In selected, locally validated implementations, accelerated prostate MRI may be performed more efficiently while retaining the PI-RADS v2.1 diagnostic requirements, and deep learning reconstruction for accelerated 2D T2-weighted imaging has the most mature supporting clinical evidence.
Automated prostate cancer (PCa) lesion segmentation using deep learning remains constrained by limited tissue contrast in standard diffusion-based MRI sequences, with state-of-the-art methods reporting Dice scores of 32% or lower on large patient cohorts. Synthetic correlated diffusion imaging (CDIs) offers a promising...
Jarett Dewbury, Chi-en Amy Tai, Alexander Wong· Signals· 0 citations
DL-based reconstruction enables faster WB-DWI acquisition without compromising image quality or ADC quantification, supporting clinical implementation in metastatic prostate cancer staging and therapy assessment.
E. Kousi, M. Rata, Doh-Mu Koh et al.· medRxiv· 0 citations
This is the first comprehensive evaluation of ADC-only breast cancer pipelines for classification, detection, and segmentation tasks and evaluates the application of different state-of-the-art deep learning techniques for detection and segmentation of breast cancer using ADC-only images.
Pablo García Marcos, Paula Puerta Gonzĺez, Guillermo Lorenzo et al.· 0 citations
DWI is pivotal for personalized breast cancer care, though standardized protocols and further refinement are needed for broader clinical adoption, and artificial intelligence and radiomics enhance its utility.
Yong Ye, Wen-Wen Shao, Ying Yang· Diagnostic and Interventiona...· 0 citations
Comparing image quality and diagnostic performance among conventional sequences and an automated accelerated deep learning-based reconstruction (DLR) knee algorithm method in patients with knee pain revealed no significant differences in overall diagnosis, image quality, and artifacts.
Rola Husain, Stephen Belmustakov, Etan Dayan et al.· Diagnostics· 0 citations
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