BackgroundMulticompartmental modeling outperforms conventional diffusion‐weighted imaging (DWI) in the assessment of prostate cancer. Optimized multicompartmental models could further improve the detection and characterization of prostate cancer.PurposeTo optimize multicompartmental signal models and apply them to study diffusion in normal and cancerous prostate tissue in vivo.Study TypeRetrospective.SubjectsForty‐six patients who underwent MRI examination for suspected prostate cancer; 23 had prostate cancer and 23 had no detectable cancer.Field Strength/Sequence3T multishell diffusion‐weighted sequence.AssessmentMulticompartmental models with 2–5 tissue compartments were fit to DWI data from the prostate to determine optimal compartmental apparent diffusion coefficients (ADCs). These ADCs were used to compute signal contributions from the different compartments. The Bayesian Information Criterion (BIC) and model‐fitting residuals were calculated to quantify model complexity and goodness‐of‐fit. Tumor contrast‐to‐noise ratio (CNR) and tumor‐to‐background signal intensity ratio (SIR) were computed for conventional DWI and multicompartmental signal‐contribution maps.Statistical TestsAnalysis of variance (ANOVA) and two‐sample t‐tests (α = 0.05) were used to compare fitting residuals between prostate regions and between multicompartmental models. T‐tests (α = 0.05) were also used to assess differences in compartmental signal‐fraction between tissue types and CNR/SIR between conventional DWI and multicompartmental models.ResultsThe lowest BIC was observed from the 4‐compartment model, with optimal ADCs of 5.2e‐4, 1.9e‐3, 3.0e‐3, and >3.0e‐2 mm2/sec. Fitting residuals from multicompartmental models were significantly lower than from conventional ADC mapping (P < 0.05). Residuals were lowest in the peripheral zone and highest in tumors. Tumor tissue showed the largest reduction in fitting residual by increasing model order. Tumors had a greater proportion of signal from compartment 1 than normal tissue (P < 0.05). Tumor CNR and SIR were greater on compartment‐1 signal maps than conventional DWI (P < 0.05) and increased with model order.Data ConclusionThe 4‐compartment signal model best described diffusion in the prostate. Compartmental signal contributions revealed by this model may improve assessment of prostate cancer.Level of Evidence 3Technical Efficacy Stage 3J. MAGN. RESON. IMAGING 2021;53:628–639.

Competing Interest Statement Dr. Dale reports that he was a Founder of and holds equity in CorTechs Labs, Inc., and serves on its Scientific Advisory Board. He is a member of the Scientific Advisory Board of Human Longevity, Inc. He receives funding through research grants from GE Healthcare to UCSD. Dr. Rakow-Penner is a consultant for Human Longevity, Inc. and receives funding through research grants from GE Healthcare. The terms of these arrangements have been reviewed by and approved by UCSD in accordance with its conflict of interest policies. Dr. Igor Vidić is employed as a consultant for Cortechs Labs, Inc. Dr. Seibert reports personal honoraria in the past three years from Varian Medical Systems, Multimodal Imaging Services Corporation, and WebMD.

Restriction spectrum imaging (RSI) decomposes the diffusion-weighted MRI signal into separate components of known apparent diffusion coefficients (ADCs). The number of diffusion components and optimal ADCs for RSI are organ-specific and determined empirically. The purpose of this work was to determine the RSI model for breast tissues. Methods:The diffusion-weighted MRI signal was described using a linear combination of multiple exponential components. A set of ADC values was estimated to fit voxels in cancer and control ROIs. Later, the signal contributions of each diffusion component were estimated using these fixed ADC values. Relative-fitting residuals and Bayesian information criterion were assessed. Contrast-to-noise ratio between cancer and fibroglandular tissue in RSI-derived signal contribution maps was compared to DCE imaging.Results: A total of 74 women with breast cancer were scanned at 3.0 Tesla MRI.The fitting residuals of conventional ADC and Bayesian information criterion suggest that a 3-component model improves the characterization of the diffusion signal over a biexponential model. Estimated ADCs of triexponential model were

Background Diffusion magnetic resonance imaging (MRI) is integral to detection of prostate cancer (PCa), but conventional apparent diffusion coefficient (ADC) cannot capture the complexity of prostate tissues and tends to yield noisy images that do not distinctly highlight cancer. A four‐compartment restriction spectrum imaging (RSI4) model was recently found to optimally characterize pelvic diffusion signals, and the model coefficient for the slowest diffusion compartment, RSI4‐C1, yielded greatest tumor conspicuity. Purpose To evaluate the slowest diffusion compartment of a four‐compartment spectrum imaging model (RSI4‐C1) as a quantitative voxel‐level classifier of PCa. Study Type Retrospective. Subjects Forty‐six men who underwent an extended MRI acquisition protocol for suspected PCa. Twenty‐three men had benign prostates, and the other 23 men had PCa. Field Strength/Sequence A 3 T, multishell diffusion‐weighted and axial T2‐weighted sequences. Assessment High‐confidence cancer voxels were delineated by expert consensus, using imaging data and biopsy results. The entire prostate was considered benign in patients with no detectable cancer. Diffusion images were used to calculate RSI4‐C1 and conventional ADC. Classifier images were also generated. Statistical Tests Voxel‐level discrimination of PCa from benign prostate tissue was assessed via receiver operating characteristic (ROC) curves generated by bootstrapping with patient‐level case resampling. RSI4‐C1 was compared to conventional ADC for two metrics: area under the ROC curve (AUC) and false‐positive rate for a sensitivity of 90% (FPR90). Statistical significance was assessed using bootstrap difference with two‐sided α = 0.05. Results RSI4‐C1 outperformed conventional ADC, with greater AUC (mean 0.977 [95% CI: 0.951–0.991] vs. 0.922 [0.878–0.948]) and lower FPR90 (0.032 [0.009–0.082] vs. 0.201 [0.132–0.290]). These improvements were statistically significant (P < 0.05). Data Conclusion RSI4‐C1 yielded a quantitative, voxel‐level classifier of PCa that was superior to conventional ADC. RSI classifier images with a low false‐positive rate might improve PCa detection and facilitate clinical applications like targeted biopsy and treatment planning. Evidence Level 3 Technical Efficacy Stage 2

Purpose-To propose and validate a method for accurately quantifying renal plasma flow (RPF) with arterial spin labeling (ASL).Materials and methods-The proposed method employs a tracer-kinetic approach and derives perfusion from the slope of the ASL difference signal sampled at multiple inversion-times (TIs). To validate the method's accuracy, we performed a HIPAA-compliant and IRB-approved study with 15 subjects (9 male, 6 female; age range 24-73) to compare RPF estimates obtained from ASL to those from a more established dynamic contrast-enhanced (DCE) MRI method. We also investigated the impact of TI-sampling density on the accuracy of estimated RPF.Results-Good agreement was found between ASL-and DCE-measured RPF, with a mean difference of 9 ± 30 ml/min and a correlation coefficient R = 0.92 when ASL signals were acquired at 16 TIs and a mean difference of 9 ± 57 ml/min and R = 0.81 when ASL signals were acquired at 5 TIs. RPF estimated from ASL signals acquired at only 2 TIs (400 and 1200 ms) showed a low correlation with DCE-measured values (R = 0.30). Conclusion-The proposed ASL method is capable of measuring RPF with an accuracy that is comparable to DCE MRI. At least 5 TIs are recommended for the ASL acquisition to ensure reliability of RPF measurements.