Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Purpose: This study aimed to optimize the imaging time and image quality of T2WI-FS through the integration of Artificial Intelligence-Assisted Compressed Sensing (ACS) and respiratory triggering (RT).
Materials And Methods: A prospective cohort study was conducted on one hundred thirty-four patients (99 males, 35 females; average age: 57.93 ± 9.40 years) undergoing liver MRI between March and July 2024. All patients were scanned using both breath-hold ACS-assisted T2WI (BH-ACS-T2WI) and respiratory-triggered ACS-assisted T2WI (RT-ACS-T2WI) sequences. Two experienced radiologists retrospectively analyzed regions of interest (ROIs), recorded primary lesions, and assessed key metrics including signal intensity (SI), standard deviation (SD), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), motion artifacts, hepatic vessel clarity, liver edge sharpness, lesion conspicuity, and overall image quality. Statistical comparisons were conducted using Mann-Whitney U test, Wilcoxon signed-rank test and intraclass correlation coefficient (ICC).
Results: Compared to BH-ACS-T2WI, RT-ACS-T2WI significantly reduced average imaging time from 38 s to 22.91 ± 3.36 s, achieving a 40 % reduction in scan duration. Additionally, RT-ACS-T2WI demonstrated superior performance across multiple parameters, including SI, SD, SNR, CNR, motion artifact reduction, hepatic vessel clarity, liver edge sharpness, lesion conspicuity (≤5 mm), and overall image quality (P < 0.05). Notably, the lesion detection rate was slightly higher with RT-ACS-T2WI (94 %) compared to BH-ACS-T2WI (90 %).
Conclusion: The RT-ACS-T2WI sequence not only enhanced image quality but also reduced imaging time to approximately 23 s, making it particularly beneficial for patients unable to perform prolonged breath-holding maneuvers. This approach represents a promising advancement in optimizing liver MRI protocols.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.mri.2025.110475 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Clinical Role of the Noninvasive Abdominal Fetal ECG in the Detection and Monitoring of Fetal Tachycardia.
Circ Arrhythm Electrophysiol
September 2025
Department of Congenital Heart Disease, Evelina London Children's Hospital, United Kingdom (S. Chivers, T.V., V.Z., S.M., G.M., W.R., E.R., D.F.A.L., T.G.D., O.I.M., G.K.S., J.M.S.).
Background: Fetal tachycardias can cause adverse fetal outcomes including ventricular dysfunction, hydrops, and fetal demise. Postnatally, ECG is the gold standard, but, in fetal practice, echocardiography is used most frequently to diagnose and monitor fetal arrhythmias. Noninvasive extraction of the fetal ECG (fECG) may provide additional information about the electrophysiological mechanism and monitoring of intermittent arrhythmias.
View Article and Find Full Text PDFLeveraging Deep Learning to Address Diagnostic Challenges with Insufficient Image Data.
ACS Sens
September 2025
Institute of Applied Mechanics, National Taiwan University, Taipei 106, Taiwan.
In recent AI-driven disease diagnosis, the success of models has depended mainly on extensive data sets and advanced algorithms. However, creating traditional data sets for rare or emerging diseases presents significant challenges. To address this issue, this study introduces a direct-self-attention Wasserstein generative adversarial network (DSAWGAN) designed to improve diagnostic capabilities in infectious diseases with limited data availability.
View Article and Find Full Text PDFFew-shot learning for highly accelerated 3D time-of-flight MRA reconstruction.
Magn Reson Med
September 2025
Centre for Integrative Neuroimaging, FMRIB Division, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Purpose: To develop a deep learning-based reconstruction method for highly accelerated 3D time-of-flight MRA (TOF-MRA) that achieves high-quality reconstruction with robust generalization using extremely limited acquired raw data, addressing the challenge of time-consuming acquisition of high-resolution, whole-head angiograms.
Methods: A novel few-shot learning-based reconstruction framework is proposed, featuring a 3D variational network specifically designed for 3D TOF-MRA that is pre-trained on simulated complex-valued, multi-coil raw k-space datasets synthesized from diverse open-source magnitude images and fine-tuned using only two single-slab experimentally acquired datasets. The proposed approach was evaluated against existing methods on acquired retrospectively undersampled in vivo k-space data from five healthy volunteers and on prospectively undersampled data from two additional subjects.
Improving magnetic field homogeneity in prostate MR imaging and spectroscopy using an add-on local external shim coil array.
MAGMA
September 2025
Department of Medical Imaging, (766), Radboud University Medical Center, Geert Grooteplein 10Radboudumc, P.O. Box 9101, 6500 HB, Nijmegen, the Netherlands.
Objective: To improve B field homogeneity in prostate MR imaging and spectroscopy using a custom-designed 16-channel external local shim coil array.
Methods: In vivo prostate imaging was performed in seven healthy volunteers (mean age: 40.7 years) without bowel preparation.
MRI-based radiomics signatures for predicting the efficacy of targeted therapy with lenvatinib in hepatocellular carcinoma: a retrospective cohort study.
J Cancer Res Clin Oncol
September 2025
Department of Radiology, Guizhou Provincial People's Hospital, No. 83 East Zhongshan Road, Guiyang, 550002, Guizhou, China.
Purpose: Targeted therapy with lenvatinib is a preferred option for advanced hepatocellular carcinoma, however, predicting its efficacy remains challenging. This study aimed to build a nomogram integrating clinicoradiological indicators and radiomics features to predict the response to lenvatinib in patients with hepatocellular carcinoma.
Methods: This study included 211 patients with hepatocellular carcinoma from two centers, who were allocated into the training (107 patients), internal test (46 patients) and external test set(58 patients).