Beyond the Conventional Structural MRI: Clinical Application of Deep Learning Image Reconstruction and Synthetic MRI of the Brain

Y Choi, JS Ko, JE Park, G Jeong, M Seo… - Investigative …, 2025 - journals.lww.com
Recent technological advancements have revolutionized routine brain magnetic resonance
imaging (MRI) sequences, offering enhanced diagnostic capabilities in intracranial disease …

Preoperatively predicting Ki67 expression in pituitary adenomas using deep segmentation network and radiomics analysis based on multiparameter MRI

H Li, Z Liu, F Li, F Shi, Y **a, Q Zhou, Q Zeng - Academic Radiology, 2024 - Elsevier
Rationale and Objectives Ki67 proliferation index is associated with more aggressive tumor
behavior and recurrence of pituitary adenomas (PAs). Recently, radiomics and deep …

Radiomics and machine learning for predicting the consistency of benign tumors of the central nervous system: A systematic review

C Koechli, DR Zwahlen, P Schucht… - European journal of …, 2023 - Elsevier
Purpose Predicting the consistency of benign central nervous system (CNS) tumors prior to
surgery helps to improve surgical outcomes. This review summarizes and analyzes the …

[HTML][HTML] Deep learning reconstruction for accelerated high-resolution upper abdominal MRI improves lesion detection without time penalty

JM Brendel, J Jacoby, R Dehdab, J Herrmann… - Diagnostic and …, 2024 - Elsevier
Purpose The purpose of this study was to compare a conventional T1-weighted volumetric
interpolated breath-hold examination (VIBE) sequence with a DL-reconstructed accelerated …

Thin-slice two-dimensional T2-weighted imaging with deep learning-based reconstruction: improved lesion detection in the brain of patients with multiple sclerosis

M Iwamura, S Ide, K Sato, A Kakuta… - … Resonance in Medical …, 2024 - jstage.jst.go.jp
Purpose: Brain MRI with high spatial resolution allows for a more detailed delineation of
multiple sclerosis (MS) lesions. The recently developed deep learning-based reconstruction …

[HTML][HTML] Improving Diagnostic Performance of MRI for Temporal Lobe Epilepsy With Deep Learning-Based Image Reconstruction in Patients With Suspected Focal …

PS Suh, JE Park, YH Roh, S Kim, M Jung… - Korean Journal of …, 2024 - ncbi.nlm.nih.gov
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) …

Deep learning‐based reconstruction can improve canine thoracolumbar magnetic resonance image quality and reduce slice thickness

H Kang, D Noh, SK Lee, S Choi… - Veterinary Radiology & …, 2023 - Wiley Online Library
In veterinary practice, thin‐sliced thoracolumbar MRI is useful in detecting small lesions,
especially in small‐breed dogs. However, it is challenging due to the partial volume …

Deep learning‐based reconstruction for canine brain magnetic resonance imaging could improve image quality while reducing scan time

H Choi, SK Lee, H Choi, Y Lee… - Veterinary Radiology & …, 2023 - Wiley Online Library
Optimal magnetic resonance imaging (MRI) quality and shorter scan time are challenging to
achieve in veterinary practices. Recently, deep learning‐based reconstruction (DLR) has …

Thin-slice elbow MRI with deep learning reconstruction: Superior diagnostic performance of elbow ligament pathologies

J Yi, S Hahn, HJ Lee, Y Lee, JY Bang, Y Kim… - European Journal of …, 2024 - Elsevier
Purpose With the slice thickness routinely used in elbow MRI, small or subtle lesions may be
overlooked or misinterpreted as insignificant. To compare 1 mm slice thickness MRI (1 mm …

[HTML][HTML] Multidisciplinary quantitative and qualitative assessment of IDH-mutant gliomas with full diagnostic deep learning image reconstruction

C Ruff, P Bombach, C Roder, E Weinbrenner… - European Journal of …, 2024 - Elsevier
Abstract Rationale and Objectives: Diagnostic accuracy and therapeutic decision-making for
IDH-mutant gliomas in tumor board reviews are based on MRI and multidisciplinary …