The ISMRM Open Science Initiative for Perfusion Imaging (OSIPI): Results from the OSIPI-Dynamic Contrast-Enhanced challenge.

PURPOSE: K trans $$ {K}^{\mathrm{trans}} $$ has often been proposed as a quantitative imaging biomarker for diagnosis, prognosis, and treatment response assessment for various tumors. None of the many software tools for K trans $$ {K}^{\mathrm{trans}} $$ quantification are standardized. The ISMRM Open Science Initiative for Perfusion Imaging-Dynamic Contrast-Enhanced (OSIPI-DCE) challenge was designed to benchmark methods to better help the efforts to standardize K trans $$ {K}^{\mathrm{trans}} $$ measurement. METHODS: A framework was created to evaluate K trans $$ {K}^{\mathrm{trans}} $$ values produced by DCE-MRI analysis pipelines to enable benchmarking. The perfusion MRI community was invited to apply their pipelines for K trans $$ {K}^{\mathrm{trans}} $$ quantification in glioblastoma from clinical and synthetic patients. Submissions were required to include the entrants' K trans $$ {K}^{\mathrm{trans}} $$ values, the applied software, and a standard operating procedure. These were evaluated using the proposed OSIP I gold $$ \mathrm{OSIP}{\mathrm{I}}_{\mathrm{gold}} $$ score defined with accuracy, repeatability, and reproducibility components. RESULTS: Across the 10 received submissions, the OSIP I gold $$ \mathrm{OSIP}{\mathrm{I}}_{\mathrm{gold}} $$ score ranged from 28% to 78% with a 59% median. The accuracy, repeatability, and reproducibility scores ranged from 0.54 to 0.92, 0.64 to 0.86, and 0.65 to 1.00, respectively (0-1 = lowest-highest). Manual arterial input function selection markedly affected the reproducibility and showed greater variability in K trans $$ {K}^{\mathrm{trans}} $$ analysis than automated methods. Furthermore, provision of a detailed standard operating procedure was critical for higher reproducibility. CONCLUSIONS: This study reports results from the OSIPI-DCE challenge and highlights the high inter-software variability within K trans $$ {K}^{\mathrm{trans}} $$ estimation, providing a framework for ongoing benchmarking against the scores presented. Through this challenge, the participating teams were ranked based on the performance of their software tools in the particular setting of this challenge. In a real-world clinical setting, many of these tools may perform differently with different benchmarking methodology.

Understanding Spatial Correlation Between Multiparametric MRI Performance and Prostate Cancer.

BACKGROUND: Multiparametric MRI (mpMRI) has shown a substantial impact on prostate cancer (PCa) diagnosis. However, the understanding of the spatial correlation between mpMRI performance and PCa location is still limited. PURPOSE: To investigate the association between mpMRI performance and tumor spatial location within the prostate using a prostate sector map, described by Prostate Imaging Reporting and Data System (PI-RADS) v2.1. STUDY TYPE: Retrospective. SUBJECTS: One thousand one hundred forty-three men who underwent mpMRI before radical prostatectomy between 2010 and 2022. FIELD STRENGTH/SEQUENCE: 3.0 T. T2-weighted turbo spin-echo, a single-shot spin-echo EPI sequence for diffusion-weighted imaging, and a gradient echo sequence for dynamic contrast-enhanced MRI sequences. ASSESSMENT: Integrated relative cancer prevalence (rCP), detection rate (DR), and positive predictive value (PPV) maps corresponding to the prostate sector map for PCa lesions were created. The relationship between tumor location and its detection/missing by radiologists on mpMRI compared to WMHP as a reference standard was investigated. STATISTICAL TESTS: A weighted chi-square test was performed to examine the statistical differences for rCP, DR, and PPV of the aggregated sectors within the zone, anterior/posterior, left/right prostate, and different levels of the prostate with a statistically significant level of 0.05. RESULTS: A total of 1665 PCa lesions were identified in 1143 patients, and from those 1060 lesions were clinically significant (cs)PCa tumors (any Gleason score [GS] ≥7). Our sector-based analysis utilizing weighted chi-square tests suggested that the left posterior part of PZ had a high likelihood of missing csPCa lesions at a DR of 67.0%. Aggregated sector analysis indicated that the anterior or apex locations in PZ had the significantly lowest csPCa detection at 67.3% and 71.5%, respectively. DATA CONCLUSION: Spatial characteristics of the per-lesion-based mpMRI performance for diagnosis of PCa were studied. Our results demonstrated that there is a spatial correlation between mpMRI performance and locations of PCa on the prostate. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Clinical Significance of Background Parenchymal Enhancement in Breast Cancer Risk Stratification.

BACKGROUND: Background parenchymal enhancement (BPE) is an established breast cancer risk factor. However, the relationship between BPE levels and breast cancer risk stratification remains unclear. PURPOSE: To evaluate the clinical relationship between BPE levels and breast cancer risk with covariate adjustments for age, ethnicity, and hormonal status. STUDY TYPE: Retrospective. POPULATION: 954 screening breast MRI datasets representing 721 women divided into four cohorts: women with pathogenic germline breast cancer (BRCA) mutations (Group 1, N = 211), women with non-BRCA germline mutations (Group 2, N = 60), women without high-risk germline mutations but with a lifetime breast cancer risk of ≥20% using the Tyrer-Cuzick model (Group 3, N = 362), and women with <20% lifetime risk (Group 4, N = 88). FIELD STRENGTH/SEQUENCE: 3 T/axial non-fat-saturated T1, short tau inversion recovery, fat-saturated pre-contrast, and post-contrast T1-weighted images. ASSESSMENT: Data on age, body mass index, ethnicity, menopausal status, genetic predisposition, and hormonal therapy use were collected. BPE levels were evaluated by two breast fellowship-trained radiologists independently in accordance with BI-RADS, with a third breast fellowship-trained radiologist resolving any discordance. STATISTICAL TESTS: Propensity score matching (PSM) was utilized to adjust covariates, including age, ethnicity, menopausal status, hormonal treatments, and prior bilateral oophorectomy. The Mann-Whitney U test, chi-squared test, and univariate and multiple logistic regression analysis were performed, with an odds ratio (OR) and corresponding 95% confidence interval. Weighted Kappa statistic was used to assess inter-reader variation. A P value <0.05 indicated a significant result. RESULTS: In the assessment of BPE, there was substantial agreement between the two interpreting radiologists (κ = 0.74). Patient demographics were not significantly different between patient groups after PSM. The BPE of Group 1 was significantly lower than that of Group 4 and Group 3 among premenopausal women. In estimating the BPE level, the OR of gene mutations was 0.35. DATA CONCLUSION: Adjusting for potential confounders, the BPE level of premenopausal women with BRCA mutations was significantly lower than that of non-high-risk women. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 3.

Investigating MRI-Associated Biological Aspects of Racial Disparities in Prostate Cancer for African American and White Men.

BACKGROUND: Understanding the characteristics of multiparametric MRI (mpMRI) in patients from different racial/ethnic backgrounds is important for reducing the observed gaps in clinical outcomes. PURPOSE: To investigate the diagnostic performance of mpMRI and quantitative MRI parameters of prostate cancer (PCa) in African American (AA) and matched White (W) men. STUDY TYPE: Retrospective. SUBJECTS: One hundred twenty-nine patients (43 AA, 86 W) with histologically proven PCa who underwent mpMRI before radical prostatectomy. FIELD STRENGTH/SEQUENCE: 3.0 T, T2-weighted turbo spin echo imaging, a single-shot spin-echo EPI sequence diffusion-weighted imaging, and a gradient echo sequence dynamic contrast-enhanced MRI with an ultrafast 3D spoiled gradient-echo sequence. ASSESSMENT: The diagnostic performance of mpMRI in AA and W men was assessed using detection rates (DRs) and positive predictive values (PPVs) in zones defined by the PI-RADS v2.1 prostate sector map. Quantitative MRI parameters, including Ktrans and ve of clinically significant (cs) PCa (Gleason score ≥ 7) tumors were compared between AA and W sub-cohorts after matching age, prostate-specific antigen (PSA), and prostate volume. STATISTICAL TESTS: Weighted Pearson's chi-square and Mann-Whitney U tests with a statistically significant level of 0.05 were used to examine differences in DR and PPV and to compare parameters between AA and matched W men, respectively. RESULTS: A total number of 264 PCa lesions were identified in the study cohort. The PPVs in the peripheral zone (PZ) and posterior prostate of mpMRI for csPCa lesions were significantly higher in AA men than in matched W men (87.8% vs. 68.1% in PZ, and 89.3% vs. 69.6% in posterior prostate). The Ktrans of index csPCa lesions in AA men was significantly higher than in W men (0.25 ± 0.12 vs. 0.20 ± 0.08 min-1; P < 0.01). DATA CONCLUSION: This study demonstrated race-related differences in the diagnostic performances and quantitative MRI measures of csPCa that were not reflected in age, PSA, and prostate volume. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Quantitative assessment of background parenchymal enhancement is associated with lifetime breast cancer risk in screening MRI.

OBJECTIVES: To compare the quantitative background parenchymal enhancement (BPE) in women with different lifetime risks and BRCA mutation status of breast cancer using screening MRI. MATERIALS AND METHODS: This study included screening MRI of 535 women divided into three groups based on lifetime risk: nonhigh-risk women, high-risk women without BRCA mutation, and BRCA1/2 mutation carriers. Six quantitative BPE measurements, including percent enhancement (PE) and signal enhancement ratio (SER), were calculated on DCE-MRI after segmentation of the whole breast and fibroglandular tissue (FGT). The associations between lifetime risk factors and BPE were analyzed via linear regression analysis. We adjusted for risk factors influencing BPE using propensity score matching (PSM) and compared the BPE between different groups. A two-sided Mann-Whitney U-test was used to compare the BPE with a threshold of 0.1 for multiple testing issue-adjusted p values. RESULTS: Age, BMI, menopausal status, and FGT level were significantly correlated with quantitative BPE based on the univariate and multivariable linear regression analyses. After adjusting for age, BMI, menopausal status, hormonal treatment history, and FGT level using PSM, significant differences were observed between high-risk non-BRCA and BRCA groups in PEFGT (11.5 vs. 8.0%, adjusted p = 0.018) and SERFGT (7.2 vs. 9.3%, adjusted p = 0.066). CONCLUSION: Quantitative BPE varies in women with different lifetime breast cancer risks and BRCA mutation status. These differences may be due to the influence of multiple lifetime risk factors. Quantitative BPE differences remained between groups with and without BRCA mutations after adjusting for known risk factors associated with BPE. CLINICAL RELEVANCE STATEMENT: BRCA germline mutations may be associated with quantitative background parenchymal enhancement, excluding the effects of known confounding factors. This finding can provide potential insights into the cancer pathophysiological mechanisms behind lifetime risk models. KEY POINTS: Expanding understanding of breast cancer pathophysiology allows for improved risk stratification and optimized screening protocols. Quantitative BPE is significantly associated with lifetime risk factors and differs between BRCA mutation carriers and noncarriers. This research offers a possible understanding of the physiological mechanisms underlying quantitative BPE and BRCA germline mutations.

High-resolution prostate diffusion MRI using eddy current-nulled convex optimized diffusion encoding and random matrix theory-based denoising.

OBJECTIVE: To develop and evaluate a technique combining eddy current-nulled convex optimized diffusion encoding (ENCODE) with random matrix theory (RMT)-based denoising to accelerate and improve the apparent signal-to-noise ratio (aSNR) and apparent diffusion coefficient (ADC) mapping in high-resolution prostate diffusion-weighted MRI (DWI). MATERIALS AND METHODS: Eleven subjects with clinical suspicion of prostate cancer were scanned at 3T with high-resolution (HR) (in-plane: 1.0 × 1.0 mm2) ENCODE and standard-resolution (1.6 × 2.2 mm2) bipolar DWI sequences (both had 7 repetitions for averaging, acquisition time [TA] of 5 min 50 s). HR-ENCODE was retrospectively analyzed using three repetitions (accelerated effective TA of 2 min 30 s). The RMT-based denoising pipeline utilized complex DWI signals and Marchenko-Pastur distribution-based principal component analysis to remove additive Gaussian noise in images from multiple coils, b-values, diffusion encoding directions, and repetitions. HR-ENCODE with RMT-based denoising (HR-ENCODE-RMT) was compared with HR-ENCODE in terms of aSNR in prostate peripheral zone (PZ) and transition zone (TZ). Precision and accuracy of ADC were evaluated by the coefficient of variation (CoV) between repeated measurements and mean difference (MD) compared to the bipolar ADC reference, respectively. Differences were compared using two-sided Wilcoxon signed-rank tests (P < 0.05 considered significant). RESULTS: HR-ENCODE-RMT yielded 62% and 56% higher median aSNR than HR-ENCODE (b = 800 s/mm2) in PZ and TZ, respectively (P < 0.001). HR-ENCODE-RMT achieved 63% and 70% lower ADC-CoV than HR-ENCODE in PZ and TZ, respectively (P < 0.001). HR-ENCODE-RMT ADC and bipolar ADC had low MD of 22.7 × 10-6 mm2/s in PZ and low MD of 90.5 × 10-6 mm2/s in TZ. CONCLUSIONS: HR-ENCODE-RMT can shorten the acquisition time and improve the aSNR of high-resolution prostate DWI and achieve accurate and precise ADC measurements in the prostate.

Evaluation of Spatial Attentive Deep Learning for Automatic Placental Segmentation on Longitudinal MRI.

BACKGROUND: Automated segmentation of the placenta by MRI in early pregnancy may help predict normal and aberrant placenta function, which could improve the efficiency of placental assessment and the prediction of pregnancy outcomes. An automated segmentation method that works at one gestational age may not transfer effectively to other gestational ages. PURPOSE: To evaluate a spatial attentive deep learning method (SADL) for automated placental segmentation on longitudinal placental MRI scans. STUDY TYPE: Prospective, single-center. SUBJECTS: A total of 154 pregnant women who underwent MRI scans at both 14-18 weeks of gestation and at 19-24 weeks of gestation, divided into training (N = 108), validation (N = 15), and independent testing datasets (N = 31). FIELD STRENGTH/SEQUENCE: A 3 T, T2-weighted half Fourier single-shot turbo spin-echo (T2-HASTE) sequence. ASSESSMENT: The reference standard of placental segmentation was manual delineation on T2-HASTE by a third-year neonatology clinical fellow (B.L.) under the supervision of an experienced maternal-fetal medicine specialist (C.J. with 20 years of experience) and an MRI scientist (K.S. with 19 years of experience). STATISTICAL TESTS: The three-dimensional Dice similarity coefficient (DSC) was used to measure the automated segmentation performance compared to the manual placental segmentation. A paired t-test was used to compare the DSCs between SADL and U-Net methods. A Bland-Altman plot was used to analyze the agreement between manual and automated placental volume measurements. A P value < 0.05 was considered statistically significant. RESULTS: In the testing dataset, SADL achieved average DSCs of 0.83 ± 0.06 and 0.84 ± 0.05 in the first and second MRI, which were significantly higher than those achieved by U-Net (0.77 ± 0.08 and 0.76 ± 0.10, respectively). A total of 6 out of 62 MRI scans (9.6%) had volume measurement differences between the SADL-based automated and manual volume measurements that were out of 95% limits of agreement. DATA CONCLUSIONS: SADL can automatically detect and segment the placenta with high performance in MRI at two different gestational ages. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 2.

Integrative Machine Learning Prediction of Prostate Biopsy Results From Negative Multiparametric MRI.

BACKGROUND: Multiparametric MRI (mpMRI) is commonly recommended as a triage test prior to any prostate biopsy. However, there exists limited consensus on which patients with a negative prostate mpMRI could avoid prostate biopsy. PURPOSE: To identify which patient could safely avoid prostate biopsy when the prostate mpMRI is negative, via a radiomics-based machine learning approach. STUDY TYPE: Retrospective. SUBJECTS: Three hundred thirty patients with negative prostate 3T mpMRI between January 2016 and December 2018 were included. FIELD STRENGTH/SEQUENCE: A 3.0 T/T2-weighted turbo spin echo (TSE) imaging (T2 WI) and diffusion-weighted imaging (DWI). ASSESSMENT: The integrative machine learning (iML) model was trained to predict negative prostate biopsy results, utilizing both radiomics and clinical features. The final study cohort comprised 330 consecutive patients with negative mpMRI (PI-RADS < 3) who underwent systematic transrectal ultrasound-guided (TRUS) or MR-ultrasound fusion (MRUS) biopsy within 6 months. A secondary analysis of biopsy naïve subcohort (n = 227) was also conducted. STATISTICAL TESTS: The Mann-Whitney U test and Chi-Squared test were utilized to evaluate the significance of difference of clinical features between prostate biopsy positive and negative groups. The model performance was validated using leave-one-out cross-validation (LOOCV) and measured by AUC, sensitivity, specificity, and negative predictive value (NPV). RESULTS: Overall, 306/330 (NPV 92.7%) of the final study cohort patients had negative biopsies, and 207/227 (NPV 91.2%) of the biopsy naïve subcohort patients had negative biopsies. Our iML model achieved NPVs of 98.3% and 98.0% for the study cohort and subcohort, respectively, superior to prostate-specific antigen density (PSAD)-based risk assessment with NPVs of 94.9% and 93.9%, respectively. DATA CONCLUSION: The proposed iML model achieved high performance in predicting negative prostate biopsy results for patients with negative mpMRI. With improved NPVs, the proposed model can be used to stratify patients who in whom we might obviate biopsies, thus reducing the number of unnecessary biopsies. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Multiparametric MRI-based radiomics model to predict pelvic lymph node invasion for patients with prostate cancer.

OBJECTIVE: To identify which patient with prostate cancer (PCa) could safely avoid extended pelvic lymph node dissection (ePLND) by predicting lymph node invasion (LNI), via a radiomics-based machine learning approach. METHODS: An integrative radiomics model (IRM) was proposed to predict LNI, confirmed by the histopathologic examination, integrating radiomics features, extracted from prostatic index lesion regions on MRI images, and clinical features via SVM. The study cohort comprised 244 PCa patients with MRI and followed by radical prostatectomy (RP) and ePLND within 6 months between 2010 and 2019. The proposed IRM was trained in training/validation set and evaluated in an internal independent testing set. The model's performance was measured by area under the curve (AUC), sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). AUCs were compared via Delong test with 95% confidence interval (CI), and the rest measurements were compared via chi-squared test or Fisher's exact test. RESULTS: Overall, 17 (10.6%) and 14 (16.7%) patients with LNI were included in training/validation set and testing set, respectively. Shape and first-order radiomics features showed usefulness in building the IRM. The proposed IRM achieved an AUC of 0.915 (95% CI: 0.846-0.984) in the testing set, superior to pre-existing nomograms whose AUCs were from 0.698 to 0.724 (p < 0.05). CONCLUSION: The proposed IRM could be potentially feasible to predict the risk of having LNI for patients with PCa. With the improved predictability, it could be utilized to assess which patients with PCa could safely avoid ePLND, thus reduce the number of unnecessary ePLND. KEY POINTS: • The combination of MRI-based radiomics features with clinical information improved the prediction of lymph node invasion, compared with the model using only radiomics features or clinical features. • With improved prediction performance on predicting lymph node invasion, the number of extended pelvic lymph node dissection (ePLND) could be reduced by the proposed integrative radiomics model (IRM), compared with the existing nomograms.

A Description of the Imaging Innovations for Placental Assessment in Response to Environmental Pollution Study.

OBJECTIVE: The aim of Placental Assessment in Response to Environmental Pollution Study (PARENTs) was to determine whether imaging of the placenta by novel multiparametric magnetic resonance imaging (MRI) techniques in early pregnancy could help predict adverse pregnancy outcomes (APOs) due to ischemic placental disease (IPD). Additionally, we sought to determine maternal characteristics and environmental risk factors that contribute to IPD and secondary APOs. STUDY DESIGN: Potential patients in their first trimester of pregnancy, who agreed to MRI of the placenta and measures of assessment of environmental pollution, were recruited into PARENTs, a prospective population-based cohort study. Participants were seen at three study visits during pregnancy and again at their delivery from 2015 to 2019. We collected data from interviews, chart abstractions, and imaging. Maternal biospecimens (serum, plasma, and urine) at antepartum study visits and delivery specimens (placenta, cord, and maternal blood) were collected, processed, and stored. The primary outcome was a composite of IPD, which included any of the following: placental abruption, hypertensive disease of pregnancy, fetal growth restriction, or a newborn of small for gestational age. RESULTS: In this pilot cohort, of the 190 patients who completed pregnancy to viable delivery, 50 (26%) developed IPD. Among demographic characteristics, having a history of prior IPD in multiparous women was associated with the development of IPD. In the multiple novel perfusion measurements taken of the in vivo placenta using MRI, decreased high placental blood flow (mL/100 g/min) in early pregnancy (between 14 and 16 weeks) was found to be significantly associated with the later development of IPD. CONCLUSION: Successful recruitment of the PARENTs prospective cohort demonstrated the feasibility and acceptability of the use of MRI in human pregnancy to study the placenta in vivo and at the same time collect environmental exposure data. Analysis is ongoing and we hope these methods will assist researchers in the design of prospective imaging studies of pregnancy. KEY POINTS: · MRI was acceptable and feasible for the study of the human placenta in vivo.. · Functional imaging of the placenta by MRI showed a significant decrease in high placental blood flow.. · Measures of environmental exposures are further being analyzed to predict IPD..

Tissue clearing techniques for three-dimensional optical imaging of intact human prostate and correlations with multi-parametric MRI.

BACKGROUND: Tissue clearing technologies have enabled remarkable advancements for in situ characterization of tissues and exploration of the three-dimensional (3D) relationships between cells, however, these studies have predominantly been performed in non-human tissues and correlative assessment with clinical imaging has yet to be explored. We sought to evaluate the feasibility of tissue clearing technologies for 3D imaging of intact human prostate and the mapping of structurally and molecularly preserved pathology data with multi-parametric volumetric MR imaging (mpMRI). METHODS: Whole-mount prostates were processed with either hydrogel-based CLARITY or solvent-based iDISCO. The samples were stained with a nuclear dye or fluorescently labeled with antibodies against androgen receptor, alpha-methylacyl coenzyme-A racemase, or p63, and then imaged with 3D confocal microscopy. The apparent diffusion coefficient and Ktrans maps were computed from preoperative mpMRI. RESULTS: Quantitative analysis of cleared normal and tumor prostate tissue volumes displayed differences in 3D tissue architecture, marker-specific cell staining, and cell densities that were significantly correlated with mpMRI measurements in this initial, pilot cohort. CONCLUSIONS: 3D imaging of human prostate volumes following tissue clearing is a feasible technique for quantitative radiology-pathology correlation analysis with mpMRI and provides an opportunity to explore functional relationships between cellular structures and cross-sectional clinical imaging.

Performance of Deep Learning and Genitourinary Radiologists in Detection of Prostate Cancer Using 3-T Multiparametric Magnetic Resonance Imaging.

BACKGROUND: Several deep learning-based techniques have been developed for prostate cancer (PCa) detection using multiparametric magnetic resonance imaging (mpMRI), but few of them have been rigorously evaluated relative to radiologists' performance or whole-mount histopathology (WMHP). PURPOSE: To compare the performance of a previously proposed deep learning algorithm, FocalNet, and expert radiologists in the detection of PCa on mpMRI with WMHP as the reference. STUDY TYPE: Retrospective, single-center study. SUBJECTS: A total of 553 patients (development cohort: 427 patients; evaluation cohort: 126 patients) who underwent 3-T mpMRI prior to radical prostatectomy from October 2010 to February 2018. FIELD STRENGTH/SEQUENCE: 3-T, T2-weighted imaging and diffusion-weighted imaging. ASSESSMENT: FocalNet was trained on the development cohort to predict PCa locations by detection points, with a confidence value for each point, on the evaluation cohort. Four fellowship-trained genitourinary (GU) radiologists independently evaluated the evaluation cohort to detect suspicious PCa foci, annotate detection point locations, and assign a five-point suspicion score (1: least suspicious, 5: most suspicious) for each annotated detection point. The PCa detection performance of FocalNet and radiologists were evaluated by the lesion detection sensitivity vs. the number of false-positive detections at different thresholds on suspicion scores. Clinically significant lesions: Gleason Group (GG) ≥ 2 or pathological size ≥ 10 mm. Index lesions: the highest GG and the largest pathological size (secondary). STATISTICAL TESTS: Bootstrap hypothesis test for the detection sensitivity between radiologists and FocalNet. RESULTS: For the overall differential detection sensitivity, FocalNet was 5.1% and 4.7% below the radiologists for clinically significant and index lesions, respectively; however, the differences were not statistically significant (P = 0.413 and P = 0.282, respectively). DATA CONCLUSION: FocalNet achieved slightly lower but not statistically significant PCa detection performance compared with GU radiologists. Compared with radiologists, FocalNet demonstrated similar detection performance for a highly sensitive setting (suspicion score ≥ 1) or a highly specific setting (suspicion score = 5), while lower performance in between. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

Prostate Microstructure in Prostate Cancer Using 3-T MRI with Diffusion-Relaxation Correlation Spectrum Imaging: Validation with Whole-Mount Digital Histopathology.

Background Microstructural MRI has the potential to improve diagnosis and characterization of prostate cancer (PCa), but validation with histopathology is lacking. Purpose To validate ex vivo diffusion-relaxation correlation spectrum imaging (DR-CSI) in the characterization of microstructural tissue compartments in prostate specimens from men with PCa by using registered whole-mount digital histopathology (WMHP) as the reference standard. Materials and Methods Men with PCa who underwent 3-T MRI and robotic-assisted radical prostatectomy between June 2018 and January 2019 were prospectively studied. After prostatectomy, the fresh whole prostate specimens were imaged in patient-specific three-dimensionally printed molds by using 3-T MRI with DR-CSI and were then sliced to create coregistered WMHP slides. The DR-CSI spectral signal component fractions (fA, fB, fC) were compared with epithelial, stromal, and luminal area fractions (fepithelium, fstroma, flumen) quantified in PCa and benign tissue regions. A linear mixed-effects model assessed the correlations between (fA, fB, fC) and (fepithelium, fstroma, flumen), and the strength of correlations was evaluated by using Spearman correlation coefficients. Differences between PCa and benign tissues in terms of DR-CSI signal components and microscopic tissue compartments were assessed using two-sided t tests. Results Prostate specimens from nine men (mean age, 65 years ± 7 [standard deviation]) were evaluated; 20 regions from 17 PCas, along with 20 benign tissue regions of interest, were analyzed. Three DR-CSI spectral signal components (spectral peaks) were consistently identified. The fA, fB, and fC were correlated with fepithelium, fstroma, and flumen (all P < .001), with Spearman correlation coefficients of 0.74 (95% confidence interval [CI]: 0.62, 0.83), 0.80 (95% CI: 0.66, 0.89), and 0.67 (95% CI: 0.51, 0.81), respectively. PCa exhibited differences compared with benign tissues in terms of increased fA (PCa vs benign, 0.37 ± 0.05 vs 0.27 ± 0.06; P < .001), decreased fC (PCa vs benign, 0.18 ± 0.06 vs 0.31 ± 0.13; P = .01), increased fepithelium (PCa vs benign, 0.44 ± 0.13 vs 0.26 ± 0.16; P < .001), and decreased flumen (PCa vs benign, 0.14 ± 0.08 vs 0.27 ± 0.18; P = .004). Conclusion Diffusion-relaxation correlation spectrum imaging signal components correlate with microscopic tissue compartments in the prostate and differ between cancer and benign tissue. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Lee and Hectors in this issue.

Human Placenta Blood Flow During Early Gestation With Pseudocontinuous Arterial Spin Labeling MRI.

BACKGROUND: Noninvasive measurement of placental blood flow is the major technical challenge for predicting ischemic placenta (IPD). Pseudocontinuous arterial spin labeling (pCASL) MRI was recently shown to be promising, but the potential value in predicting the subsequence development of IPD is not known. PURPOSE: To derive global and regional placental blood flow parameters from longitudinal measurements of pCASL MRI and to assess the associations between perfusion-related parameters and IPD. STUDY TYPE: Prospective. POPULATION: Eighty-four women completed two pCASL MRI scans (first; 14-18 weeks and second; 19-24 weeks) from prospectively recruited 118 subjects. A total of 69 subjects were included for the analysis, of which 15 subjects developed IPD. FIELD STRENGTH/SEQUENCE: 3T/T2 -weighted half-Fourier single-shot turbo spin-echo (HASTE) and pCASL. ASSESSMENT: Four perfusion-related parameters in the placenta were derived: placenta volume, placental blood flow (PBF), high PBF (hPBF), and relative hPBF. The longitudinal changes of the parameters and their association with IPD were tested after being normalizing to the 16th and 20th weeks of gestation. STATISTICAL TESTS: Comparisons between two gestational ages within subjects were performed using the paired Wilcoxon tests, and comparisons between normal and IPD groups were performed using the unpaired Wilcoxon tests. RESULTS: The difference between the first and second MRI scans was statistically significant for volume (156.6 cm3 vs. 269.7 cm3 , P < 0.001) and PBF (104.9 ml/100g/min vs. 111.3 ml/100g/min, P = 0.02) for normal subjects, indicating an increase in pregnancy with advancing gestation. Of the parameters tested, the difference between the normal and IPD subjects was most pronounced in hPBF (278.1 ml/100g/min vs. 180.7 ml/100g/min, P < 0.001) and relative hPBF (259.1% vs. 183.2%, P < 0.001) at 16 weeks. DATA CONCLUSION: The high perfusion-related image parameters for IPD were significantly decreased from normal pregnancy at 14-18 weeks of gestation. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 1 J. Magn. Reson. Imaging 2020;51:1247-1257.

Modified MR dispersion imaging in prostate dynamic contrast-enhanced MRI.

BACKGROUND: An estimation of an intravascular dispersion parameter was previously proposed to improve the overall accuracy and precision of the model parameters, but the high computation complexity can limit its practical usability in prostate dynamic contrast-enhanced MRI (DCE-MRI). PURPOSE: To compare and evaluate the model fitting uncertainty and error in the model parameter estimation using different DCE-MRI analysis models and to evaluate the ability of the intravascular dispersion parameter to delineate between noncancerous and cancerous prostate tissue in the transition and peripheral zones. STUDY TYPE: Retrospective. POPULATION: Fifty-three patients who underwent radical prostatectomy. FIELD STRENGTH/SEQUENCE: 3 T/3D RF-spoiled gradient echo sequence. ASSESSMENT: The coefficient of variation was used to assess the model fitting uncertainty by adding random noise to the time-concentration curves, and the Akaike information criterion was used to assess the model fitting error. The parametric maps derived from four DCE-MRI analysis models were evaluated by evaluating the delineation between noncancerous tissue and prostate cancer or clinically significant prostate cancer. STATISTICAL TESTS: The receiver operating curve analysis was performed to compare the ability to delineate between noncancerous and prostate cancer tissue in the transition and peripheral zones. RESULTS: Both MR dispersion imaging (MRDI) and Weinmann analysis models had the maximum coefficient of variation in different tissue types, while the model fitting uncertainty of modified (m)MRDI was similar to the standard Toft model. In mMRDI, the model fitting error was minimum, and the delineation between noncancerous and clinically significant prostate cancer tissue was improved in both transition (area under the curve [AUC] = 0.92) and peripheral zones (AUC = 0.92), in comparison with MRDI (AUC = 0.89 and AUC = 0.85, respectively). DATA CONCLUSION: The mMRDI showed promising results in detecting prostate cancer while maintaining a similar model fitting uncertainty. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1307-1317.

Repeatability and reproducibility of variable flip angle T1 quantification in the prostate at 3 T.

BACKGROUND: Variable flip angle (VFA) imaging is widely used for the estimation of T1 relaxation in the prostate, but may have limited repeatability and reproducibility due to its sensitivity to B1 + inhomogeneity. PURPOSE: To assess the repeatability and reproducibility of prostate T1 estimation with and without compensating for B1 + variation. STUDY TYPE: Prospective. POPULATION: Twenty-one volunteers were prospectively recruited and scanned twice on two 3 T MRI scanners, resulting in 84 VFA T1 exams. FIELD STRENGTH/SEQUENCE: 3 T/2D saturated turbo fast low angle shot (FLASH) and 3D dual-echo FLASH. ASSESSMENT: Two B1 + mapping techniques, including reference region VFA (RR-VFA) and saturated turbo FLASH (satTFL), were used for B1 + correction, and T1 maps with and without B1 + correction were tested for intrascanner repeatability and interscanner reproducibility. Volumetric regions of interest (ROIs) were drawn on the transition zone, peripheral zone of the prostate, and the obturator internus left and right muscles in the corresponding slices. STATISTICAL TESTS: The average T1 within each ROI for each scan was compared for both intra- and interscanner variability using concordance correlation coefficient and a Bland-Altman plot. RESULTS: Both RR-VFA-corrected T1 and satTFL-corrected T1 showed higher intra- and interscanner correlation (0.89/0.87 and 0.87/0.84, respectively) than VFA T1 (0.84 and 0.74). Bland-Altman plots showed that VFA T1 had wider 95% limits of agreement and a larger range of T1 for each tissue compared with T1 with B1 + correction. DATA CONCLUSION: The application of B1 + correction (both RR-VFA and satTFL) to VFA T1 results in more repeatable and reproducible T1 estimation than VFA T1 . This can potentially provide improved quantification of the prostate dynamic contrast-enhanced MRI parameters. Level of Evidence 1. Technical Efficacy Stage 1. J. Magn. Reson. Imaging 2018.

3D R2* mapping of the placenta during early gestation using free-breathing multiecho stack-of-radial MRI at 3T.

BACKGROUND: Multiecho gradient-echo Cartesian MRI characterizes placental oxygenation by quantifying R2* . Previous research was performed at 1.5T using breath-held 2D imaging during later gestational age (GA). PURPOSE: To evaluate the accuracy and repeatability of a free-breathing (FB) 3D multiecho gradient-echo stack-of-radial technique (radial) for placental R2* mapping at 3T and report placental R2* during early GA. STUDY TYPE: Prospective. POPULATION: Thirty subjects with normal pregnancies and three subjects with ischemic placental disease (IPD) were scanned twice: between 14-18 and 19-23 weeks GA. FIELD STRENGTH: 3T. SEQUENCE: FB radial. ASSESSMENT: Linear correlation (concordance coefficient, ρc ) and Bland-Altman analyses (mean difference, MD) were performed to evaluate radial R2* mapping accuracy compared to Cartesian in a phantom. Radial R2* mapping repeatability was characterized using the coefficient of repeatability (CR) between back-to-back scans. The mean and spatial coefficient of variation (CV) of R2* was determined for all subjects, and separately for anterior and posterior placentas, at each GA range. STATISTICAL TESTS: ρc was tested for significance. Differences in mean R2* and CV were tested using Wilcoxon Signed-Rank and Rank-Sum tests. P < 0.05 was considered significant. Z-scores for the IPD subjects were determined. RESULTS: FB radial demonstrated accurate (ρc ≥0.996; P < 0.001; |MD|<0.2s-1 ) and repeatable (CR<4s-1 ) R2* mapping in a phantom, and repeatable (CR≤4.6s-1 ) R2* mapping in normal subjects. At 3T, placental R2* mean ± standard deviation was 12.9s-1 ± 2.7s-1 for 14-18 and 13.2s-1 ± 1.9s-1 for 19-23 weeks GA. The CV was significantly greater (P = 0.043) at 14-18 (0.63 ± 0.12) than 19-23 (0.58 ± 0.13) weeks GA. At 19-23 weeks, the CV was significantly lower (P < 0.001) for anterior (0.49 ± 0.08) than posterior (0.67 ± 0.11) placentas. One IPD subject had a lower mean R2* than normal subjects at both GA ranges (Z<-2). DATA CONCLUSION: FB radial provides accurate and repeatable 3D R2* mapping for the entire placenta at 3T during early GA. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:291-303.

A system using patient-specific 3D-printed molds to spatially align in vivo MRI with ex vivo MRI and whole-mount histopathology for prostate cancer research.

BACKGROUND: Patient-specific 3D-printed molds and ex vivo MRI of the resected prostate have been two important strategies to align MRI with whole-mount histopathology (WMHP) for prostate cancer (PCa) research, but the combination of these two strategies has not been systematically evaluated. PURPOSE: To develop and evaluate a system that combines patient-specific 3D-printed molds with ex vivo MRI (ExV) to spatially align in vivo MRI (InV), ExV, and WMHP in PCa patients. STUDY TYPE: Prospective cohort study. POPULATION: Seventeen PCa patients who underwent 3T MRI and robotic-assisted laparoscopic radical prostatectomy (RALP). FIELD STRENGTH/SEQUENCES: T2 -weighted turbo spin-echo sequences at 3T. ASSESSMENT: Immediately after RALP, the fresh whole prostate specimens were imaged in patient-specific 3D-printed molds by 3T MRI and then sectioned to create WMHP slides. The time required for ExV was measured to assess impact on workflow. InV, ExV, and WMHP images were registered. Spatial alignment was evaluated using: slide offset (mm) between ExV slice locations and WMHP slides; overlap of the 3D prostate contour on InV versus ExV using Dice's coefficient (0 to 1); and 2D target registration error (TRE, mm) between corresponding landmarks on InV, ExV, and WMHP. Data are reported as mean ± standard deviation (SD). STATISTICAL TESTING: Differences in 2D TRE before versus after registration were compared using the Wilcoxon signed-rank test (P < 0.05 considered significant). RESULTS: ExV (duration 115 ± 15 min) was successfully incorporated into the workflow for all cases. Absolute slide offset was 1.58 ± 1.57 mm. Dice's coefficient was 0.865 ± 0.035. 2D TRE was significantly reduced after registration (P < 0.01) with mean (±SD of per patient means) of 1.9 ± 0.6 mm for InV versus ExV, 1.4 ± 0.5 mm for WMHP versus ExV, and 2.0 ± 0.5 mm for WMHP versus InV. DATA CONCLUSION: The proposed system combines patient-specific 3D-printed molds with ExV to achieve spatial alignment between InV, ExV, and WMHP with mean 2D TRE of 1-2 mm. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:270-279.

Apparent Diffusion Coefficient (ADC) Ratio Versus Conventional ADC for Detecting Clinically Significant Prostate Cancer With 3-T MRI.

OBJECTIVE. The purpose of this study is to evaluate the performance of the apparent diffusion coefficient ratio (ADCratio; the ADC of the suspected prostate cancer [PCa] focus on MRI divided by the ADC in a noncancerous reference area) with that of conventional ADC for detection of high-grade PCa (Gleason score [GS] ≥ 3 + 4) versus low-grade PCa (GS = 3 + 3) with whole-mount (WM) histopathologic analysis used as a reference. MATERIALS AND METHODS. The cohort of this retrospective study included 218 men with 240 unilateral PCa lesions assessed by both 3-T multiparametric MRI and whole-mount histopathologic analysis. ROIs were placed on individual lesions verified by WM histopathologic analysis, to calculate the mean ADC (ADCtumor_mean) and lowest ADC within each lesion (ADCtumor_min), and within non-tumor-containing regions of the same tumor zone but on the contralateral side (ADCbenign), to calculate the background ADC. The ADCratio_mean (the ADCtumor_mean divided by the ADCbenign) was calculated. The performance of individual ADCtumor and ADCratio_mean values in discriminating PCa with a GS of 3 + 3 from PCa with a GS of 3 + 4 or greater was assessed using the AUC value. RESULTS. The ADCratio_mean had a higher AUC value for discriminating PCa lesions with a GS of 3 + 3 from those with a GS of 3 + 4 or greater (the AUC value increased from 0.70 using the ADCtumor_mean and 0.67 using the ADCtumor_min [the minimum ADC of the PCa lesion] to 0.80 for the ADCratio_mean and 0.72 for the ADCratio_min [the ADCtumor_min divided by the ADCbenign]; p = 0.043). When stratified by PCa zonal location, the ADCratio_mean had significantly more robust accuracy in the transition zone (the AUC value increased from 0.63 for ADCtumor_mean to 0.87 for ADCratio_mean; p = 0.019) compared with the peripheral zone (the AUC value increased from 0.74 for ADCtumor_mean to 0.78 for ADCratio_mean; p = 0.44). When analyzed on the basis of endorectal coil use, the ADCratio_mean performed nonsignificantly better in both the endorectal coil and non-endorectal coil subcohorts, although it performed better in the former. CONCLUSION. As an intrapatient-normalized diagnostic tool, the ADC ratio provided the best AUC value for discrimination of low-grade from high-grade PCa on 3-T MRI.

Free-breathing liver fat quantification using a multiecho 3D stack-of-radial technique.

PURPOSE: The diagnostic gold standard for nonalcoholic fatty liver disease is an invasive biopsy. Noninvasive Cartesian MRI fat quantification remains limited to a breath-hold (BH). In this work, a novel free-breathing 3D stack-of-radial (FB radial) liver fat quantification technique is developed and evaluated in a preliminary study. METHODS: Phantoms and healthy subjects (n = 11) were imaged at 3 Tesla. The proton-density fat fraction (PDFF) determined using FB radial (with and without scan acceleration) was compared to BH single-voxel MR spectroscopy (SVS) and BH 3D Cartesian MRI using linear regression (correlation coefficient ρ and concordance coefficient ρc ) and Bland-Altman analysis. RESULTS: In phantoms, PDFF showed significant correlation (ρ > 0.998, ρc > 0.995) and absolute mean differences < 2.2% between FB radial and BH SVS, as well as significant correlation (ρ > 0.999, ρc > 0.998) and absolute mean differences < 0.6% between FB radial and BH Cartesian. In the liver and abdomen, PDFF showed significant correlation (ρ > 0.986, ρc > 0.985) and absolute mean differences < 1% between FB radial and BH SVS, as well as significant correlation (ρ > 0.996, ρc > 0.995) and absolute mean differences < 0.9% between FB radial and BH Cartesian. CONCLUSION: Accurate 3D liver fat quantification can be performed in 1 to 2 min using a novel FB radial technique. Magn Reson Med 79:370-382, 2018. © 2017 International Society for Magnetic Resonance in Medicine.