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Oncol."],"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>To minimize computation latency using a predictive strategy to retrieve and project tumor volume onto 2D MR beam eye\u2019s view (BEV) cine from time-resolved four-dimensional magnetic resonance imaging (TR-4DMRI) libraries (inhalation\/exhalation) for personalized MR-guided intensity-modulated radiotherapy (IMRT) or volumetric-modulated arc therapy (VMAT).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Two time-series forecasting algorithms, autoregressive (AR) modeling and deep-learning-based long short-term memory (LSTM), were applied to predict the diaphragm position in the next 2D BEV cine to identify a motion-matched and hysteresis-accounted image to retrieve the tumor volume from the inhalation\/exhalation TR-4DMRI libraries. Three 40-s TR-4DMRI (2\u00a0Hz, 3 \u00d7 80 images) per patient of eight lung cancer patients were used to create patient-specific inhalation\/exhalation 4DMRI libraries, extract diaphragmatic waveforms, and interpolate them to <jats:italic>f =<\/jats:italic> 4 and 8\u00a0Hz to match 2D cine frame rates. Along a (40\u2022<jats:italic>f<\/jats:italic>)-timepoint waveform, 30\u2022<jats:italic>f<\/jats:italic> training timepoints were moved forward to produce 3\u00d7(10\u2022<jats:italic>f<\/jats:italic>-1) predictions. The accuracy of position prediction was assessed against the waveform ground truth. The accuracy of tumor volume projections was evaluated using the center-of-mass difference (\u2206COM) and Dice similarity index against the TR-4DMRI ground truth for both IMRT (six beam angles, 30\u00b0 interval) and VMAT (240\/480 beam angles, 1.5\u00b0\/0.75\u00b0 interval, at 4\/8\u00a0Hz, respectively).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The accuracy of the first-timepoint prediction is 0.36 \u00b1 0.10\u00a0mm (AR) and 0.62 \u00b1 0.21\u00a0mm (LSTM) at 4\u00a0Hz and 0.06 \u00b1 0.02\u00a0mm (AR) and 0.18 \u00b1 0.06\u00a0mm (LSTM) at 8\u00a0Hz. A 10%\u201320% random error in prediction-library matching increases the overall uncertainty slightly. For both IMRT and VMAT, the accuracy of projected tumor volume contours on 2D BEV cine is \u2206COM = 0.39 \u00b1 0.13\u00a0mm and DICE = 0.97 \u00b1 0.02 at 4\u00a0Hz and \u2206COM = 0.10 \u00b1 0.04\u00a0mm and DICE = 1.00 \u00b1 0.00 at 8Hz.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This study demonstrates the feasibility of accurately predicting respiratory motion during 2D BEV cine imaging, identifying a motion-matched and hysteresis-accounted tumor volume, and projecting tumor volume contour on 2D BEV cine for real-time assessment of beam-to-tumor conformality, promising for optimal personalized MR-guided radiotherapy.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fonc.2022.898771","type":"journal-article","created":{"date-parts":[[2022,6,29]],"date-time":"2022-06-29T06:32:16Z","timestamp":1656484336000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Real-Time 2D MR Cine From Beam 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