Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE‐MRI derived biomarkers in multicenter oncology trials

Physiological properties of tumors can be measured both in vivo and noninvasively by diffusion‐weighted imaging and dynamic contrast‐enhanced magnetic resonance imaging. Although these techniques have been used for more than two decades to study tumor diffusion, perfusion, and/or permeability, the methods and studies on how to reduce measurement error and bias in the derived imaging metrics is still lacking in the literature. This is of paramount importance because the objective is to translate these quantitative imaging biomarkers (QIBs) into clinical trials, and ultimately in clinical practice. Standardization of the image acquisition using appropriate phantoms is the first step from a technical performance standpoint. The next step is to assess whether the imaging metrics have clinical value and meet the requirements for being a QIB as defined by the Radiological Society of North America's Quantitative Imaging Biomarkers Alliance (QIBA). The goal and mission of QIBA and the National Cancer Institute Quantitative Imaging Network (QIN) initiatives are to provide technical performance standards (QIBA profiles) and QIN tools for producing reliable QIBs for use in the clinical imaging community. Some of QIBA's development of quantitative diffusion‐weighted imaging and dynamic contrast‐enhanced QIB profiles has been hampered by the lack of literature for repeatability and reproducibility of the derived QIBs. The available research on this topic is scant and is not in sync with improvements or upgrades in MRI technology over the years. This review focuses on the need for QIBs in oncology applications and emphasizes the importance of the assessment of their reproducibility and repeatability.

[1]  Johannes Buurman,et al.  The influence of temporal resolution in determining pharmacokinetic parameters from DCE‐MRI data , 2010, Magnetic resonance in medicine.

[2]  W. Willinek,et al.  Contrast‐enhanced timing robust acquisition order with a preparation of the longitudinal signal component (CENTRA plus) for 3D contrast‐enhanced abdominal imaging , 2008, Journal of magnetic resonance imaging : JMRI.

[3]  D. Collins,et al.  Development of a temperature-controlled phantom for magnetic resonance quality assurance of diffusion, dynamic, and relaxometry measurements. , 2016, Medical physics.

[4]  Yousef Mazaheri,et al.  Prostate cancer aggressiveness: assessment with whole-lesion histogram analysis of the apparent diffusion coefficient. , 2014, Radiology.

[5]  Xiangyu Yang,et al.  Improving the pharmacokinetic parameter measurement in dynamic contrast‐enhanced MRI by use of the arterial input function: Theory and clinical application , 2008, Magnetic resonance in medicine.

[6]  Thomas Hambrock,et al.  Relationship between apparent diffusion coefficients at 3.0-T MR imaging and Gleason grade in peripheral zone prostate cancer. , 2011, Radiology.

[7]  A. Sahgal,et al.  The predictive capacity of apparent diffusion coefficient (ADC) in response assessment of brain metastases following radiation , 2016, Clinical & Experimental Metastasis.

[8]  Katarzyna J Macura,et al.  Reply to Erik Rud and Eduard Baco's Letter to the Editor re: Re: Jeffrey C. Weinreb, Jelle O. Barentsz, Peter L. Choyke, et al. PI-RADS Prostate Imaging - Reporting and Data System: 2015, Version 2. Eur Urol 2016;69:16-40. , 2016, European urology.

[9]  J. Bussink,et al.  Diffusion-weighted MR imaging in liver metastases of colorectal cancer: reproducibility and biological validation , 2013, European Radiology.

[10]  J. M. Taylor,et al.  Diffusion magnetic resonance imaging: an early surrogate marker of therapeutic efficacy in brain tumors. , 2000, Journal of the National Cancer Institute.

[11]  T. Chenevert,et al.  Diffusion MRI in early cancer therapeutic response assessment , 2017, NMR in biomedicine.

[12]  Wei Huang,et al.  A feasible high spatiotemporal resolution breast DCE-MRI protocol for clinical settings. , 2012, Magnetic resonance imaging.

[13]  G. Weinstein,et al.  Diffusion-Weighted Magnetic Resonance Imaging for Predicting and Detecting Early Response to Chemoradiation Therapy of Squamous Cell Carcinomas of the Head and Neck , 2009, Clinical Cancer Research.

[14]  Erich P Huang,et al.  Metrology Standards for Quantitative Imaging Biomarkers. , 2015, Radiology.

[15]  C. Ng,et al.  Reproducibility of perfusion parameters in dynamic contrast-enhanced MRI of lung and liver tumors: effect on estimates of patient sample size in clinical trials and on individual patient responses. , 2010, AJR. American journal of roentgenology.

[16]  D. Barboriak,et al.  Repeatability of quantitative parameters derived from diffusion tensor imaging in patients with glioblastoma multiforme , 2009, Journal of magnetic resonance imaging : JMRI.

[17]  Anwar R. Padhani,et al.  Diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) for monitoring anticancer therapy , 2010, Targeted Oncology.

[18]  Thomas E Yankeelov,et al.  A novel AIF tracking method and comparison of DCE-MRI parameters using individual and population-based AIFs in human breast cancer , 2011, Physics in medicine and biology.

[19]  Nola M. Hylton,et al.  Diffusion-weighted MRI: influence of intravoxel fat signal and breast density on breast tumor conspicuity and apparent diffusion coefficient measurements. , 2011, Magnetic resonance imaging.

[20]  Xiangyu Yang,et al.  Quantifying Tumor Vascular Heterogeneity with Dynamic Contrast-Enhanced Magnetic Resonance Imaging: A Review , 2011, Journal of biomedicine & biotechnology.

[21]  Thomas E. Yankeelov,et al.  Multisite concordance of apparent diffusion coefficient measurements across the NCI Quantitative Imaging Network , 2017, Journal of medical imaging.

[22]  Namkug Kim,et al.  Apparent diffusion coefficient: Prostate cancer versus noncancerous tissue according to anatomical region , 2008, Journal of magnetic resonance imaging : JMRI.

[23]  L. Turnbull,et al.  Repeatability of echo-planar-based diffusion measurements of the human prostate at 3 T. , 2007, Magnetic resonance imaging.

[24]  B K Rutt,et al.  Temporal sampling requirements for the tracer kinetics modeling of breast disease. , 1998, Magnetic resonance imaging.

[25]  C Coolens,et al.  Development of a dynamic flow imaging phantom for dynamic contrast-enhanced CT. , 2011, Medical physics.

[26]  Nancy A Obuchowski,et al.  Quantitative imaging biomarkers: Effect of sample size and bias on confidence interval coverage , 2018, Statistical methods in medical research.

[27]  S. Venneti,et al.  Non-invasive metabolic imaging of brain tumours in the era of precision medicine , 2016, Nature Reviews Clinical Oncology.

[28]  Wei Huang,et al.  Accuracy, repeatability, and interplatform reproducibility of T1 quantification methods used for DCE‐MRI: Results from a multicenter phantom study , 2018, Magnetic resonance in medicine.

[29]  Yong Chen,et al.  MR Fingerprinting for Rapid Quantitative Abdominal Imaging. , 2016, Radiology.

[30]  S. Furui,et al.  Brain gadolinium deposition after administration of gadolinium-based contrast agents , 2015, Japanese Journal of Radiology.

[31]  D. Nishimura,et al.  Reduced field‐of‐view DWI with robust fat suppression and unrestricted slice coverage using tilted 2D RF excitation , 2016, Magnetic resonance in medicine.

[32]  H. Merisaari,et al.  Optimization of b‐value distribution for biexponential diffusion‐weighted MR imaging of normal prostate , 2014, Journal of magnetic resonance imaging : JMRI.

[33]  Mitchell D Schnall,et al.  Neoadjuvant Chemotherapy for Breast Cancer: Functional Tumor Volume by MR Imaging Predicts Recurrence-free Survival-Results from the ACRIN 6657/CALGB 150007 I-SPY 1 TRIAL. , 2016, Radiology.

[34]  Amita Shukla-Dave,et al.  Role of MRI in prostate cancer detection , 2014, NMR in biomedicine.

[35]  Wei Huang,et al.  Early Prediction and Evaluation of Breast Cancer Response to Neoadjuvant Chemotherapy Using Quantitative DCE-MRI1 , 2016, Translational oncology.

[36]  Geoffrey S. Payne,et al.  DCE-MRI, DW-MRI, and MRS in Cancer: Challenges and Advantages of Implementing Qualitative and Quantitative Multi-parametric Imaging in the Clinic , 2016, Topics in magnetic resonance imaging : TMRI.

[37]  Amita Shukla-Dave,et al.  Tumor metabolism and perfusion in head and neck squamous cell carcinoma: pretreatment multimodality imaging with 1H magnetic resonance spectroscopy, dynamic contrast-enhanced MRI, and [18F]FDG-PET. , 2012, International journal of radiation oncology, biology, physics.

[38]  Xia Li,et al.  Analyzing Spatial Heterogeneity in DCE- and DW-MRI Parametric Maps to Optimize Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer. , 2014, Translational oncology.

[39]  N. Obuchowski,et al.  Linearity, Bias, and Precision of Hepatic Proton Density Fat Fraction Measurements by Using MR Imaging: A Meta-Analysis. , 2017, Radiology.

[40]  C. Meyer,et al.  Diffusion coefficient measurement using a temperature‐controlled fluid for quality control in multicenter studies , 2011, Journal of magnetic resonance imaging : JMRI.

[41]  D. Bihan Apparent Diffusion Coefficient and Beyond: What Diffusion MR Imaging Can Tell Us about Tissue Structure , 2013 .

[42]  M. Holz,et al.  Temperature-dependent self-diffusion coefficients of water and six selected molecular liquids for calibration in accurate 1H NMR PFG measurements , 2000 .

[43]  Mehmet Kocak,et al.  Pediatric brain tumor consortium multisite assessment of apparent diffusion coefficient z-axis variation assessed with an ice-water phantom. , 2015, Academic radiology.

[44]  A. Pfefferbaum,et al.  Replicability of diffusion tensor imaging measurements of fractional anisotropy and trace in brain , 2003, Journal of magnetic resonance imaging : JMRI.

[45]  D. Schnyer,et al.  Toward Precision and Reproducibility of Diffusion Tensor Imaging: A Multicenter Diffusion Phantom and Traveling Volunteer Study , 2016, American Journal of Neuroradiology.

[46]  Brian D Ross,et al.  Predicting and monitoring cancer treatment response with diffusion‐weighted MRI , 2010, Journal of magnetic resonance imaging : JMRI.

[47]  L. Axel,et al.  Rapid B1+ mapping using a preconditioning RF pulse with TurboFLASH readout , 2010, Magnetic resonance in medicine.

[48]  C. Thng,et al.  Dynamic contrast‐enhanced MRI of neuroendocrine hepatic metastases: A feasibility study using a dual‐input two‐compartment model , 2011, Magnetic resonance in medicine.

[49]  Noam Nissan,et al.  Diffusion‐weighted breast MRI: Clinical applications and emerging techniques , 2017, Journal of magnetic resonance imaging : JMRI.

[50]  Hamid Soltanian-Zadeh,et al.  Model selection for DCE‐T1 studies in glioblastoma , 2012, Magnetic resonance in medicine.

[51]  Kay Nehrke,et al.  T1 corrected B1 mapping using multi‐TR gradient echo sequences , 2010, Magnetic resonance in medicine.

[52]  Stuart A. Taylor,et al.  UK quantitative WB-DWI technical workgroup: consensus meeting recommendations on optimisation, quality control, processing and analysis of quantitative whole-body diffusion-weighted imaging for cancer , 2017, The British journal of radiology.

[53]  Sandra Nuyts,et al.  Diffusion-weighted magnetic resonance imaging early after chemoradiotherapy to monitor treatment response in head-and-neck squamous cell carcinoma. , 2012, International journal of radiation oncology, biology, physics.

[54]  J R Griffiths,et al.  Clinical studies. , 2005, Advances in pharmacology.

[55]  Elise Bannier,et al.  Dynamic contrast‐enhanced MRI: Study of inter‐software accuracy and reproducibility using simulated and clinical data , 2016, Journal of magnetic resonance imaging : JMRI.

[56]  Nicholas Petrick,et al.  Statistical Issues in Testing Conformance with the Quantitative Imaging Biomarker Alliance (QIBA) Profile Claims. , 2016, Academic radiology.

[57]  D Le Bihan,et al.  Temperature mapping with MR imaging of molecular diffusion: application to hyperthermia. , 1989, Radiology.

[58]  Ron Kikinis,et al.  Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge. , 2014, Translational oncology.

[59]  A. Jackson,et al.  Candidate Biomarkers of Extravascular Extracellular Space: A Direct Comparison of Apparent Diffusion Coefficient and Dynamic Contrast-Enhanced MR Imaging—Derived Measurement of the Volume of the Extravascular Extracellular Space in Glioblastoma Multiforme , 2010, American Journal of Neuroradiology.

[60]  B. Taouli,et al.  Comparison Between 3-Scan Trace and Diagonal Body Diffusion-Weighted Imaging Acquisitions: A Phantom and Volunteer Study , 2016, Tomography.

[61]  D. Margolis,et al.  PI-RADS Prostate Imaging - Reporting and Data System: 2015, Version 2. , 2016, European urology.

[62]  Rafal Panek,et al.  The emerging potential of magnetic resonance imaging in personalizing radiotherapy for head and neck cancer: an oncologist's perspective. , 2017, The British journal of radiology.

[63]  A. Padhani,et al.  Functional magnetic resonance imaging of the liver: parametric assessments beyond morphology. , 2010, Magnetic resonance imaging clinics of North America.

[64]  A. Padhani,et al.  Apparent diffusion coefficient measurements as very early predictive markers of response to chemotherapy in hepatic metastasis: A preliminary investigation of reproducibility and diagnostic value , 2014, Journal of magnetic resonance imaging : JMRI.

[65]  Jianhua Yao,et al.  Automatic Determination of Arterial Input Function for Dynamic Contrast Enhanced MRI in Tumor Assessment , 2008, MICCAI.

[66]  James F. Gimpel,et al.  Performance Observations of Scanner Qualification of NCI-Designated Cancer Centers: Results From the Centers of Quantitative Imaging Excellence (CQIE) Program. , 2017, Academic radiology.

[67]  A. Jackson,et al.  Experimentally‐derived functional form for a population‐averaged high‐temporal‐resolution arterial input function for dynamic contrast‐enhanced MRI , 2006, Magnetic resonance in medicine.

[68]  D. Koh,et al.  Diffusion-weighted imaging of the liver: an update , 2013, Cancer imaging : the official publication of the International Cancer Imaging Society.

[69]  Wendy B DeMartini,et al.  Breast DCE-MRI: influence of postcontrast timing on automated lesion kinetics assessments and discrimination of benign and malignant lesions. , 2014, Academic radiology.

[70]  A. Oto,et al.  Arterial input functions (AIFs) measured directly from arteries with low and standard doses of contrast agent, and AIFs derived from reference tissues. , 2016, Magnetic resonance imaging.

[71]  P. Tofts Modeling tracer kinetics in dynamic Gd‐DTPA MR imaging , 1997, Journal of magnetic resonance imaging : JMRI.

[72]  Wei Huang,et al.  Discrimination of benign and malignant breast lesions by using shutter-speed dynamic contrast-enhanced MR imaging. , 2011, Radiology.

[73]  T. Yankeelov,et al.  Repeatability, reproducibility, and accuracy of quantitative mri of the breast in the community radiology setting , 2018, Journal of magnetic resonance imaging : JMRI.

[74]  P. Choyke,et al.  Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. , 2009, Neoplasia.

[75]  H. Huisman,et al.  Imaging vascular function for early stage clinical trials using dynamic contrast-enhanced magnetic resonance imaging , 2012, European Radiology.

[76]  H. Huisman,et al.  Interpatient variation in normal peripheral zone apparent diffusion coefficient: effect on the prediction of prostate cancer aggressiveness. , 2012, Radiology.

[77]  Kaori Togashi,et al.  MRI artifact reduction and quality improvement in the upper abdomen with PROPELLER and prospective acquisition correction (PACE) technique. , 2008, AJR. American journal of roentgenology.

[78]  P. Basser,et al.  Polyvinylpyrrolidone (PVP) water solutions as isotropic phantoms for diffusion MRI studies , 2008 .

[79]  A. Oto,et al.  Diffusion-weighted and dynamic contrast-enhanced MRI of prostate cancer: correlation of quantitative MR parameters with Gleason score and tumor angiogenesis. , 2011, AJR. American journal of roentgenology.

[80]  James G Pipe,et al.  Multishot diffusion‐weighted FSE using PROPELLER MRI , 2002, Magnetic resonance in medicine.

[81]  Li Feng,et al.  Dynamic contrast‐enhanced MRI of the prostate with high spatiotemporal resolution using compressed sensing, parallel imaging, and continuous golden‐angle radial sampling: Preliminary experience , 2015, Journal of magnetic resonance imaging : JMRI.

[82]  David Bonekamp,et al.  Diffusion tensor imaging in children and adolescents: Reproducibility, hemispheric, and age-related differences , 2007, NeuroImage.

[83]  Yue Cao,et al.  Correction of arterial input function in dynamic contrast‐enhanced MRI of the liver , 2012, Journal of magnetic resonance imaging : JMRI.

[84]  B. Dale,et al.  Improved T1, contrast concentration, and pharmacokinetic parameter quantification in the presence of fat with two‐point dixon for dynamic contrast‐enhanced magnetic resonance imaging , 2016, Magnetic resonance in medicine.

[85]  Allen W. Song,et al.  A robust multi-shot scan strategy for high-resolution diffusion weighted MRI enabled by multiplexed sensitivity-encoding (MUSE) , 2013, NeuroImage.

[86]  M. Miquel,et al.  Diffusion-weighted magnetic resonance imaging in cancer: Reported apparent diffusion coefficients, in-vitro and in-vivo reproducibility. , 2016, World journal of radiology.

[87]  Kim Mouridsen,et al.  The QUASAR reproducibility study, Part II: Results from a multi-center Arterial Spin Labeling test–retest study , 2010, NeuroImage.

[88]  Erich P Huang,et al.  Meta-analysis of the technical performance of an imaging procedure: Guidelines and statistical methodology , 2015, Statistical methods in medical research.

[89]  John Kornak,et al.  Gradient nonlinearity correction to improve apparent diffusion coefficient accuracy and standardization in the american college of radiology imaging network 6698 breast cancer trial , 2015, Journal of magnetic resonance imaging : JMRI.

[90]  C. Roehrborn,et al.  Multiparametric Magnetic Resonance Imaging of the Prostate: Technical Aspects and Role in Clinical Management , 2014, Topics in magnetic resonance imaging : TMRI.

[91]  J. Kurhanewicz,et al.  Practical aspects of prostate MRI: hardware and software considerations, protocols, and patient preparation , 2016, Abdominal Radiology.

[92]  Lawrence Tanenbaum,et al.  Diffusion‐weighted imaging outside the brain: Consensus statement from an ISMRM‐sponsored workshop , 2016, Journal of magnetic resonance imaging : JMRI.

[93]  Yonggang Lu,et al.  Evaluation of Head and Neck Tumors with Functional MR Imaging. , 2016, Magnetic resonance imaging clinics of North America.

[94]  John Kornak,et al.  Real-Time Measurement of Functional Tumor Volume by MRI to Assess Treatment Response in Breast Cancer Neoadjuvant Clinical Trials: Validation of the Aegis SER Software Platform. , 2014, Translational oncology.

[95]  R. Jain,et al.  Perfusion Imaging in Neuro-Oncology: Basic Techniques and Clinical Applications. , 2016, Magnetic resonance imaging clinics of North America.

[96]  W E Reddick,et al.  MR imaging of tumor microcirculation: Promise for the new millenium , 1999, Journal of magnetic resonance imaging : JMRI.

[97]  H. Hricak,et al.  The expanding landscape of diffusion-weighted MRI in prostate cancer , 2016, Abdominal Radiology.

[98]  A. Jackson,et al.  Comparative study into the robustness of compartmental modeling and model‐free analysis in DCE‐MRI studies , 2006, Journal of magnetic resonance imaging : JMRI.

[99]  Mark S. Bolding,et al.  Portable perfusion phantom for quantitative DCE‐MRI of the abdomen , 2017, Medical physics.

[100]  F. Wiesinger,et al.  B1 mapping by Bloch‐Siegert shift , 2010, Magnetic resonance in medicine.

[101]  Baris Turkbey,et al.  Overview of dynamic contrast-enhanced MRI in prostate cancer diagnosis and management. , 2012, AJR. American journal of roentgenology.

[102]  H. Hricak,et al.  Dynamic contrast-enhanced magnetic resonance imaging of prostate cancer: A review of current methods and applications , 2017, World journal of radiology.

[103]  J. Kuijer,et al.  Diffusion-weighted (DW) MRI in lung cancers: ADC test-retest repeatability , 2017, European Radiology.

[104]  Mithat Gönen,et al.  Quantitative imaging biomarkers: A review of statistical methods for technical performance assessment , 2015, Statistical methods in medical research.

[105]  R. Boubertakh,et al.  In vitro and in vivo repeatability of abdominal diffusion-weighted MRI. , 2012, The British journal of radiology.

[106]  Sandra Nuyts,et al.  Detection of head and neck squamous cell carcinoma with diffusion weighted MRI after (chemo)radiotherapy: correlation between radiologic and histopathologic findings. , 2007, International journal of radiation oncology, biology, physics.

[107]  A. Padhani,et al.  Tumor response assessments with diffusion and perfusion MRI , 2012, Journal of magnetic resonance imaging : JMRI.

[108]  Paul S. Tofts,et al.  Quantitative MRI of the brain : measuring changes caused by disease , 2003 .

[109]  Stuart A. Taylor,et al.  Imaging biomarker roadmap for cancer studies , 2016, Nature Reviews Clinical Oncology.

[110]  B. Taouli,et al.  Diffusion and perfusion imaging of the liver. , 2010, European journal of radiology.

[111]  S. Vos,et al.  Reliability of brain volume measurements: A test-retest dataset , 2014, Scientific Data.

[112]  Manojkumar Saranathan,et al.  DIfferential subsampling with cartesian ordering (DISCO): A high spatio‐temporal resolution dixon imaging sequence for multiphasic contrast enhanced abdominal imaging , 2012, Journal of magnetic resonance imaging : JMRI.

[113]  T. Bathen,et al.  Diffusion‐weighted and dynamic contrast‐enhanced MRI in evaluation of early treatment effects during neoadjuvant chemotherapy in breast cancer patients , 2011, Journal of magnetic resonance imaging : JMRI.

[114]  David J Collins,et al.  Reproducibility and correlation between quantitative and semiquantitative dynamic and intrinsic susceptibility‐weighted MRI parameters in the benign and malignant human prostate , 2010, Journal of magnetic resonance imaging : JMRI.

[115]  M. Holz,et al.  Biological applications of scanning tunnelling microscopy , 1993 .

[116]  E. Merkle,et al.  Short- and midterm reproducibility of apparent diffusion coefficient measurements at 3.0-T diffusion-weighted imaging of the abdomen. , 2009, Radiology.

[117]  Yousef Mazaheri,et al.  Prostate MRI: Evaluating Tumor Volume and Apparent Diffusion Coefficient as Surrogate Biomarkers for Predicting Tumor Gleason Score , 2014, Clinical Cancer Research.

[118]  L. Esserman,et al.  Locally advanced breast cancer: MR imaging for prediction of response to neoadjuvant chemotherapy--results from ACRIN 6657/I-SPY TRIAL. , 2012, Radiology.

[119]  Xia Li,et al.  DCE‐MRI analysis methods for predicting the response of breast cancer to neoadjuvant chemotherapy: Pilot study findings , 2014, Magnetic resonance in medicine.

[120]  K. Peck,et al.  Diagnostic Accuracy of T1-Weighted Dynamic Contrast-Enhanced–MRI and DWI-ADC for Differentiation of Glioblastoma and Primary CNS Lymphoma , 2016, American Journal of Neuroradiology.

[121]  Glen R Morrell,et al.  Pharmacokinetic mapping for lesion classification in dynamic breast MRI , 2010, Journal of magnetic resonance imaging : JMRI.

[122]  Timothy D Johnson,et al.  Multi‐system repeatability and reproducibility of apparent diffusion coefficient measurement using an ice‐water phantom , 2013, Journal of magnetic resonance imaging : JMRI.

[123]  John Kurhanewicz,et al.  Reduced-FOV excitation decreases susceptibility artifact in diffusion-weighted MRI with endorectal coil for prostate cancer detection. , 2015, Magnetic resonance imaging.

[124]  B. Taouli,et al.  Diffusion-weighted MR imaging of the liver. , 2010, Radiology.

[125]  Thomas L Chenevert,et al.  Diffusion imaging for therapy response assessment of brain tumor. , 2009, Neuroimaging clinics of North America.

[126]  T. Chenevert,et al.  Practical estimate of gradient nonlinearity for implementation of apparent diffusion coefficient bias correction , 2014, Journal of magnetic resonance imaging : JMRI.

[127]  C. Gatsonis,et al.  MRI evaluation of the contralateral breast in women with recently diagnosed breast cancer. , 2007, The New England journal of medicine.

[128]  H. Rusinek,et al.  DCE-MRI of the Liver: Reconstruction of the Arterial Input Function Using a Low Dose Pre-Bolus Contrast Injection , 2014, PloS one.

[129]  J. Duerk,et al.  Magnetic Resonance Fingerprinting , 2013, Nature.

[130]  Ryan J Bosca,et al.  Novel High Spatiotemporal Resolution Versus Standard-of-Care Dynamic Contrast-Enhanced Breast MRI: Comparison of Image Quality , 2017, Investigative radiology.

[131]  A. Jackson,et al.  Reproducibility of quantitative dynamic contrast-enhanced MRI in newly presenting glioma. , 2003, The British journal of radiology.

[132]  T W Redpath,et al.  The accuracy of pharmacokinetic parameter measurement in DCE-MRI of the breast at 3 T , 2010, Physics in medicine and biology.

[133]  M. Knopp,et al.  Estimating kinetic parameters from dynamic contrast‐enhanced t1‐weighted MRI of a diffusable tracer: Standardized quantities and symbols , 1999, Journal of magnetic resonance imaging : JMRI.

[134]  D. Le Bihan Apparent diffusion coefficient and beyond: what diffusion MR imaging can tell us about tissue structure. , 2013, Radiology.

[135]  H. Merisaari,et al.  Evaluation of different mathematical models for diffusion‐weighted imaging of normal prostate and prostate cancer using high b‐values: A repeatability study , 2015, Magnetic resonance in medicine.

[136]  Thomas E Yankeelov,et al.  Integration of quantitative DCE-MRI and ADC mapping to monitor treatment response in human breast cancer: initial results. , 2007, Magnetic resonance imaging.

[137]  N. Hylton,et al.  Diagnostic architectural and dynamic features at breast MR imaging: multicenter study. , 2006, Radiology.

[138]  H. Barnhart,et al.  The emerging science of quantitative imaging biomarkers terminology and definitions for scientific studies and regulatory submissions , 2015, Statistical methods in medical research.

[139]  Elizabeth A Morris,et al.  The potential of multiparametric MRI of the breast. , 2017, The British journal of radiology.