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BackgroundUnderstanding the characteristics of multiparametric MRI (mpMRI) in patients from different racial/ethnic backgrounds is important for reducing the observed gaps in clinical outcomes.PurposeTo investigate the diagnostic performance of mpMRI and quantitative MRI parameters of prostate cancer (PCa) in African American (AA) and matched White (W) men.Study typeRetrospective.SubjectsOne hundred twenty-nine patients (43 AA, 86 W) with histologically proven PCa who underwent mpMRI before radical prostatectomy.Field strength/sequence3.0 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.AssessmentThe 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 testsWeighted 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.ResultsA 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

Multi-parametric MRI (mpMRI) is widely used for prostate cancer (PCa) diagnosis. Deep learning models show good performance in detecting PCa on mpMRI, but domain-specific PCa-related anatomical information is sometimes overlooked and not fully explored even by state-of-the-art deep learning models, causing potential suboptimal performances in PCa detection. Symmetric-related anatomical information is commonly used when distinguishing PCa lesions from other visually similar but benign prostate tissue. In addition, different combinations of mpMRI findings are used for evaluating the aggressiveness of PCa for abnormal findings allocated in different prostate zones. In this study, we investigate these domain-specific anatomical properties in PCa diagnosis and how we can adopt them into the deep learning framework to improve the models detection performance. We propose an anatomical-aware PCa detection Network (AtPCa-Net) for PCa detection on mpMRI. Experiments show that the AtPCa-Net can better utilize the anatomical-related information, and the proposed anatomical-aware designs help improve the overall model performance on both PCa detection and patient-level classification.

BackgroundMultiparametric 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.PurposeTo 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 typeRetrospective.SubjectsOne thousand one hundred forty-three men who underwent mpMRI before radical prostatectomy between 2010 and 2022.Field strength/sequence3.0 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.AssessmentIntegrated 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 testsA 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.ResultsA 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 conclusionSpatial 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 level4 TECHNICAL EFFICACY: Stage 2.

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.

Purpose To describe the design, conduct, and results of the Breast Multiparametric MRI for prediction of neoadjuvant chemotherapy Response (BMMR2) challenge. Materials and Methods The BMMR2 computational challenge opened on May 28, 2021, and closed on December 21, 2021. The goal of the challenge was to identify image-based markers derived from multiparametric breast MRI, including diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI, along with clinical data for predicting pathologic complete response (pCR) following neoadjuvant treatment. Data included 573 breast MRI studies from 191 women (mean age [±SD], 48.9 years ± 10.56) in the I-SPY 2/American College of Radiology Imaging Network (ACRIN) 6698 trial (ClinicalTrials.gov: NCT01042379). The challenge cohort was split into training (60%) and test (40%) sets, with teams blinded to test set pCR outcomes. Prediction performance was evaluated by area under the receiver operating characteristic curve (AUC) and compared with the benchmark established from the ACRIN 6698 primary analysis. Results Eight teams submitted final predictions. Entries from three teams had point estimators of AUC that were higher than the benchmark performance (AUC, 0.782 [95% CI: 0.670, 0.893], with AUCs of 0.803 [95% CI: 0.702, 0.904], 0.838 [95% CI: 0.748, 0.928], and 0.840 [95% CI: 0.748, 0.932]). A variety of approaches were used, ranging from extraction of individual features to deep learning and artificial intelligence methods, incorporating DCE and DWI alone or in combination. Conclusion The BMMR2 challenge identified several models with high predictive performance, which may further expand the value of multiparametric breast MRI as an early marker of treatment response. Clinical trial registration no. NCT01042379 Keywords: MRI, Breast, Tumor Response Supplemental material is available for this article. © RSNA, 2024.

Introduction: Dynamic contrast-enhanced (DCE) MRI has important clinical value for early detection, accurate staging, and therapeutic monitoring of cancers. However, conventional multi-phasic abdominal DCE-MRI has limited temporal resolution and provides qualitative or semi-quantitative assessments of tissue vascularity. In this study, the feasibility of retrospectively quantifying multi-phasic abdominal DCE-MRI by using pharmacokinetics-informed deep learning to improve temporal resolution was investigated. Method: Forty-five subjects consisting of healthy controls, pancreatic ductal adenocarcinoma (PDAC), and chronic pancreatitis (CP) were imaged with a 2-s temporal-resolution quantitative DCE sequence, from which 30-s temporal-resolution multi-phasic DCE-MRI was synthesized based on clinical protocol. A pharmacokinetics-informed neural network was trained to improve the temporal resolution of the multi-phasic DCE before the quantification of pharmacokinetic parameters. Through ten-fold cross-validation, the agreement between pharmacokinetic parameters estimated from synthesized multi-phasic DCE after deep learning inference was assessed against reference parameters from the corresponding quantitative DCE-MRI images. The ability of the deep learning estimated parameters to differentiate abnormal from normal tissues was assessed as well. Results: The pharmacokinetic parameters estimated after deep learning have a high level of agreement with the reference values. In the cross-validation, all three pharmacokinetic parameters (transfer constant Ktrans, fractional extravascular extracellular volume ve, and rate constant kep) achieved intraclass correlation coefficient and R2 between 0.84–0.94, and low coefficients of variation (10.1%, 12.3%, and 5.6%, respectively) relative to the reference values. Significant differences were found between healthy pancreas, PDAC tumor and non-tumor, and CP pancreas. Discussion: Retrospective quantification (RoQ) of clinical multi-phasic DCE-MRI is possible by deep learning. This technique has the potential to derive quantitative pharmacokinetic parameters from clinical multi-phasic DCE data for a more objective and precise assessment of cancer.

IntroductionTo characterize early-gestation changes in placental structure, perfusion, and oxygenation in the context of ischemic placental disease (IPD) as a composite outcome and in individual sub-groups.MethodsIn a single-center prospective cohort study, 199 women were recruited from antenatal clinics between February 2017 and February 2019. Maternal magnetic resonance imaging (MRI) studies of the placenta were temporally conducted at two timepoints: 14-16 weeks gestational age (GA) and 19-24 weeks GA. The pregnancy was monitored via four additional study visits, including at delivery. Placental volume, perfusion, and oxygenation were assessed at both MRI timepoints. The primary outcome was defined as pregnancy complicated by IPD, with group assignment confirmed after delivery.ResultsIn early gestation, mothers with IPD who subsequently developed fetal growth restriction (FGR) and/or delivered small-for gestational age (SGA) infants showed significantly decreased MRI indices of placental volume, perfusion, and oxygenation compared to controls. The prediction of FGR or SGA by multiple logistic regression using placental volume, perfusion, and oxygenation revealed receiver operator characteristic curves with areas under the curve of 0.81 (Positive predictive value (PPV) = 0.84, negative predictive value (NPV) = 0.75) at 14-16 weeks GA and 0.66 (PPV = 0.78, NPV = 0.60) at 19-24 weeks GA.DiscussionMRI indices showing decreased placental volume, perfusion and oxygenation in early pregnancy were associated with subsequent onset of IPD, with the greatest deviation evident in subjects with FGR and/or SGA. These early-gestation MRI changes may be predictive of the subsequent development of FGR and/or SGA.

BackgroundAutomated 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.PurposeTo evaluate a spatial attentive deep learning method (SADL) for automated placental segmentation on longitudinal placental MRI scans.Study typeProspective, single-center.SubjectsA 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/sequenceA 3 T, T2-weighted half Fourier single-shot turbo spin-echo (T2-HASTE) sequence.AssessmentThe 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 testsThe 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

Conditional image generation plays a vital role in medical image analysis as it is effective in tasks such as super-resolution, denoising, and inpainting, among others. Diffusion models have been shown to perform at a state-of-the-art level in natural image generation, but they have not been thoroughly studied in medical image generation with specific conditions. Moreover, current medical image generation models have their own problems, limiting their usage in various medical image generation tasks. In this paper, we introduce the use of conditional Denoising Diffusion Probabilistic Models (cDDPMs) for medical image generation, which achieve state-of-the-art performance on several medical image generation tasks.

High-resolution magnetic resonance imaging (MRI) sequences, such as 3D turbo or fast spin-echo (TSE/FSE) imaging, are clinically desirable but suffer from long scanning time-related blurring when reformatted into preferred orientations. Instead, multi-slice two-dimensional (2D) TSE imaging is commonly used because of its high in-plane resolution but is limited clinically by poor through-plane resolution due to elongated voxels and the inability to generate multi-planar reformations due to staircase artifacts. Therefore, multiple 2D TSE scans are acquired in various orthogonal imaging planes, increasing the overall MRI scan time. In this study, we propose a novel slice-profile transformation super-resolution (SPTSR) framework with deep generative learning for through-plane super-resolution (SR) of multi-slice 2D TSE imaging. The deep generative networks were trained by synthesized low-resolution training input via slice-profile downsampling (SP-DS), and the trained networks inferred on the slice profile convolved (SP-conv) testing input for 5.5x through-plane SR. The network output was further slice-profile deconvolved (SP-deconv) to achieve an isotropic super-resolution. Compared to SMORE SR method and the networks trained by conventional downsampling, our SPTSR framework demonstrated the best overall image quality from 50 testing cases, evaluated by two abdominal radiologists. The quantitative analysis cross-validated the expert reader study results. 3D simulation experiments confirmed the quantitative improvement of the proposed SPTSR and the effectiveness of the SP-deconv step, compared to 3D ground-truths. Ablation studies were conducted on the individual contributions of SP-DS and SP-conv, networks structure, training dataset size, and different slice profiles.

Prostate cancer is the second leading cause of cancer death among men in the United States. The diagnosis of prostate MRI often relies on accurate prostate zonal segmentation. However, state-of-the-art automatic segmentation methods often fail to produce well-contained volumetric segmentation of the prostate zones since certain slices of prostate MRI, such as base and apex slices, are harder to segment than other slices. This difficulty can be overcome by leveraging important multi-scale image-based information from adjacent slices, but current methods do not fully learn and exploit such cross-slice information. In this paper, we propose a novel cross-slice attention mechanism, which we use in a Transformer module to systematically learn cross-slice information at multiple scales. The module can be utilized in any existing deep-learning-based segmentation framework with skip connections. Experiments show that our cross-slice attention is able to capture cross-slice information significant for prostate zonal segmentation in order to improve the performance of current state-of-the-art methods. Cross-slice attention improves segmentation accuracy in the peripheral zones, such that segmentation results are consistent across all the prostate slices (apex, mid-gland, and base). The code for the proposed model is available at https://bit.ly/CAT-Net.

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 models 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 Fishers 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.

BackgroundMultiparametric 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.PurposeTo identify which patient could safely avoid prostate biopsy when the prostate mpMRI is negative, via a radiomics-based machine learning approach.Study typeRetrospective.SubjectsThree hundred thirty patients with negative prostate 3T mpMRI between January 2016 and December 2018 were included.Field strength/sequenceA 3.0 T/T2-weighted turbo spin echo (TSE) imaging (T2 WI) and diffusion-weighted imaging (DWI).AssessmentThe 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

The current standardized scheme for interpreting MRI requires a high level of expertise and exhibits a significant degree of inter-reader and intra-reader variability. An automated prostate cancer (PCa) classification can improve the ability of MRI to assess the spectrum of PCa. The purpose of the study was to evaluate the performance of a texture-based deep learning model (Textured-DL) for differentiating between clinically significant PCa (csPCa) and non-csPCa and to compare the Textured-DL with Prostate Imaging Reporting and Data System (PI-RADS)-based classification (PI-RADS-CLA), where a threshold of PI-RADS ≥ 4, representing highly suspicious lesions for csPCa, was applied. The study cohort included 402 patients (60% (n = 239) of patients for training, 10% (n = 42) for validation, and 30% (n = 121) for testing) with 3T multiparametric MRI matched with whole-mount histopathology after radical prostatectomy. For a given suspicious prostate lesion, the volumetric patches of T2-Weighted MRI and apparent diffusion coefficient images were cropped and used as the input to Textured-DL, consisting of a 3D gray-level co-occurrence matrix extractor and a CNN. PI-RADS-CLA by an expert reader served as a baseline to compare classification performance with Textured-DL in differentiating csPCa from non-csPCa. Sensitivity and specificity comparisons were performed using Mcnemar's test. Bootstrapping with 1000 samples was performed to estimate the 95% confidence interval (CI) for AUC. CIs of sensitivity and specificity were calculated by the Wald method. The Textured-DL model achieved an AUC of 0.85 (CI [0.79, 0.91]), which was significantly higher than the PI-RADS-CLA (AUC of 0.73 (CI [0.65, 0.80]); p < 0.05) for PCa classification, and the specificity was significantly different between Textured-DL and PI-RADS-CLA (0.70 (CI [0.59, 0.82]) vs. 0.47 (CI [0.35, 0.59]); p < 0.05). In sub-analyses, Textured-DL demonstrated significantly higher specificities in the peripheral zone (PZ) and solitary tumor lesions compared to the PI-RADS-CLA (0.78 (CI [0.66, 0.90]) vs. 0.42 (CI [0.28, 0.57]); 0.75 (CI [0.54, 0.96]) vs. 0.38 [0.14, 0.61]; all p values < 0.05). Moreover, Textured-DL demonstrated a high negative predictive value of 92% while maintaining a high positive predictive value of 58% among the lesions with a PI-RADS score of 3. In conclusion, the Textured-DL model was superior to the PI-RADS-CLA in the classification of PCa. In addition, Textured-DL demonstrated superior performance in the specificities for the peripheral zone and solitary tumors compared with PI-RADS-based risk assessment.

BackgroundTissue 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).MethodsWhole-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.ResultsQuantitative 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.Conclusions3D 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.

Although analysis of maternal plasma cell-free content has been employed for screening of genetic abnormalities within a pregnancy, limited attention has been paid to its use for the detection of adverse pregnancy outcomes (APOs) based on placental function. Here we investigated cell-free DNA and RNA content of 102 maternal and 25 cord plasma samples. Employing a novel deconvolution methodology, we found that during the first trimester, placenta-specific DNA increased prior to the subsequent development of gestational diabetes with no change in patients with preeclampsia while decreasing with maternal obesity. Moreover, using cell-free RNA sequencing, APOs revealed 71 differentially expressed genes early in pregnancy. We noticed the upregulation of S100A8, MS4A3, and MMP8 that have been already associated with APOs but also the upregulation of BCL2L15 and the downregulation of ALPL that have never been associated with APOs. We constructed a classifier with a positive predictive ability (AUC) of 0.91 for APOs, 0.86 for preeclampsia alone and 0.64 for GDM. We conclude that placenta-specific cell-free nucleic acids during early gestation provide the possibility of predicting APOs prior to the emergence of characteristic clinical features.

Multi-parametric MRI (mpMRI) is a powerful non-invasive tool for diagnosing prostate cancer (PCa) and is widely recommended to be performed before prostate biopsies. Prostate Imaging Reporting and Data System version (PI-RADS) is used to interpret mpMRI. However, when the pre-biopsy mpMRI is negative, PI-RADS 1 or 2, there exists no consensus on which patients should undergo prostate biopsies. Recently, radiomics has shown great abilities in quantitative imaging analysis with outstanding performance on computer-aid diagnosis tasks. We proposed an integrative radiomics-based approach to predict the prostate biopsy results when pre-biopsy mpMRI is negative. Specifically, the proposed approach combined radiomics features and clinical features with machine learning to stratify positive and negative biopsy groups among negative mpMRI patients. We retrospectively reviewed all clinical prostate MRIs and identified 330 negative mpMRI scans, followed by biopsy results. Our proposed model was trained and validated with 10-fold cross-validation and reached the negative predicted value (NPV) of 0.99, the sensitivity of 0.88, and the specificity of 0.63 in receiver operating characteristic (ROC) analysis. Compared with results from existing methods, ours achieved 11.2% higher NPV and 87.2% higher sensitivity with a cost of 23.2% less specificity.

Background: Several deep learning-based techniques have been developed for prostate cancer (PCa) detection using multi-parametric MRI (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: 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 versus the number of false-positive detections at different thresholds on suspicion scores. Clinically significant lesions: Gleason Group≥2 or pathological size≥10 mm. Index lesions: the highest Gleason Group 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 to 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.

Whole-prostate gland (WPG) segmentation plays a significant role in prostate volume measurement, treatment, and biopsy planning. This study evaluated a previously developed automatic WPG segmentation, deep attentive neural network (DANN), on a large, continuous patient cohort to test its feasibility in a clinical setting. With IRB approval and HIPAA compliance, the study cohort included 3,698 3T MRI scans acquired between 2016 and 2020. In total, 335 MRI scans were used to train the model, and 3,210 and 100 were used to conduct the qualitative and quantitative evaluation of the model. In addition, the DANN-enabled prostate volume estimation was evaluated by using 50 MRI scans in comparison with manual prostate volume estimation. For qualitative evaluation, visual grading was used to evaluate the performance of WPG segmentation by two abdominal radiologists, and DANN demonstrated either acceptable or excellent performance in over 96% of the testing cohort on the WPG or each prostate sub-portion (apex, midgland, or base). Two radiologists reached a substantial agreement on WPG and midgland segmentation (κ = 0.75 and 0.63) and moderate agreement on apex and base segmentation (κ = 0.56 and 0.60). For quantitative evaluation, DANN demonstrated a dice similarity coefficient of 0.93 ± 0.02, significantly higher than other baseline methods, such as DeepLab v3+ and UNet (both p values < 0.05). For the volume measurement, 96% of the evaluation cohort achieved differences between the DANN-enabled and manual volume measurement within 95% limits of agreement. In conclusion, the study showed that the DANN achieved sufficient and consistent WPG segmentation on a large, continuous study cohort, demonstrating its great potential to serve as a tool to measure prostate volume.

Magnetic resonance imaging (MRI) is a promising non-invasive imaging technique that can be safely used to study placental development and function. However, studies of the human placenta performed by MRI are limited by uterine motion and motion in the uterus during MRI remains one of the major limiting factors. Here, we aimed to investigate the characterization of uterine activity during MRI in the second trimester of pregnancy using MRI-based motion tracking. In total, 46 pregnant women were scanned twice (first scan between 14 and 18 weeks and second scan between 19 and 24 weeks), and 20 pregnant subjects underwent a single MRI between 14 and 18 weeks GA, resulting in 112 MRI scans. An MRI-based algorithm was used to track uterine motion in the superior-inferior and left-right directions. Uterine contraction and maternal motion cases were separated by the experts, and unpaired Wilcoxon tests were performed within the groups of gestational age (GA), fetal sex, and placental location in terms of the overall intensity measures of the uterine activity. In total, 22.3% of cases had uterine contraction during MRI, which increased from 18.6% at 14-18 weeks to 26.4% at 19-24 weeks GA. The dominant direction of the uterine contraction and maternal motion was the superior to the inferior direction during early gestation.

PurposeTo investigate the role of qualitative and quantitative DCE-MRI parameters in prostate cancer (PCa) stratified by whole-mount histopathology (WMHP) Gleason score (GS) and PI-RADSv2.MethodsThis retrospective study included 323 PCa tumors in 254 men, who underwent 3T MRI prior to prostatectomy, 7/2009-12/2016. Qualitative DCE curve types included type 1 (progressive), type 2 (plateau) and type 3 (washout). Quantitative DCE-MRI pharmacokinetic (PK) parameters included Ktrans (influx volume transfer coefficient), Kep (efflux reflux rate constant) and iAUC (initial area under the curve). DCE-MRI features of true positive lesions were evaluated for overall, index, transition zone (TZ) and peripheral zone (PZ), based on GS grade (low = 6, high > 6) and PI-RADSv2 score using SPSSv24.ResultsThere were 57 (17.6%) low-grade and 266 (82.4%) high-grade PCa lesions. PI-RADSv2 3, 4 and 5 included 106, 120 and 97 lesions, respectively. 251 (77.7%) and 72 (22.3%) lesions were located in PZ and TZ, respectively. High-grade lesions had significantly higher proportion of Type 3 curves compared to low-grade lesions in overall (70.3% vs. 54.4%) and TZ (73.5% vs. 43.5%). As PI-RADSv2 increased, the proportion of type 3 curve significantly increased for overall (80.4-51.9%), index (80.4-54.7%) and PZ (78.7-52.1%) lesions. Among PK parameters, Ktrans (0.43 vs 0.32) and iAUC (8.99 vs 6.9) for overall PCa, Ktrans (0.43 vs 0.31) and iAUC (9 vs 6.67) for PZ PCa, and iAUC (8.94 vs 7.42) for index PCa were significantly higher for high-grade versus low-grade lesions. Also, Ktrans (0.51-0.34), Kep (1.75-1.29) and iAUC (9.79-7.6) for overall PCa, Ktrans (0.53-0.32), Kep (1.81-1.26) and iAUC (9.83-7.34) for PZ PCa; and Kep (1.79-1.17) and iAUC (11.3-8.45) for index PCa increased significantly with a higher PI-RADSv2 score.ConclusionsThe results of study show the possible utility of qualitative and quantitative DCE-MRI parameters for assessment of PCa GS and PI-RADSv2 categorization.

BackgroundNoninvasive 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.PurposeTo 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 typeProspective.PopulationEighty-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/sequence3T/T2 -weighted half-Fourier single-shot turbo spin-echo (HASTE) and pCASL.AssessmentFour 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 testsComparisons 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.ResultsThe difference between the first and second MRI scans was statistically significant for volume (156.6 cm3 vs. 269.7 cm3 , P

Automatic segmentation of prostatic zones on multiparametric MRI (mpMRI) can improve the diagnostic workflow of prostate cancer. We designed a spatial attentive Bayesian deep learning network for the automatic segmentation of the peripheral zone (PZ) and transition zone (TZ) of the prostate with uncertainty estimation. The proposed method was evaluated by using internal and external independent testing datasets, and overall uncertainties of the proposed model were calculated at different prostate locations (apex, middle, and base). The study cohort included 351 MRI scans, of which 304 scans were retrieved from a de-identified publicly available datasets (PROSTATEX) and 47 scans were extracted from a large U.S. tertiary referral center (external testing dataset; ETD)). All the PZ and TZ contours were drawn by research fellows under the supervision of expert genitourinary radiologists. Within the PROSTATEX dataset, 259 and 45 patients (internal testing dataset; ITD) were used to develop and validate the model. Then, the model was tested independently using the ETD only. The segmentation performance was evaluated using the Dice Similarity Coefficient (DSC). For PZ and TZ segmentation, the proposed method achieved mean DSCs of 0.80±0.05 and 0.89±0.04 on ITD, as well as 0.79±0.06 and 0.87±0.07 on ETD. For both PZ and TZ, there was no significant difference between ITD and ETD for the proposed method. This DL-based method enabled the accuracy of the PZ and TZ segmentation, which outperformed the state-of-art methods (Deeplab V3+, Attention U-Net, R2U-Net, USE-Net and U-Net). We observed that segmentation uncertainty peaked at the junction between PZ, TZ and AFS. Also, the overall uncertainties were highly consistent with the actual model performance between PZ and TZ at three clinically relevant locations of the prostate.

PurposeTo determine the accuracy and test-retest repeatability of fast radiofrequency (RF) transmit measurement approaches used in Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI). Spatial variation in the transmitted RF field introduces bias and increased variance in quantitative DCE-MRI metrics including tracer kinetic parameter maps. If unaccounted for, these errors can dominate all other sources of bias and variance. The amount and pattern of variation depend on scanner-specific hardware and software.MethodsHuman tissue mimicking torso and brain phantoms were constructed. RF transmit maps were measured and compared across eight different commercial scanners, from three major vendors, and three clinical sites. Vendor-recommended rapid methods for RF mapping were compared to a slower reference method. Imaging was repeated at all sites after 2 months. Ranges and magnitude of RF inhomogeneity were compared scanner-wise at two time points. Limits of Agreement of vendor-recommended methods and double-angle reference method were assessed.ResultsAt 3 T, B1 + inhomogeneity spans across 35% in the head and 120% in the torso. Fast vendor provided methods are within 30% agreement with the reference double angle method for both the head and the torso phantom.ConclusionsIf unaccounted for, B1 + inhomogeneity can severely impact tracer-kinetic parameter estimation. Depending on the scanner, fast vendor provided B1 + mapping sequences allow unbiased and reproducible measurements of B1 + inhomogeneity to correct for this source of bias.

BackgroundMultiecho 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).PurposeTo 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 typeProspective.PopulationThirty subjects with normal pregnancies and three subjects with ischemic placental disease (IPD) were scanned twice: between 14-18 and 19-23 weeks GA.Field strength3T.SequenceFB radial.AssessmentLinear 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 testsρc 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.ResultsFB radial demonstrated accurate (ρc ≥0.996; P < 0.001; |MD|

BackgroundWhile low-field MRI is disadvantaged by a reduced signal-to-noise ratio (SNR) compared to higher fields, it has a number of useful features such as decreased SAR and shorter T1, and has shown promise for diagnostic imaging. This study demonstrates the feasibility of cardiac balanced steady-state free precession (bSSFP) MRI at 0.35 T and compares cardiac bSSFP MRI images at 0.35 T with those at 1.5 T.MethodsCardiac images were acquired in 7 healthy volunteers using an ECG-gated bSSFP cine sequence on a 0.35 T superconducting MR system as well as a clinical 1.5 T system. Blood and myocardium SNR and contrast-to-noise ratio (CNR) were computed. Subjective image scoring was used to compare the image quality between 0.35 and 1.5 T.ResultsCardiac images at 0.35 T were successfully acquired in all volunteers. While the 0.35 T images were noisier than those at 1.5 T, blood, myocardium and papillary muscles could be clearly delineated. At 0.35 T, bSSFP images were acquired at flip angles as high as 150°. Maximum CNR was achieved at 130°. Image quality scoring showed that while at lower flip angles, the 0.35 T images had poorer quality than the 1.5 T, but with flip angles of 110 and 130, the image quality at 0.35 T had scores similar to those at 1.5 T.ConclusionsThis study demonstrates that cardiac bSSFP imaging is highly feasible at 0.35 T.

BackgroundPlacenta influences the health of both a woman and her fetus during pregnancy. Maternal blood supply to placenta can be measured noninvasively using arterial spin labeling (ASL).PurposeTo present a multidelay pseudocontinuous arterial spin labeling (pCASL) combined with a fast 3D inner-volume gradient- and spin-echo (GRASE) imaging technique to simultaneously measure placental blood flow (PBF) and arterial transit time (ATT), and to study PBF and ATT evolution with gestational age during the second trimester. The PBF values were compared with uterine arterial Doppler ultrasound to assess its potential clinical utility.Study typeThis was a prospective study.SubjectsThirty-four pregnant women.Field strength/sequenceMultidelay 3D inner-volume GRASE pCASL sequence on 3T MR scanners.AssessmentSubjects underwent two longitudinal MRI scans within the second trimester, conducted between 14-16 and 19-22 weeks of gestational age, respectively. Placental perfusion was measured using the free-breathing pCASL sequence at three postlabeling delays (PLDs), followed by offline motion correction and model fitting for estimation of PBF and ATT.Statistical testsA paired t-test was conducted to evaluate the significance of PBF/ATT variations with placental development. A two-sample t-test was conducted to evaluate the significance of PBF difference in subjects with and without early diastolic notch.ResultsThe mean PBF and ATT for the second trimester were 111.4 ± 26.7 ml/100g/min and 1387.5 ± 88.0 msec, respectively. The average PBF increased by 10.4% (P < 0.05), while no significant change in ATT (P = 0.72) was found along gestational ages during the second trimester. PBF decreased 20.3% (P < 0.01) in subjects with early diastolic notches in ultrasound flow waveform patterns.Data conclusionMultidelay pCASL with inner-volume 3D GRASE is promising for noninvasive assessment of PBF during pregnancy. Its clinical use for the detection of aberrations in placental function and prediction of fetal developmental disorders awaits evaluation.Level of evidence1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1667-1676.

PurposeThe 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.MethodsPhantoms 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.ResultsIn phantoms, PDFF showed significant correlation (ρ > 0.998, ρc  > 0.995) and absolute mean differences  0.999, ρc  > 0.998) and absolute mean differences  0.986, ρc  > 0.985) and absolute mean differences  0.996, ρc  > 0.995) and absolute mean differences

Purpose:To develop a novel combination of controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA) with integrated SSFP (CAIPI-iSSFP) for accelerated SSFP imaging without banding artifacts at 3T. Materials and Methods:CAIPI-iSSFP was developed by adding a dephasing gradient to the balanced SSFP (bSSFP) pulse sequence with a gradient area that results in 2π dephasing across a single pixel. Extended phase graph (EPG) simulations were performed to show the signal behaviors of iSSFP, bSSFP, and RF-spoiled gradient echo (SPGR) sequences. In vivo experiments were performed for brain and abdominal imaging at 3T with simultaneous multi-slice (SMS) acceleration factors of 2, 3 and 4 with CAIPI-iSSFP and CAIPI-bSSFP. The image quality was evaluated by measuring the relative contrast-to-noise ratio (CNR) and by qualitatively assessing banding artifact removal in the brain. Results:Banding artifacts were removed using CAIPI-iSSFP compared to CAIPI-bSSFP up to an SMS factor of 4 and 3 on brain and liver imaging, respectively. The relative CNRs between gray and white matter were on average 18% lower in CAIPI-iSSFP compared to that of CAIPI-bSSFP. Conclusion:This study demonstrated that CAIPI-iSSFP provides up to a factor of four acceleration, while minimizing the banding artifacts with up to a 20% decrease in the relative CNR.

PurposeTo compare the accuracy and precision of four different T1 estimation algorithms for modified Look-Locker inversion recovery (MOLLI).MethodsFour T1 estimation algorithms, including the original fit, inversion group (IG) fit, instantaneous signal loss simulation (InSiL), and Bloch equation simulation with slice profile correction (BLESSPC) were studied. T1 estimation accuracy, precision, reproducibility, and sensitivity to heart rate (HR), flip angle (FA), and acquisition scheme (AcS) variations were compared in simulation, phantom, and volunteer studies.ResultsT1 estimation accuracy of IG (-2.4% ± 3.9%) and original fit (-3.2% ± 1.4%) were worse than BLESSPC (0.2% ± 1.5%) and InSiL (-0.7% ± 2.1%). The original fit had the best precision for T1 from 409-1884 ms for the same FA (0.67% ± 0.16% versus 0.90% ± 0.23% using IG, 0.78% ± 0.11% using InSiL, 0.77% ± 0.12% using BLESSPC). BLESSPC generated the most consistent in vivo T1 values over different FAs and AcS, and the T1 estimation reproducibility was similar (P > 0.3) among the four methods when FA = 35°. When using FA = 50°, the reproducibility was significantly improved only when using BLESSPC (1.6% ± 0.9 versus 2.6% ± 1.9%, P

PurposeTo develop a free-breathing multidelay pseudocontinuous arterial spin labeling (pCASL) technique for quantitative measurement of liver perfusion of the hepatic artery and portal vein, respectively.Materials and methodsA navigator-gated pCASL sequence with balanced steady-state free precession (bSSFP) readout was developed and applied on five healthy young volunteers at 3T. Two labeling schemes were performed with the labeling plane applied on the descending aorta above the liver, and perpendicular to the portal vein before its entry to liver to label the hepatic artery and portal vein, respectively. For each labeling scheme, pCASL scans were performed at five or six postlabeling delays between 200 and 2000 msec or 2500 msec with an interval of 400 or 500 msec. Multidelay pCASL images were processed offline with nonrigid motion correction, outlier removal, and fitted for estimation of liver perfusion and transit time.ResultsEstimated liver perfusion of the hepatic artery and hepatic portal vein were 21.8 ± 1.9 and 95.1 ± 8.9 mL/100g/min, with the corresponding transit time of 1227.3 ± 355.5 and 667.2 ± 85.0 msec, respectively. The estimated liver perfusion and transit time without motion correction were less reliable with greater residual variance compared to those processed with motion correction (P < 0.05).ConclusionThe liver perfusion measurement using multidelay pCASL showed good correspondence with values noted in the literature. The capability to noninvasively and selectively label the hepatic artery and portal vein is a unique strength of pCASL as compared to other liver perfusion imaging techniques, such as computed tomography perfusion and dynamic contrast-enhanced MRI.

The identification of early distant metastases (DM) in patients with newly diagnosed, previously untreated nasopharyngeal carcinoma (NPC) plays an important role in selecting the most appropriate treatment approach. Here, we sought to investigate the predictive value of distinct MRI parameters for the detection of early DM.Between November 2010 and June 2011, a total of 51 newly diagnosed NPC patients were included. All of the study participants were followed until December 2014 at a single institution after completion of therapy. DM was defined as early when they were detected on pretreatment FDG-PET scans or within 6 months after initial diagnosis. The following parameters were tested for their ability to predict early DM: pretreatment FDG-PET standardized uptake value (SUV), MRI-derived AJCC tumor staging, tumor volume, and dynamic contrast-enhanced (DCE) values. The DCE-derived ve was defined as the volume fraction of the extravascular, extracellular space.Compared with patients without early DM, patients with early DM had higher SUV, tumor volume, DCE mean (median) ve, ve skewness, ve kurtosis, and the largest mean ve selected among sequential slices (P

PurposeThree-dimensional (3D) T2 -weighted fast spin echo (FSE) imaging of the prostate currently requires long acquisition times. Our objective was to develop a fast 3D T2 -weighted sequence for prostate imaging at 3T using a variable flip angle transition into driven equilibrium (T2 -TIDE) scheme.Methods3D T2 -TIDE uses interleaved spiral-out phase encode ordering to efficiently sample the ky -kz phase encodes and also uses the transient balanced steady-state free precession signal to acquire the center of k-space for T2 -weighted imaging. Bloch simulations and images from 10 healthy subjects were acquired to evaluate the performance of 3D T2 -TIDE compared to 3D FSE.Results3D T2 -TIDE images were acquired in 2:54 minutes compared to 7:02 minutes for 3D FSE with identical imaging parameters. The signal-to-noise ratio (SNR) efficiency was significantly higher for 3D T2 -TIDE compared to 3D FSE in nearly all tissues, including periprostatic fat (45 ± 12 vs. 31 ± 7, P

PurposeTo incorporate a newly developed shape-based motion estimation scheme into magnetic resonance urography (MRU) and verify its efficacy in facilitating quantitative functional analysis.MethodsThe authors propose a motion compensation scheme in MRU that consists of three sequential modules: MRU image acquisition, motion compensation, and quantitative functional analysis. They designed two sets of complementary experiments to evaluate the performance of the proposed method. In the first experiment, dynamic contrast enhanced (DCE) MR images were acquired from three sedated subjects, from which clinically valid estimates were derived and served as the "ground truth." Physiologically sound motion was then simulated to synthesize image sequences influenced by respiratory motion. Quantitative assessment and comparison were performed on functional estimates of Patlak number, glomerular filtration rate, and Patlak differential renal function without and with motion compensation against the ground truth. In the second experiment, the authors acquired a temporal series of noncontrast MR images under free breathing from a healthy adult subject. The performance of the proposed method on compensating real motion was evaluated by comparing the standard deviation of the obtained temporal intensity curves before and after motion compensation.ResultsOn DCE-MR images with simulated motion, the generated relative enhancement curves exhibited large perturbations and the Patlak numbers of the left and right kidney were significantly underestimated up to 35% and 34%, respectively, compared with the ground truth. After motion compensation, the relative enhancement curves exhibited much less perturbations and Patlak estimation errors reduced within 3% and 4% for the left and right kidneys, respectively. On clinical free-breathing MR images, the temporal intensity curves exhibited significantly reduced variations after motion compensation, with standard deviation decreased from 30.3 and 38.2 to 8.3 and 11.7 within two manually selected regions of interest, respectively.ConclusionsThe developed motion compensation method has demonstrated its ability to facilitate quantitative MRU functional analysis, with improved accuracy of pharmacokinetic modeling and quantitative parameter estimations. Future work will consider performing more intensive clinical verifications with sophisticated pharmacokinetic models and generalizing the proposed method to other quantitative DCE analysis, such as on liver or prostate function.