A hybrid 2D/4D‐MRI methodology using simultaneous multislice imaging for radiotherapy guidance

Abstract

Purpose Respiratory motion management is important in abdominothoracic radiotherapy. Fast imaging of the tumor can facilitate multileaf collimator (MLC) tracking that allows for smaller treatment margins, while repeatedly imaging the full field‐of‐view is necessary for 4D dose accumulation. This study introduces a hybrid 2D/4D‐MRI methodology that can be used for simultaneous MLC tracking and dose accumulation on a 1.5 T Unity MR‐linac (Elekta AB, Stockholm, Sweden). Methods We developed a hybrid 2D/4D‐MRI methodology that uses a simultaneous multislice (SMS) accelerated MRI sequence, which acquires two coronal slices simultaneously and repeatedly cycles through slice positions over the image volume. As a result, the fast 2D imaging can be used prospectively for MLC tracking and the SMS slices can be sorted retrospectively into respiratory‐correlated 4D‐MRIs for dose accumulation. Data were acquired in five healthy volunteers with an SMS‐bTFE and SMS‐TSE MRI sequence. For each sequence, a prebeam dataset and a beam‐on dataset were acquired simulating the two phases of MR‐linac treatments. Prebeam data were used to generate a 4D‐based motion model and a reference mid‐position volume, while beam‐on data were used for real‐time motion extraction and reconstruction of beam‐on 4D‐MRIs. In addition, an in‐silico computational phantom was used for validation of the hybrid 2D/4D‐MRI methodology. MLC tracking experiments were performed with the developed methodology, for which real‐time SMS data reconstruction was enabled on the scanner. A 15‐beam 8× 7.5 Gy intensity‐modulated radiotherapy plan for lung stereotactic body radiotherapy with isotropic 3 mm GTV‐to‐PTV margins was created. Dosimetry experiments were performed using a 4D motion phantom. The latency between target motion and updating the radiation beam was determined and compensated. Local gamma analyses were performed to quantify dose differences compared to a static reference delivery, and dose area histograms (DAHs) were used to quantify the GTV and PTV coverage. Results In‐vivo data acquisition and MLC tracking experiments were successfully performed with the developed hybrid 2D/4D‐MRI methodology. Real‐time liver–lung interface motion estimation had a Pearson's correlation of 0.996 (in‐vivo) and 0.998 (in‐silico). A median (5th–95th percentile) error of 0.0 (−0.9 to 0.7) mm and 0.0 (−0.2 to 0.2) mm was found for real‐time motion estimation for in‐vivo and in‐silico, respectively. Target motion prediction beyond the liver–lung interface had a median root mean square error of 1.6 mm (in‐vivo) and 0.5 mm (in‐silico). Beam‐on 4D MRI reconstruction required a median amount of data equal to an acquisition time of 2:21–3:17 min, which was 20% less data compared to the prebeam‐derived 4D‐MRI. System latency was reduced from 501 ± 12 ms to −1 ± 3 ms (SMS‐TSE) and from 398 ± 10 ms to −10 ± 4 ms (SMS‐bTFE) by a linear regression prediction filter. The local gamma analysis agreed within −3.8%$-3.8\%$ to 3.3% (SMS‐bTFE) and −5.3%$-5.3\%$ to 10% (SMS‐TSE) with a...

Multi‐parametric MRI for radiotherapy simulation

“…Retrospective 4D-MRI can be achieved by continuously acquiring images with a respiratory signal recorded simultaneously and then assigning 2D slices to corresponding respiratory phases using internal or external surrogates. [187][188][189][190][191][192][193][194][195] Many studies have used fast MR sequences, including spoiled gradient echo and balanced steady-state free precession (bSSFP) sequences. For example, Cai et al developed a retrospective 4D-MRI technique that uses an image-based surrogate for respiratory motion.…”

Accelerated respiratory‐resolved 4D‐MRI with separable spatio‐temporal neural networks

“…A straightforward way to accelerate MRI is by undersampling the acquisition, violating the Shannon-Nyquist data sufficiency criterion, 25 and introducing image artifacts that may preclude accurate motion quantification. 24 Several techniques have been proposed to reconstruct high-quality MRI from undersampled acquisitions, such as parallel imaging, 26,27 simultaneous multi-slice acquisitions, [28][29][30] or compressed sensing. 31 Some algorithms have been specifically developed to reconstruct high-quality respiratory-resolved 4D-MRI by taking advantage of all spatio-temporal information in the images, such as XD-GRASP 32 or HDTV-MoCo.…”

Experimental comparison of linear regression and LSTM motion prediction models for MLC‐tracking on an MRI‐linac

“…In two follow up studies with the same motion predictor, they used Lujan motion and additionally one patient-derived motion trace to investigate MLC-tracking with VMAT plans 24 and with a hybrid 2D/4D-MRI methodology. 25 A limitation of all four studies is that either sinusoidal or a single patient-derived trace was used.…”

“…showed that an online LR can compensate the latency for the delivery of intensity modulated radiotherapy (IMRT) plans with MLC‐tracking to a phantom moving with Lujan motion (cos 4 ). In two follow up studies with the same motion predictor, they used Lujan motion and additionally one patient‐derived motion trace to investigate MLC‐tracking with VMAT plans 24 and with a hybrid 2D/4D‐MRI methodology 25 . A limitation of all four studies is that either sinusoidal or a single patient‐derived trace was used.…”

“…This study aimed at experimentally validating the in-silico comparison of LSTM and LR motion prediction models 20 for MLC-tracking with a prototype MRI-linac. Compared to previous MRIgRT studies with MLC-tracking and motion prediction, [22][23][24][25] this was the first time multiple motion traces with different complexities were used and conventional and machine learning based predictors were compared. Specifically, we compared models trained on retrospective data (i.e., offline) with online models updated in real-time during the experiments.…”