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  4. MRI Super-Resolution Reconstruction for MRI-Guided Adaptive Radiotherapy Using Cascaded Deep Learning: In the Presence of Limited Training Data and Unknown Translation Model

MRI Super-Resolution Reconstruction for MRI-Guided Adaptive Radiotherapy Using Cascaded Deep Learning: In the Presence of Limited Training Data and Unknown Translation Model

J Chun1,3*, H Gach1,2 , S Olberg1 , T Mazur1 , O Green1 , T Kim1 , H Kim1 , J Kim3 , S Mutic1 , J Park1 , (1) Department of Radiation Oncology, Washington University in St. Louis, St. Louis, MO, (2) Departments of Radiology and Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, (3) Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea

Presentations

(Tuesday, 7/16/2019) 4:30 PM - 6:00 PM

Room: 225BCD

Purpose: Deep learning (DL)-based super-resolution (SR) reconstruction for MRI has recently been receiving attention, however challenges hindering the implementation of these approaches remain. Low-resolution (LR) MRIs captured in the clinic exhibit complex tissue structures obfuscated by noise while training a robust network for a SR task requires abundant, perfectly matched pairs of LR and high-resolution (HR) images. We present the development of a novel MRI SR technique based on the concept of cascaded DL that allows for SR reconstruction in the presence of insufficient training data, an unknown translation model, and noise.

Methods: The proposed cascaded DL framework consists of three components: 1) a de-noising auto-encoder (DAE) trained using clinical LR noisy MRI scans processed with a non-local means filter that generates denoised LR data; 2) a down-sampling network (DSN) trained with a small amount of paired LR/HR data from volunteers that allows for the generation of perfectly paired LR/HR data; and 3) the proposed SR generative model (p-SRG) trained with data generated by the DSN that maps from LR inputs to HR outputs. After training, LR clinical images may be fed through the DAE and p-SRG to yield SR reconstructions of the LR input. The application of this framework was explored in two settings: 3D LR breath-hold MRI scans (< 3 sec/vol) and 4D-MRI LR acquisitions (0.5 sec/vol).

Results: The DSN produces LR scans from HR inputs with a higher fidelity to true LR clinical scans compared to conventional k-space down-sampling methods. Furthermore, HR outputs generated by the p-SRG exhibit considerably improved results compared to conventional approaches to MRI SR.

Conclusion: Four-fold enhancements in spatial resolution using the proposed method facilitate target delineation and motion management during radiation therapy, enabling precise MRI-guided radiation therapy with 3D breath-hold MRI within 3 seconds and 4D-MRI within 2 frames/sec.

Keywords

Image-guided Therapy, MRI, Resolution

Taxonomy

IM/TH- MRI in Radiation Therapy: MRI/Linear accelerator combined- IGRT and tracking

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