All T1-weighted images are built upon one of two fundamental pulse sequences, spin-echo and gradient echo, each of which has distinct signal characteristics and clinical applications. Moreover, within each broadly defined category of T1-weighting, acquisition parameters can be modified to affect image quality, contrast, and scan duration; each tailored sequence has unique advantages, drawbacks, clinical indications, and potential artifacts. In this review, we describe key features that distinguish different types of T1-weighted sequences and discuss the utility of each sequence for specific clinical settings, including neuro-oncology, vasculopathy, and pediatric neuroradiology. In addition, we provide case examples from our institution that illustrate common artifacts and pitfalls associated with image interpretation. The findings described herein provide a framework to individualize the imaging protocol based on patient presentation and clinical indication.

Using deep learning-based techniques to overcome physical limitations and explore the potential performance of 0.2 T low-field unshielded MRI in terms of imaging quality and speed.

First, fast and high-quality unshielded imaging is achieved using active electromagnetic shielding and basic super-resolution. Then, the speed of basic super-resolution imaging is further improved by reducing the number of excitations. Next, the feasibility of using cross-field super-resolution to map low-field low-resolution images to high-field ultra-high-resolution images is analyzed. Finally, by cascading basic and cross-field super-resolution, the quality of the low-field low-resolution image is improved to the level of the high-field ultra-high-resolution image.

Under unshielded conditions, our 0.2 T scanner can achieve image quality comparable to that of a 1.5 T scanner (acquisition resolution of 512 × 512, spatial resolution of 0.45 mm2), and a single-orientation imaging time of less than 3.3 min.

The proposed strategy overcomes the physical limitations of the hardware and rapidly acquires images close to the high-field level on a low-field unshielded MRI scanner. These findings have significant practical implications for the advances in MRI technology, supporting the shift from conventional scanners to point-of-care imaging systems.

Muscle invasive bladder cancer (MIBC) is aggressive and requires radical cystectomy and neoadjuvant therapy, yet over 40% of patients face recurrence. The loss of the bladder also significantly reduces quality of life. Accurate staging, crucial for treatment decisions, is typically done through transurethral resection (TURBT), but inconsistencies in pathology affect diagnosis in 25% of cases. MRI is the most precise imaging method for evaluating local tumor invasiveness. This review discusses recent advances in MRI for staging MIBC and predicting responses to neoadjuvant therapy.

Vesical imaging - reporting and data system (VI-RADS) accuracy may improve if combined with ADC maps and tumor contact length, while a bi-parametric MRI approach without contrast could reduce side effects without losing diagnostic precision, though evidence is mixed. VI-RADS shows promise in predicting neoadjuvant therapy responses, and the new nacVI-RADS score is in development. Non-Gaussian diffusion-weighted imaging techniques and machine learning could enhance accuracy but need more integration with mpMRI. VI-RADS may assist in evaluating responses in bladder-sparing regimens. Urodrill, an MRI-guided biopsy, aims to replace diagnostic TURBT but needs more accuracy data.

MRI in MIBC is evolving, offering potential for accurate local staging and reduced side effects by avoiding TURBT. Predicting neoadjuvant treatment response could guide personalized treatment and bladder preservation. Larger trials are needed to validate these findings.

Medical imaging agents, i.e., contrast agents for magnetic resonance imaging (MRI) and radiopharmaceuticals, play a vital role in the diagnosis of diseases. Yet, they mostly contain harmful and non-biodegradable substances, such as per- and polyfluoroalkyl substances (PFAS), heavy metals or radionuclides. As a result of their increasing clinical use, these agents are entering various water bodies and soil, posing risks to environment and human health. Here, the environmental effects of the application of imaging agents are outlined for the major imaging modalities, and the respective chemistry of the contrast agents with environmental implications is linked. Recommendations are introduced for the design and application of contrast agents: the 3Cs of imaging agents: control, change, and combine; and recent approaches for more sustainable imaging strategies are highlighted. This combination of measures should engage an open discussion, inspire solutions to reduce pollution by imaging agents, and increase awareness for the impact of toxic waste related to imaging agents.

Novel noninvasive imaging modalities such as quantitative magnetic resonance imaging (qMRI) and shear wave elastography (SWE) allow for assessment of soft tissue microstructure and composition, which ultimately may be associated with functional and material properties.

To provide a narrative review of the scientific techniques and clinical applications of qMRI and SWE for the evaluation of soft tissue about the knee and shoulder, including the meniscus, the anterior cruciate ligament (ACL), and the rotator cuff.

Review.

A literature search was performed in October 2022 via PubMed using the following keywords: "quantitative MRI tendon," quantitative MRI ligament,""quantitative MRI cartilage," or "shear wave elastography tendon." Only articles related to clinical applications were included in this review.

Conventional imaging techniques, including standard morphologic magnetic resonance imaging (MRI) and ultrasound imaging, have limited ability to evaluate the material and functional properties of soft tissue; qMRI builds on the limitations of conventional morphologic MRI by allowing for detection of early articular cartilage changes, differentiation of healed versus unhealed meniscal tissue, and quantification of ACL graft maturity. SWE can evaluate the material properties of rotator cuff and Achilles tendons after injury, which may provide insight into both the chronicity and the healing status of the aforementioned injuries.

Our review of the literature showed that quantitative imaging techniques, including qMRI and SWE, may both improve early detection of pathology and aid in comprehensive evaluation after treatment.

Magnetic resonance imaging (MRI) remains a cornerstone of diagnostic imaging, offering unparalleled insights into anatomical structures and pathological conditions. Gadolinium-based contrast agents have long been the standard in MRI enhancement, yet concerns over nephrogenic systemic fibrosis have spurred interest in metal-free alternatives. Nitroxide radical-based MRI contrast agents (NO-CAs) have emerged as promising candidates, leveraging their biocompatibility and imaging capabilities. This review summaries the latest advancements in NO-CAs, focusing on synthesis methodologies, influencing effects of structures of NO-CAs on relaxation efficiency and their applications across various clinical contexts. Comprehensive discussions encompass small molecular, polymeric, and nano-sized NO-CAs, detailing their unique properties and potential clinical utilities. Despite challenges, NO-CAs represent a dynamic area of research poised to revolutionize MRI diagnostics. This review serves as a critical resource for researchers and practitioners seeking to navigate the evolving landscape of MRI contrast agents.

Advances in magnetic resonance imaging (MRI) have revolutionized disease detection and treatment planning. However, as the volume and complexity of MRI data grow with increasing heterogeneity between institutions in imaging protocol, scanner technology, and data labeling, there is a need for a standardized methodology to efficiently identify, characterize, and label MRI sequences. Such a methodology is crucial for advancing research efforts that incorporate MRI data from diverse populations to develop robust machine learning models. This research utilizes convolutional neural networks (CNNs) to automatically classify sequence, orientation, and contrast, specifically tailored for abdominal MRI. Three distinct CNN models with similar backbone architectures were trained to classify single image slices into one of 12 sequences, 4 orientations, and 2 contrast classes. Results derived from this method demonstrate high levels of performance for the three specialized CNN models, with model accuracies for sequence, orientation, and contrast of 96.9%, 97.4%, and 97.3%, respectively.

To propose a deep learning-based low-field mobile MRI strategy for fast, high-quality, unshielded imaging using minimal hardware resources.

Firstly, we analyze the correlation of EMI signals between the sensing coil and the MRI coil to preliminarily verify the feasibility of active EMI shielding using a single sensing coil. Then, a powerful deep learning EMI elimination model is proposed, which can accurately predict the EMI components in the MRI coil signals using EMI signals from at least one sensing coil. Further, deep learning models with different task objectives (super-resolution and denoising) are strategically stacked for multi-level post-processing to enable fast and high-quality low-field MRI. Finally, extensive phantom and brain experiments were conducted on a home-built 0.2 T mobile brain scanner for the evaluation of the proposed strategy.

20 healthy volunteers were recruited to participate in the experiment. The results show that the proposed strategy enables the 0.2 T scanner to generate images with sufficient anatomical information and diagnostic value under unshielded conditions using a single sensing coil. In particular, the EMI elimination outperforms the state-of-the-art deep learning methods and numerical computation methods. In addition, 2 × super-resolution (DDSRNet) and denoising (SwinIR) techniques enable further improvements in imaging speed and quality.

The proposed strategy enables low-field mobile MRI scanners to achieve fast, high-quality imaging under unshielded conditions using minimal hardware resources, which has great significance for the widespread deployment of low-field mobile MRI scanners.

Autism Spectrum Disorder (ASD) necessitates comprehensive management, addressing complex challenges in social communication, behavioral regulation, and sensory processing, for which wearable technologies offer valuable tools to monitor and support interventions. Therefore, this review explores recent advancements in wearable technology, categorizing devices based on executive function, psychomotor skills, and the behavioral/emotional/sensory domain, highlighting their potential to improve ongoing management and intervention. To ensure rigor and comprehensiveness, the review employs a PRISMA-based methodology. Specifically, literature searches were conducted across diverse databases, focusing on studies published between 2014 and 2024, to identify the most commonly used wearables in ASD research. Notably, 55.45% of the 110 devices analyzed had an undefined FDA status, 23.6% received 510(k) clearance, and only a small percentage were classified as FDA Breakthrough Devices or in the submission process. Additionally, approximately 50% of the devices utilized sensors like ECG, EEG, PPG, and EMG, highlighting their widespread use in real-time physiological monitoring. Our work comprehensively analyzes a wide array of wearable technologies, including emerging and advanced. While these technologies have the potential to transform ASD management through real-time data collection and personalized interventions, improved clinical validation and user-centered design are essential for maximizing their effectiveness and user acceptance.

Neuropathic pain is a severe form of chronic pain due to a lesion or disease of the somatosensory nervous system. Here we provide an overview of the neuroimaging approaches that can be used to assess brain abnormalities in a chronic pain condition, with particular focus on people with neuropathic pain and then summarize the findings of studies that applied these methodologies to study neuropathic pain. First, we review the most commonly used approaches to examine grey and white matter abnormalities using magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) and then review functional neuroimaging techniques to measure regional activity and inter-regional communication using functional MRI, electroencephalography (EEG) and magnetoencephalography (MEG). In neuropathic pain the most prominent structural abnormalities have been found to be in the primary somatosensory cortex, insula, anterior cingulate cortex and thalamus, with differences in volume directionality linked to neuropathic pain symptomology. Functional connectivity findings related to treatment outcome point to a potential clinical utility. Some prominent abnormalities in neuropathic pain identified with EEG and MEG throughout the dynamic pain connectome are slowing of alpha activity and higher regional oscillatory activity in the theta and alpha band, lower low beta and higher high beta band power. Finally, connectivity and coupling findings placed into context how regional abnormalities impact the networks and pathways of the dynamic pain connectome. Overall, functional and structural neuroimaging have the potential to identify predictive biomarkers that can be used to guide development of personalized pain management of neuropathic pain.

There is a growing interest in developing paramagnetic nanoparticles as responsive magnetic resonance imaging (MRI) contrast agents, which feature switchable T1 image contrast of water protons upon biochemical cues for better discerning diseases. However, performing an MRI is pragmatically limited by its cost and availability. Hence, a facile, routine method for measuring the T1 contrast is highly desired in early-stage development. This work presents a single-point inversion recovery (IR) nuclear magnetic resonance (NMR) method that can rapidly evaluate T1 contrast change by employing a single, optimized IR pulse sequence that minimizes water signal for "off-state" nanoparticles and allows for sensitively measuring the signal change with "switch-on" T1 contrast. Using peptide-induced liposomal gadopentetic acid (Gd3+-DTPA) release and redox-sensitive manganese oxide (MnO2) nanoparticles as a demonstration of generality, this method successfully evaluates the T1 shortening of water protons caused by liposomal Gd3+-DTPA release and Mn2+ formation from MnO2 reduction. Furthermore, the NMR measurement is highly correlated to T1-weighted MRI scans, suggesting its feasibility to predict the MRI results at the same field strength. This NMR method can be a low-cost, time-saving alternative for pre-MRI evaluation for a diversity of responsive T1 contrast systems.

According to the Relational Developmental Systems perspective, the development of individual differences in spatial thinking (e.g., mental rotation, spatial reorientation, and spatial language) are attributed to various psychological (e.g., children's cognitive strategies), biological (e.g., structure and function of hippocampus), and cultural systems (e.g., caregiver spatial language input). Yet, measuring the development of individual differences in spatial thinking in young children, as well as the psychological, biological, and cultural systems that influence the development of these abilities, presents unique challenges. The current paper outlines ways to harness available technology including eye-tracking, eye-blink conditioning, MRI, Zoom, and LENA technology, to study the development of individual differences in young children's spatial thinking. The technologies discussed offer ways to examine children's spatial thinking development from different levels of analyses (i.e., psychological, biological, cultural), thereby allowing us to advance the study of developmental theory. We conclude with a discussion of the use of artificial intelligence.

Multimodal medical imaging plays a pivotal role in clinical diagnosis and research, as it combines information from various imaging modalities to provide a more comprehensive understanding of the underlying pathology. Recently, deep learning-based multimodal fusion techniques have emerged as powerful tools for improving medical image classification. This review offers a thorough analysis of the developments in deep learning-based multimodal fusion for medical classification tasks. We explore the complementary relationships among prevalent clinical modalities and outline three main fusion schemes for multimodal classification networks: input fusion, intermediate fusion (encompassing single-level fusion, hierarchical fusion, and attention-based fusion), and output fusion. By evaluating the performance of these fusion techniques, we provide insight into the suitability of different network architectures for various multimodal fusion scenarios and application domains. Furthermore, we delve into challenges related to network architecture selection, handling incomplete multimodal data management, and the potential limitations of multimodal fusion. Finally, we spotlight the promising future of Transformer-based multimodal fusion techniques and give recommendations for future research in this rapidly evolving field.

Challenges arise in achieving high-resolution Magnetic Resonance Imaging (MRI) to improve disease diagnosis accuracy due to limitations in hardware, patient discomfort, long acquisition times, and high costs. While Convolutional Neural Networks (CNNs) have shown promising results in MRI super-resolution, they often don't look into the structural similarity and prior information available in consecutive MRI slices. By leveraging information from sequential slices, more robust features can be obtained, potentially leading to higher-quality MRI slices. We propose a multi-slice two-dimensional (2D) MRI super-resolution network that combines a Generative Adversarial Network (GAN) with feature fusion and a pre-trained slice interpolation network to achieve three-dimensional (3D) super-resolution. The proposed model requires consecutively acquired three low-resolution (LR) MRI slices along a specific axis, and achieves the reconstruction of the MRI slices in the remaining two axes. The network effectively enhances both in-plane and out-of-plane resolution along the sagittal axis while addressing computational and memory constraints in 3D super-resolution. The proposed generator has a in-plane and out-of-plane Attention (IOA) network that fuses both in-plane and out-plane features of MRI dynamically. In terms of out-of-plane attention, the network merges features by considering the similarity distance between features and for in-plane attention, the network employs a two-level pyramid structure with varying receptive fields to extract features at different scales, ensuring the inclusion of both global and local features. Subsequently, to achieve 3D MRI super-resolution, a pre-trained slice interpolation network is used that takes two consecutive super-resolved MRI slices to generate a new intermediate slice. To further enhance the network performance and perceptual quality, we introduce a feature up-sampling layer and a feature extraction block with Scaled Exponential Linear Unit (SeLU). Moreover, our super-resolution network incorporates VGG loss from a fine-tuned VGG-19 network to provide additional enhancement. Through experimental evaluations on the IXI dataset and BRATS dataset, using the peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM) and the number of training parameters, we demonstrate the superior performance of our method compared to the existing techniques. Also, the proposed model can be adapted or modified to achieve super-resolution for both 2D and 3D MRI data.

The purpose of this review is to serve as a concise learning tool for clinicians interested in quickly learning more about cardiac magnetic resonance imaging (CMR) and its physical principles. There is heavy coverage of the basic physical fundamentals of CMR as well as updates on the history, clinical indications, cost-effectiveness, role of artificial intelligence in CMR, and examples of common late gadolinium enhancement (LGE) patterns. This literature review was performed by searching the PubMed database for the most up-to-date literature regarding these topics. Relevant, less up-to-date articles, covering the history and physics of CMR, were also obtained from the PubMed database. Clinical indications for CMR include adult congenital heart disease, cardiac ischemia, cardiomyopathies, and heart failure. CMR has a projected cost-benefit ratio of 0.58, leading to potential savings for patients. Despite its utility, CMR has some drawbacks including long image processing times, large space requirements for equipment, and patient discomfort during imaging. Artificial intelligence-based algorithms can address some of these drawbacks by decreasing image processing times and may have reliable diagnostic capabilities. CMR is quickly rising as a high-resolution, non-invasive cardiac imaging modality with an increasing number of clinical indications. Thanks to technological advancements, especially in artificial intelligence, the benefits of CMR often outweigh its drawbacks.

Alzheimer's disease is the most common cause of dementia and a leading cause of mortality in the elderly population. Diagnosis of Alzheimer's disease has traditionally relied on evaluation of clinical symptoms for cognitive impairment with a definitive diagnosis requiring post-mortem demonstration of neuropathology. However, advances in disease pathogenesis have revealed that patients exhibit Alzheimer's disease pathology several decades before the manifestation of clinical symptoms. Magnetic resonance imaging (MRI) plays an important role in the management of patients with Alzheimer's disease. The clinical availability of molecular MRI (mMRI) contrast agents can revolutionize the diagnosis of Alzheimer's disease. In this article, we review advances in nanoparticle contrast agents, also referred to as nanoprobes, for mMRI of Alzheimer's disease. This article is categorized under: Diagnostic Tools > In Vivo Nanodiagnostics and Imaging Therapeutic Approaches and Drug Discovery > Nanomedicine for Neurological Disease.

Brain extraction, or skull-stripping, is an essential data preprocessing step for machine learning approaches to brain MRI analysis. Currently, there are limited extraction algorithms for the neonatal brain. We aim to adapt an established deep learning algorithm for the automatic segmentation of neonatal brains from MRI, trained on a large multi-institutional dataset for improved generalizability across image acquisition parameters. Our model, ANUBEX (automated neonatal nnU-Net brain MRI extractor), was designed using nnU-Net and was trained on a subset of participants (N = 433) enrolled in the High-dose Erythropoietin for Asphyxia and Encephalopathy (HEAL) study. We compared the performance of our model to five publicly available models (BET, BSE, CABINET, iBEATv2, ROBEX) across conventional and machine learning methods, tested on two public datasets (NIH and dHCP). We found that our model had a significantly higher Dice score on the aggregate of both data sets and comparable or significantly higher Dice scores on the NIH (low-resolution) and dHCP (high-resolution) datasets independently. ANUBEX performs similarly when trained on sequence-agnostic or motion-degraded MRI, but slightly worse on preterm brains. In conclusion, we created an automatic deep learning-based neonatal brain extraction algorithm that demonstrates accurate performance with both high- and low-resolution MRIs with fast computation time.

Climate change mandates that we take steps to understand and mitigate the negative environmental consequences of the practice of health care, so that health care advances sustainably. In this article, the authors review and discuss a sample of technical and administrative advances required to align the practice of radiology with principles of environmental sustainability.

Structural magnetic resonance imaging (MRI) is a powerful tool to visualize 3D neuroanatomy and assess pathology and disease progression in neurodegenerative disorders such as Huntington's disease (HD). The development of mouse models of HD that reproduce many of the psychiatric, motor and cognitive impairments observed in human HD has improved our understanding of the disease and provided opportunities for testing novel therapies. Similar to the clinical scenario, MRI of mouse models of HD demonstrates onset and progression of brain pathology. Here, we provided an overview of the articles that used structural MRI in mouse models of HD to date, highlighting the differences between studies and models and describing gaps in the current state of knowledge and recommendations for future studies.

Medical imaging, also known as radiology, is the field of medicine in which medical professionals recreate various images of parts of the body for diagnostic or treatment purposes. Medical imaging procedures include non-invasive tests that allow doctors to diagnose injuries and diseases without being intrusive TechTarget (n.d.). A number of tools and techniques are used to automate the analysis of medical images acquired with various image processing methods. The brain is one of the largest and most complex organs of the human body and anomaly detection from brain images (i.e., MRI, CT, PET, etc.) is one of the major research areas of medical image analysis. Image processing methods such as filtering and thresholding models, geometry models, graph models, region-based analysis, connected component analysis, machine learning (ML) models, the recent deep learning (DL) models, and various hybrid models are used in brain image analysis. Brain tumors are one of the most common brain diseases with a high mortality rate, and it is difficult to analyze from brain images for the versatility of the shape, location, size, texture, and other characteristics. In this paper, a comprehensive review on brain tumor image analysis is presented with basic ideas of brain tumor, brain imaging, brain image analysis tasks, brain image analysis models, brain tumor image features, performance metrics used for evaluating the models, and some available datasets on brain tumor/medical images. Some challenges of brain tumor analysis are also discussed including suggestions for future research directions. The graphical abstract summarizes the contributions of this paper.

Since the introduction of MRI as a sustainable diagnostic modality, global accessibility to its services has revealed a wide discrepancy between populations-leaving most of the population in LMICs without access to this important imaging modality. Several factors lead to the scarcity of MRI in LMICs; for example, inadequate infrastructure and the absence of a dedicated workforce are key factors in the scarcity observed. RAD-AID has contributed to the advancement of radiology globally by collaborating with our partners to make radiology more accessible for medically underserved communities. However, progress is slow and further investment is needed to ensure improved global access to MRI.

Skeletal metastatic disease accounts for significant overall morbidity in cancer patients. Accurate and accessible imaging forms an integral part of the investigation for patients with suspected or known skeletal metastatic disease; it is considered indispensable in making appropriate oncological treatment decisions. Magnetic resonance imaging (MRI) is a contemporary imaging modality that provides excellent spatial and contrast resolution for bone and soft tissues. Therefore, it is particularly useful for imaging patients suffering from metastatic skeletal disease. This review provides a fundamental overview of the physics and image generation of MRI. The most commonly used MRI sequences in the investigation of metastatic skeletal disease are also discussed. Additionally, a review of the pathophysiological basis of metastatic bone disease is presented, along with an introduction to the interpretation of MRI sequences obtained for metastatic bone disease. Finally, the strengths and drawbacks of MRI are considered in comparison to alternative imaging modalities for the investigation of this common and important oncological complication.

The concept of a 2D cylindrical High Pass Ladder (2D c-HPL) is used in the development of this ultra high radio frequency (UHRF) volumetric head coil for 7T tuned at the Larmor frequency of 298 MHz. The architecture of the 2D c-HPL helps to overcome the challenges associated with non-uniform magnetic field distribution. The prototype consists of an individual resonating array of inductance-capacitance (LC) elements and each component is tuned to the precisef o frequency. The tuning of the (i) inductance, (ii) capacitance, (iii) mesh size, and (iv) coupling coefficient play critical roles to attain the desired Larmor frequency. For this proof-of-concept, the prototype of a volumetric head coil consists of a cylindrical array size of 4 ×6, with individual LC components of inductance magnitude, 98 nH and four fixed value capacitors and one tunable capacitor that allowed to achieve the desired precession frequency,f r = 298 M H z . The model was tested for three differentf o values of 269 MHz, 275 MHz and 286 MHz. The mutual coupling and the eigenfrequencies were compared through bench testing and dispersion equation. The experimental data were in good agreement (< 5%) with the theoretical eigenfrequencies from the dispersion relation. The theoretical eigenfrequencies and the experimental eigenfrequencies are in good agreement for eigenmodes (1,2), (1,3), (2,2), (2,3) and (4,3).

Advances in artificial intelligence (AI), especially deep learning (DL), have facilitated magnetic resonance imaging (MRI) data analysis, enabling AI-assisted medical image diagnoses and prognoses. However, most of the DL models are considered as "black boxes". There is an unmet need to demystify DL models so domain experts can trust these high-performance DL models. This has resulted in a sub-domain of AI research called explainable artificial intelligence (XAI). In the last decade, many experts have dedicated their efforts to developing novel XAI methods that are competent at visualizing and explaining the logic behind data-driven DL models. However, XAI techniques are still in their infancy for medical MRI image analysis. This study aims to outline the XAI applications that are able to interpret DL models for MRI data analysis. We first introduce several common MRI data modalities. Then, a brief history of DL models is discussed. Next, we highlight XAI frameworks and elaborate on the principles of multiple popular XAI methods. Moreover, studies on XAI applications in MRI image analysis are reviewed across the tissues/organs of the human body. A quantitative analysis is conducted to reveal the insights of MRI researchers on these XAI techniques. Finally, evaluations of XAI methods are discussed. This survey presents recent advances in the XAI domain for explaining the DL models that have been utilized in MRI applications.

Medical imaging is an important tool for accurate medical diagnosis, while state-of-the-art image reconstruction algorithms raise critical challenges in massive data processing for high-speed and high-quality imaging. Here, we present a memristive image reconstructor (MIR) to greatly accelerate image reconstruction with discrete Fourier transformation (DFT) by computing-in-memory (CIM) with memristor arrays. A high-accuracy quasi-analogue mapping (QAM) method and generic complex matrix transfer (CMT) scheme was proposed to improve the mapping precision and transfer efficiency, respectively. High-fidelity magnetic resonance imaging (MRI) and computed tomography (CT) image reconstructions were demonstrated, achieving software-equivalent qualities and DICE scores after segmentation with nnU-Net algorithm. Remarkably, our MIR exhibited 153× and 79× improvements in energy efficiency and normalized image reconstruction speed, respectively, compared to graphics processing unit (GPU). This work demonstrates MIR as a promising high-fidelity image reconstruction platform for future medical diagnosis, and also largely extends the application of memristor-based CIM beyond artificial neural networks.

A diagnosis of pancreatic cancer carries a 5-y survival rate of less than 10%. Furthermore, the detection of pancreatic cancer occurs most often in later stages of the disease due to its location in the retroperitoneum and lack of symptoms (in most cases) until tumors become more advanced. Once diagnosed, cross-sectional imaging techniques are heavily utilized to determine the tumor stage and the potential for surgical resection. However, a major determinant of resectability is the extent of local vascular involvement of the mesenteric vessels and critical tributaries; current imaging techniques have limited capacity to accurately determine vascular involvement. Surrounding inflammation and fibrosis can be difficult to discriminate from viable tumor, making determination of the degree of vascular involvement unreliable. New innovations in fluorescence and optoacoustic imaging techniques may overcome these limitations and make determination of resectability more accurate. These imaging modalities are able to more clearly discern between viable tumor tissue and non-neoplastic inflammation or desmoplasia, allowing clinicians to more reliably characterize vascular involvement and develop individualized treatment plans for patients. This review will discuss the current imaging techniques used to diagnose pancreatic cancer, the barriers that current techniques raise to accurate staging, and novel fluorescence and optoacoustic imaging techniques that may provide more accurate clinical staging of pancreatic cancer.

Novel nanomaterials are of interest in biology, medicine, and imaging applications. Multimodal fluorescent-magnetic nanoparticles demand special attention because they have the potential to be employed as diagnostic and medication-delivery tools, which, in turn, might make it easier to diagnose and treat cancer, as well as a wide variety of other disorders. The most recent advancements in the development of magneto-fluorescent nanocomposites and their applications in the biomedical field are the primary focus of this review. We describe the most current developments in synthetic methodologies and methods for the fabrication of magneto-fluorescent nanocomposites. The primary applications of multimodal magneto-fluorescent nanoparticles in biomedicine, including biological imaging, cancer treatment, and drug administration, are covered in this article, and an overview of the future possibilities for these technologies is provided.

Magnetic resonance imaging (MRI) uses a large magnetic field and radio waves to generate images of tissues in the body. Conventional MRI techniques have been developed to image and quantify tissues and fluids with long transverse relaxation times (T2s), such as muscle, cartilage, liver, white matter, gray matter, spinal cord, and cerebrospinal fluid. However, the body also contains many tissues and tissue components such as the osteochondral junction, menisci, ligaments, tendons, bone, lung parenchyma, and myelin, which have short or ultrashort T2s. After radio frequency excitation, their transverse magnetizations typically decay to zero or near zero before the receiving mode is enabled for spatial encoding with conventional MR imaging. As a result, these tissues appear dark, and their MR properties are inaccessible. However, when ultrashort echo times (UTEs) are used, signals can be detected from these tissues before they decay to zero. This review summarizes recent technical developments in UTE MRI of tissues with short and ultrashort T2 relaxation times. A series of UTE MRI techniques for high-resolution morphological and quantitative imaging of these short-T2 tissues are discussed. Applications of UTE imaging in the musculoskeletal, nervous, respiratory, gastrointestinal, and cardiovascular systems of the body are included.

Imaging is integral part of cervical cancer management. Currently, MRI is used for staging, follow up and image guided adaptive brachytherapy. The ongoing IQ-EMBRACE sub-study is evaluating the use of MRI for functional imaging to aid in the assessment of hypoxia, metabolism, hemodynamics and tissue structure. This study reviews the current and potential future utilization of functional MRI imaging in diagnosis and management of cervical cancer.

We searched PubMed for articles characterizing the uses of functional MRI (fMRI) for cervical cancer. The current literature regarding these techniques in diagnosis and outcomes for cervical cancer were then reviewed.

The most used fMRI techniques identified for use in cervical cancer include diffusion weighted imaging (DWI) and dynamic contrast enhancement (DCE). DCE-MRI indirectly reflects tumor perfusion and hypoxia. This has been utilized to either characterize a functional risk volume of tumor with low perfusion or to characterize at-risk tumor voxels by analyzing signal intensity both pre-treatment and during treatment. DCE imaging in these situations has been associated with local control and disease-free survival and may have predictive/prognostic significance, however this has not yet been clinically validated. DWI allows for creation of ADC maps, that assists with diagnosis of local malignancy or nodal disease with high sensitivity and specificity. DWI findings have also been correlated with local control and overall survival in patients with an incomplete response after definitive chemoradiotherapy and thus may assist with post-treatment follow up. Other imaging techniques used in some instances are MR-spectroscopy and perfusion weighted imaging. T2-weighted imaging remains the standard technique used for diagnosis and radiation treatment planning. In many instances, it is unclear what additional information functional-MRI techniques provide compared to standard MRI imaging.

Functional MRI provides potential for improved diagnosis, prediction of treatment response and prognostication in cervical cancer. Specific sequences such as DCE, DWI and ADC need to be validated in a large prospective setting prior to widespread use. The ongoing IQ-EMBRACE study will provide important clinical information regarding these imaging modalities.

Pain is an unpleasant sensory and emotional experience. Understanding the neural mechanisms of acute and chronic pain and the brain changes affecting pain factors is important for finding pain treatment methods. The emergence and progress of non-invasive neuroimaging technology can help us better understand pain at the neural level. Recent developments in identifying brain-based biomarkers of pain through advances in advanced imaging can provide some foundations for predicting and detecting pain. For example, a neurologic pain signature (involving brain regions that receive nociceptive afferents) and a stimulus intensity-independent pain signature (involving brain regions that do not show increased activity in proportion to noxious stimulus intensity) were developed based on multivariate modeling to identify processes related to the pain experience. However, an accurate and comprehensive review of common neuroimaging techniques for evaluating pain is lacking. This paper reviews the mechanism, clinical application, reliability, strengths, and limitations of common neuroimaging techniques for assessing pain to promote our further understanding of pain.

Nowadays, about 30% of magnetic resonance imaging (MRI) exams need contrast agents (CAs) to improve the sensitivity and quality of the images for accurate diagnosis. Here, a multifunctional nano-agent with ring-like vortex-domain iron oxide as core and gadolinium oxide as shell (vortex nanoring Fe₃O₄ @Gd₂O₃, abbreviated as VNFG) was firstly designed and prepared for highly enhanced T₁-T₂ dual-modality magnetic resonance imaging (MRI)-guided magnetic thermal cancer therapy. After thorough characterization, the core-shell structure of VNFG was confirmed. Moreover, the excellent heat generation property (SAR=984.26 W/g) of the proposed VNFG under alternating magnetic fields was firmly demonstrated. Furthermore, both in vitro and in vivo studies have revealed a good preliminary indication of VNFG's biological compatibility, dual-modality enhancing feature and antitumor efficacy. This work demonstrates that the proposed VNFG can be a high-performance tumor diagnosis and theranostic treatment agent and may have great potential for clinical application in the future.

Magnetic resonance imaging (MRI) has a key role to play at multiple steps of the radiotherapy (RT) treatment planning and delivery process. Development of high-precision RT techniques such as intensity-modulated RT, stereotactic ablative RT, and particle beam therapy has enabled oncologists to escalate RT dose to the target while restricting doses to organs at risk (OAR). MRI plays a critical role in target volume delineation in various disease sites, thus ensuring that these high-precision techniques can be safely implemented. Accurate identification of gross disease has also enabled selective dose escalation as a means to widen the therapeutic index. Morphological and functional MRI sequences have also facilitated an understanding of temporal changes in target volumes and OAR during a course of RT, allowing for midtreatment volumetric and biological adaptation. The latest advancement in linear accelerator technology has led to the incorporation of an MRI scanner in the treatment unit. MRI-guided RT provides the opportunity for MRI-only workflow along with online adaptation for either target or OAR or both. MRI plays a key role in post-treatment response evaluation and is an important tool for guiding decision making. In this review, we briefly discuss the RT-related applications of MRI in the management of brain, prostate, and GI malignancies.

Anterior cruciate ligament (ACL) tear represents a common orthopedic traumatic issue that often leads to an early development of osteoarthritis. To improve the diagnostic and prognostic techniques involved in the assessment of the joint after the trauma and during the healing process, the present work proposes a multi-parametric approach that aims to investigate the relationship between joint function and soft tissue status before and after ACL reconstruction.

Thirteen consecutive patients who underwent ACL reconstruction were preliminarily enrolled in this study. Joint laxity assessment as well as magnetic resonance imaging with T2 mapping were performed in the pre-operative stage, at four and 18 months after surgery to acquire objective information to correlate knee function and soft tissue condition.

Correlations were found between graft and cartilage T2 signal, suggesting an interplay between these tissues within the knee joint. Moreover, graft maturation resulted in being connected to joint laxity, as underlined by the correlation between the graft T2 signal and the temporal evolution of knee function.

This preliminary study represents a step forward in assessing the effects of ACL graft maturation on knee biomechanics, and vice versa. The presented integrated framework underlines the possibility to quantitatively assess the impact of ACL reconstruction on trauma recovery and cartilage homeostasis. Moreover, the reported findings-despite the preliminary nature of the clinical impacts-evidence the possibility of monitoring the surgery outcomes using a multi-parametric prognostic investigation tool.

Magnetic resonance-guided focused ultrasound (MRgFUS) is a completely non-invasive technology that has been approved by FDA to treat several diseases. This report, prepared by the American Association of Physicist in Medicine (AAPM) Task Group 241, provides background on MRgFUS technology with a focus on clinical body MRgFUS systems. The report addresses the issues of interest to the medical physics community, specific to the body MRgFUS system configuration, and provides recommendations on how to successfully implement and maintain a clinical MRgFUS program. The following sections describe the key features of typical MRgFUS systems and clinical workflow and provide key points and best practices for the medical physicist. Commonly used terms, metrics and physics are defined and sources of uncertainty that affect MRgFUS procedures are described. Finally, safety and quality assurance procedures are explained, the recommended role of the medical physicist in MRgFUS procedures is described, and regulatory requirements for planning clinical trials are detailed. Although this report is limited in scope to clinical body MRgFUS systems that are approved or currently undergoing clinical trials in the United States, much of the material presented is also applicable to systems designed for other applications.

To enable use of clinical magnetic resonance images (MRIs) to quantify abnormalities in normal appearing (NA) white matter (WM) and gray matter (GM) in multiple sclerosis (MS) and to determine associations with MS-related disability. Identification of these abnormalities heretofore has required specialized scans not routinely available in clinical practice.

We developed an analytic technique which normalizes image intensities based on an intensity atlas for quantification of WM and GM abnormalities in standardized MRIs obtained with clinical sequences. Gaussian mixture modeling is applied to summarize image intensity distributions from T1-weighted and 3D-FLAIR (T2-weighted) images from 5010 participants enrolled in a multinational database of MS patients which collected imaging, neuroperformance and disability measures.

Intensity distribution metrics distinguished MS patients from control participants based on normalized non-lesional signal differences. This analysis revealed non-lesional differences between relapsing MS versus progressive MS subtypes. Further, the correlation between our non-lesional measures and disability was approximately three times greater than that between total lesion volume and disability, measured using the patient derived disease steps. Multivariate modeling revealed that measures of extra-lesional tissue integrity and atrophy contribute uniquely, and approximately equally, to the prediction of MS-related disability.

These results support the notion that non-lesional abnormalities correlate more strongly with MS-related disability than lesion burden and provide new insight into the basis of abnormalities in NA WM. Non-lesional abnormalities distinguish relapsing from progressive MS but do not distinguish between progressive subtypes suggesting a common progressive pathophysiology. Image intensity parameters and existing biomarkers each independently correlate with MS-related disability.

Eu(HP-DO3A) is present in solution as a mixture of two diastereoisomers whose alcoholic groups are the source of the mobile protons for the CEST effect. The exchange is base catalyzed. Two novel Eu^III complexes of HP-DO3A-like ligands containing an amino or a carboxylate functionality in the proximity of the -OH groups showed the occurrence of intramolecular catalysis of the prototropic exchange. New insights into the role of the intramolecular proton exchange on the CEST properties have been gained.

Time-resolved donor-detected Förster resonance energy transfer (trDDFRET) allows the observation of molecular interactions of dye-labeled biomolecules in the ∼10-100 Å region. However, we can observe longer-range interactions when using time-resolved acceptor-detected FRET (trADFRET), since the signal/noise ratio can be improved when observing the acceptor emission. Therefore, we propose a new methodology based on trADFRET to construct a new fluorescence lifetime microscopy (FLIM-trADFRET) technique to observe biological machinery in the range of 100-300 Å in vivo, the last frontier in biomolecular medicine. The integrated trADFRET signal is extracted in such a way that noise is canceled, and more photons are collected, even though trADFRET and trDDFRET have the same rate of transfer. To assess our new methodology, proof of concept was demonstrated with a set of well-defined DNA scaffolds.

Flavor-dependent neutrino emission is critical to the evolution of a supernova and its neutrino signal. In the dense anisotropic interior of the star, neutrino-neutrino forward scattering can lead to fast collective neutrino oscillations, which has striking consequences. We present a theory of fast flavor depolarization, explaining how neutrino flavor differences become smaller, i.e., depolarize, due to diffusion to smaller angular scales. We show that transverse relaxation determines the epoch of this irreversible depolarization. We give a method to compute the depolarized fluxes, presenting an explicit formula for simple initial conditions, which can be a crucial input for supernova theory and neutrino phenomenology.

Complementary information obtained from multiple contrasts of tissue facilitates physicians assessing, diagnosing and planning treatment of a variety of diseases. However, acquiring multiple contrasts magnetic resonance images (MRI) for every patient using multiple pulse sequences is time-consuming and expensive, where, medical image synthesis has been demonstrated as an effective alternative. The purpose of this study is to develop a unified framework for multimodal MR image synthesis.

A unified generative adversarial network consisting of only a single generator and a single discriminator was developed to learn the mappings among images of four different modalities. The generator took an image and its modality label as inputs and learned to synthesize the image in the target modality, while the discriminator was trained to distinguish between real and synthesized images and classify them to their corresponding modalities. The network was trained and tested using multimodal brain MRI consisting of four different contrasts which are T1-weighted (T1), T1-weighted and contrast-enhanced (T1c), T2-weighted (T2), and fluid-attenuated inversion recovery (Flair). Quantitative assessments of our proposed method were made through computing normalized mean absolute error (NMAE), peak signal-to-noise ratio (PSNR), structural similarity index measurement (SSIM), visual information fidelity (VIF), and naturalness image quality evaluator (NIQE).

The proposed model was trained and tested on a cohort of 274 glioma patients with well-aligned multi-types of MRI scans. After the model was trained, tests were conducted by using each of T1, T1c, T2, Flair as a single input modality to generate its respective rest modalities. Our proposed method shows high accuracy and robustness for image synthesis with arbitrary MRI modality that is available in the database as input. For example, with T1 as input modality, the NMAEs for the generated T1c, T2, Flair respectively are 0.034 ± 0.005, 0.041 ± 0.006, and 0.041 ± 0.006, the PSNRs respectively are 32.353 ± 2.525 dB, 30.016 ± 2.577 dB, and 29.091 ± 2.795 dB, the SSIMs are 0.974 ± 0.059, 0.969 ± 0.059, and 0.959 ± 0.059, the VIF are 0.750 ± 0.087, 0.706 ± 0.097, and 0.654 ± 0.062, and NIQE are 1.396 ± 0.401, 1.511 ± 0.460, and 1.259 ± 0.358, respectively.

We proposed a novel multimodal MR image synthesis method based on a unified generative adversarial network. The network takes an image and its modality label as inputs and synthesizes multimodal images in a single forward pass. The results demonstrate that the proposed method is able to accurately synthesize multimodal MR images from a single MR image.

Correcting or reducing the effects of voxel intensity non-uniformity (INU) within a given tissue type is a crucial issue for quantitative magnetic resonance (MR) image analysis in daily clinical practice. Although having no severe impact on visual diagnosis, the INU can highly degrade the performance of automatic quantitative analysis such as segmentation, registration, feature extraction and radiomics. In this study, we present an advanced deep learning based INU correction algorithm called residual cycle generative adversarial network (res-cycle GAN), which integrates the residual block concept into a cycle-consistent GAN (cycle-GAN). In cycle-GAN, an inverse transformation was implemented between the INU uncorrected and corrected magnetic resonance imaging (MRI) images to constrain the model through forcing the calculation of both an INU corrected MRI and a synthetic corrected MRI. A fully convolution neural network integrating residual blocks was applied in the generator of cycle-GAN to enhance end-to-end raw MRI to INU corrected MRI transformation. A cohort of 55 abdominal patients with T1-weighted MR INU images and their corrections with a clinically established and commonly used method, namely, N4ITK were used as a pair to evaluate the proposed res-cycle GAN based INU correction algorithm. Quantitatively comparisons of normalized mean absolute error (NMAE), peak signal-to-noise ratio (PSNR), normalized cross-correlation (NCC) indices, and spatial non-uniformity (SNU) were made among the proposed method and other approaches. Our res-cycle GAN based method achieved an NMAE of 0.011 ± 0.002, a PSNR of 28.0 ± 1.9 dB, an NCC of 0.970 ± 0.017, and a SNU of 0.298 ± 0.085. Our proposed method has significant improvements (p < 0.05) in NMAE, PSNR, NCC and SNU over other algorithms including conventional GAN and U-net. Once the model is well trained, our approach can automatically generate the corrected MR images in a few minutes, eliminating the need for manual setting of parameters.