MR SIGnature MAtching (MRSIGMA) with retrospective self-evaluation for real-time volumetric motion imaging

Kim, Nathanael; Tringale, Kathryn R.; Crane, Christopher H.; Tyagi, Neetu; Otazo, Ricardo

doi:10.1088/1361-6560/ac2dd2

Cited by 8 publications

(9 citation statements)

References 30 publications

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“…By treating such patients on a hybrid MRI-Linac, motion can quickly be quantified without repositioning the patient. Moreover, high-quality 4D-MRI can also be used for high-quality time-resolved imaging 65,70 and could be helpful for real-time intra-fraction radiation treatment adaptation. 22…”

Section: Discussionmentioning

confidence: 99%

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

Terpstra,

Maspero,

Verhoeff

et al. 2023

Medical Physics

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BackgroundRespiratory‐resolved four‐dimensional magnetic resonance imaging (4D‐MRI) provides essential motion information for accurate radiation treatments of mobile tumors. However, obtaining high‐quality 4D‐MRI suffers from long acquisition and reconstruction times.PurposeTo develop a deep learning architecture to quickly acquire and reconstruct high‐quality 4D‐MRI, enabling accurate motion quantification for MRI‐guided radiotherapy (MRIgRT).MethodsA small convolutional neural network called MODEST is proposed to reconstruct 4D‐MRI by performing a spatial and temporal decomposition, omitting the need for 4D convolutions to use all the spatio‐temporal information present in 4D‐MRI. This network is trained on undersampled 4D‐MRI after respiratory binning to reconstruct high‐quality 4D‐MRI obtained by compressed sensing reconstruction. The network is trained, validated, and tested on 4D‐MRI of 28 lung cancer patients acquired with a T1‐weighted golden‐angle radial stack‐of‐stars (GA‐SOS) sequence. The 4D‐MRI of 18, 5, and 5 patients were used for training, validation, and testing. Network performances are evaluated on image quality measured by the structural similarity index (SSIM) and motion consistency by comparing the position of the lung‐liver interface on undersampled 4D‐MRI before and after respiratory binning. The network is compared to conventional architectures such as a U‐Net, which has 30 times more trainable parameters.ResultsMODEST can reconstruct high‐quality 4D‐MRI with higher image quality than a U‐Net, despite a thirty‐fold reduction in trainable parameters. High‐quality 4D‐MRI can be obtained using MODEST in approximately 2.5 min, including acquisition, processing, and reconstruction.ConclusionHigh‐quality accelerated 4D‐MRI can be obtained using MODEST, which is particularly interesting for MRIgRT.

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Section: Discussionmentioning

confidence: 99%

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

Terpstra,

Maspero,

Verhoeff

et al. 2023

Medical Physics

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“…Natick, MA) using the XD-GRASP algorithm, as described previously (Feng et al 2016, Feng et al 2020, Kim et al 2021. First, a respiratory signal was extracted from the center of k-space data (kx = ky = 0) along the kz direction using fast Fourier transform, principal component analysis (PCA), and coil clustering (Feng et al 2016, Zhang et al 2016.…”

Section: D Motion Dictionarymentioning

confidence: 99%

“…Magnetic Resonance SIGnature MAtching (MRSIGMA) is a recent technique that shifts the acquisition and reconstruction burden to a motion learning step and can achieve a total imaging latency (including acquisition and reconstruction) of less than 300 ms (Feng et al 2020). Another advantage of MRSIGMA is the calculation of 4D reference directly from the real-time acquisition data that can be used to retrospectively evaluate the performance of real-time motion tracking (Kim et al 2021). While the MRSIGMA methodology has been previously developed, it has only been implemented on 3 T MRI scanners.…”

Section: Introductionmentioning

confidence: 99%

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Real-time 4D MRI using MR signature matching (MRSIGMA) on a 1.5T MR-Linac system

Wu,

Murray,

Siddiq

et al. 2023

Phys. Med. Biol.

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Objective: To develop real-time 4D MRI using MR signature matching (MRSIGMA) for volumetric motion imaging in patients with pancreatic cancer on a 1.5T MR-Linac system.Approach: Two consecutive MRI scans with 3D golden-angle radial stack-of-stars acquisitions were performed on ten patients with inoperable pancreatic cancer. The complete first scan (905 angles) was used to compute a 4D motion dictionary including ten pairs of 3D motion images and signatures. The second scan was used for real-time imaging, where each angle (275 ms) was processed separately to match it to one of the dictionary entries. The complete second scan was also used to compute a 4D reference to assess motion tracking performance. Dice coefficients of the gross tumor volume (GTV) and two organs-at-risk (duodenum-stomach and small bowel) were calculated between signature matching and reference. In addition, volume changes, displacements, center of mass shifts, and Dice scores over time were calculated to characterize motion. Main Results: Total imaging latency of MRSIGMA (acquisition + matching) was less than 300 ms. The Dice coefficients were 0.87±0.06 (GTV), 0.86±0.05 (duodenum-stomach), and 0.85±0.05 (small bowel), which indicate high accuracy (high mean value) and low uncertainty (low standard deviation) of MRSIGMA for real-time motion tracking. The center of mass shift was 3.1±2.0 mm (GTV), 5.3±3.0 mm (duodenum-stomach), and 3.4±1.5 mm (small bowel). The Dice scores over time (0.97± [0.01-0.03]) were similarly high for MRSIGMA and reference scans in all the three contours.Significance: This work demonstrates the feasibility of real-time 4D MRI using MRSIGMA for volumetric motion tracking on a 1.5T MR-Linac system. The high accuracy and low uncertainty of real-time MRSIGMA is an essential step towards continuous treatment adaptation of tumors affected by real-time respiratory motion and could ultimately improve treatment safety by optimizing ablative dose delivery near gastrointestinal organs.

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“…One of the first approaches was MR signature matching (MRSIGMA) that uses a stack-of-stars acquisition and compressed sensing reconstruction to generate a dictionary of 3D motion states and corresponding signatures during motion learning and leaves fast signature-only acquisition and matching during the real-time beam-on step, enabling 3D MRI with total imaging latency of less than 300 ms. 25,26 By using the same stack-of-stars acquisition during signature matching, a retrospective motion-resolved 4D reference can be computed using the signature-only data to evaluate the performance of real-time imaging. 27 An extension of MRSIGMA, called single projection driven real-time multi-contrast (SPIDERM), uses an MR Multitasking approach to generate a multi-contrast dictionary including 3D T 1 -weighted, T 2 -weighted, and PD-weighted images and single projection signatures with a 55 ms latency on a 3T scanner. 28 A more recent extension of MRSIGMA is live-view GRASP which employs 2D coronal navigators and a dictionary of time-resolved 4D images (non-sorted) to perform signature matching with a latency of 500 ms on a 3T scanner.…”

Section: Introductionmentioning

confidence: 99%

MR signature matching (MRSIGMA) implementation for true real‐time free‐breathing volumetric imaging with sub‐200 ms latency on an MR‐Linac

Siddiq,

Murray,

Tyagi

et al. 2024

Magnetic Resonance in Med

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PurposeDevelop a true real‐time implementation of MR signature matching (MRSIGMA) for free‐breathing 3D MRI with sub‐200 ms latency on the Elekta Unity 1.5T MR‐Linac.MethodsMRSIGMA was implemented on an external computer with a network connection to the MR‐Linac. Stack‐of‐stars with partial kz sampling was used to accelerate data acquisition and ReconSocket was employed for simultaneous data transmission. Movienet network computed the 4D MRI motion dictionary and correlation analysis was used for signature matching. A programmable 4D MRI phantom was utilized to evaluate MRSIGMA with respect to a ground‐truth translational motion reference. In vivo validation was performed on patients with pancreatic cancer, where 15 patients were employed to train Movienet and 7 patients to test the real‐time implementation of MRSIGMA. Dice coefficients between real‐time MRSIGMA and a retrospectively computed 4D reference were used to evaluate motion tracking performance.ResultsMotion dictionary was computed in under 5 s. Signature acquisition and matching presented 173 ms latency on the phantom and 193 ms on patients. MRSIGMA presented a mean error of 1.3–1.6 mm for all phantom experiments, which was below the 2 mm acquisition resolution along the motion direction. The Dice coefficient over time between MRSIGMA and reference contours was 0.88 ± 0.02 (GTV), 0.87 ± 0.02(duodenum‐stomach), and 0.78 ± 0.02(small bowel), demonstrating high motion tracking performance for both tumor and organs at risk.ConclusionThe real‐time implementation of MRSIGMA enabled true real‐time free‐breathing 3D MRI with sub‐200 ms imaging latency on a clinical MR‐Linac system, which can be used for treatment monitoring, adaptive radiotherapy and dose accumulation mapping in tumors affected by respiratory motion.

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MR SIGnature MAtching (MRSIGMA) with retrospective self-evaluation for real-time volumetric motion imaging

Cited by 8 publications

References 30 publications

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

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

Real-time 4D MRI using MR signature matching (MRSIGMA) on a 1.5T MR-Linac system

MR signature matching (MRSIGMA) implementation for true real‐time free‐breathing volumetric imaging with sub‐200 ms latency on an MR‐Linac

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