Does Amsterdam criteria applied to largely unsubmitted term placentas with favorable fetal outcomes show significant maternal clinicopathologic correlation?

Objectives: Before the Amsterdam Placental Workshop Group Consensus Statement, standardization in placental pathology assessment did not exist. This study evaluated the Amsterdam criteria's utility in correlating ischemic placental disease (IPD) with placental pathologic lesions in a cohort of largely unsubmitted term placentas with favorable outcomes.

Methods: In this prospective case-controlled study at a single institution, all placentas were examined using Amsterdam protocols for gross sampling and microscopic review by 2 reviewers who were blinded to clinical history.

Quantitative 3-T Multiparametric MRI Parameters as Predictors of Aggressive Prostate Cancer.

Purpose To determine which quantitative 3-T multiparametric MRI (mpMRI) parameters correlate with and help predict the presence of aggressive large cribriform pattern (LCP) and intraductal carcinoma (IDC) prostate cancer (PCa) at whole-mount histopathology (WMHP). Materials and Methods This retrospective study included 130 patients (mean age ± SD, 62.6 years ± 7.

Cross-Slice Attention and Evidential Critical Loss for Uncertainty-Aware Prostate Cancer Detection.

Current deep learning-based models typically analyze medical images in either 2D or 3D albeit disregarding volumetric information or suffering sub-optimal performance due to the anisotropic resolution of MR data. Furthermore, providing an accurate uncertainty estimation is beneficial to clinicians, as it indicates how confident a model is about its prediction. We propose a novel 2.

NExpR: Neural Explicit Representation for fast arbitrary-scale medical image super-resolution.

Medical images often require rescaling to various spatial resolutions to ensure interpretations at different levels. Conventional deep learning-based image super-resolution (SR) enhances the fixed-scale resolution. Implicit neural representation (INR) is a promising way of achieving arbitrary-scale image SR.

MRI Super-Resolution with Partial Diffusion Models.

Diffusion models have achieved impressive performance on various image generation tasks, including image super-resolution. Despite their impressive performance, diffusion models suffer from high computational costs due to the large number of denoising steps. In this paper, we proposed a novel accelerated diffusion model, termed Partial Diffusion Models (PDMs), for magnetic resonance imaging (MRI) super-resolution.

A Deep Learning-Based Framework for Highly Accelerated Prostate MR Dispersion Imaging.

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.

CSAM: A 2.5D Cross-Slice Attention Module for Anisotropic Volumetric Medical Image Segmentation.

A large portion of volumetric medical data, especially magnetic resonance imaging (MRI) data, is anisotropic, as the through-plane resolution is typically much lower than the in-plane resolution. Both 3D and purely 2D deep learning-based segmentation methods are deficient in dealing with such volumetric data since the performance of 3D methods suffers when confronting anisotropic data, and 2D methods disregard crucial volumetric information. Insufficient work has been done on 2.

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.

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).

AtPCa-Net: anatomical-aware prostate cancer detection network on multi-parametric MRI.

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.

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.

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.

Breast Multiparametric MRI for Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer: The BMMR2 Challenge.

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.

Maternal dietary patterns and placental outcomes among pregnant women in Los Angeles.

Introduction: Epidemiological studies have linked prenatal maternal diet to fetal growth, but whether diet affects placental outcomes is poorly understood.

Methods: We collected past month dietary intake from 148 women in mid-pregnancy enrolled at University of California Los Angeles (UCLA) antenatal clinics from 2016 to 2019. We employed the food frequency Diet History Questionnaire II and generated the Healthy Eating Index-2015 (HEI-2015), the Alternate Healthy Eating Index for Pregnancy (AHEI-P), and the Alternate Mediterranean Diet (aMED).

Med-cDiff: Conditional Medical Image Generation with Diffusion Models.

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.

Flexible in-cavity MRI receiving coil for ultra-high-resolution imaging of the pituitary gland.

Objective: The objective of this study was the preclinical design and construction of a flexible intrasphenoid coil aiming for submillimeter resolution of the human pituitary gland.

Methods: Sphenoid sinus measurements determined coil design constraints for use in > 95% of adult patients. Temperature safety parameters were tested.

Retrospective quantification of clinical abdominal DCE-MRI using pharmacokinetics-informed deep learning: a proof-of-concept study.

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.

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.

High-Resolution 3D MRI With Deep Generative Networks via Novel Slice-Profile Transformation Super-Resolution.

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.

Early pregnancy imaging predicts ischemic placental disease.

Introduction: To 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.

Methods: In 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.

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.

Racial Disparities in Quantitative MRI for African American and White Men with Prostate Cancer.

The risk of prostate cancer (PCa) is strongly influenced by race and ethnicity. The purpose of this study is to investigate differences in the diagnostic performance of multiparametric MRI (mpMRI) in African American (AA) and white (W) men. 111 patients (37 AA and 74 W men) were selected from the study's initial cohort of 885 patients after matching age, prostate-specific antigen, and prostate volume.

Utility of In Vivo Magnetic Resonance Imaging Is Predictive of Gestational Diabetes Mellitus During Early Pregnancy.

Context: Gestational diabetes (GDM) imposes long-term adverse health effects on the mother and fetus. The role of magnetic resonance imaging (MRI) during early gestation in GDM has not been well-studied.

Objective: To investigate the role of quantitative MRI measurements of placental volume and perfusion, with distribution of maternal adiposity, during early gestation in GDM.