Meeting Banner
Abstract #0608

Ultra-Fast Dynamic MRI for Lung Tumor Tracking Based on Compressed Sensing

Manoj K. Sarma1, M. Albert Thomas1, Peng Hu2, Daniel B. Ennis2, Ke K. Sheng3

1Radiological Sciences, UCLA School of Medicine, Los Angeles, CA, United States; 2Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States; 3Radiation Oncology, UCLA School of Medicine, Los Angeles, CA, United States

Radiotherapy guided by MRI has afforded the hardware potential to treat a moving tumor more accurately but existing imaging speed is inadequate for 3D real-time lung and lung tumor imaging. By exploiting the intrinsic coherence of the patient anatomy during time, we adapted a k-t SLR compressed sensing method to dramatically reduce the amount of data that is needed to update a new dynamic imaging without losing details. We were able to accurately track moving tumors of nine patients based on images reconstructed with very high data under-sampling ratio up to 5% of the original data.

Abstract PDF Presentation Video

Keywords

able accelerate accuracy accurately achieved acquisition adequate afforded anatomy approved audience board body cancer characterize coefficients coherence complication compressed coronal correlation cross dataset datasets decrease degrading describe despite details determination determine dynamic efficient emergence error evaluated even expected exploit exploiting fast fine folds frame fully functional golden guided house human implementation importantly imposed incoherently indicated indicating institution integrity intrinsically little lung markedly matrix medicine minimization monitoring motion moving needed nine norm noticeably nuclear optimization ordinate ordinates original overall patient patients performance period phys posed potential preserving problem protocol pulmonary quality quantified questionable radial radiation radiological radiotherapy randomly rank read real receive reconstruct reconstructed reconstruction reconstructions recovery reduced regularization represent resolution review sampled samples sampling scanner scheme school sciences sensing sets simulated since singular slice solutions solved space sparsity spatial spectral speed strategy subject substantially successfully supported temporal therapy though track tracking trajectories transform treatment tumor tumors ultra update usefulness visually volume