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Objective: To evaluate the diagnostic performance and image quality of 1.5-mm slice thickness MRI with deep learning-based image reconstruction (1.5-mm MRI + DLR) compared to routine 3-mm slice thickness MRI (routine MRI) and 1.5-mm slice thickness MRI without DLR (1.5-mm MRI without DLR) for evaluating temporal lobe epilepsy (TLE).
Materials And Methods: This retrospective study included 117 MR image sets comprising 1.5-mm MRI + DLR, 1.5-mm MRI without DLR, and routine MRI from 117 consecutive patients (mean age, 41 years; 61 female; 34 patients with TLE and 83 without TLE). Two neuroradiologists evaluated the presence of hippocampal or temporal lobe lesions, volume loss, signal abnormalities, loss of internal structure of the hippocampus, and lesion conspicuity in the temporal lobe. Reference standards for TLE were independently constructed by neurologists using clinical and radiological findings. Subjective image quality, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were analyzed. Performance in diagnosing TLE, lesion findings, and image quality were compared among the three protocols.
Results: The pooled sensitivity of 1.5-mm MRI + DLR (91.2%) for diagnosing TLE was higher than that of routine MRI (72.1%, < 0.001). In the subgroup analysis, 1.5-mm MRI + DLR showed higher sensitivity for hippocampal lesions than routine MRI (92.7% vs. 75.0%, = 0.001), with improved depiction of hippocampal T2 high signal intensity change ( = 0.016) and loss of internal structure ( < 0.001). However, the pooled specificity of 1.5-mm MRI + DLR (76.5%) was lower than that of routine MRI (89.2%, = 0.004). Compared with 1.5-mm MRI without DLR, 1.5-mm MRI + DLR resulted in significantly improved pooled accuracy (91.2% vs. 73.1%, = 0.010), image quality, SNR, and CNR (all, < 0.001).
Conclusion: The use of 1.5-mm MRI + DLR enhanced the performance of MRI in diagnosing TLE, particularly in hippocampal evaluation, because of improved depiction of hippocampal abnormalities and enhanced image quality.
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http://dx.doi.org/10.3348/kjr.2023.0842 | DOI Listing |
Clin Transl Oncol
September 2025
Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, China.
Background: The primary aim of this research was to create and rigorously assess a deep learning radiomics (DLR) framework utilizing magnetic resonance imaging (MRI) to forecast the histological differentiation grades of oropharyngeal cancer.
Methods: This retrospective analysis encompassed 122 patients diagnosed with oropharyngeal cancer across three medical institutions in China. The participants were divided at random into two groups: a training cohort comprising 85 individuals and a test cohort of 37.
J Imaging Inform Med
September 2025
Department of Radiology, Peking University People's Hospital, No.11, Xizhimen South Street, Xicheng District, Beijing, 100044, China.
Soft tissue sarcomas (STS) are heterogeneous malignancies with high recurrence rates (33-39%) post-surgery, necessitating improved prognostic tools. This study proposes a fusion model integrating deep transfer learning and radiomics from MRI to predict postoperative STS recurrence. Axial T2-weighted fat-suppressed imaging (TWI) of 803 STS patients from two institutions was retrospectively collected and divided into training (n = 527), internal validation (n = 132), and external validation (n = 144) cohorts.
View Article and Find Full Text PDFEur Stroke J
August 2025
Department of Neurosciences, Experimental Neurology, KU Leuven - University of Leuven, Leuven, Belgium.
Introduction: In Acute Ischemic Stroke (AIS), mismatch between Diffusion-Weighted Imaging (DWI) and Fluid-Attenuated Inversion-Recovery (FLAIR) helps identify patients who can benefit from thrombolysis when stroke onset time is unknown (15% of AIS). However, visual assessment has suboptimal observer agreement. Our study aims to develop and validate a Deep-Learning model for predicting DWI-FLAIR mismatch using solely DWI data.
View Article and Find Full Text PDFAJNR Am J Neuroradiol
August 2025
From the Guilloz Imaging Department, Central Hospital, University Hospital of Nancy, 54000 Nancy, France (F.B, U.P,PA.G-T, A.B,R.G); From Department of Radiology, Mayo Clinic, Rochester, MN 55901, USA (JI.L, RJ.W). From Université de Lorraine, CIC, Innovation Technologique, University Hospital Cent
Background And Purpose: The labyrinth is a complex anatomical structure in the temporal bone. However, high-resolution imaging of its membranous portion is challenging due to its small size and the limitations of current MRI techniques. Deep Learning Reconstruction (DLR) represents a promising approach to advancing MRI image quality, enabling higher spatial resolution and reduced noise.
View Article and Find Full Text PDFBMC Cancer
August 2025
Department of Otorhinolaryngology, The First Affiliated Hospital, Nanjing Medical University, Nanjing, 210029, China.
Objective: To develop a deep learning-based MRI model for predicting tongue cancer T-stage.
Methods: This retrospective study analyzed clinical and MRI data from 579 tongue cancer patients (Xiangya Cancer Hospital and Jiangsu Province Hospital). T2-weighted (T2WI) and contrast-enhanced T1-weighted (CET1) sequences were preprocessed (anonymization/resampling/calibration).