Super-resolution synthetic MRI using deep learning reconstruction for accurate diagnosis of knee osteoarthritis.

Objective: To assess the accuracy of deep learning reconstruction (DLR) technique on synthetic MRI (SyMRI) including T2 measurements and diagnostic performance of DLR synthetic MRI (SyMRI) in patients with knee osteoarthritis (KOA) using conventional MRI as standard reference.

Materials And Methods: This prospective study recruited 36 volunteers and 70 patients with suspected KOA from May to October 2023. DLR and non-DLR synthetic T2 measurements (T2-SyMRI, T2-SyMRI) for phantom and in vivo knee cartilage were compared with multi-echo fast-spin-echo (MESE) sequence acquired standard T2 values (T2).

Texture analysis of T1- and T2-weighted images identifies myofiber atrophy and grip strength decline in streptozotocin-induced type 1 diabetic sarcopenia rats.

Background: Type 1 diabetes mellitus is associated with accelerated skeletal muscle aging and sarcopenia, a condition characterized by muscle mass and function loss. Early and noninvasive evaluation of muscle microstructural damage is critical for managing sarcopenia in diabetes. This study evaluated the potential of MRI texture analysis as a noninvasive imaging tool to assess myofiber size and grip strength alterations in a rat model of diabetic sarcopenia.

Comprehensive proteomic analysis and multidimensional model construction of peritoneal metastasis in gastric cancer.

Peritoneal metastasis following gastric cancer surgery is often associated with a poor prognosis. This study aimed to investigate the mechanisms underlying peritoneal metastasis and to develop a predictive model for the risk of postoperative peritoneal metastases in gastric cancer. We performed a comprehensive analysis of the protein mass spectra and tumor microenvironment in paraffin-embedded primary tumor sections from gastric cancer patients, both with and without postoperative peritoneal metastases.

Enhancing repeatability of follicle counting with deep learning reconstruction high-resolution MRI in PCOS patients.

Follicle count, a pivotal metric in the adjunct diagnosis of polycystic ovary syndrome (PCOS), is often underestimated when assessed via transvaginal ultrasonography compared to MRI. Nevertheless, the repeatability of follicle counting using traditional MR images is still compromised by motion artifacts or inadequate spatial resolution. In this prospective study involving 22 PCOS patients, we employed periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) and single-shot fast spin-echo (SSFSE) T2-weighted sequences to suppress motion artifacts in high-resolution ovarian MRI.

Integration of texture analysis based on DCE-MRI K map and metabolomics of early bone marrow microvascular changes in alloxan-induced diabetic rabbits.

Objective: To evaluate early bone marrow microvascular changes in alloxan-induced diabetic rabbits using IDEAL-IQ fat quantification, texture analysis based on DCE-MRI K map, and metabolomics.

Materials And Methods: 24 male Japanese rabbits were randomly divided into diabetic (n = 12) and control (n = 12) groups. All rabbits underwent sagittal MRI of the lumbar vertebrae at the 0th,4th, 8th, 12th, and 16th week, respectively.

Matrix metalloproteinase 9 expression and glioblastoma survival prediction using machine learning on digital pathological images.

This study aimed to apply pathomics to predict Matrix metalloproteinase 9 (MMP9) expression in glioblastoma (GBM) and investigate the underlying molecular mechanisms associated with pathomics. Here, we included 127 GBM patients, 78 of whom were randomly allocated to the training and test cohorts for pathomics modeling. The prognostic significance of MMP9 was assessed using Kaplan-Meier and Cox regression analyses.

Evaluation of deep learning-based reconstruction late gadolinium enhancement images for identifying patients with clinically unrecognized myocardial infarction.

Background: The presence of infarction in patients with unrecognized myocardial infarction (UMI) is a critical feature in predicting adverse cardiac events. This study aimed to compare the detection rate of UMI using conventional and deep learning reconstruction (DLR)-based late gadolinium enhancement (LGE and LGE, respectively) and evaluate optimal quantification parameters to enhance diagnosis and management of suspected patients with UMI.

Methods: This prospective study included 98 patients (68 men; mean age: 55.

Deep learning-based radiomics of computed tomography angiography to predict adverse events after initial endovascular repair for acute uncomplicated Stanford type B aortic dissection.

Purpose: This study aimed to construct a predictive model integrating deep learning-derived radiomic features from computed tomography angiography (CTA) and clinical biomarkers to forecast postoperative adverse events (AEs) in patients with acute uncomplicated Stanford type B aortic dissection (uTBAD) undergoing initial thoracic endovascular aortic repair (TEVAR).

Methods: We retrospectively evaluated 369 patients treated with TEVAR for acute uTBAD from January 2015 to December 2022. A three-dimensional (3D) deep convolutional neural network (CNN) automated radiomic feature extraction from CTA images.

Neurodegeneration within the rostral spinal cord is associated with brain gray matter volume atrophy in the early stage of cervical spondylotic myelopathy.

Study Design: Case-control study.

Objectives: Investigating the association between neurodegeneration within rostral spinal cord and brain gray matter volume (GMV) and assessing the relationship between remote neurodegenerative changes and clinical outcomes at the early phase of Cervical Spondylotic Myelopathy (CSM).

Setting: University/hospital.

Development and Validation of a Deep Learning Model to Reduce the Interference of Rectal Artifacts in MRI-based Prostate Cancer Diagnosis.

Purpose To develop an MRI-based model for clinically significant prostate cancer (csPCa) diagnosis that can resist rectal artifact interference. Materials and Methods This retrospective study included 2203 male patients with prostate lesions who underwent biparametric MRI and biopsy between January 2019 and June 2023. Targeted adversarial training with proprietary adversarial samples (TPAS) strategy was proposed to enhance model resistance against rectal artifacts.

Atrial and Ventricular Strain Imaging Using CMR in the Prediction of Ventricular Arrhythmia in Patients with Myocarditis.

(1) : Myocarditis can be associated with ventricular arrhythmia (VA), individual non-invasive risk stratification through cardiovascular magnetic resonance (CMR) is of great clinical significance. Our study aimed to explore whether left atrial (LA) and left ventricle (LV) myocardial strain serve as independent predictors of VA in patients with myocarditis. (2) This retrospective study evaluated CMR scans in 141 consecutive patients diagnosed with myocarditis based on the updated Lake Louise criteria (29 females, mean age 41 ± 20).

Microvascular permeability and texture analysis of bone marrow in diabetic rabbits with critical limb ischemia based on dynamic contrast-enhanced magnetic resonance imaging.

Background: Early on in the development of diabetes, skeletal muscles can exhibit microarchitectural changes that can be detected using texture analysis (TA) based on volume transfer constant (K) maps. Nevertheless, there have been few studies and thus we evaluated microvascular permeability and the TA of the bone marrow in diabetics with critical limb ischemia (CLI).

Methods: Eighteen male rabbits were randomly assigned equally into an operation group with hindlimb ischemia and diabetes, a sham-operated group with diabetes only, and a control group.

Usefulness of T2-Weighted Images with Deep-Learning-Based Reconstruction in Nasal Cartilage.

Objective: This study aims to evaluate the feasibility of visualizing nasal cartilage using deep-learning-based reconstruction (DLR) fast spin-echo (FSE) imaging in comparison to three-dimensional fast spoiled gradient-echo (3D FSPGR) images.

Materials And Methods: This retrospective study included 190 set images of 38 participants, including axial T1- and T2-weighted FSE images using DLR (T1WI and T2WI, belong to FSE) and without using DLR (T1WI and T2WI, belong to FSE) and 3D FSPGR images. Subjective evaluation (overall image quality, noise, contrast, artifacts, and identification of anatomical structures) was independently conducted by two radiologists.

Radiomics Features on Magnetic Resonance Images Can Predict C5aR1 Expression Levels and Prognosis in High-Grade Glioma.

Background: The complement component C5a receptor 1 (C5aR1) regulates cancer immunity. This retrospective study aimed to assess its prognostic value in high-grade glioma (HGG) and predict C5aR1 expression using a radiomics approach.

Methods: Among 298 patients with HGG, 182 with MRI data were randomly divided into training and test groups for radiomics analysis.

An integrated convolutional neural network for classifying small pulmonary solid nodules.

Achieving accurate classification of benign and malignant pulmonary nodules is essential for treating some diseases. However, traditional typing methods have difficulty obtaining satisfactory results on small pulmonary solid nodules, mainly caused by two aspects: (1) noise interference from other tissue information; (2) missing features of small nodules caused by downsampling in traditional convolutional neural networks. To solve these problems, this paper proposes a new typing method to improve the diagnosis rate of small pulmonary solid nodules in CT images.

Hippocampal Subfield Volumes in Amateur Marathon Runners.

Purpose: Numerous studies have implicated the involvement of structure and function of the hippocampus in physical exercise, and the larger hippocampal volume is one of the relevant benefits reported in exercise. It remains to be determined how the different subfields of hippocampus respond to physical exercise.

Methods: A 3D T1-weighted magnetic resonance imaging was acquired in 73 amateur marathon runners (AMR) and 52 healthy controls (HC) matched with age, sex, and education.

Predicting prognosis of nasopharyngeal carcinoma based on deep learning: peritumoral region should be valued.

Background: The purpose of this study was to explore whether incorporating the peritumoral region to train deep neural networks could improve the performance of the models for predicting the prognosis of NPC.

Methods: A total of 381 NPC patients who were divided into high- and low-risk groups according to progression-free survival were retrospectively included. Deeplab v3 and U-Net were trained to build segmentation models for the automatic segmentation of the tumor and suspicious lymph nodes.

CENP-A is a potential prognostic biomarker and correlated with immune infiltration levels in glioma patients.

Centromeric protein A (), an essential protein involved in chromosomal segregation during cell division, is associated with several cancer types. However, its role in gliomas remains unclear. This study examined the clinical and prognostic significance of in gliomas.

Two-stage hybrid network for segmentation of COVID-19 pneumonia lesions in CT images: a multicenter study.

COVID-19 has been spreading continuously since its outbreak, and the detection of its manifestations in the lung via chest computed tomography (CT) imaging is essential to investigate the diagnosis and prognosis of COVID-19 as an indispensable step. Automatic and accurate segmentation of infected lesions is highly required for fast and accurate diagnosis and further assessment of COVID-19 pneumonia. However, the two-dimensional methods generally neglect the intraslice context, while the three-dimensional methods usually have high GPU memory consumption and calculation cost.