1. Deep Convolutional Neural Network for Dedicated Regions-of-Interest Based Multi-Parameter Quantitative Ultrashort Echo Time (UTE) Magnetic Resonance Imaging of the Knee Joint
- Author
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Lu, Xing, Ma, Yajun, Chang, Eric Y, Athertya, Jiyo, Jang, Hyungseok, Jerban, Saeed, Covey, Dana C, Bukata, Susan, Chung, Christine B, and Du, Jiang
- Subjects
Biomedical and Clinical Sciences ,Clinical Sciences ,Arthritis ,Clinical Research ,Biomedical Imaging ,Bioengineering ,Networking and Information Technology R&D (NITRD) ,Machine Learning and Artificial Intelligence ,Osteoarthritis ,Health Disparities ,Musculoskeletal ,Humans ,Magnetic Resonance Imaging ,Knee Joint ,Neural Networks ,Computer ,Male ,Female ,Deep Learning ,Middle Aged ,Adult ,Osteoarthritis ,Knee ,Image Processing ,Computer-Assisted ,Cartilage ,Articular ,Aged ,Image Interpretation ,Computer-Assisted ,Quantitative MRI ,Automated segmentation ,DCNN ,RMQ-Net ,UTE ,Knee joint ,OA - Abstract
We proposed an end-to-end deep learning convolutional neural network (DCNN) for region-of-interest based multi-parameter quantification (RMQ-Net) to accelerate quantitative ultrashort echo time (UTE) MRI of the knee joint with automatic multi-tissue segmentation and relaxometry mapping. The study involved UTE-based T1 (UTE-T1) and Adiabatic T1ρ (UTE-AdiabT1ρ) mapping of the knee joint of 65 human subjects, including 20 normal controls, 29 with doubtful-minimal osteoarthritis (OA), and 16 with moderate-severe OA. Comparison studies were performed on UTE-T1 and UTE-AdiabT1ρ measurements using 100%, 43%, 26%, and 18% UTE MRI data as the inputs and the effects on the prediction quality of the RMQ-Net. The RMQ-net was modified and retrained accordingly with different combinations of inputs. Both ROI-based and voxel-based Pearson correlation analyses were performed. High Pearson correlation coefficients were achieved between the RMQ-Net predicted UTE-T1 and UTE-AdiabT1ρ results and the ground truth for segmented cartilage with acceleration factors ranging from 2.3 to 5.7. With an acceleration factor of 5.7, the Pearson r-value achieved 0.908 (ROI-based) and 0.945 (voxel-based) for UTE-T1, and 0.733 (ROI-based) and 0.895 (voxel-based) for UTE-AdiabT1ρ, correspondingly. The results demonstrated that RMQ-net can significantly accelerate quantitative UTE imaging with automated segmentation of articular cartilage in the knee joint.
- Published
- 2024