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Lombardo E, Liu PZY, Waddington DEJ, Grover J, Whelan B, Wong E, Reiner M, Corradini S, Belka C, Riboldi M, Kurz C, Landry G, Keall PJ. Experimental comparison of linear regression and LSTM motion prediction models for MLC-tracking on an MRI-linac. Med Phys 2023; 50:7083-7092. [PMID: 37782077 DOI: 10.1002/mp.16770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 08/30/2023] [Accepted: 09/17/2023] [Indexed: 10/03/2023] Open
Abstract
BACKGROUND Magnetic resonance imaging (MRI)-guided radiotherapy with multileaf collimator (MLC)-tracking is a promising technique for intra-fractional motion management, achieving high dose conformality without prolonging treatment times. To improve beam-target alignment, the geometric error due to system latency should be reduced by using temporal prediction. PURPOSE To experimentally compare linear regression (LR) and long-short-term memory (LSTM) motion prediction models for MLC-tracking on an MRI-linac using multiple patient-derived traces with different complexities. METHODS Experiments were performed on a prototype 1.0 T MRI-linac capable of MLC-tracking. A motion phantom was programmed to move a target in superior-inferior (SI) direction according to eight lung cancer patient respiratory motion traces. Target centroid positions were localized from sagittal 2D cine MRIs acquired at 4 Hz using a template matching algorithm. The centroid positions were input to one of four motion prediction models. We used (1) a LSTM network which had been optimized in a previous study on patient data from another cohort (offline LSTM). We also used (2) the same LSTM model as a starting point for continuous re-optimization of its weights during the experiment based on recent motion (offline+online LSTM). Furthermore, we implemented (3) a continuously updated LR model, which was solely based on recent motion (online LR). Finally, we used (4) the last available target centroid without any changes as a baseline (no-predictor). The predictions of the models were used to shift the MLC aperture in real-time. An electronic portal imaging device (EPID) was used to visualize the target and MLC aperture during the experiments. Based on the EPID frames, the root-mean-square error (RMSE) between the target and the MLC aperture positions was used to assess the performance of the different motion predictors. Each combination of motion trace and prediction model was repeated twice to test stability, for a total of 64 experiments. RESULTS The end-to-end latency of the system was measured to be (389 ± 15) ms and was successfully mitigated by both LR and LSTM models. The offline+online LSTM was found to outperform the other models for all investigated motion traces. It obtained a median RMSE over all traces of (2.8 ± 1.3) mm, compared to the (3.2 ± 1.9) mm of the offline LSTM, the (3.3 ± 1.4) mm of the online LR and the (4.4 ± 2.4) mm when using the no-predictor. According to statistical tests, differences were significant (p-value <0.05) among all models in a pair-wise comparison, but for the offline LSTM and online LR pair. The offline+online LSTM was found to be more reproducible than the offline LSTM and the online LR with a maximum deviation in RMSE between two measurements of 10%. CONCLUSIONS This study represents the first experimental comparison of different prediction models for MRI-guided MLC-tracking using several patient-derived respiratory motion traces. We have shown that among the investigated models, continuously re-optimized LSTM networks are the most promising to account for the end-to-end system latency in MRI-guided radiotherapy with MLC-tracking.
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Affiliation(s)
- Elia Lombardo
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Paul Z Y Liu
- Image X Institute, University of Sydney Central Clinical School, Sydney, New South Wales, Australia
- Department of Medical Physics, Ingham Institute of Applied Medical Research, Liverpool, New South Wales, Australia
| | - David E J Waddington
- Image X Institute, University of Sydney Central Clinical School, Sydney, New South Wales, Australia
- Department of Medical Physics, Ingham Institute of Applied Medical Research, Liverpool, New South Wales, Australia
| | - James Grover
- Image X Institute, University of Sydney Central Clinical School, Sydney, New South Wales, Australia
- Department of Medical Physics, Ingham Institute of Applied Medical Research, Liverpool, New South Wales, Australia
| | - Brendan Whelan
- Image X Institute, University of Sydney Central Clinical School, Sydney, New South Wales, Australia
- Department of Medical Physics, Ingham Institute of Applied Medical Research, Liverpool, New South Wales, Australia
| | - Esther Wong
- Department of Medical Physics, Ingham Institute of Applied Medical Research, Liverpool, New South Wales, Australia
| | - Michael Reiner
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Stefanie Corradini
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Claus Belka
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), partner site Munich, a partnership between DKFZ and LMU University Hospital Munich, Munich, Germany
- Bavarian Cancer Research Center (BZKF), Munich, Germany
| | - Marco Riboldi
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Garching, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Paul J Keall
- Image X Institute, University of Sydney Central Clinical School, Sydney, New South Wales, Australia
- Department of Medical Physics, Ingham Institute of Applied Medical Research, Liverpool, New South Wales, Australia
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Wu C, Murray V, Siddiq SS, Tyagi N, Reyngold M, Crane C, Otazo R. Real-time 4D MRI using MR signature matching (MRSIGMA) on a 1.5T MR-Linac system. Phys Med Biol 2023; 68:10.1088/1361-6560/acf3cc. [PMID: 37619588 PMCID: PMC10513779 DOI: 10.1088/1361-6560/acf3cc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 08/24/2023] [Indexed: 08/26/2023]
Abstract
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.Dicecoefficients 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, andDicescores over time were calculated to characterize motion.Main results. Total imaging latency of MRSIGMA (acquisition + matching) was less than 300 ms. TheDicecoefficients 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). TheDicescores 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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Affiliation(s)
- Can Wu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Victor Murray
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Syed S. Siddiq
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Neelam Tyagi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Marsha Reyngold
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Christopher Crane
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
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Lombardo E, Rabe M, Xiong Y, Nierer L, Cusumano D, Placidi L, Boldrini L, Corradini S, Niyazi M, Reiner M, Belka C, Kurz C, Riboldi M, Landry G. Evaluation of real-time tumor contour prediction using LSTM networks for MR-guided radiotherapy. Radiother Oncol 2023; 182:109555. [PMID: 36813166 DOI: 10.1016/j.radonc.2023.109555] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 01/24/2023] [Accepted: 02/05/2023] [Indexed: 02/24/2023]
Abstract
BACKGROUND AND PURPOSE Magnetic resonance imaging guided radiotherapy (MRgRT) with deformable multileaf collimator (MLC) tracking would allow to tackle both rigid displacement and tumor deformation without prolonging treatment. However, the system latency must be accounted for by predicting future tumor contours in real-time. We compared the performance of three artificial intelligence (AI) algorithms based on long short-term memory (LSTM) modules for the prediction of 2D-contours 500ms into the future. MATERIALS AND METHODS Models were trained (52 patients, 3.1h of motion), validated (18 patients, 0.6h) and tested (18 patients, 1.1h) with cine MRs from patients treated at one institution. Additionally, we used three patients (2.9h) treated at another institution as second testing set. We implemented 1) a classical LSTM network (LSTM-shift) predicting tumor centroid positions in superior-inferior and anterior-posterior direction which are used to shift the last observed tumor contour. The LSTM-shift model was optimized both in an offline and online fashion. We also implemented 2) a convolutional LSTM model (ConvLSTM) to directly predict future tumor contours and 3) a convolutional LSTM combined with spatial transformer layers (ConvLSTM-STL) to predict displacement fields used to warp the last tumor contour. RESULTS The online LSTM-shift model was found to perform slightly better than the offline LSTM-shift and significantly better than the ConvLSTM and ConvLSTM-STL. It achieved a 50% Hausdorff distance of 1.2mm and 1.0mm for the two testing sets, respectively. Larger motion ranges were found to lead to more substantial performance differences across the models. CONCLUSION LSTM networks predicting future centroids and shifting the last tumor contour are the most suitable for tumor contour prediction. The obtained accuracy would allow to reduce residual tracking errors during MRgRT with deformable MLC-tracking.
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Affiliation(s)
- Elia Lombardo
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich 81377, Germany
| | - Moritz Rabe
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich 81377, Germany
| | - Yuqing Xiong
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich 81377, Germany
| | - Lukas Nierer
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich 81377, Germany
| | - Davide Cusumano
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome 00168, Italy
| | - Lorenzo Placidi
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome 00168, Italy
| | - Luca Boldrini
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome 00168, Italy
| | - Stefanie Corradini
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich 81377, Germany
| | - Maximilian Niyazi
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich 81377, Germany
| | - Michael Reiner
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich 81377, Germany
| | - Claus Belka
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich 81377, Germany; German Cancer Consortium (DKTK), Munich 81377, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich 81377, Germany
| | - Marco Riboldi
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Garching b. München 85748, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich 81377, Germany.
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Nie X, Li G. Real-Time 2D MR Cine From Beam Eye's View With Tumor-Volume Projection to Ensure Beam-to-Tumor Conformality for MR-Guided Radiotherapy of Lung Cancer. Front Oncol 2022; 12:898771. [PMID: 35847879 PMCID: PMC9277147 DOI: 10.3389/fonc.2022.898771] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 05/20/2022] [Indexed: 12/02/2022] Open
Abstract
Purpose To minimize computation latency using a predictive strategy to retrieve and project tumor volume onto 2D MR beam eye’s 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). Methods 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 Hz, 3 × 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 f = 4 and 8 Hz to match 2D cine frame rates. Along a (40•f)-timepoint waveform, 30•f training timepoints were moved forward to produce 3×(10•f-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 (∆COM) and Dice similarity index against the TR-4DMRI ground truth for both IMRT (six beam angles, 30° interval) and VMAT (240/480 beam angles, 1.5°/0.75° interval, at 4/8 Hz, respectively). Results The accuracy of the first-timepoint prediction is 0.36 ± 0.10 mm (AR) and 0.62 ± 0.21 mm (LSTM) at 4 Hz and 0.06 ± 0.02 mm (AR) and 0.18 ± 0.06 mm (LSTM) at 8 Hz. A 10%–20% 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 ∆COM = 0.39 ± 0.13 mm and DICE = 0.97 ± 0.02 at 4 Hz and ∆COM = 0.10 ± 0.04 mm and DICE = 1.00 ± 0.00 at 8Hz. Conclusion 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.
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Affiliation(s)
- Xingyu Nie
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States.,Department of Radiology, University of Kentucky, Lexington, KY, United States
| | - Guang Li
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
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Meschini G, Vai A, Barcellini A, Fontana G, Molinelli S, Mastella E, Pella A, Vitolo V, Imparato S, Orlandi E, Ciocca M, Baroni G, Paganelli C. Time-resolved MRI for off-line treatment robustness evaluation in carbon-ion radiotherapy of pancreatic cancer. Med Phys 2022; 49:2386-2395. [PMID: 35124811 PMCID: PMC9306947 DOI: 10.1002/mp.15510] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 09/28/2021] [Accepted: 01/20/2022] [Indexed: 11/25/2022] Open
Abstract
PURPOSE In this study, we investigate the use of magnetic resonance imaging (MRI) for the clinical evaluation of gating treatment robustness in carbon-ion radiotherapy (CIRT) of pancreatic cancer. Indeed, MRI allows radiation-free repeated scans and fast dynamic sequences for time-resolved (TR) imaging (cine-MRI), providing information on inter- and intra-fraction cycle-to-cycle variations of respiratory motion. MRI can therefore support treatment planning and verification, overcoming the limitations of the current clinical standard, that is, four-dimensional computed tomography (4DCT), which describes an "average" breathing cycle neglecting breathing motion variability. METHODS We integrated a technique to generate a virtual CT (vCT) from 3D MRI with a method for 3D reconstruction from 2D cine-MRI, to produce TR vCTs for dose recalculations. For eight patients, the method allowed evaluating inter-fraction variations at end-exhale and intra-fraction cycle-to-cycle variability within the gating window in terms of tumor displacement and dose to the target and organs at risk. RESULTS The median inter-fraction tumor motion was in the range 3.33-12.16 mm, but the target coverage was robust (-0.4% median D95% variation). Concerning cycle-to-cycle variations, the gating technique was effective in limiting tumor displacement (1.35 mm median gating motion) and corresponding dose variations (-3.9% median D95% variation). The larger exposure of organs at risk (duodenum and stomach) was caused by inter-fraction motion, whereas intra-fraction cycle-to-cycle dose variations were limited. CONCLUSIONS This study proposed a method for the generation of TR vCTs from MRI, which enabled an off-line evaluation of gating treatment robustness and suggested its feasibility to support treatment planning of pancreatic tumors in CIRT.
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Affiliation(s)
- Giorgia Meschini
- Department of Electronics, Information and BioengineeringPolitecnico di MilanoMilanItaly
| | - Alessandro Vai
- Medical Physics UnitNational Center for Oncological Hadrontherapy (Fondazione CNAO)PaviaItaly
| | - Amelia Barcellini
- Clinical DepartmentNational Center for Oncological Hadrontherapy (Fondazione CNAO)PaviaItaly
| | - Giulia Fontana
- Clinical Bioengineering UnitNational Center for Oncological Hadrontherapy (Fondazione CNAO)PaviaItaly
| | - Silvia Molinelli
- Medical Physics UnitNational Center for Oncological Hadrontherapy (Fondazione CNAO)PaviaItaly
| | - Edoardo Mastella
- Medical Physics UnitNational Center for Oncological Hadrontherapy (Fondazione CNAO)PaviaItaly
| | - Andrea Pella
- Clinical Bioengineering UnitNational Center for Oncological Hadrontherapy (Fondazione CNAO)PaviaItaly
| | - Viviana Vitolo
- Clinical DepartmentNational Center for Oncological Hadrontherapy (Fondazione CNAO)PaviaItaly
| | - Sara Imparato
- Radiology UnitNational Center for Oncological Hadrontherapy (Fondazione CNAO)PaviaItaly
| | - Ester Orlandi
- Clinical DepartmentNational Center for Oncological Hadrontherapy (Fondazione CNAO)PaviaItaly
| | - Mario Ciocca
- Medical Physics UnitNational Center for Oncological Hadrontherapy (Fondazione CNAO)PaviaItaly
| | - Guido Baroni
- Department of Electronics, Information and BioengineeringPolitecnico di MilanoMilanItaly
- Clinical Bioengineering UnitNational Center for Oncological Hadrontherapy (Fondazione CNAO)PaviaItaly
| | - Chiara Paganelli
- Department of Electronics, Information and BioengineeringPolitecnico di MilanoMilanItaly
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