Deep Learning-Enhanced Opportunistic Osteoporosis Screening in 100 kV Low-Voltage Chest CT: A Novel Way Toward Bone Mineral Density Measurement and Radiation Dose Reduction.

Rationale And Objectives: To explore the feasibility and accuracy of a deep learning (DL) method for fully automated vertebral body (VB) segmentation, region of interest (ROI) extraction, and bone mineral density (BMD) calculation using 100kV low-voltage chest CT performed for lung cancer screening across various scanners from different manufacturers and hospitals.

Materials And Methods: This study included 1167 patients who underwent 100 kV low-voltage chest and 120 kV lumbar CT from October 2022 to August 2024. Patients were divided into a training set (495 patients), a validation set (169 patients), and three test sets (245, 128, and 130 patients).

Clinical Diagnostic Value of 5 T MRI for Knee Injuries: A Comparison Study With 1.5 or 3 T.

Background: Preliminary studies have shown the potential of 5 T MRI in cardiac, neurovascular, and abdominal imaging. However, the clinical diagnostic value of 5 T for assessing knee injuries remains unclear.

Purpose: To compare the image quality, anatomic visibility, and diagnostic performance of 1.

Dissecting human cortical similarity networks across the lifespan.

The human cortex exhibits remarkable morphometric similarity between regions; however, the form and extent of lifespan network remodeling remain unknown. Here, we show the spatiotemporal maturation of morphometric brain networks, using multimodal neuroimaging data from 33,937 healthy participants aged 0-80 years. Global architecture matures from birth to early adulthood through enhanced modularity and small worldness.

Population-specific brain charts reveal Chinese-Western differences in neurodevelopmental trajectories.

Human brain charts provide unprecedented opportunities for decoding neurodevelopmental milestones and establishing clinical benchmarks for precision brain medicine . However, current lifespan brain charts are primarily derived from European and North American cohorts, with Asian populations severely underrepresented. Here, we present the first population-specific brain charts for China, developed through the Chinese Lifespan Brain Mapping Consortium (Phase I) using neuroimaging data from 43,037 participants (aged 0-100 years) across 384 sites nationwide.

Feasibility of 100 kV Low-voltage Bone Mineral Density Measurement with Quantitative CT: Phantom and Clinical Research.

To investigate the feasibility and accuracy of 100 kV low-voltage quantitative CT (QCT) for bone mineral density (BMD) measurement using the European Spine Phantom and patients. The accuracy and precision of the BMD measurements were assessed using relative measurement error (RME%), coefficient of variation (CV%), root mean square standard deviation (RMS-SD), and root mean square CV (RMS-%CV). Linear regression and Bland‒Altman analyses were used to assess the agreement between 100 and 120 kV QCT-based BMD measurements.

CT delta-radiomics predicts the risks of blood transfusion and massive bleeding during spinal tumor surgery.

Background: Intraoperative bleeding is a serious complication of spinal tumor surgery. Preoperative identification of patients at high risk of intraoperative blood transfusion (IBT) and intraoperative massive bleeding (IMB) before spinal tumor resection surgery is difficult but critical for surgical planning and blood management. This study aims to develop and validate delta radiomics prediction models for IBT and IMB in spinal tumor surgery.

Diagnostic value of dual-energy CT virtual monochromatic imaging for supraspinatus tendon injuries: a comparison with standard CT and MRI.

Objectives: This study aimed to compare the diagnostic value of dual-energy computed tomography (DECT) virtual monochromatic imaging with that of standard computed tomography (SCT) in evaluating supraspinatus tendon injuries.

Materials And Methods: This retrospective study involved patients who underwent a single-source DECT system, 3.0-T MRI, and shoulder arthroscopy within 14 days.

Improving Deep Learning-Based Grading of Partial-thickness Supraspinatus Tendon Tears with Guided Diffusion Augmentation.

Rationale And Objectives: To develop and validate a deep learning system with guided diffusion-based data augmentation for grading partial-thickness supraspinatus tendon (SST) tears and to compare its performance with experienced radiologists, including external validation.

Methods: This retrospective study included 1150 patients with arthroscopically confirmed SST tears, divided into a training set (741 patients), validation set (185 patients), and internal test set (185 patients). An independent external test set of 224 patients was used for generalizability assessment.

Deep Learning-enhanced Opportunistic Osteoporosis Screening in Ultralow-Voltage (80 kV) Chest CT: A Preliminary Study.

Rationale And Objectives: To explore the feasibility of deep learning (DL)-enhanced, fully automated bone mineral density (BMD) measurement using the ultralow-voltage 80 kV chest CT scans performed for lung cancer screening.

Materials And Methods: This study involved 987 patients who underwent 80 kV chest and 120 kV lumbar CT from January to July 2024. Patients were collected from six CT scanners and divided into the training, validation, and test sets 1 and 2 (561: 177: 112: 137).

Quantification of the Proton Density Fat Fraction and Iron Content: A Comparative Study Between 3.0 T and 5.0 T MRI.

Background: Proton density fat fraction (PDFF) and R2* are noninvasive MRI biomarkers for quantifying fat and iron in abdominal organs. While 3.0 T MRI is widely used clinically, 5.

Craniocaudal cyclic load improves risk assessment of lumbar pedicle screw loosening: finite element analysis based on computer tomography.

Background: Pedicle screw loosening (PSL) is a frequent complication in osteoporotic patients undergoing spinal fixation, yet effective risk assessment methods are limited. This study explores the impact of craniocaudal cyclic load on pedicle screw fixation strength using computed tomography-based finite element analysis (CT-FEA) and evaluates its predictive value for PSL.

Methods: A total of 23 PSL cases (7 men and 16 women) and 29 matched controls were analyzed using CT-FEA.

Human lifespan changes in the brain's functional connectome.

Functional connectivity of the human brain changes through life. Here, we assemble task-free functional and structural magnetic resonance imaging data from 33,250 individuals at 32 weeks of postmenstrual age to 80 years from 132 global sites. We report critical inflection points in the nonlinear growth curves of the global mean and variance of the connectome, peaking in the late fourth and late third decades of life, respectively.

Participant Discomfort During 5 T MRI Examinations and Its Contributing Factors.

Background: 5 T magnetic resonance imaging (MRI)-induced patient discomfort and the associated contributing factors remain unclear.

Purpose: To assess the frequency of discomfort during 5 T MRI examinations and analyze the contributing factors that may lead to discomfort, understand the potential challenges, and improve patient experience with 5 T systems.

Study Type: Prospective study.

Evaluation of brain microstructure changes in surviving fetus of monochorionic twin pregnancies with single intrauterine fetal death using diffusion weighted imaging: a MRI-based cohort study.

Background: Single intrauterine fetal death (sIUFD) will lead to an increased risk of adverse events such as fetal brain abnormalities in the survivor. However, how to detect these anomalies in the early stages remains to be explored.

Objective: To compare apparent diffusion coefficient (ADC) values of fetal brain in cases of single intrauterine fetal death (sIUFD) with twins control and singleton control using diffusion weighted imaging (DWI), and to perform follow-up study to reveal the underlying cerebral microstructure changes.

A machine learning model based on preoperative multiparametric quantitative DWI can effectively predict the survival and recurrence risk of pancreatic ductal adenocarcinoma.

Purpose: To develop a machine learning (ML) model combining preoperative multiparametric diffusion-weighted imaging (DWI) and clinical features to better predict overall survival (OS) and recurrence-free survival (RFS) following radical surgery for pancreatic ductal adenocarcinoma (PDAC).

Materials And Methods: A retrospective analysis was conducted on 234 PDAC patients who underwent radical resection at two centers. Among 101 ML models tested for predicting postoperative OS and RFS, the best-performing model was identified based on comprehensive evaluation metrics, including C-index, Brier scores, AUC curves, clinical decision curves, and calibration curves.

Deep learning-based image domain reconstruction enhances image quality and pulmonary nodule detection in ultralow-dose CT with adaptive statistical iterative reconstruction-V.

Objectives: To evaluate the image quality and lung nodule detectability of ultralow-dose CT (ULDCT) with adaptive statistical iterative reconstruction-V (ASiR-V) post-processed using a deep learning image reconstruction (DLIR)-based image domain compared to low-dose CT (LDCT) and ULDCT without DLIR.

Materials And Methods: A total of 210 patients undergoing lung cancer screening underwent LDCT (mean ± SD, 0.81 ± 0.

Multiparametric quantitative diffusion weighted magnetic resonance imaging can effectively predict the response to neoadjuvant therapy in borderline resectable pancreatic ductal adenocarcinoma.

Purpose: To investigate whether multiparametric quantitative diffusion weighted magnetic resonance imaging (DWI) can effectively predict the neoadjuvant therapy (NAT) response in borderline resectable pancreatic ductal adenocarcinoma (BRPC).

Methods: The clinicopathological data, including tumor size, location, and CA19-9 values, as well as DWI parameters(ADC, D, and f values) from 72 patients with BRPC, were analyzed. The differences and changes in these factors before and after NAT were compared to identify those most accurately reflect the response to NAT.

Deep learning model for the automated detection and classification of central canal and neural foraminal stenosis upon cervical spine magnetic resonance imaging.

Background: A deep learning (DL) model that can automatically detect and classify cervical canal and neural foraminal stenosis using cervical spine magnetic resonance imaging (MRI) can improve diagnostic accuracy and efficiency.

Methods: A method comprising region-of-interest (ROI) detection and cascade prediction was formulated for diagnosing cervical spinal stenosis based on a DL model. First, three part-specific convolutional neural networks were employed to detect the ROIs in different parts of the cervical MR images.

Utilizing artificial intelligence to determine bone mineral density using spectral CT.

Background: Dual-energy computed tomography (DECT) has demonstrated the feasibility of using HAP-water to respond to BMD changes without requiring dedicated software or calibration. Artificial intelligence (AI) has been utilized for diagnosising osteoporosis in routine CT scans but has rarely been used in DECT. This study investigated the diagnostic performance of an AI system for osteoporosis screening using DECT images with reference quantitative CT (QCT).

Feasibility of Weight-based Tube Voltage and Iodine Delivery Rate for Coronary Artery CT Angiography.

Purpose: The objective of this study was to evaluate the feasibility of weight-based tube voltage and iodine delivery rate (IDR) for coronary artery CT angiography (CCTA).

Methods: A total of 193 patients (mean age: 58 ± 12 years) with suspected coronary heart disease indicated for CCTA between May and October 2022 were prospectively enrolled. The subjects were divided into five groups according to body weight: < 60 kg, 60 – 69 kg, 70 – 79 kg, 80 – 89 kg, and ≥ 90 kg.

Deep Learning Imaging Reconstruction Algorithm for Carotid Dual Energy CT Angiography: Opportunistic Evaluation of Cervical Intervertebral Discs-A Preliminary Study.

Thus, the aim of this study is to evaluate the performance of deep learning imaging reconstruction (DLIR) algorithm in different image sets derived from carotid dual-energy computed tomography angiography (DECTA) for evaluating cervical intervertebral discs (IVDs) and compare them with those reconstructed using adaptive statistical iterative reconstruction-Veo (ASiR-V). Forty-two patients who underwent carotid DECTA were included in this retrospective analysis. Three types of image sets (70 keV, water-iodine, and water-calcium) were reconstructed using 50% ASiR-V and DLIR at medium and high levels (DLIR-M and DLIR-H).

Quantified fat fraction as biomarker assessing disease severity in rare Charcot-Marie-Tooth subtypes.

Objective: Charcot-Marie-Tooth (CMT) disease is the most common inherited neuromuscular disorder. Multi-echo Dixon MRI technique is a highly sensitive method for quantifying muscle fatty infiltration, which may provide excellent value for the assessment of CMT. Due to the rareness of the disease, its use in CMT disease has been rarely evaluated, especially in subtypes.