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Zhao K, Pang K, Hung AL, Zheng H, Yan R, Sung K. A Deep Learning-Based Framework for Highly Accelerated Prostate MR Dispersion Imaging. Cancers (Basel) 2024; 16:2983. [PMID: 39272841 PMCID: PMC11393971 DOI: 10.3390/cancers16172983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2024] [Revised: 08/15/2024] [Accepted: 08/17/2024] [Indexed: 09/15/2024] Open
Abstract
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) measures microvascular perfusion by capturing the temporal changes of an MRI contrast agent in a target tissue, and it provides valuable information for the diagnosis and prognosis of a wide range of tumors. Quantitative DCE-MRI analysis commonly relies on the nonlinear least square (NLLS) fitting of a pharmacokinetic (PK) model to concentration curves. However, the voxel-wise application of such nonlinear curve fitting is highly time-consuming. The arterial input function (AIF) needs to be utilized in quantitative DCE-MRI analysis. and in practice, a population-based arterial AIF is often used in PK modeling. The contribution of intravascular dispersion to the measured signal enhancement is assumed to be negligible. The MR dispersion imaging (MRDI) model was recently proposed to account for intravascular dispersion, enabling more accurate PK modeling. However, the complexity of the MRDI hinders its practical usability and makes quantitative PK modeling even more time-consuming. In this paper, we propose fast MR dispersion imaging (fMRDI) to effectively represent the intravascular dispersion and highly accelerated PK parameter estimation. We also propose a deep learning-based, two-stage framework to accelerate PK parameter estimation. We used a deep neural network (NN) to estimate PK parameters directly from enhancement curves. The estimation from NN was further refined using several steps of NLLS, which is significantly faster than performing NLLS from random initializations. A data synthesis module is proposed to generate synthetic training data for the NN. Two data-processing modules were introduced to improve the model's stability against noise and variations. Experiments on our in-house clinical prostate MRI dataset demonstrated that our method significantly reduces the processing time, produces a better distinction between normal and clinically significant prostate cancer (csPCa) lesions, and is more robust against noise than conventional DCE-MRI analysis methods.
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Affiliation(s)
- Kai Zhao
- Department of Radiological Sciences, University of California, Los Angeles, CA 92521, USA
| | - Kaifeng Pang
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 92521, USA;
| | - Alex LingYu Hung
- Department of Computer Science, University of California, Los Angeles, CA 92521, USA; (A.L.H.); (H.Z.)
| | - Haoxin Zheng
- Department of Computer Science, University of California, Los Angeles, CA 92521, USA; (A.L.H.); (H.Z.)
| | - Ran Yan
- Department of Bioengineering, University of California, Los Angeles, CA 92521, USA;
| | - Kyunghyun Sung
- Department of Radiological Sciences, University of California, Los Angeles, CA 92521, USA
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Zhang Q, Luo X, Zhou L, Nguyen TD, Prince MR, Spincemaille P, Wang Y. Fluid Mechanics Approach to Perfusion Quantification: Vasculature Computational Fluid Dynamics Simulation, Quantitative Transport Mapping (QTM) Analysis of Dynamics Contrast Enhanced MRI, and Application in Nonalcoholic Fatty Liver Disease Classification. IEEE Trans Biomed Eng 2023; 70:980-990. [PMID: 36107908 DOI: 10.1109/tbme.2022.3207057] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE We quantify liver perfusion using quantitative transport mapping (QTM) method that is free of arterial input function (AIF). QTM method is validated in a vasculature computational fluid dynamics (CFD) simulation and is applied for processing dynamic contrast enhanced (DCE) MRI images in differentiating liver with nonalcoholic fatty liver disease (NAFLD) from healthy controls using pathology reference in a preclinical rabbit model. METHODS QTM method was validated on a liver perfusion simulation based on fluid dynamics using a rat liver vasculature model and the mass transport equation. In the NAFLD grading task, DCE MRI images of 7 adult rabbits with methionine choline-deficient diet-induced nonalcoholic steatohepatitis (NASH), 8 adult rabbits with simple steatosis (SS) were acquired and processed using QTM method and dual-input two compartment Kety's method respectively. Statistical analysis was performed on six perfusion parameters: velocity magnitude | u | derived from QTM, liver arterial blood flow LBFa, liver venous blood flow LBFv, permeability Ktrans, blood volume Vp and extravascular space volume Ve averaged in liver ROI. RESULTS In the simulation, QTM method successfully reconstructed blood flow, reduced error by 48% compared to Kety's method. In the preclinical study, only QTM |u| showed significant difference between high grade NAFLD group and low grade NAFLD group. CONCLUSION QTM postprocesses DCE-MRI automatically through deconvolution in space and time to solve the inverse problem of the transport equation. Comparing with Kety's method, QTM method showed higher accuracy and better differentiation in NAFLD classification task. SIGNIFICANCE We propose to apply QTM method in liver DCE MRI perfusion quantification.
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Keil VC, Mädler B, Gieseke J, Fimmers R, Hattingen E, Schild HH, Hadizadeh DR. Effects of arterial input function selection on kinetic parameters in brain dynamic contrast-enhanced MRI. Magn Reson Imaging 2017; 40:83-90. [PMID: 28438713 DOI: 10.1016/j.mri.2017.04.006] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2016] [Revised: 03/20/2017] [Accepted: 04/20/2017] [Indexed: 12/01/2022]
Abstract
PURPOSE Kinetic parameters derived from dynamic contrast-enhanced MRI (DCE-MRI) were suggested as a possible instrument for multi-parametric lesion characterization, but have not found their way into clinical practice yet due to inconsistent results. The quantification is heavily influenced by the definition of an appropriate arterial input functions (AIF). Regarding brain tumor DCE-MRI, there are currently several co-existing methods to determine the AIF frequently including different brain vessels as sources. This study quantitatively and qualitatively analyzes the impact of AIF source selection on kinetic parameters derived from commonly selected AIF source vessels compared to a population-based AIF model. MATERIAL AND METHODS 74 patients with brain lesions underwent 3D DCE-MRI. Kinetic parameters [transfer constants of contrast agent efflux and reflux Ktrans and kep and, their ratio, ve, that is used to measure extravascular-extracellular volume fraction and plasma volume fraction vp] were determined using extended Tofts model in 821 ROI from 4 AIF sources [the internal carotid artery (ICA), the closest artery to the lesion, the superior sagittal sinus (SSS), the population-based Parker model]. The effect of AIF source alteration on kinetic parameters was evaluated by tissue type selective intra-class correlation (ICC) and capacity to differentiate gliomas by WHO grade [area under the curve analysis (AUC)]. RESULTS Arterial AIF more often led to implausible ve >100% values (p<0.0001). AIF source alteration rendered different absolute kinetic parameters (p<0.0001), except for kep. ICC between kinetic parameters of different AIF sources and tissues were variable (0.08-0.87) and only consistent >0.5 between arterial AIF derived kinetic parameters. Differentiation between WHO III and II glioma was exclusively possible with vp derived from an AIF in the SSS (p=0.03; AUC 0.74). CONCLUSION The AIF source has a significant impact on absolute kinetic parameters in DCE-MRI, which limits the comparability of kinetic parameters derived from different AIF sources. The effect is also tissue-dependent. The SSS appears to be the best choice for AIF source vessel selection in brain tumor DCE-MRI as it exclusively allowed for WHO grades II/III and III/IV glioma distinction (by vp) and showed the least number of implausible ve values.
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Affiliation(s)
- Vera C Keil
- Department of Radiology, University Hospital Bonn, Sigmund-Freud-Strasse 25, 53127 Bonn, Germany.
| | - Burkhard Mädler
- Philips Healthcare, Röntgenstrasse 22, 22335 Hamburg, Germany.
| | - Jürgen Gieseke
- Department of Radiology, University Hospital Bonn, Sigmund-Freud-Strasse 25, 53127 Bonn, Germany; Philips Healthcare, Röntgenstrasse 22, 22335 Hamburg, Germany.
| | - Rolf Fimmers
- IMBIE (Statistics Department), University Hospital Bonn, Sigmund-Freud-Strasse 25, 53127 Bonn, Germany.
| | - Elke Hattingen
- Department of Radiology, University Hospital Bonn, Sigmund-Freud-Strasse 25, 53127 Bonn, Germany.
| | - Hans H Schild
- Department of Radiology, University Hospital Bonn, Sigmund-Freud-Strasse 25, 53127 Bonn, Germany.
| | - Dariusch R Hadizadeh
- Department of Radiology, University Hospital Bonn, Sigmund-Freud-Strasse 25, 53127 Bonn, Germany.
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Ramamonjisoa N, Ackerstaff E. Characterization of the Tumor Microenvironment and Tumor-Stroma Interaction by Non-invasive Preclinical Imaging. Front Oncol 2017; 7:3. [PMID: 28197395 PMCID: PMC5281579 DOI: 10.3389/fonc.2017.00003] [Citation(s) in RCA: 74] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2016] [Accepted: 01/05/2017] [Indexed: 12/13/2022] Open
Abstract
Tumors are often characterized by hypoxia, vascular abnormalities, low extracellular pH, increased interstitial fluid pressure, altered choline-phospholipid metabolism, and aerobic glycolysis (Warburg effect). The impact of these tumor characteristics has been investigated extensively in the context of tumor development, progression, and treatment response, resulting in a number of non-invasive imaging biomarkers. More recent evidence suggests that cancer cells undergo metabolic reprograming, beyond aerobic glycolysis, in the course of tumor development and progression. The resulting altered metabolic content in tumors has the ability to affect cell signaling and block cellular differentiation. Additional emerging evidence reveals that the interaction between tumor and stroma cells can alter tumor metabolism (leading to metabolic reprograming) as well as tumor growth and vascular features. This review will summarize previous and current preclinical, non-invasive, multimodal imaging efforts to characterize the tumor microenvironment, including its stromal components and understand tumor-stroma interaction in cancer development, progression, and treatment response.
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Affiliation(s)
- Nirilanto Ramamonjisoa
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ellen Ackerstaff
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Lu-Emerson C, Duda DG, Emblem KE, Taylor JW, Gerstner ER, Loeffler JS, Batchelor TT, Jain RK. Lessons from anti-vascular endothelial growth factor and anti-vascular endothelial growth factor receptor trials in patients with glioblastoma. J Clin Oncol 2015; 33:1197-213. [PMID: 25713439 PMCID: PMC4517055 DOI: 10.1200/jco.2014.55.9575] [Citation(s) in RCA: 140] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Treatment of glioblastoma (GBM), the most common primary malignant brain tumor in adults, remains a significant unmet need in oncology. Historically, cytotoxic treatments provided little durable benefit, and tumors recurred within several months. This has spurred a substantial research effort to establish more effective therapies for both newly diagnosed and recurrent GBM. In this context, antiangiogenic therapy emerged as a promising treatment strategy because GBMs are highly vascular tumors. In particular, GBMs overexpress vascular endothelial growth factor (VEGF), a proangiogenic cytokine. Indeed, many studies have demonstrated promising radiographic response rates, delayed tumor progression, and a relatively safe profile for anti-VEGF agents. However, randomized phase III trials conducted to date have failed to show an overall survival benefit for antiangiogenic agents alone or in combination with chemoradiotherapy. These results indicate that antiangiogenic agents may not be beneficial in unselected populations of patients with GBM. Unfortunately, biomarker development has lagged behind in the process of drug development, and no validated biomarker exists for patient stratification. However, hypothesis-generating data from phase II trials that reveal an association between increased perfusion and/or oxygenation (ie, consequences of vascular normalization) and survival suggest that early imaging biomarkers could help identify the subset of patients who most likely will benefit from anti-VEGF agents. In this article, we discuss the lessons learned from the trials conducted to date and how we could potentially use recent advances in GBM biology and imaging to improve outcomes of patients with GBM who receive antiangiogenic therapy.
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Affiliation(s)
- Christine Lu-Emerson
- All authors, Massachusetts General Hospital Cancer Center and Harvard Medical School, Boston, MA
| | - Dan G Duda
- All authors, Massachusetts General Hospital Cancer Center and Harvard Medical School, Boston, MA
| | - Kyrre E Emblem
- All authors, Massachusetts General Hospital Cancer Center and Harvard Medical School, Boston, MA
| | - Jennie W Taylor
- All authors, Massachusetts General Hospital Cancer Center and Harvard Medical School, Boston, MA
| | - Elizabeth R Gerstner
- All authors, Massachusetts General Hospital Cancer Center and Harvard Medical School, Boston, MA
| | - Jay S Loeffler
- All authors, Massachusetts General Hospital Cancer Center and Harvard Medical School, Boston, MA
| | - Tracy T Batchelor
- All authors, Massachusetts General Hospital Cancer Center and Harvard Medical School, Boston, MA
| | - Rakesh K Jain
- All authors, Massachusetts General Hospital Cancer Center and Harvard Medical School, Boston, MA.
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Hsu YHH, Huang Z, Ferl GZ, Ng CM. GPU-accelerated compartmental modeling analysis of DCE-MRI data from glioblastoma patients treated with bevacizumab. PLoS One 2015; 10:e0118421. [PMID: 25786263 PMCID: PMC4364976 DOI: 10.1371/journal.pone.0118421] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2014] [Accepted: 12/25/2014] [Indexed: 01/21/2023] Open
Abstract
The compartment model analysis using medical imaging data is the well-established but extremely time consuming technique for quantifying the changes in microvascular physiology of targeted organs in clinical patients after antivascular therapies. In this paper, we present a first graphics processing unit-accelerated method for compartmental modeling of medical imaging data. Using this approach, we performed the analysis of dynamic contrast-enhanced magnetic resonance imaging data from bevacizumab-treated glioblastoma patients in less than one minute per slice without losing accuracy. This approach reduced the computation time by more than 120-fold comparing to a central processing unit-based method that performed the analogous analysis steps in serial and more than 17-fold comparing to the algorithm that optimized for central processing unit computation. The method developed in this study could be of significant utility in reducing the computational times required to assess tumor physiology from dynamic contrast-enhanced magnetic resonance imaging data in preclinical and clinical development of antivascular therapies and related fields.
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Affiliation(s)
- Yu-Han H. Hsu
- Division of Clinical Pharmacology and Therapeutics, The Children's Hospital of Philadelphia, Philadelphia, PA, United States of America
| | - Ziyin Huang
- Division of Clinical Pharmacology and Therapeutics, The Children's Hospital of Philadelphia, Philadelphia, PA, United States of America
| | - Gregory Z. Ferl
- Early Development Pharmacokinetics and Pharmacodynamics, Genentech, South San Francisco, CA, United States of America
| | - Chee M. Ng
- Division of Clinical Pharmacology and Therapeutics, The Children's Hospital of Philadelphia, Philadelphia, PA, United States of America
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
- * E-mail:
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Wang CH, Yin FF, Horton J, Chang Z. Review of treatment assessment using DCE-MRI in breast cancer radiation therapy. World J Methodol 2014; 4:46-58. [PMID: 25332905 PMCID: PMC4202481 DOI: 10.5662/wjm.v4.i2.46] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2013] [Revised: 12/31/2013] [Accepted: 02/18/2014] [Indexed: 02/06/2023] Open
Abstract
As a noninvasive functional imaging technique, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is being used in oncology to measure properties of tumor microvascular structure and permeability. Studies have shown that parameters derived from certain pharmacokinetic models can be used as imaging biomarkers for tumor treatment response. The use of DCE-MRI for quantitative and objective assessment of radiation therapy has been explored in a variety of methods and tumor types. However, due to the complexity in imaging technology and divergent outcomes from different pharmacokinetic approaches, the method of using DCE-MRI in treatment assessment has yet to be standardized, especially for breast cancer. This article reviews the basic principles of breast DCE-MRI and recent studies using DCE-MRI in treatment assessment. Technical and clinical considerations are emphasized with specific attention to assessment of radiation treatment response.
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Bergamino M, Bonzano L, Levrero F, Mancardi GL, Roccatagliata L. A review of technical aspects of T1-weighted dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in human brain tumors. Phys Med 2014; 30:635-43. [PMID: 24793824 DOI: 10.1016/j.ejmp.2014.04.005] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2013] [Revised: 03/18/2014] [Accepted: 04/08/2014] [Indexed: 12/11/2022] Open
Abstract
In the last few years, several imaging methods, such as magnetic resonance imaging (MRI) and computed tomography, have been used to investigate the degree of blood-brain barrier (BBB) permeability in patients with neurological diseases including multiple sclerosis, ischemic stroke, and brain tumors. One promising MRI method for assessing the BBB permeability of patients with neurological diseases in vivo is T1-weighted dynamic contrast-enhanced (DCE)-MRI. Here we review the technical issues involved in DCE-MRI in the study of human brain tumors. In the first part of this paper, theoretical models for the DCE-MRI analysis will be described, including the Toft-Kety models, the adiabatic approximation to the tissue homogeneity model and the two-compartment exchange model. These models can be used to estimate important kinetic parameters related to BBB permeability. In the second part of this paper, details of the data acquisition, issues related to the arterial input function, and procedures for DCE-MRI image analysis are illustrated.
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Affiliation(s)
- M Bergamino
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Genoa, Italy; Magnetic Resonance Research Centre on Nervous System Diseases, University of Genoa, Genoa, Italy.
| | - L Bonzano
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Genoa, Italy; Magnetic Resonance Research Centre on Nervous System Diseases, University of Genoa, Genoa, Italy
| | - F Levrero
- Department of Medical Physics, San Martino Hospital, Genoa, Italy
| | - G L Mancardi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Genoa, Italy; Magnetic Resonance Research Centre on Nervous System Diseases, University of Genoa, Genoa, Italy
| | - L Roccatagliata
- Magnetic Resonance Research Centre on Nervous System Diseases, University of Genoa, Genoa, Italy; Department of Health Sciences, University of Genoa, Genoa, Italy
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