The glial lymphatic system (GLS) is responsible for removing metabolic waste and aberrantly deposited substances from brain by exchanging materials with interstitial fluid (ISF), thereby maintaining cerebral homeostasis. Dysfunction in this system can result in the abnormal accumulation of amyloid-beta (Aβ) and tau proteins, leading to cognitive impairments. Recent advancements in neuroimaging have enhanced the evaluation of GLS function, forming a vital component of Alzheimer's disease (AD) diagnostics. This article offers a comprehensive overview of the imaging performance of various methods used to visualize the glial lymphatic system in Alzheimer's disease (AD), highlighting their respective advantages and limitations.

The management of non-obstructive azoospermia (NOA) remains challenging because no predictive test for the presence of localized spermatogenesis exists. Previous work considered MRI techniques, such as spectroscopy (MRS) and diffusion weighted imaging (DWI), in this role. We report here data from a prospective study evaluating additional advanced MRI sequences for predicting spermatogenesis in patients with NOA.

9 fertile volunteers and 18 men with NOA were prospectively recruited. Each participant underwent a novel multi-parametric MRI consisting of T1 and T2 mapping as well as intravoxel incoherent motion (IVIM) and diffusion weighted imaging (DWI). A single radiologist drew representative regions of interest on the best quality images for each sequence and recorded the mean values. Sperm extraction procedure results were recorded. Two-end points were evaluated: NOA versus fertile controls and the presence of viable sperm within the NOA cohort. The data were analyzed per patient. Nonparametric and logistic regression statistical analysis were used.

9 fertile men (median 43 years old, 2 children) and 18 men with NOA (median 37 years old, 0 children) were studied. 11 of the 18 men with NOA had testicle sampling. 4 men with NOA had viable sperm. Follicle-stimulating hormone and testosterone levels were not significantly different among NOAmen with and without sperm (p-value = 0.58 and 0.25). Nonparametric analysis with the Wilcoxon rank sum test showed T2 relaxation time was lower among NOA patients (median 101 vs 135 ms, p-value = 0.002), apparent diffusion coefficient (ADC) was higher among NOA patients (median 127.9 vs. 106.7 × 10-5 mm2/sec, p-value = 0.005). T1 relaxation time, alpha (Water diffusion heterogeneity index), D (IVIM-based apparent diffusion coefficient), DDC (Distributed diffusion coefficient) and D* (pseudodiffusion) were also significantly different. On logistic regression analysis, both T2 and ADC were associated with NOA; The odds of NOA decreased by 6% for each msec increase in T2 (p-value = 0.02) while the odds of NOA increased by 11% for each 10⁻5 mm2/sec increase in ADC, (p-value = 0.02). T2 yielded a larger area under the receiver operating characteristic curve than ADC (0.87 versus 0.84). Alpha, D, DDC and D* also predicted NOA. Amongst men with NOA who underwent testicle sampling, T2 was lower in testicles of patients with no sperm retrieved (median 73 vs 134. msec, p-value = 0.02). The remaining variables were not significantly different between the cohorts.

In spite of the small sample size, particularly for men with NOA who underwent sperm extraction, these results suggest that several novel MRI parameters, such as T2 relaxation time and certain IVIM/DWI parameters, are able to distinguish between fertile men and men with NOA as well as potentially predict successful sperm extraction in men with NOA. Additional larger prospective studies of men with NOA undergoing sperm extraction are warranted.

Diffusion-weighted imaging (DWI), a quantitative magnetic resonance imaging (qMRI) technique, has the potential to aid in disease characterization and treatment response monitoring. MR-Linacs (MRLs) enable simultaneous DWI acquisitions during radiotherapy, uniquely aiding in the collection of large-scale datasets for imaging biomarkers, such as the DWI-derived apparent diffusion coefficient (ADC), without additional patient burden. However, the limited data reporting on variability in MRL scanner performance characteristics, and a lack of established clinical trial quality assurance (QA) procedures, are barriers to this route for biomarker validation.

This study aims to quantify the accuracy, intra-scanner repeatability, and inter-scanner reproducibility of ADC measurements across three MRLs in Australia in both a phantom and in vivo. These measurements will inform the feasibility of carrying out prospective multi-center studies in Australia investigating ADC as a biomarker and form a core set of QA procedures and baselines to assess biomarker and sequence suitability.

An isotropic diffusion phantom (at 0°C) and one healthy volunteer were scanned on three Unity MRLs (Elekta AB, Stockholm, Sweden). Standardized (QIBA Diffusion Profile) and anatomy-specific DWI sequences, including sequences recommended by the MR-Linac Consortium Imaging Biomarker Working Group, were used to image the phantom and volunteer. ADC maps generated using the MRL scanner software (inline ADC) and diffusion-weighted (b-value) images were exported from the scanner console. The latter was used to generate ADC maps using commercial software (offline ADC) for a separate comparative analysis. Performance metrics were computed for each sequence, including a coefficient of variation to assess between-session intra-scanner repeatability (CVBS) and inter-scanner reproducibility (CV), for each phantom vial and contoured organ. Additionally, using the phantoms' known ADC vial values, a percentage bias (bias) was calculated to determine ADC accuracy.

Phantom-based measurements for the standardized QIBA sequence had intra- and inter-scanner CV and bias well within recommended guideline (QIBA Diffusion Profile) tolerance limits of 2.2% and ±3.6%, respectively. All anatomy-specific phantom DWI sequences were also within these tolerances, except for the cervix sequence at one site which showed an average intra-scanner bias of +4.5%. Both accuracy and reproducibility for all sequences were worse for lower diffusivity vials measured in the phantom. Additionally, inline and offline ADC maps had high similarity with average percent differences of +0.2%. Volunteer-based results had worse reproducibility, with the average inter-scanner CV for the brain and pancreas sequences within 9.0%, however, reaching up to 27.1% for pelvis and abdomen sequences.

This study demonstrated accuracy, intra-scanner repeatability, and inter-scanner reproducibility comparable to metrics reported in the literature, using both the phantom and volunteer datasets. The cervix sequence had the largest variability in both phantom and volunteer results and was recommended for further investigation. This study suggests that qMRI techniques utilizing DWI could be a viable option for future multi-centered patient-based studies utilizing Australian MRLs, with phantom-based quality assurance recommended alongside patient imaging.

Eclampsia is a serious pregnancy complication and is associated with cerebral edema and infarctions. However, the underlying pathophysiology of eclampsia remains poorly explored.

This study aimed to assess the pathophysiology of eclampsia using specialized magnetic resonance imaging to measure diffusion, perfusion, and vasospasm.

This was a cross-sectional study recruiting consecutive pregnant women between April 2018 and November 2021 at Tygerberg Hospital, Cape Town, South Africa. Women with eclampsia, preeclampsia, and normotensive pregnancies who underwent magnetic resonance imaging after birth were recruited. The main outcome measures were cerebral infarcts, edema, and perfusion using intravoxel incoherent motion imaging and vasospasm using magnetic resonance imaging angiography. The imaging protocol was established before inclusion.

Here, 49 women with eclampsia, 20 women with preeclampsia, and 10 normotensive women were included. Cerebral infarcts were identified in 34% of women with eclampsia and 5% of women with preeclampsia (risk difference, 0.29; 95% confidence interval, 0.06-0.52; P=.012). However, no cerebral infarct was identified in normotensive controls. Women with eclampsia were more likely to have vasogenic cerebral edema than women with preeclampsia (80% vs 20%, respectively; risk difference, 0.60; 95% confidence interval, 0.34-0.85; P<.001) and normotensive women (risk difference, 0.80; 95% confidence interval, 0.47-1.00; P<.001). Diffusion was increased in women with eclampsia in the parieto-occipital white matter (mean difference, 0.02 × 10-3 mm2/s; 95% confidence interval, 0.00-0.05; P=.045) and caudate nucleus (mean difference, 0.02 × 10-3 mm2/s; 95% confidence interval, 0.00-0.04; P=.033) compared with women with preeclampsia. In addition, diffusion was increased in women with eclampsia in the frontal white matter (mean difference, 0.07 × 10-3 mm2/s; 95% confidence interval, 0.02-0.12; P=.012), parieto-occipital white matter (mean difference, 0.05 × 10-3 mm2/s; 95% confidence interval, 0.02-0.07; P=.03), and caudate nucleus (mean difference, 0.04 × 10-3 mm2/s; 95% confidence interval, 0.00-0.07; P=.028) compared with normotensive women. Perfusion was decreased in edematous regions. Hypoperfusion was present in the caudate nucleus in eclampsia (mean difference, -0.17 × 10-3 mm2/s; 95% confidence interval, -0.27 to -0.06; P=.003) compared with preeclampsia. There was no sign of hyperperfusion. Vasospasm was present in 18% of women with eclampsia and 6% of women with preeclampsia. However, no vasospasm was present in the controls.

Eclampsia was associated with cerebral infarcts, vasogenic cerebral edema, vasospasm, and decreased perfusion, which are not usually evident on standard clinical imaging. This finding may explain why some patients have cerebral symptoms and signs despite having normal conventional imaging.

To develop and evaluate a deep learning technique for the differentiation of hepatocellular carcinoma (HCC) using "simplified intravoxel incoherent motion (IVIM) parameters" derived from only 3 b-value images.

Ninety-eight retrospective magnetic resonance imaging data were collected (68 men, 30 women; mean age 59 ± 14 years), including T2-weighted imaging with fat suppression, in-phase, out-of-phase, and diffusion-weighted imaging (b = 0, 100, 800 s/mm2). Ninety percent of data were used for stratified 10-fold cross-validation. After data preprocessing, diffusion-weighted imaging images were used to compute simplified IVIM and apparent diffusion coefficient (ADC) maps. A 17-layer 3D convolutional neural network (3D-CNN) was implemented, and the input channels were modified for different strategies of input images.

The 3D-CNN with IVIM maps (ADC, f, and D*) demonstrated superior performance compared with other strategies, achieving an accuracy of 83.25 ± 6.24% and area under the receiver-operating characteristic curve of 92.70 ± 8.24%, significantly surpassing the baseline of 50% (P < 0.05) and outperforming other strategies in all evaluation metrics. This success underscores the effectiveness of simplified IVIM parameters in combination with a 3D-CNN architecture for enhancing HCC differentiation accuracy.

Simplified IVIM parameters derived from 3 b-values, when integrated with a 3D-CNN architecture, offer a robust framework for HCC differentiation.

The aim was to evaluate visual breast lesion assessment using single binary index maps (IDf) in comparison to the use of combined regions of interest (ROI) analysis of estimated diffusion coefficient (D') AND perfusion fraction (f'), which proved to be the best method in a previous simplified intravoxel incoherent motion DWI, if diffusion-weighted imaging (DWI) is used as stand-alone tool. IDf, was constructed voxel-wise from cut-off values of D' and f'. The cut-off values, the data of 105 malignant and 86 benign lesions and the ROIs were re-used. For visual assessment, IDf was displayed as two-colour b800 overlay with red representing "malignant" and green "benign" voxels. A lesion was rated as "malignant", if a red hot spot was found within translucent hyperintensity on b800, otherwise as "benign". Intraindividual comparison of quantitative analysis and visual assessment of IDf showed comparable accuracy, both to each other and to combined ROI-analysis of D' and f' maps (0.927 vs. 0.937, p = 0.157, and 0.921 vs. 0.937, p = 0.157, respectively). Thus, visual assessment of IDf can replace combined ROI analysis of D' and f' without loss in accuracy enabling a considerable facilitation in clinical routine.

Autosomal dominant polycystic kidney disease (ADPKD) is a prevalent hereditary disorder characterized by the development and growth of fluid-filled cysts, resulting in a decline in kidney function. Beyond total kidney and cyst volume quantification, non-cystic tissue characterization by multi-parametric MRI (mp-MRI) and radiomics holds promise. We conducted a radiomic analysis based on reproducible and informative features extracted from non-cystic tissue on mp-MRI in ADPKD patients. T2-weighted (T2-w), T1-weighted MRI (T1-w), and IntraVoxel Incoherent Motion (IVIM) maps from Diffusion Weighted Imaging (DWI) were considered. The reliability of radiomic features was evaluated using five different segmentation methods. The impact of segmentation variability on radiomic reproducibility was assessed through Intraclass Correlation Coefficients (ICC), and a preliminary correlation analysis with relevant clinical parameters, such as age and eGFR, was also performed. The results from 14 patients indicate that radiomic features derived from IVIM maps exhibit greater reliability compared to features from T1-w and T2-w for characterizing non-cystic tissue in ADPKD patients, also showing a moderate correlation with age and eGFR. Additionally, lower-order features, including those computed from histograms and co-occurrence matrices, demonstrate higher reproducibility than other texture features.

To validate multi-site and multi-vendor ADC measurements using the QIBA/NIST diffusion MRI phantom at room temperature.

ADC measurements were performed on 12 scanners (evenly split between 1.5 and 3 T) from three vendors at five sites and compared with reference values at room temperature. We adopted Pearson's correlation (r) and accuracy error for comparison with reference values; within scanner coefficient of variation (CVintra%) for intra-session repeatability and inter-scanner for agreement (CVinter%); Bland-Altman plots and precision error for short-term reproducibility; generalized linear mixed models and post-hoc tests ( α =0.05) to compare accuracy, repeatability and precision across field strengths, vendors, and scanners.

Temperature adjusted ADCs were well correlated with NIST reference values (r ≥ 0.997 for 1.5 T, r ≥ 0.996 for 3 T). Median accuracy error was lower than 5% for all scanners. In the renal physiologic range (ADC > 0.83 × 10-3 mm2/s), accuracy error was < 10% and CVintra < 2%. Across all scanners, good short-term reproducibility with limits of agreement < 10% and excellent agreement (median CVinter < 2%) were found.

Despite using abdominal receive coils and room temperature measurements, all quantitative parameters were within literature findings. High accuracy, repeatability and precision within the renal physiologic range support the feasibility of scanner evaluation using QIBA standardization process for diffusion measurements in renal studies.

Adult patients with suspected or histologically proven WHO Grade II diffuse glioma. QUESTION 1: In adult patients with suspected or histologically proven WHO Grade II diffuse glioma, do advanced MRI techniques using magnetic resonance spectroscopy, perfusion weighted imaging or diffusion weighted imaging provide superior assessment of tumor grade, margins, progression, treatment-related effects, and prognosis compared to standard neuroimaging?

Level II: The use of diffusion imaging and dynamic susceptibility contrast (DSC), dynamic contrast enhancement (DCE) and arterial spin labeling (ASL) sequences are suggested to differentiate WHO Grade II diffuse glioma from higher grade gliomas when this is not accomplished by T2 weighted and pre- and post-gadolinium contrast enhanced T1 weighted imaging.

The use of diffusion and perfusion is suggested for obtaining information in genomics, prognosis, and post treatment monitoring when this information would be of value to the clinician and is not obtained through other methods.

The use of MR Spectroscopy is suggested to differentiate WHO Grade II diffuse glioma from higher grade gliomas when this is not accomplished by standard MRI, perfusion and diffusion techniques and when such information would be of value to the clinician. QUESTION 2: In adult patients with suspected or histologically proven WHO Grade II diffuse glioma, does molecular imaging using amino acid PET tracers provide superior assessment of tumor grade, margins, progression, treatment-related effects, and prognosis compared to standard neuroimaging?

Level III: If not already evident by MRI studies, the addition of amino acid PET with FET and FDOPA as a tracer is suggested to help determine if a brain lesion is a low grade glioma or high grade glioma.

If the standard clinical prognostic parameters are unclear and novel PET tracers are available, the clinician may consider FET to assist in determination of prognosis in an individual with grade II diffuse glioma.

Clinicians may use FDOPA PET in addition to MRI if additional information is required for detection of tumor progression.

Oscillating gradient spin echo (OGSE) diffusion MRI (dMRI) can probe the diffusive dynamics on short time scales ≲10 ms, which translates into the sensitivity to tissue microstructure at the short length scales≲ 10 μ $$ \lesssim 10\kern0.3em \upmu $$ m. OGSE-based tissue microstructure imaging techniques able to characterize the cell diameter and cellular density have been established in pre-clinical studies. The unique image contrast of OGSE dMRI has been shown to differentiate tumor types and malignancies, enable early diagnosis of treatment effectiveness, and reveal different pathophysiology of lesions in stroke and neurological diseases. Recent innovations in high-performance gradient human MRI systems provide an opportunity to translate OGSE research findings in pre-clinical studies to human research and the clinic. The implementation of OGSE dMRI in human studies has the promise to advance our understanding of human brain microstructure and improve patient care. Compared to the clinical standard (pulsed gradient spin echo), engineering OGSE diffusion encoding for human imaging is more challenging. This review summarizes the impact of hardware and human biophysical safety considerations on the waveform design, imaging parameter space, and image quality of OGSE dMRI. Here we discuss the effects of the gradient amplitude, slew rate, peripheral nerve stimulation, cardiac stimulation, gradient driver, acoustic noise and mechanical vibration, eddy currents, gradient nonlinearity, concomitant gradient, motion and flow, and signal-to-noise ratio. We believe that targeted engineering for safe, high-quality, and reproducible imaging will enable the translation of OGSE dMRI techniques into the clinic.

Diffusion-weighted imaging (DWI) has significant value in clinical application, which however suffers from a serious low signal-to-noise ratio (SNR) problem, especially at high spatial resolution and/or high diffusion sensitivity factor.

Here, we propose a denoising method for magnitude DWI. The method consists of two modules: pre-denoising and post-filtering, the former mines the diffusion redundancy by local kernel principal component analysis, and the latter fully mines the non-local self-similarity using patch-based non-local mean.

Validated by simulation and in vivo datasets, the experiment results show that the proposed method is capable of improving the SNR of the whole brain, thus enhancing the performance for diffusion metrics estimation, crossing fiber discrimination, and human brain fiber tractography tracking compared with the different three state-of-the-art comparison methods. More importantly, the proposed method consistently exhibits superior performance to comparison methods when used for denoising diffusion data acquired with sensitivity encoding (SENSE).

The proposed denoising method is expected to show significant practicability in acquiring high-quality whole-brain diffusion data, which is crucial for many neuroscience studies.

To determine whether quantitative parameters derived using diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) imaging reflect pathological changes in fibrosarcoma.

Thirty nude mouse models of fibrosarcoma underwent T1/T2-weighted imaging, DKI, and IVIM imaging on a 3.0-T scanner. Immunohistochemistry was utilized for the hematoxylin and eosin, aquaporin 1 (AQP1), aquaporin 4 (AQP4), and Ki-67 staining of fibrosarcoma tissue, and AQP1 and AQP4 staining of normal muscle tissue (NMT). The independent-sample t-test was used to compare AQP1 and AQP4 expression in fibrosarcoma and NMT. Pearson and Spearman correlation analyses were conducted to evaluate the correlation between imaging parameters and pathological indicators. Multiple linear regression analysis was employed to identify the pathological indicators independently associated with quantitative DKI and IVIM parameters.

Apparent diffusion coefficient (ADC), D, f, and mean kurtosis (MK) indicated cell density and Ki-67 and AQP1 expression intensity. D values reflected AQP4 expression intensity, while MD reflected cell density and AQP1 expression intensity. Cell density (CD) independently influenced ADC and f values, while CD and AQP1 independently influenced D values.

CD and Ki-67 independently influenced MK. DKI- and IVIM imaging-derived ADC, D, f, MD, and MK were correlated with AQP1, AQP4, Ki-67, and CD in nude mice with fibrosarcoma.

To characterize the diffusion time (Δeff) dependence of apparent diffusion coefficient (ADC) and intravoxel incoherent motion-related parameters in the human kidney at 3 T.

Sixteen healthy volunteers underwent an MRI examination at 3 T including diffusion-weighted imaging at different Δeff ranging from 24.1 to 104.1 ms. The extended mono-exponential ADC and intravoxel incoherent motion models were fitted to the data for each Δeff and the medullary and cortical ADC, (pseudo-)diffusion coefficients (D* and D) and flow-related signal fraction (f) were calculated.

When all the data were used for fitting, a significant trend toward higher ADC with increasing Δeff was observed between 24.1 and 104.1 ms (median and interquartile range: 2.38 [2.19, 2.47] to 2.84 [2.36, 2.90] × 10-3 mm2/s for cortex, and 2.28 [2.18, 2.37] to 2.82 [2.58, 3.11] × 10-3 mm2/s for medulla). In contrast, no significant differences in ADC were found when only the data acquired at b-values higher than 200 s/mm2 were used for fitting. When the intravoxel incoherent motion model was applied, cortical and medullary f increased significantly (cortex: 0.21 [0.15 0.27] to 0.37 [0.32, 0.49] × 10-3 mm2/s; medulla: 0.15 [0.13 0.29] to 0.41 [0.36 0.51] × 10-3 mm2/s). No significant changes in cortical and medullary D and D* were observed as diffusion time increased.

Renal perfusion and tubular flow substantially contribute to the observed increase in ADC over a wide range of Δeff between 24 and 104 ms.

To evaluate whole-tumor histogram analysis of diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM), and dynamic contrast-enhanced MRI (DCE-MRI) in predicting the efficacy of imatinib, a c-KIT inhibitor, for treating patient-derived models derived from sinonasal mucosal melanomas (MM).

This study included 38 patients with histologically confirmed sinonasal MM, who underwent DKI, IVIM, and DCE-MRI. Patient-derived tumor xenograft models and precision-cut tumor slices were established to evaluate tumor response to imatinib. Whole-tumor histogram analysis was conducted on imaging parameters, and logistic regression models were applied to determine the predictive value of these metrics in differentiating responders from nonresponders.

Among the 38 patients with sinonasal MM, 12 were classified as responders and 26 as nonresponders based on patient-derived tumor xenograft and precision-cut tumor slice model responses to imatinib. The DKI model revealed significant differences in mean, median, 10th percentile, and 90th percentile values of Dk and K between responders and nonresponders (P < 0.05). The IVIM model indicated significant differences in 10th percentile and mean values of D, with kurtosis f being a strong predictor. The DCE-MRI model, using the 90th percentile Ktrans metric, demonstrated robust predictive performance, achieving an AUC of 0.89, with 80.77% specificity and 91.67% sensitivity. The combined logistic model integrating DKI, IVIM, and DCE-MRI metrics produced the highest predictive accuracy, with an AUC of 0.90.

Whole-tumor histogram analysis of DKI, IVIM, and DCE-MRI offers a noninvasive method for predicting the efficacy of c-KIT inhibitors in sinonasal MMs, presenting valuable implications for guiding targeted treatment in this rare cancer type.

BackgroundDiffusion-weighted imaging (DWI) is an important technique to study brain microstructure. However, diffusion-weighted (DW) images suffer from severe low signal-to-noise ratio (SNR) problem, affecting subsequent diffusion analysis.ObjectiveThe goal of this paper is to develop advanced DWI denoising technique to effectively reduce noise while improving the accuracy and reliability of subsequent diffusion model fitting and diffusion analysis, thereby facilitating the research and analysis of brain science.MethodsWe propose a new method for denoising DW images based on patch-matching with higher-order singular value decomposition (HOSVD) by combined with the variance-stabilizing transformation technique. It starts with introducing a novel non-local mean algorithm as a prefiltering stage, and then denoises the noisy data using a local HOSVD algorithm based on the HOSVD bases learned from prefiltered images.ResultsExperiments are performed on simulation, HCP and in vivo brain DWI datasets. Results show that the proposed method significantly reduces spatially invariant and variant noise, improving the most reliable diffusion analysis compared with the different denoising methods.ConclusionsThe proposed method achieves state-of-the-art performance which can improve image quality and enable accurate diffusion analysis.

To evaluate the performance of virtual MR elastography (vMRE) for predicting microvascular invasion (MVI) in Barcelona Clinic Liver Cancer (BCLC) stage A (≤ 5.0 cm) hepatocellular carcinoma (HCC) and to construct a combined nomogram based on vMRE, multi-b-value DWI models, and clinical-radiological (CR) features.

Consecutive patients with suspected HCC who underwent multi-b-value DWI examinations were prospectively collected. Quantitative parameters from vMRE, mono-exponential, intravoxel incoherent motion, and diffusion kurtosis imaging models were obtained. Multivariate logistic regression was used to identify independent MVI predictors and build prediction models. A combined MRI_Score was constructed using independent quantitative parameters. A visualized nomogram was built based on significant CR features and MRI_Score. The predictive performance of quantitative parameters and models was evaluated.

The study included 103 patients (median age: 56 years; range: 35-70 years; 87 males and 16 females). Diffusion-based shear modulus (μDiff) exhibited a predictive performance for MVI with area under the curve (AUC) of 0.735. The MRI_Score was developed employing true diffusion coefficient (D), mean kurtosis (MK), and μDiff. CR model and MRI_Score achieved AUCs of 0.787 and 0.840, respectively. The combined nomogram based on AFP, corona enhancement, tumor capsule, TTPVI, and MRI_Score significantly improved the predictive performance to an AUC of 0.931 (Delong test p < 0.05).

vMRE exhibited great potential for predicting MVI in BCLC stage A HCC. The combined nomogram integrating CR features, vMRE, and quantitative diffusion parameters significantly improved the predictive accuracy and could potentially assist clinicians in identifying appropriate treatment options.

In this review article, we describe the development and application of diffusion-based MR imaging methods for studying glymphatic physiology. Fluid exchange and solute transport are the 2 key components of the glymphatic system. Here we describe the use of low b-value imaging, free water fraction imaging, and diffusion time sensitization to leverage cerebral spinal fluid, as well as interstitial fluid motion in the parenchyma. We also describe multiple b-value diffusion imaging to better delineate diffusion components within the brain. Finally, we touch upon newer approaches that use advanced models of the diffusion signal, including high b-value imaging.

Hypoxia inducible factor (HIF-1α) is a major transcriptional factor regulating gene expression under hypoxic conditions. HIF-1α expression was closely correlated with the oxygenation status of tumor and could serve as an important biomarker for tumor hypoxia, aggressiveness, or radiation resistance. High expression of HIF-1α contributes to high aggressiveness or poor prognosis of endometrial cancer.

This study aimed to investigate correlations between multimodal MRI parameters (derived from amide proton transfer weighted imaging [APTw], conventional diffusion weighted imaging [DWI], intravoxel incoherent motion [IVIM] imaging and diffusion kurtosis imaging [DKI]) and HIF-1α expression, and to determine whether multimodal MRI can be used for quantitative evaluation of HIF-1α expression.

Retrospective.

A total of 94 patients with EC were examined with 32 cases finally included in the high HIF-1α expression group and 40 cases included in the low expression group according to the exclusion and inclusion criteria.

3.0T/APTw, DWI, IVIM, and DKI.

The asymmetry of magnetization transfer rate (MTRasym), apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudo diffusion coefficient (D*), perfusion fraction (f), mean kurtosis (MK), and mean diffusivity (MD) were calculated from multimodal MRI and compared between HIF-1α high expression and HIF-1α low expression groups.

Mann-Whitney U-test; Chi-square test or Fisher exact test; logistic regression analysis; Area under the receiver operating characteristic (ROC) curve (AUC); The Delong test; Pearson or Spearman correlation coefficients. The significance threshold was set at P < 0.05.

MTRasym, ADC, D, D*, MK and MD values were significantly higher in high HIF-1α expression than in low HIF-1α expression groups, whereas f value was significantly lower in high HIF-1α expression than in low HIF-1α expression groups. The AUC of HIF-1 α expression evaluated by MTRasym, ADC, D, D*, f, MD, MK and their combination were 0.894 (0.740, 0.973), 0.746 (0.568, 0.879), 0.716 (0.528, 0.904), 0.920 (0.772, 0.984), 0.756 (0.578, 0.886), and 0.973 (0.851-1.000), respectively. Multivariate analysis revealed that only f, MK, and MD values were independent predictors for evaluating HIF-1α expression in EC.

APTw combined with multi-model diffusion imaging can quantitatively evaluate the expression of HIF-1α in EC, and the combination of multiple quantitative parameters can improve the evaluation efficiency.

The aim of this study was to determine whether intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) can differentiate benign, intermediate, and malignant soft-tissue tumours (STTs) of the extremities and trunk.

We prospectively recruited 100 STT patients (32, 15, and 53 patients with benign, intermediate, and malignant tumours, respectively). The patients underwent IVIM and DKI, and the following parameters were measured: standard apparent diffusion coefficient (ADC), perfusion fraction (f), true diffusion coefficient (Dslow), pseudo-diffusion coefficient (Dfast), water diffusion heterogeneity index (α), distributed diffusion coefficient (DDC), mean diffusivity (MD), and mean kurtosis (MK). Statistical analyses were performed using receiver operating characteristic curves, the Kruskal-Wallis H test, and post hoc test with Bonferroni correction.

Standard ADC, Dslow, DDC, and MD values gradually decreased from benign to intermediate and malignant STTs. Intermediate STTs displayed a lower f value than benign tumours (P=0.029). The MK value was higher in malignant tumours than in intermediate and benign tumours (P=0.021 and <0.001, respectively). The DDC value best differentiated benign tumours from nonbenign (intermediate and malignant) tumours (area under the curve [AUC] = 0.884, 0853, and 0.892, respectively). The optimal MK cut-off value for differentiating intermediate and malignant tumours was 0.65 (sensitivity: 73.33%, specificity: 81.13%, accuracy: 79.41%).

IVIM and DKI parameters were helpful for differentiating benign, intermediate, and malignant STTs and can complement conventional MRI, with DDC and MK values showing high diagnostic efficacy.

This study compared the diagnostic performance of diffusion biomarkers estimated from an abbreviated diffusion-weighted imaging (DWI) protocol and assessed their potential to reduce unnecessary biopsies of benign BI-RADS 4 lesions identified on dynamic contrast-enhanced (DCE) MRI.

A retrospective study was conducted from 2019 to 2023. All patients underwent abbreviated DWI at 3 T with four b-values (0 s/mm2, 200 s/mm2, 800 s/mm2, and 1500 s/mm2). Regions of interest were manually placed on DWI, and biomarkers, including the apparent diffusion coefficient (ADC0-800), perfusion fraction intravoxel incoherent motion, non-Gaussian diffusion (ADC0 and kurtosis [K]), signature index (S-index), and shifted ADC (sADC), were estimated. Diagnostic performance and the potential to reduce unnecessary biopsies were evaluated for each parameter.

In total, 168 female patients (mean age ± standard deviation, 56.2 ± 13.5 years) with 178 BI-RADS 4 lesions on DCE MRI were analyzed. The median ADC0-800, sADC, and ADC0 were significantly lower in malignant lesions, while S-index and K were significantly higher (all p ≤ 0.001). The diagnostic performance to reclassify lesions as benign or malignant was identical for ADC0-800 (area under the curve = 0.67), sADC (0.69), S-index (0.69), ADC0 (0.68), and K (0.66). Applying an ad-hoc threshold cutoff, all parameters reduced unnecessary biopsies (around 16%), while K resulted in a slightly higher reduction rate than ADC0-800 (20.5% vs 15.9%, p = 0.317) without reducing sensitivity.

Diffusion MRI biomarkers obtained using an abbreviated DWI protocol reduced unnecessary biopsies in BI-RADS 4 lesions, with K performing slightly better than ADC.

Question MRI BI-RADS category 4 includes a substantial number of benign lesions, and reducing unnecessary biopsies remains a critical clinical concern. Findings The parameters from abbreviated DWI show lesion differentiation comparable to ADC and have greater potential to reduce unnecessary biopsies. Clinical relevance This study underscores the potential of imaging biomarkers from abbreviated DWI for assessing breast MRI BI-RADS 4 lesions. These biomarkers may be comparable or superior to standard ADC in reducing unnecessary biopsies and could aid in improving patient management decisions.

To determine the value of intravoxel incoherent motion (IVIM) for quantitative tumor budding (TB) evaluation and prognostic stratification in patients with rectal cancer (RC).

This study enrolled 189 RC patients (training set 148, validation set 41) who underwent IVIM and were subsequently treated surgically within 2 weeks between January 2022 and April 2023. Hematoxylin-eosin staining was used for TB scoring. IVIM metrics were calculated on MRI images using biexponential fitting and histogram analysis. Differences in IVIM histogram metrics between the low-intermediate grade budding (Bd 1 + 2) and the high-grade budding (Bd 3) were analyzed. Multivariate logistic regression analysis was used to build the Combined model. The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic performance of the IVIM histogram metrics and the Combined model. Kaplan-Meier analysis was employed to estimate disease-free and overall survival rates for patients.

Multivariate logistic analysis showed that the D_25th percentile, D_75th percentile, D_90th percentile, and D_95th percentile were independent predictors of Bd 3 (all p < 0.05). The Combined model incorporating these four factors had the best diagnostic performance, with the AUC, sensitivity, and specificity of 0.852, 73.02%, and 82.35% in the training set and 0.856, 75.00%, and 86.21% in the validation set. Furthermore, the score of the Combined model was significantly associated with worse 2-year overall survival (hazard ratio 6.804, 95% confidence interval 2.214 to 20.909, p = 0.001).

The IVIM histogram metrics could distinguish different TB grades and be used as a preoperative risk stratification tool.

Questions Does intravoxel incoherent motion based on histogram analysis predict tumor budding grades and its prognosis in patients with rectal cancer? Findings The histogram metrics of slow diffusion coefficient are an independent prediction factor of high-grade tumor budding and a risk factor of poor 2-year overall survival. Clinical relevance This combined model, based on slow diffusion coefficient, is a reliable tool for preoperative predicting 2-year overall survival in patients with rectal cancer, contributing to risk stratification and individual treatment.

Vascular cognitive impairment (VCI) encompasses a diverse range of syndromes, including mild cognitive impairment and vascular dementia (VaD), primarily attributed to cerebrovascular lesions and vascular risk factors. Its prevalence ranks second only to Alzheimer's disease (AD) in neuro diseases. The advancement of medical imaging technology, particularly magnetic resonance imaging (MRI), has enabled the early detection of structural, functional, metabolic, and cerebral connectivity alterations in individuals with VCI. This paper examines the utility of multimodal MRI in evaluating structural changes in the cerebral cortex, integrity of white matter fiber tracts, alterations in the blood-brain barrier (BBB) and glymphatic system (GS) activity, alteration of neurovascular coupling function, assessment of brain connectivity, and assessment of metabolic changes in patients with VCI.

To develop a multi-echo, diffusion-encoded chemical exchange saturation transfer (dCEST) imaging technique for estimating the intracellular and extracellular/intravascular contributions to the conventional CEST signal.

A dCEST pulse sequence was developed to quantify the signal fractions, transverse relaxation times (T2), and apparent diffusion coefficient (ADC) of the intracellular and extracellular/intravascular water compartments. dCEST images were acquired across a wide range of TE, b-values, RF saturation strengths, and frequency offsets. The data were analyzed using a two-compartment model with distinct diffusivities and T2 values. Intracellular and extracellular fractions of conventional water-saturation spectra (Z-spectra) and corresponding amide proton transfer (APT) signals were estimated from human brain scans of healthy volunteers at 3 T.

The multi-echo diffusion results showed that the intracellular water fractions were significantly higher than the extracellular water fractions, whereas the intracellular T2 values were shorter than those of the extracellular/intravascular compartments. The ADC for the intracellular compartment was significantly lower than that of the extracellular compartment. The dCEST analysis showed that the average intracellular and extracellular fractions of the Z-spectra were 85 ± 7% and 15 ± 4%, respectively. The overall intracellular APT-weighted values were higher than the total (i.e., intracellular + extracellular) APT-weighted values.

The dCEST imaging technique provides valuable insight into the source of signals in conventional CEST MRI, offering potential utility for clinical applications.

The intravoxel incoherent motion (IVIM) parameter estimation is affected by noise, while existing CNN-based fitting methods utilize neighborhood spatial features around voxels to obtain more robust parameters. However, due to the heterogeneity of tissue, neighborhood features with low similarity can lead to excessively smooth parameter maps and even loss of tissue details.

To propose a novel neural network fitting approach, IVIM-CNNsimilar, which utilizes similar neighborhood information of voxels to assist in the estimation of IVIM parameters in diffusion-weighted imaging (DWI).

The proposed fitting model is based on convolutional neural network (CNN), which first identifies the similar neighborhoods of voxels through cluster analysis and then uses CNN to learn the spatial features of similar neighborhoods to reduce the impact of noise on the parameter estimation of the voxel. To evaluate the performance of the proposed method, comparisons were conducted with the least squares (LSQ), Bayesian, PI-DNN, and IVIM-CNNunet algorithms on both simulated and in vivo brains, including 23 healthy brains and three brain tumors, in terms of root mean square error (RMSE) of IVIM parameters and the parameter contrast ratio between the tumor and normal regions.

The CNN-based methods, such as IVIM-CNNsimilar and IVIM-CNNunet, yield smoother parameter maps compared to voxel-based methods like nonlinear least squares, segmented nonlinear least squares, Bayesian, and PI-DNN. Additionally, the IVIM-CNNsimilar retains more local tissue details while maintaining smoothness of parameter maps compared to the IVIM-CNNunet. In simulated experiments, IVIM-CNNsimilar outperforms IVIM-CNNunet in terms of parameter estimation accuracy (SNR = 30; RMSE [ D $D$ ] = 0.0168 vs. 0.0253; RMSE ( F $F$ ) = 0.0001 vs. 0.0002; RMSE [D ∗ $D^{*}$ ] = 0.0266 vs. 0.0416). In addition, compared with other methods, the proposed IVIM-CNNsimilar is more robust to noise, which is reflected in the lower RMSE of each parameter at different SNRs. For in vivo brains, compared to other methods, IVIM-CNNsimilar achieved the highest PCR for most parameters when comparing the normal and tumor regions.

The IVIM-CNNsimilar method uses similar neighborhood information to assist IVIM parameter fitting by reducing the impact of noise on voxel parameter estimation, thereby improving the accuracy of parameter estimation and increasing the potential for IVIM clinical application.

The present study aimed to investigate whether diffusion-weighted imaging (DWI) can qualify and quantify cerebrospinal fluid (CSF) dynamics in the brains of healthy subjects. For this purpose, we developed new DWI-based fluidography and compared the CSF dynamics seen on the fluidography with two apparent diffusion coefficients obtained with different DWI signal models at anatomical spaces filled by CSF.

DWI with multiple b values was performed for 10 subjects using a 7T MRI scanner. DWI-fluidography based on the DWI signal variations in different motion probing gradient directions was developed for visualizing the CSF dynamics voxel-by-voxel. DWI signals were measured using an ROI in the representative CSF-filled anatomical spaces in the brain. For the multiple DWI signals, the mono-exponential and kurtosis models were fitted and two kinds of apparent diffusion coefficients (ADCC and ADCK) were estimated in each space using the Gaussian and non-Gaussian diffusion models, respectively.

DWI-fluidography could qualitatively represent the features of CSF dynamics in each anatomical space. ADCs indicated that the motions at the foramen of Monro, the cistern of the velum interpositum, the quadrigeminal cistern, the Sylvian cisterns, and the fourth ventricle were more drastic than those at the subarachnoid space and anterior horns of the lateral ventricle. Those results seen in ADCs were identical to the findings on DWI-fluidography.

DWI-fluidography based on the features of DWI signals could show differences of CSF dynamics among anatomical spaces.

This study by the EUSOBI International Breast Diffusion-weighted Imaging (DWI) working group aimed to evaluate the current and future applications of advanced DWI in breast imaging.

A literature search and a comprehensive survey of EUSOBI members to explore the clinical use and potential of advanced DWI techniques and a literature search were involved. Advanced DWI approaches such as intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), and diffusion tensor imaging (DTI) were assessed for their current status and challenges in clinical implementation.

Although a literature search revealed an increasing number of publications and growing academic interest in advanced DWI, the survey revealed limited adoption of advanced DWI techniques among EUSOBI members, with 32% using IVIM models, 17% using non-Gaussian diffusion techniques for kurtosis analysis, and only 8% using DTI. A variety of DWI techniques are used, with IVIM being the most popular, but less than half use it, suggesting that the study identified a gap between the potential benefits of advanced DWI and its actual use in clinical practice.

The findings highlight the need for further research, standardization and simplification to transition advanced DWI from a research tool to regular practice in breast imaging. The study concludes with guidelines and recommendations for future research directions and clinical implementation, emphasizing the importance of interdisciplinary collaboration in this field to improve breast cancer diagnosis and treatment.

Advanced DWI in breast imaging, while currently in limited clinical use, offers promising improvements in diagnosis, staging, and treatment monitoring, highlighting the need for standardized protocols, accessible software, and collaborative approaches to promote its broader integration into routine clinical practice.

Increasing number of publications on advanced DWI over the last decade indicates growing research interest. EUSOBI survey shows that advanced DWI is used primarily in research, not extensively in clinical practice. More research and standardization are needed to integrate advanced DWI into routine breast imaging practice.

To explore the predictive value of quantitative parameters from intravoxel incoherent movement (IVIM) imging and dynamic contrast enhancement MRI (DCE-MRI) for HER2 expression in breast cancer.

This retrospective study included 167 women with breast cancer who underwent MRI from December 2019 to December 2023, categorized into 48 HER2-positive, 78 HER2-low and 41 HER2-zero cancers. All patients underwent IVIM imaging and DCE-MRI. Statistical analyses, including one-way ANOVA, Kruskal-Wallis test and χ2 test, were employed to compare clinical data, MRI features, and MRI quantitative parameters including standard ADC(ADC), pure diffusion coefficient(D), perfusion-related diffusion coefficient(D*), perfusion fraction(f), volume transfer constant(Ktrans), extravascular extracellular interstitial volume ratio(Ve) and rate constant(Kep) between the three groups. Multivariable logistic regression was used to identify independent predictors for distinguishing HER2 expressions. The diagnostic efficacy of significant IVIM and DCE parameters for different HER2 expressions was analyzed using receiver operator characteristic (ROC) curves.

Peritumoral edema, histological grade and Kep achieved an AUC of 0.86(95%CI:0.78,0.91) in distinguishing HER2-positive tumors from HER2-low expressing tumors and were independent predictors for differentiating these two groups. Among HER2-positive and -zero breast cancers, the combined model of D*, Ktrans and Kep had an AUC of 0.74(95%CI:0.63,0.82) for the prediction of HER2-positive versus HER2-zero cancers, and its prediction efficiency was not improved compared with that of a single parameter(P > .05).

Quantitative parameters from intravoxel incoherent movement imaging and dynamic contrast enhancement MRI can predict different HER2 expressions in breast cancer from different perspectives, with implications for therapy.

Magnetic resonance imaging (MRI) is an indispensable tool used in both the lab and the clinic. Part of the strength of MRI comes from its ability to deliver anatomical information highlighted with different types of contrasts, including functional and diffusion-oriented acquisitions that are often incompatible with normal, multi-shot scans. For these problems, Nobel-award-winning techniques such as Echo Planar Imaging (EPI) have been essential in opening a manifold of new applications. EPI, however, has challenges when dealing with sharp changes in magnetic susceptibility, including those arising in the presence of air/tissue or air/fat interfaces, from non-ferromagnetic metal implants, as well when the main magnetic field cannot be shimmed to achieve the desired degree of homogeneity, as often is the case in systems built using permanent magnets. Among the techniques being proposed to deal with this kind of problem is spatiotemporally-encoded (SPEN) MRI. The present review focuses on the principles of this technique, with an emphasis on: i) explaining SPEN's resilience to field inhomogeneities, on the basis of expanded bandwidth considerations vis-à-vis EPI; ii) "the good, the bad and the ugly" associated with the undersampling that SPEN usually has to carry out when employing expanded bandwidths; iii) recent developments in data processing algorithms seeking to alleviate the "bad and the ugly" part of these experiments by formulating SPEN image reconstruction as an optimization problem, and then relying on compressed sensing and parallel imaging concepts to achieve improved image quality; and iv) the incorporation of experimental improvements including scan interleaving, simultaneous multi-banding and multi-echo elements, to keep in line with advancements in other areas of fast MRI. The strengths and weaknesses of these data sampling and processing strategies are assessed, and examples of their leverage in functional, but foremost diffusion-weighted, imaging applications, are presented.

In medical image analysis, the utilization of biophysical models for signal analysis offers valuable insights into the underlying tissue types and microstructural processes. In diffusion-weighted magnetic resonance imaging (DWI), a major challenge lies in accurately estimating model parameters from the acquired data due to the inherently low signal-to-noise ratio (SNR) of the signal measurements and the complexity of solving the ill-posed inverse problem. Conventional model fitting approaches treat individual voxels as independent. However, the tissue microenvironment is typically homogeneous in a local environment, where neighboring voxels may contain correlated information. To harness the potential benefits of exploiting correlations among signals in adjacent voxels, this study introduces a novel approach to deep learning parameter estimation that effectively incorporates relevant spatial information. This is achieved by training neural networks on patches of synthetic data encompassing plausible combinations of direct correlations between neighboring voxels. We evaluated the approach on the intravoxel incoherent motion (IVIM) model in DWI. We explored the potential of several deep learning architectures to incorporate spatial information using self-supervised and supervised learning. We assessed performance quantitatively using novel fractal-noise-based synthetic data, which provide ground truths possessing spatial correlations. Additionally, we present results of the approach applied to in vivo DWI data consisting of twelve repetitions from a healthy volunteer. We demonstrate that supervised training on larger patch sizes using attention models leads to substantial performance improvements over both conventional voxelwise model fitting and convolution-based approaches.

To explore the value of histogram parameters derived from intravoxel incoherent motion (IVIM) for predicting response to neoadjuvant chemoradiation (nCRT) in patients with locally advanced rectal cancer (LARC).

A total of 112 patients diagnosed with LARC who underwent IVIM-DWI prior to nCRT were enrolled in this study. The true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and microvascular volume fraction (f) calculated from IVIM were recorded along with the histogram parameters. The patients were classified into the pathological complete response (pCR) group and the non-pCR group according to the tumor regression grade (TRG) system. Additionally, the patients were divided into low T stage (yp T0-2) and high T stage (ypT3-4) according to the pathologic T stage (ypT stage). Univariate logistic regression analysis was implemented to identify independent risk factors, including both clinical characteristics and IVIM histogram parameters. Subsequently, models for Clinical, Histogram, and Combined Clinical and Histogram were constructed using multivariable binary logistic regression analysis for the purpose of predicting pCR. The area under the receiver operating characteristic (ROC) curve (AUCs) was employed to evaluate the diagnostic performance of the three models.

The values of D_ kurtosis, f_mean, and f_ median were significantly higher in the pCR group compared with the non-pCR group (all P < 0.05). The value of D*_ entropy was significantly lower in the pCR group compared with the non-pCR group (P < 0.05). The values of D_ kurtosis, f_mean, and f_ median were significantly higher in the low T stage group compared with the high T stage group (all P < 0.05). The value of D*_ entropy was significantly lower in the low T stage group compared with the high T stage group (P < 0.05). The ROC curves indicated that the Combined Clinical and Histogram model exhibited the best diagnostic performance in predicting the pCR patients with AUCs, sensitivity, specificity, and accuracy of 0.916, 83.33%, 85.23%, and 84.82%.

The histogram parameters derived from IVIM have the potential to identify patients who have achieved pCR. Moreover, the combination of IVIM histogram parameters and clinical characteristics enhanced the diagnostic performance of IVIM histogram parameters.

In conjunction with an epidemiologically determined treatment window, current radiological acute ischemic stroke practice discerns two lesion (stage) types: core (dead tissue, identified by diffusion-weighted imaging (DWI)) and penumbra (tissue region receiving just enough blood flow to be potentially salvageable, identified by the perfusion diffusion mismatch). However, advancements in preclinical and clinical studies have indicated that this approach may be too rigid, warranting a more fine-grained patient-tailored approach. This study aimed to demonstrate the ability to noninvasively provide insights into the current in vivo stroke lesion cascade.

To elucidate a finer-grained depiction of the acute focal ischemic stroke cascade in vivo, we retrospectively applied our multimodal apparent diffusion (MAD) method to multi-b-value DWI, up to a b-value of 10,000 s/mm2 in 34 patients with acute focal ischemic stroke. Fuzzy C Means was used to cluster the MAD parameters.

We discerned 18 clusters consistent with normal appearing tissue (NAT) types and 14 potential ischemic lesion (stage) types, providing insights into the variability and aggressiveness of lesion progression and current anomalous stroke-related imaging features. Of the 529 ischemic stroke lesion instances previously identified by two radiologists, 493 (92 %) were autonomously identified; 460 (87 %) were identified as efficaciously or better than the radiologists.

The data analyzed included a small number of clinical patients without follow-up or contemporaneous histology; therefor, the findings and theorizing should be treated as conjecture. Nevertheless, each identified NAT and lesion type is consistent with the known underpinnings of physiological tissues and pathological ischemic stroke lesion (stage) types. Several findings should be considered in current clinical imaging: WM fluid accumulation, BBB compromise conundrum, b1000 identified core may not be dead tissue, and a practical reason for DWI (pseudo) normalization.

To investigate and explain observed features of the placental DWI signal in healthy and compromised pregnancies using a mathematical model of maternal blood flow.

Thirteen healthy and nine compromised third trimester pregnancies underwent pulse gradient spin echo DWI MRI, with the results compared to MRI data simulated from a 2D mathematical model of maternal blood flow through the placenta. Both sets of data were fitted to an intravoxel incoherent motion (IVIM) model, and a rebound model (defined within text), which described voxels that did not decay monotonically. Both the in vivo and simulated placentas were split into regions of interest (ROIs) to analyze how the signal varies and how IVIM and rebounding parameters change across the placental width.

There was good agreement between the in vivo MRI data, and the data simulated from the mathematical model. Both sets of data included voxels showing a rebounding signal and voxels showing fast signal decay focused near the maternal side of the placenta. In vivo we found higherf IVIM $$ {f}_{IVIM} $$ in the uterine wall and near the maternal side of the placenta, with the slow diffusion coefficientD $$ D $$ reduced in all ROIs in compromised pregnancy.

A simulation based entirely on maternal blood explains key features observed in placental DWI, indicating the importance of maternal blood flow in interpreting placental MRI data, and providing potential new metrics for understanding changes in compromised placentas.

To compare intravoxel incoherent motion (IVIM) imaging to dynamic susceptibility-weighted contrast (DSC) perfusion MRI in differentiating tumor recurrence from treatment-induced changes.

Our prospective study included patients previously treated with radiotherapy for intracranial tumors who later developed a new or increasing contrast-enhancing lesion. The final diagnosis was based on neuropathology or 6-month follow-up. MR examinations were performed for calculation of the perfusion fraction (f) using the IVIM technique and relative blood volume (rCBV) using DSC perfusion. Measurements of f and rCBV were made by two independent readers in hotspots when possible, but otherwise in the whole enhancing region. Measures of rCBV were normalized to the contralateral region. Receiver operating characteristics (ROC) analysis was performed.

Sixty patients (35 men, median age 49, range 20-77) were evaluated. Forty-four patients had tumor recurrence and 16 had treatment-induced changes. Mean f was 0.090 for tumors and 0.058 for treatment-induced changes (p = 0.002). Mean rCBV was 3.52 and 1.79, respectively (p = 0.002). The area under the curve (AUC) in the ROC analysis was 0.72 for f and 0.77 for rCBV. Cutoff values of 0.073 for f and 2.26 for rCBV yielded equal values for sensitivity (73%), specificity (75%), and accuracy (73%). The 90th percentile value of rCBV was 4.77 for tumors and 2.53 for treatment-induced changes (p = 0.0004) and yielded the highest AUC (0.79) and a sensitivity/specificity/accuracy of 80%/75%/78% at cutoff value 3.25.

The accuracy of the IVIM parameter f is similar to that of rCBV in differentiating tumor recurrence from treatment-induced changes.

This study aimed to dynamically evaluate the attenuating effects of donafenib combined with carvedilol using intra-voxel incoherent motion (IVIM) MRI at different time points of disease course in a thioacetamide (TAA)-induced hepatic fibrosis rat model.

In this study, 40 male Sprague-Dawley rats received TAA for 6 weeks to induce liver fibrosis and were divided into four groups randomly (N = 10). From week 3 to week 6 of modeling, each group of rats received daily gavage of vehicle, carvedilol (CARV), donafenib (DON), and donafenib plus carvedilol (DON + CARV), respectively. IVIM MRI was used to assess the degree of liver fibrosis in the above groups at 0, 2, 4, and 6 weeks after modeling. Liver fibrosis was classified according to the METAVIR scoring system (F0-F4). IVIM parameters were calculated using a biexponential fitting model, and a least-squares fitting approach was applied for parameter estimation.

The mean pathological collagen areas and the expression of α-SMA and collagen I in the CARV, DON, and DON + CARV groups were significantly less than that in the vehicle group (P < 0.001). IVIM-derived parameters (D, D*, and f) and ADC values were negatively correlated with the fibrosis levels (D: r2 = 0.594, P < 0.001; D*: r2 = 0.556, P < 0.001; f: r2 = 0.737, P < 0.001; ADC: r2 = 0.694, P < 0.001). At 4 and 6 weeks after modeling, the mean IVIM parameters and ADC values of the DON + CARV group were significantly higher than those of the vehicle group.

IVIM MRI is a noninvasive and valuable dynamic monitoring tool for liver fibrosis, and it was useful to monitor the dynamic inhibition process of donafenib and carvedilol on liver fibrosis in a TAA-induced rat model.

To explore whether a combination of clinico-radiological factors and histogram parameters based on monoexponential, biexponential, and stretched exponential models derived from the whole-tumor volume on diffusion-weighted imaging (DWI) could predict Ki-67 expression in hepatocellular carcinoma(HCC).

Histogram parameters based on whole-tumor volumes were derived from monoexponential model, biexponential model, and stretched exponential model. Histogram parameters were compared between HCCs with high and low Ki-67 expression. Multivariate logistic regression and receiver operating characteristic curves were used to assess the ability to predict Ki-67 expression (expression index ≤ 20% vs. >20%).

In the training and test set, the 5th percentile of distributed diffusion coefficient (DDC) yielded the area under the curve (AUC) value of 0.816 (95% CI 0.713 to 0.894) and 0.867 (95% CI 0.655 to 0.972), respectively. Multivariable analysis showed that alpha-fetoprotein (AFP) level, skewness of perfusion fraction(f), and 5th percentile of DDC were independent predictors of high Ki-67 expression in HCCs. In the training and test sets, the AUC of the combined model for predicting high Ki-67 expression in HCCs were 0.902 (95% CI 0.814 to 0.957) and 0.908 (95% CI 0.707 to 0.989), respectively.

Histogram parameters of multiple mathematical DWI models can be useful for predicting high Ki-67 expression in HCCs, and our combined model based on AFP level, skewness of f, and 5th percentile of DDC may be an effective approach for predicting Ki-67 expression in HCCs.

To evaluate how intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) histogram analysis contribute to assessing complete response (CR) to neoadjuvant therapy (NAT) in locally advanced rectal cancer (LARC).

In this prospective study, participants with LARC, who underwent NAT and subsequent surgery, with adequate MR image quality, were enrolled from November 2021 to March 2023. Conventional MRI (T2WI and DWI), IVIM, and DKI were performed before NAT (pre-NAT) and within two weeks before surgery (post-NAT). Image evaluation was independently performed by two experienced radiologists. Pathological complete response (pCR) was used as the reference standard. An IVIM-DKI-added model (a combination of IVIM and DKI histogram parameters with T2WI and DWI) was constructed. Receiver operating characteristic (ROC) curves were generated to evaluate the diagnostic performance of conventional MRI and the IVIM-DKI-added model.

A total of 59 participants (median age: 58.00 years [IQR: 52.00, 62.00]; 38 [64%] men) were evaluated, including 21 pCR and 38 non-pCR cases. The histogram parameters of DKI, including skewness of kurtosis post-NAT (post-KSkewness) and root mean squared of change ratio of diffusivity (Δ%DDKI-root mean squared), were entered into the IVIM-DKI-added model. The area under the ROC curve (AUC) of the IVIM-DKI-added model for assessing CR to NAT was significantly higher than that of conventional MRI (0.855 [95% CI: 0.749-0.960] vs 0.685 [95% CI: 0.565-0.806], p < 0.001).

IVIM and DKI provide added value in the evaluation of CR to NAT in LARC.

Question The current conventional imaging evaluation system lacks adequacy for assessing CR to NAT in LARC. Findings Significantly improved diagnostic performance was observed with the histogram analysis of IVIM and DKI in conjunction with conventional MRI. Clinical relevance IVIM and DKI provide significant value in evaluating CR to NAT in LARC, which bears significant implications for reducing surgical complications and facilitating organ preservation.

Crohn's disease (CD) is a chronic inflammatory disease of the gastrointestinal tract in which repeated episodes of acute inflammation may lead to long-term bowel damage. Cross-sectional imaging is used in conjunction with endoscopy to diagnose and monitor disease and detect complications. Magnetic resonance imaging (MRI) has demonstrable utility in evaluating inflammatory activity. However, subjective interpretation of conventional MR sequences is limited in its ability to fully phenotype the underlying histopathological processes in chronic disease. In particular, conventional MRI can be confounded by the presence of mural fibrosis and muscle hypertrophy, which can mask or sometimes mimic inflammation. Quantitative MRI (qMRI) methods provide a means to better differentiate mural inflammation from fibrosis and improve quantification of these processes. qMRI may also provide more objective measures of disease activity and enable better tailoring of treatment. Here, we review quantitative MRI methods for imaging the small bowel in CD and consider the path to their clinical translation. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

This study investigates the feasibility of multi-b-value, multi-directional diffusion MRI for assessing the anisotropy of the cerebral pseudo-diffusion (D*)-tensor. We examine D*-tensor's potential to (1) reflect CSF and blood flow, and (2) detect microvascular architectural alterations in cerebral small vessel disease (cSVD) and aging.

Multi-b-value diffusion MRI was acquired in 32 gradient directions for 11 healthy volunteers, and in six directions for 29 patients with cSVD and 14 controls at 3 T. A physics-informed neural network was used to estimate intravoxel incoherent motion (IVIM)-DTI model parameters, including the parenchymal slow diffusion (D-)tensor and the pseudo-diffusion (D*)-tensor, from which the fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were derived. Comparisons of D*-tensor metrics were made between lateral, third, and fourth ventricles and between the middle cerebral arteries and superior sagittal sinus. Group differences in D*-tensor metrics in normal-appearing white matter were analyzed using multivariable linear regression, correcting for age and sex.

D*-anisotropy aligned well with CSF flow and arterial blood flow. FA(D*), MD(D*), AD(D*), and RD(D*) were highest in the third, moderate in the fourth, and lowest in the lateral ventricles. The arteries showed higher MD(D*), AD(D*), and RD(D*) than the sagittal sinus. Higher FA(D*) in the normal-appearing white matter was related to cSVD diagnosis and older age, suggesting microvascular architecture alterations.

Multi-b-value, multi-directional diffusion analysis using the IVIM-DTI model enables assessment of the cerebral microstructure, fluid flow, and microvascular architecture, providing information on neurodegeneration, glymphatic waste clearance, and the vasculature in one measurement.