1
|
Vagni M, Tran HE, Catucci F, Chiloiro G, D’Aviero A, Re A, Romano A, Boldrini L, Kawula M, Lombardo E, Kurz C, Landry G, Belka C, Indovina L, Gambacorta MA, Cusumano D, Placidi L. Impact of bias field correction on 0.35 T pelvic MR images: evaluation on generative adversarial network-based OARs' auto-segmentation and visual grading assessment. Front Oncol 2024; 14:1294252. [PMID: 38606108 PMCID: PMC11007142 DOI: 10.3389/fonc.2024.1294252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 03/11/2024] [Indexed: 04/13/2024] Open
Abstract
Purpose Magnetic resonance imaging (MRI)-guided radiotherapy enables adaptive treatment plans based on daily anatomical changes and accurate organ visualization. However, the bias field artifact can compromise image quality, affecting diagnostic accuracy and quantitative analyses. This study aims to assess the impact of bias field correction on 0.35 T pelvis MRIs by evaluating clinical anatomy visualization and generative adversarial network (GAN) auto-segmentation performance. Materials and methods 3D simulation MRIs from 60 prostate cancer patients treated on MR-Linac (0.35 T) were collected and preprocessed with the N4ITK algorithm for bias field correction. A 3D GAN architecture was trained, validated, and tested on 40, 10, and 10 patients, respectively, to auto-segment the organs at risk (OARs) rectum and bladder. The GAN was trained and evaluated either with the original or the bias-corrected MRIs. The Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD95th) were computed for the segmented volumes of each patient. The Wilcoxon signed-rank test assessed the statistical difference of the metrics within OARs, both with and without bias field correction. Five radiation oncologists blindly scored 22 randomly chosen patients in terms of overall image quality and visibility of boundaries (prostate, rectum, bladder, seminal vesicles) of the original and bias-corrected MRIs. Bennett's S score and Fleiss' kappa were used to assess the pairwise interrater agreement and the interrater agreement among all the observers, respectively. Results In the test set, the GAN trained and evaluated on original and bias-corrected MRIs showed DSC/HD95th of 0.92/5.63 mm and 0.92/5.91 mm for the bladder and 0.84/10.61 mm and 0.83/9.71 mm for the rectum. No statistical differences in the distribution of the evaluation metrics were found neither for the bladder (DSC: p = 0.07; HD95th: p = 0.35) nor for the rectum (DSC: p = 0.32; HD95th: p = 0.63). From the clinical visual grading assessment, the bias-corrected MRI resulted mostly in either no change or an improvement of the image quality and visualization of the organs' boundaries compared with the original MRI. Conclusion The bias field correction did not improve the anatomy visualization from a clinical point of view and the OARs' auto-segmentation outputs generated by the GAN.
Collapse
Affiliation(s)
- Marica Vagni
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Huong Elena Tran
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | | | - Giuditta Chiloiro
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | | | | | - Angela Romano
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Luca Boldrini
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Maria Kawula
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Elia Lombardo
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, 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
| | - 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
| | - Luca Indovina
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Maria Antonietta Gambacorta
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Davide Cusumano
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
- Mater Olbia Hospital, Olbia, Italy
| | - Lorenzo Placidi
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| |
Collapse
|
2
|
Rabe M, Dietrich O, Forbrig R, Niyazi M, Belka C, Corradini S, Landry G, Kurz C. Repeatability quantification of brain diffusion-weighted imaging for future clinical implementation at a low-field MR-linac. Radiat Oncol 2024; 19:31. [PMID: 38448888 PMCID: PMC10916154 DOI: 10.1186/s13014-024-02424-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 02/26/2024] [Indexed: 03/08/2024] Open
Abstract
BACKGROUND Longitudinal assessments of apparent diffusion coefficients (ADCs) derived from diffusion-weighted imaging (DWI) during intracranial radiotherapy at magnetic resonance imaging-guided linear accelerators (MR-linacs) could enable early response assessment by tracking tumor diffusivity changes. However, DWI pulse sequences are currently unavailable in clinical practice at low-field MR-linacs. Quantifying the in vivo repeatability of ADC measurements is a crucial step towards clinical implementation of DWI sequences but has not yet been reported on for low-field MR-linacs. This study assessed ADC measurement repeatability in a phantom and in vivo at a 0.35 T MR-linac. METHODS Eleven volunteers and a diffusion phantom were imaged on a 0.35 T MR-linac. Two echo-planar imaging DWI sequence variants, emphasizing high spatial resolution ("highRes") and signal-to-noise ratio ("highSNR"), were investigated. A test-retest study with an intermediate outside-scanner-break was performed to assess repeatability in the phantom and volunteers' brains. Mean ADCs within phantom vials, cerebrospinal fluid (CSF), and four brain tissue regions were compared to literature values. Absolute relative differences of mean ADCs in pre- and post-break scans were calculated for the diffusion phantom, and repeatability coefficients (RC) and relative RC (relRC) with 95% confidence intervals were determined for each region-of-interest (ROI) in volunteers. RESULTS Both DWI sequence variants demonstrated high repeatability, with absolute relative deviations below 1% for water, dimethyl sulfoxide, and polyethylene glycol in the diffusion phantom. RelRCs were 7% [5%, 12%] (CSF; highRes), 12% [9%, 22%] (CSF; highSNR), 9% [8%, 12%] (brain tissue ROIs; highRes), and 6% [5%, 7%] (brain tissue ROIs; highSNR), respectively. ADCs measured with the highSNR variant were consistent with literature values for volunteers, while smaller mean values were measured for the diffusion phantom. Conversely, the highRes variant underestimated ADCs compared to literature values, indicating systematic deviations. CONCLUSIONS High repeatability of ADC measurements in a diffusion phantom and volunteers' brains were measured at a low-field MR-linac. The highSNR variant outperformed the highRes variant in accuracy and repeatability, at the expense of an approximately doubled voxel volume. The observed high in vivo repeatability confirms the potential utility of DWI at low-field MR-linacs for early treatment response assessment.
Collapse
Affiliation(s)
- Moritz Rabe
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany.
| | - Olaf Dietrich
- Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Robert Forbrig
- Institute of Neuroradiology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Maximilian Niyazi
- 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
- Department of Radiation Oncology, University of Tübingen, Tübingen, 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
| | - Stefanie Corradini
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| |
Collapse
|
3
|
Wei C, Albrecht J, Rit S, Laurendeau M, Thummerer A, Corradini S, Belka C, Steininger P, Ginzinger F, Kurz C, Riboldi M, Landry G. Reduction of cone-beam CT artifacts in a robotic CBCT device using saddle trajectories with integrated infrared tracking. Med Phys 2024; 51:1674-1686. [PMID: 38224324 DOI: 10.1002/mp.16943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 12/08/2023] [Accepted: 12/27/2023] [Indexed: 01/16/2024] Open
Abstract
BACKGROUND Cone beam computed tomography (CBCT) is widely used in many medical fields. However, conventional CBCT circular scans suffer from cone beam (CB) artifacts that limit the quality and reliability of the reconstructed images due to incomplete data. PURPOSE Saddle trajectories in theory might be able to improve the CBCT image quality by providing a larger region with complete data. Therefore, we investigated the feasibility and performance of saddle trajectory CBCT scans and compared them to circular trajectory scans. METHODS We performed circular and saddle trajectory scans using a novel robotic CBCT scanner (Mobile ImagingRing (IRm); medPhoton, Salzburg, Austria). For the saddle trajectory, the gantry executed yaw motion up to± 10 ∘ $\pm 10^{\circ }$ using motorized wheels driving on the floor. An infrared (IR) tracking device with reflective markers was used for online geometric calibration correction (mainly floor unevenness). All images were reconstructed using penalized least-squares minimization with the conjugate gradient algorithm from RTK with0.5 × 0.5 × 0.5 mm 3 $0.5 \times 0.5\times 0.5 \text{ mm}^3$ voxel size. A disk phantom and an Alderson phantom were scanned to assess the image quality. Results were correlated with the local incompleteness value represented bytan ( ψ ) $\tan (\psi)$ , which was calculated at each voxel as a function of the source trajectory and the voxel's 3D coordinates. We assessed the magnitude of CB artifacts using the full width half maximum (FWHM) of each disk profile in the axial center of the reconstructed images. Spatial resolution was also quantified by the modulation transfer function at 10% (MTF10). RESULTS When using the saddle trajectory, the region without CB artifacts was increased from 43 to 190 mm in the SI direction compared to the circular trajectory. This region coincided with low values fortan ( ψ ) $\tan (\psi)$ . Whentan ( ψ ) $\tan (\psi)$ was larger than 0.02, we found there was a linear relationship between the FWHM andtan ( ψ ) $\tan (\psi)$ . For the saddle, IR tracking allowed the increase of MTF10 from 0.37 to 0.98 lp/mm. CONCLUSIONS We achieved saddle trajectory CBCT scans with a novel CBCT system combined with IR tracking. The results show that the saddle trajectory provides a larger region with reliable reconstruction compared to the circular trajectory. The proposed method can be used to evaluate other non-circular trajectories.
Collapse
Affiliation(s)
- Chengtao Wei
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Garching, Germany
| | - Johanna Albrecht
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Garching, Germany
| | - Simon Rit
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294, F-69373, Lyon, France
| | - Matthieu Laurendeau
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294, F-69373, Lyon, France
- Thales AVS, Moirans, France
| | - Adrian Thummerer
- 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, Munich, Germany
- German Cancer Consortium (DKTK), partner site Munich, a partnership between DKFZ and LMU University Hospital Munich, Munich, Germany
| | | | | | - Christopher Kurz
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Marco Riboldi
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Garching, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| |
Collapse
|
4
|
Sui Z, Palaniappan P, Brenner J, Paganelli C, Kurz C, Landry G, Riboldi M. Intra-frame motion deterioration effects and deep-learning-based compensation in MR-guided radiotherapy. Med Phys 2024; 51:1899-1917. [PMID: 37665948 DOI: 10.1002/mp.16702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 07/07/2023] [Accepted: 07/31/2023] [Indexed: 09/06/2023] Open
Abstract
BACKGROUND Current commercially available hybrid magnetic resonance linear accelerators (MR-Linac) use 2D+t cine MR imaging to provide intra-fractional motion monitoring. However, given the limited temporal resolution of cine MR imaging, target intra-frame motion deterioration effects, resulting in effective time latency and motion artifacts in the image domain, can be appreciable, especially in the case of fast breathing. PURPOSE The aim of this work is to investigate intra-frame motion deterioration effects in MR-guided radiotherapy (MRgRT) by simulating the motion-corrupted image acquisition, and to explore the feasibility of deep-learning-based compensation approaches, relying on the intra-frame motion information which is spatially and temporally encoded in the raw data (k-space). METHODS An intra-frame motion model was defined to simulate motion-corrupted MR images, with 4D anthropomorphic digital phantoms being exploited to provide ground truth 2D+t cine MR sequences. A total number of 10 digital phantoms were generated for lung cancer patients, with randomly selected eight patients for training or validation and the remaining two for testing. The simulation code served as the data generator, and a dedicated motion pattern perturbation scheme was proposed to build the intra-frame motion database, where three degrees of freedom were designed to guarantee the diversity of intra-frame motion trajectories, enabling a thorough exploration in the domain of the potential anatomical structure positions. U-Nets with three types of loss functions: L1 or L2 loss defined in image or Fourier domain, referred to as NNImgLoss-L1 , NNFloss-L1 and NNL2-Loss were trained to extract information from the motion-corrupted image and used to estimate the ground truth final-position image, corresponding to the end of the acquisition. Images before and after compensation were evaluated in terms of (i) image mean-squared error (MSE) and mean absolute error (MAE), and (ii) accuracy of gross tumor volume (GTV) contouring, based on optical-flow image registration. RESULTS Image degradation caused by intra-frame motion was observed: for a linearly and fully acquired Cartesian readout k-space trajectory, intra-frame motion resulted in an imaging latency of approximately 50% of the acquisition time; in comparison, the motion artifacts exhibited only a negligible contribution to the overall geometric errors. All three compensation models led to a decrease in image MSE/MAE and GTV position offset compared to the motion-corrupted image. In the investigated testing dataset for GTV contouring, the average dice similarity coefficients (DSC) improved from 88% to 96%, and the 95th percentile Hausdorff distance (HD95 ) dropped from 4.8 mm to 2.1 mm. Different models showed slight performance variations across different intra-frame motion amplitude categories: NNImgLoss-L1 excelled for small/medium amplitudes, whereas NNFloss-L1 demonstrated higher DSC median values at larger amplitudes. The saliency maps of the motion-corrupted image highlighted the major contribution of the later acquired k-space data, as well as the edges of the moving anatomical structures at their final positions, during the model inference stage. CONCLUSIONS Our results demonstrate the deep-learning-based approaches have the potential to compensate for intra-frame motion by utilizing the later acquired data to drive the convergence of the earlier acquired k-space components.
Collapse
Affiliation(s)
- Zhuojie Sui
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Garching, Germany
| | - Prasannakumar Palaniappan
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Garching, Germany
| | - Jakob Brenner
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Garching, Germany
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Marco Riboldi
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Garching, Germany
| |
Collapse
|
5
|
Huang L, Kurz C, Freislederer P, Manapov F, Corradini S, Niyazi M, Belka C, Landry G, Riboldi M. Simultaneous object detection and segmentation for patient-specific markerless lung tumor tracking in simulated radiographs with deep learning. Med Phys 2024; 51:1957-1973. [PMID: 37683107 DOI: 10.1002/mp.16705] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 04/23/2023] [Accepted: 05/12/2023] [Indexed: 09/10/2023] Open
Abstract
BACKGROUND Real-time tumor tracking is one motion management method to address motion-induced uncertainty. To date, fiducial markers are often required to reliably track lung tumors with X-ray imaging, which carries risks of complications and leads to prolonged treatment time. A markerless tracking approach is thus desirable. Deep learning-based approaches have shown promise for markerless tracking, but systematic evaluation and procedures to investigate applicability in individual cases are missing. Moreover, few efforts have been made to provide bounding box prediction and mask segmentation simultaneously, which could allow either rigid or deformable multi-leaf collimator tracking. PURPOSE The purpose of this study was to implement a deep learning-based markerless lung tumor tracking model exploiting patient-specific training which outputs both a bounding box and a mask segmentation simultaneously. We also aimed to compare the two kinds of predictions and to implement a specific procedure to understand the feasibility of markerless tracking on individual cases. METHODS We first trained a Retina U-Net baseline model on digitally reconstructed radiographs (DRRs) generated from a public dataset containing 875 CT scans and corresponding lung nodule annotations. Afterwards, we used an independent cohort of 97 lung patients to develop a patient-specific refinement procedure. In order to determine the optimal hyperparameters for automatic patient-specific training, we selected 13 patients for validation where the baseline model predicted a bounding box on planning CT (PCT)-DRR with intersection over union (IoU) with the ground-truth higher than 0.7. The final test set contained the remaining 84 patients with varying PCT-DRR IoU. For each testing patient, the baseline model was refined on the PCT-DRR to generate a patient-specific model, which was then tested on a separate 10-phase 4DCT-DRR to mimic the intrafraction motion during treatment. A template matching algorithm served as benchmark model. The testing results were evaluated by four metrics: the center of mass (COM) error and the Dice similarity coefficient (DSC) for segmentation masks, and the center of box (COB) error and the DSC for bounding box detections. Performance was compared to the benchmark model including statistical testing for significance. RESULTS A PCT-DRR IoU value of 0.2 was shown to be the threshold dividing inconsistent (68%) and consistent (100%) success (defined as mean bounding box DSC > 0.6) of PS models on 4DCT-DRRs. Thirty-seven out of the eighty-four testing cases had a PCT-DRR IoU above 0.2. For these 37 cases, the mean COM error was 2.6 mm, the mean segmentation DSC was 0.78, the mean COB error was 2.7 mm, and the mean box DSC was 0.83. Including the validation cases, the model was applicable to 50 out of 97 patients when using the PCT-DRR IoU threshold of 0.2. The inference time per frame was 170 ms. The model outperformed the benchmark model on all metrics, and the comparison was significant (p < 0.001) over the 37 PCT-DRR IoU > 0.2 cases, but not over the undifferentiated 84 testing cases. CONCLUSIONS The implemented patient-specific refinement approach based on a pre-trained baseline model was shown to be applicable to markerless tumor tracking in simulated radiographs for lung cases.
Collapse
Affiliation(s)
- Lili Huang
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, München, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Philipp Freislederer
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Farkhad Manapov
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Stefanie Corradini
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Maximilian Niyazi
- 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, Germany
- Bavarian Cancer Research Center (BZKF), Munich, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Marco Riboldi
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, München, Germany
| |
Collapse
|
6
|
Vagni M, Tran HE, Romano A, Chiloiro G, Boldrini L, Zormpas-Petridis K, Kawula M, Landry G, Kurz C, Corradini S, Belka C, Indovina L, Gambacorta MA, Placidi L, Cusumano D. Auto-segmentation of pelvic organs at risk on 0.35T MRI using 2D and 3D Generative Adversarial Network models. Phys Med 2024; 119:103297. [PMID: 38310680 DOI: 10.1016/j.ejmp.2024.103297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 12/04/2023] [Accepted: 01/23/2024] [Indexed: 02/06/2024] Open
Abstract
PURPOSE Manual recontouring of targets and Organs At Risk (OARs) is a time-consuming and operator-dependent task. We explored the potential of Generative Adversarial Networks (GAN) to auto-segment the rectum, bladder and femoral heads on 0.35T MRIs to accelerate the online MRI-guided-Radiotherapy (MRIgRT) workflow. METHODS 3D planning MRIs from 60 prostate cancer patients treated with 0.35T MR-Linac were collected. A 3D GAN architecture and its equivalent 2D version were trained, validated and tested on 40, 10 and 10 patients respectively. The volumetric Dice Similarity Coefficient (DSC) and 95th percentile Hausdorff Distance (HD95th) were computed against expert drawn ground-truth delineations. The networks were also validated on an independent external dataset of 16 patients. RESULTS In the internal test set, the 3D and 2D GANs showed DSC/HD95th of 0.83/9.72 mm and 0.81/10.65 mm for the rectum, 0.92/5.91 mm and 0.85/15.72 mm for the bladder, and 0.94/3.62 mm and 0.90/9.49 mm for the femoral heads. In the external test set, the performance was 0.74/31.13 mm and 0.72/25.07 mm for the rectum, 0.92/9.46 mm and 0.88/11.28 mm for the bladder, and 0.89/7.00 mm and 0.88/10.06 mm for the femoral heads. The 3D and 2D GANs required on average 1.44 s and 6.59 s respectively to generate the OARs' volumetric segmentation for a single patient. CONCLUSIONS The proposed 3D GAN auto-segments pelvic OARs with high accuracy on 0.35T, in both the internal and the external test sets, outperforming its 2D equivalent in both segmentation robustness and volume generation time.
Collapse
Affiliation(s)
- Marica Vagni
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy
| | - Huong Elena Tran
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy
| | - Angela Romano
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy
| | - Giuditta Chiloiro
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy
| | - Luca Boldrini
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy
| | | | - Maria Kawula
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Stefanie Corradini
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Claus Belka
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany; German Cancer Consortium (DKTK), Department of Radiation Oncology, Munich, Germany
| | - Luca Indovina
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy
| | | | - Lorenzo Placidi
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy.
| | - Davide Cusumano
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy; Mater Olbia Hospital, Olbia, SS, Italy
| |
Collapse
|
7
|
Wang Y, Lombardo E, Huang L, Avanzo M, Fanetti G, Franchin G, Zschaeck S, Weingärtner J, Belka C, Riboldi M, Kurz C, Landry G. Comparison of deep learning networks for fully automated head and neck tumor delineation on multi-centric PET/CT images. Radiat Oncol 2024; 19:3. [PMID: 38191431 PMCID: PMC10773015 DOI: 10.1186/s13014-023-02388-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 12/12/2023] [Indexed: 01/10/2024] Open
Abstract
OBJECTIVES Deep learning-based auto-segmentation of head and neck cancer (HNC) tumors is expected to have better reproducibility than manual delineation. Positron emission tomography (PET) and computed tomography (CT) are commonly used in tumor segmentation. However, current methods still face challenges in handling whole-body scans where a manual selection of a bounding box may be required. Moreover, different institutions might still apply different guidelines for tumor delineation. This study aimed at exploring the auto-localization and segmentation of HNC tumors from entire PET/CT scans and investigating the transferability of trained baseline models to external real world cohorts. METHODS We employed 2D Retina Unet to find HNC tumors from whole-body PET/CT and utilized a regular Unet to segment the union of the tumor and involved lymph nodes. In comparison, 2D/3D Retina Unets were also implemented to localize and segment the same target in an end-to-end manner. The segmentation performance was evaluated via Dice similarity coefficient (DSC) and Hausdorff distance 95th percentile (HD95). Delineated PET/CT scans from the HECKTOR challenge were used to train the baseline models by 5-fold cross-validation. Another 271 delineated PET/CTs from three different institutions (MAASTRO, CRO, BERLIN) were used for external testing. Finally, facility-specific transfer learning was applied to investigate the improvement of segmentation performance against baseline models. RESULTS Encouraging localization results were observed, achieving a maximum omnidirectional tumor center difference lower than 6.8 cm for external testing. The three baseline models yielded similar averaged cross-validation (CV) results with a DSC in a range of 0.71-0.75, while the averaged CV HD95 was 8.6, 10.7 and 9.8 mm for the regular Unet, 2D and 3D Retina Unets, respectively. More than a 10% drop in DSC and a 40% increase in HD95 were observed if the baseline models were tested on the three external cohorts directly. After the facility-specific training, an improvement in external testing was observed for all models. The regular Unet had the best DSC (0.70) for the MAASTRO cohort, and the best HD95 (7.8 and 7.9 mm) in the MAASTRO and CRO cohorts. The 2D Retina Unet had the best DSC (0.76 and 0.67) for the CRO and BERLIN cohorts, and the best HD95 (12.4 mm) for the BERLIN cohort. CONCLUSION The regular Unet outperformed the other two baseline models in CV and most external testing cohorts. Facility-specific transfer learning can potentially improve HNC segmentation performance for individual institutions, where the 2D Retina Unets could achieve comparable or even better results than the regular Unet.
Collapse
Affiliation(s)
- Yiling Wang
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
- Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Elia Lombardo
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Lili Huang
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Michele Avanzo
- Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Medical Physics, Aviano, Italy
| | - Giuseppe Fanetti
- Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Radiation Oncology, Aviano, Italy
| | - Giovanni Franchin
- Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Radiation Oncology, Aviano, Italy
| | - Sebastian Zschaeck
- Radiation Oncology, Charité-Universitätsmedizin Berlin, Freie Universität Berlin, Berlin, Germany
| | - Julian Weingärtner
- Radiation Oncology, Charité-Universitätsmedizin Berlin, Freie Universität Berlin, Berlin, Germany
| | - Claus Belka
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
- Bavarian Cancer Research Center (BZKF), Munich, Germany
| | - Marco Riboldi
- Department of Medical 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.
| |
Collapse
|
8
|
Xiong Y, Rabe M, Rippke C, Kawula M, Nierer L, Klüter S, Belka C, Niyazi M, Hörner-Rieber J, Corradini S, Landry G, Kurz C. Impact of daily plan adaptation on accumulated doses in ultra-hypofractionated magnetic resonance-guided radiation therapy of prostate cancer. Phys Imaging Radiat Oncol 2024; 29:100562. [PMID: 38463219 PMCID: PMC10924058 DOI: 10.1016/j.phro.2024.100562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 01/18/2024] [Accepted: 02/19/2024] [Indexed: 03/12/2024] Open
Abstract
Background and purpose Ultra-hypofractionated online adaptive magnetic resonance-guided radiotherapy (MRgRT) is promising for prostate cancer. However, the impact of online adaptation on target coverage and organ-at-risk (OAR) sparing at the level of accumulated dose has not yet been reported. Using deformable image registration (DIR)-based accumulation, we compared the delivered adapted dose with the simulated non-adapted dose. Materials and methods Twenty-three prostate cancer patients treated at two clinics with 0.35 T magnetic resonance-guided linear accelerator (MR-linac) following the same treatment protocol (5 × 7.5 Gy with urethral sparing and daily adaptation) were included. The fraction MR images were deformably registered to the planning MR image. Both non-adapted and adapted fraction doses were accumulated with the corresponding vector fields. Two DIR approaches were implemented. PTV* (planning target volume minus urethra+2mm) D95%, CTV* (clinical target volume minus urethra) D98%, and OARs (urethra+2mm, bladder, and rectum) D0.2cc, were evaluated. Statistical significance was inferred from a two-tailed Wilcoxon signed-rank test (p < 0.05). Results Normalized to the baseline, the accumulated PTV* D95% increased significantly by 2.7 % ([1.5, 4.3]%) through adaptation, and the CTV* D98% by 1.2 % ([0.1, 1.7]%). For the OARs after adaptation, accumulated bladder D0.2cc decreased by 0.4 % ([-1.2, 0.4]%), urethra+2mmD0.2cc by 0.8 % ([-1.6, -0.1]%), while rectum D0.2cc increased by 2.6 % ([1.2, 4.9]%). For all patients, rectum D0.2cc was still below the clinical constraint. Results of both DIR approaches differed on average by less than 0.2 %. Conclusions Online adaptation in MRgRT improved target coverage and OARs sparing at the level of accumulated dose.
Collapse
Affiliation(s)
- Yuqing Xiong
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Moritz Rabe
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Carolin Rippke
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Maria Kawula
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Lukas Nierer
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Sebastian Klüter
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
- Heidelberg Institute of Radiation Oncology, National Center for Radiation Oncology, Heidelberg, 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, Germany
- Bavarian Cancer Research Center (BZKF), Munich, Germany
| | - Maximilian Niyazi
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Juliane Hörner-Rieber
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
- Heidelberg Institute of Radiation Oncology, National Center for Radiation Oncology, Heidelberg, Germany
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center, Heidelberg, Germany
- National Center for Tumor Diseases, Heidelberg, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Stefanie Corradini
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| |
Collapse
|
9
|
Lombardo E, Dhont J, Page D, Garibaldi C, Künzel LA, Hurkmans C, Tijssen RHN, Paganelli C, Liu PZY, Keall PJ, Riboldi M, Kurz C, Landry G, Cusumano D, Fusella M, Placidi L. Real-time motion management in MRI-guided radiotherapy: Current status and AI-enabled prospects. Radiother Oncol 2024; 190:109970. [PMID: 37898437 DOI: 10.1016/j.radonc.2023.109970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 10/19/2023] [Accepted: 10/22/2023] [Indexed: 10/30/2023]
Abstract
MRI-guided radiotherapy (MRIgRT) is a highly complex treatment modality, allowing adaptation to anatomical changes occurring from one treatment day to the other (inter-fractional), but also to motion occurring during a treatment fraction (intra-fractional). In this vision paper, we describe the different steps of intra-fractional motion management during MRIgRT, from imaging to beam adaptation, and the solutions currently available both clinically and at a research level. Furthermore, considering the latest developments in the literature, a workflow is foreseen in which motion-induced over- and/or under-dosage is compensated in 3D, with minimal impact to the radiotherapy treatment time. Considering the time constraints of real-time adaptation, a particular focus is put on artificial intelligence (AI) solutions as a fast and accurate alternative to conventional algorithms.
Collapse
Affiliation(s)
- Elia Lombardo
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Jennifer Dhont
- Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (H.U.B), Institut Jules Bordet, Department of Medical Physics, Brussels, Belgium; Université Libre De Bruxelles (ULB), Radiophysics and MRI Physics Laboratory, Brussels, Belgium
| | - Denis Page
- University of Manchester, Division of Cancer Sciences, Manchester, United Kingdom
| | - Cristina Garibaldi
- IEO, Unit of Radiation Research, European Institute of Oncology IRCCS, Milan, Italy
| | - Luise A Künzel
- National Center for Tumor Diseases (NCT), Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany
| | - Coen Hurkmans
- Department of Radiation Oncology, Catharina Hospital, Eindhoven, the Netherlands
| | - Rob H N Tijssen
- Department of Radiation Oncology, Catharina Hospital, Eindhoven, the Netherlands
| | - Chiara Paganelli
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy
| | - Paul Z Y Liu
- Image X Institute, University of Sydney Central Clinical School, Sydney, NSW, Australia; Department of Medical Physics, Ingham Institute of Applied Medical Research, Liverpool, NSW, Australia
| | - Paul J Keall
- Image X Institute, University of Sydney Central Clinical School, Sydney, NSW, Australia; Department of Medical Physics, Ingham Institute of Applied Medical Research, Liverpool, NSW, Australia
| | - Marco Riboldi
- Department of Medical Physics, Faculty of Physics, LMU Munich, Munich, 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; German Cancer Consortium (DKTK), Partner Site Munich, a Partnership between DKFZ and LMU University Hospital Munich, Germany; Bavarian Cancer Research Center (BZKF), Partner Site Munich, Munich, Germany
| | | | - Marco Fusella
- Department of Radiation Oncology, Abano Terme Hospital, Italy.
| | - Lorenzo Placidi
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Roma, Italy
| |
Collapse
|
10
|
Rabe M, Paganelli C, Schmitz H, Meschini G, Riboldi M, Hofmaier J, Nierer-Kohlhase L, Dinkel J, Reiner M, Parodi K, Belka C, Landry G, Kurz C, Kamp F. Continuous time-resolved estimated synthetic 4D-CTs for dose reconstruction of lung tumor treatments at a 0.35 T MR-linac. Phys Med Biol 2023; 68:235008. [PMID: 37669669 DOI: 10.1088/1361-6560/acf6f0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 09/05/2023] [Indexed: 09/07/2023]
Abstract
Objective.To experimentally validate a method to create continuous time-resolved estimated synthetic 4D-computed tomography datasets (tresCTs) based on orthogonal cine MRI data for lung cancer treatments at a magnetic resonance imaging (MRI) guided linear accelerator (MR-linac).Approach.A breathing porcine lung phantom was scanned at a CT scanner and 0.35 T MR-linac. Orthogonal cine MRI series (sagittal/coronal orientation) at 7.3 Hz, intersecting tumor-mimicking gelatin nodules, were deformably registered to mid-exhale 3D-CT and 3D-MRI datasets. The time-resolved deformation vector fields were extrapolated to 3D and applied to a reference synthetic 3D-CT image (sCTref), while accounting for breathing phase-dependent lung density variations, to create 82 s long tresCTs at 3.65 Hz. Ten tresCTs were created for ten tracked nodules with different motion patterns in two lungs. For each dataset, a treatment plan was created on the mid-exhale phase of a measured ground truth (GT) respiratory-correlated 4D-CT dataset with the tracked nodule as gross tumor volume (GTV). Each plan was recalculated on the GT 4D-CT, randomly sampled tresCT, and static sCTrefimages. Dose distributions for corresponding breathing phases were compared in gamma (2%/2 mm) and dose-volume histogram (DVH) parameter analyses.Main results.The mean gamma pass rate between all tresCT and GT 4D-CT dose distributions was 98.6%. The mean absolute relative deviations of the tresCT with respect to GT DVH parameters were 1.9%, 1.0%, and 1.4% for the GTVD98%,D50%, andD2%, respectively, 1.0% for the remaining nodulesD50%, and 1.5% for the lungV20Gy. The gamma pass rate for the tresCTs was significantly larger (p< 0.01), and the GTVD50%deviations with respect to the GT were significantly smaller (p< 0.01) than for the sCTref.Significance.The results suggest that tresCTs could be valuable for time-resolved reconstruction and intrafractional accumulation of the dose to the GTV for lung cancer patients treated at MR-linacs in the future.
Collapse
Affiliation(s)
- Moritz Rabe
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Chiara Paganelli
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy
| | - Henning Schmitz
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Giorgia Meschini
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy
| | - Marco Riboldi
- Department of Medical Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching (Munich), Germany
| | - Jan Hofmaier
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Lukas Nierer-Kohlhase
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Julien Dinkel
- Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany
- Comprehensive Pneumology Center, German Center for Lung Research (DZL), Munich, Germany
| | - Michael Reiner
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Katia Parodi
- Department of Medical Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching (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, Germany
- Bavarian Cancer Research Center (BZKF), Munich, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Florian Kamp
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
- Department of Radiation Oncology, University Hospital Cologne, Cologne, Germany
| |
Collapse
|
11
|
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] [What about the content of this article? (0)] [Affiliation(s)] [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.
Collapse
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
| |
Collapse
|
12
|
Landry G, Kurz C, Traverso A. The role of artificial intelligence in radiotherapy clinical practice. BJR Open 2023; 5:20230030. [PMID: 37942500 PMCID: PMC10630974 DOI: 10.1259/bjro.20230030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 09/13/2023] [Accepted: 09/27/2023] [Indexed: 11/10/2023] Open
Abstract
This review article visits the current state of artificial intelligence (AI) in radiotherapy clinical practice. We will discuss how AI has a place in the modern radiotherapy workflow at the level of automatic segmentation and planning, two applications which have seen real-work implementation. A special emphasis will be placed on the role AI can play in online adaptive radiotherapy, such as performed at MR-linacs, where online plan adaptation is a procedure which could benefit from automation to reduce on-couch time for patients. Pseudo-CT generation and AI for motion tracking will be introduced in the scope of online adaptive radiotherapy as well. We further discuss the use of AI for decision-making and response assessment, for example for personalized prescription and treatment selection, risk stratification for outcomes and toxicities, and AI for quantitative imaging and response assessment. Finally, the challenges of generalizability and ethical aspects will be covered. With this, we provide a comprehensive overview of the current and future applications of AI in radiotherapy.
Collapse
Affiliation(s)
| | - Christopher Kurz
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | | |
Collapse
|
13
|
Kawula M, Vagni M, Cusumano D, Boldrini L, Placidi L, Corradini S, Belka C, Landry G, Kurz C. Prior knowledge based deep learning auto-segmentation in magnetic resonance imaging-guided radiotherapy of prostate cancer. Phys Imaging Radiat Oncol 2023; 28:100498. [PMID: 37928618 PMCID: PMC10624570 DOI: 10.1016/j.phro.2023.100498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 10/03/2023] [Accepted: 10/04/2023] [Indexed: 11/07/2023] Open
Abstract
Background and purpose Automation is desirable for organ segmentation in radiotherapy. This study compared deep learning methods for auto-segmentation of organs-at-risk (OARs) and clinical target volume (CTV) in prostate cancer patients undergoing fractionated magnetic resonance (MR)-guided adaptive radiation therapy. Models predicting dense displacement fields (DDFMs) between planning and fraction images were compared to patient-specific (PSM) and baseline (BM) segmentation models. Materials and methods A dataset of 92 patients with planning and fraction MR images (MRIs) from two institutions were used. DDFMs were trained to predict dense displacement fields (DDFs) between the planning and fraction images, which were subsequently used to propagate the planning contours of the bladder, rectum, and CTV to the daily MRI. The training was performed either with true planning-fraction image pairs or with planning images and their counterparts deformed by known DDFs. The BMs were trained on 53 planning images, while to generate PSMs, the BMs were fine-tuned using the planning image of a given single patient. The evaluation included Dice similarity coefficient (DSC), the average (HDavg) and the 95th percentile (HD95) Hausdorff distance (HD). Results The DDFMs with DSCs for bladder/rectum of 0.76/0.76 performed worse than PSMs (0.91/0.90) and BMs (0.89/0.88). The same trend was observed for HDs. For CTV, DDFM and PSM performed similarly yielding DSCs of 0.87 and 0.84, respectively. Conclusions DDFMs were found suitable for CTV delineation after rigid alignment. However, for OARs they were outperformed by PSMs, as they predicted only limited deformations even in the presence of substantial anatomical changes.
Collapse
Affiliation(s)
- Maria Kawula
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Marica Vagni
- Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, Rome, Italy
| | - Davide Cusumano
- Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, Rome, Italy
- Mater Olbia Hospital, Olbia (SS), Italy
| | - Luca Boldrini
- Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, Rome, Italy
| | - Lorenzo Placidi
- Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, Rome, Italy
| | - 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, Germany
- Bavarian Cancer Research Center (BZKF), Munich, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| |
Collapse
|
14
|
Ribeiro MF, Marschner S, Kawula M, Rabe M, Corradini S, Belka C, Riboldi M, Landry G, Kurz C. Deep learning based automatic segmentation of organs-at-risk for 0.35 T MRgRT of lung tumors. Radiat Oncol 2023; 18:135. [PMID: 37574549 PMCID: PMC10424424 DOI: 10.1186/s13014-023-02330-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 08/03/2023] [Indexed: 08/15/2023] Open
Abstract
BACKGROUND AND PURPOSE Magnetic resonance imaging guided radiotherapy (MRgRT) offers treatment plan adaptation to the anatomy of the day. In the current MRgRT workflow, this requires the time consuming and repetitive task of manual delineation of organs-at-risk (OARs), which is also prone to inter- and intra-observer variability. Therefore, deep learning autosegmentation (DLAS) is becoming increasingly attractive. No investigation of its application to OARs in thoracic magnetic resonance images (MRIs) from MRgRT has been done so far. This study aimed to fill this gap. MATERIALS AND METHODS 122 planning MRIs from patients treated at a 0.35 T MR-Linac were retrospectively collected. Using an 80/19/23 (training/validation/test) split, individual 3D U-Nets for segmentation of the left lung, right lung, heart, aorta, spinal canal and esophagus were trained. These were compared to the clinically used contours based on Dice similarity coefficient (DSC) and Hausdorff distance (HD). They were also graded on their clinical usability by a radiation oncologist. RESULTS Median DSC was 0.96, 0.96, 0.94, 0.90, 0.88 and 0.78 for left lung, right lung, heart, aorta, spinal canal and esophagus, respectively. Median 95th percentile values of the HD were 3.9, 5.3, 5.8, 3.0, 2.6 and 3.5 mm, respectively. The physician preferred the network generated contours over the clinical contours, deeming 85 out of 129 to not require any correction, 25 immediately usable for treatment planning, 15 requiring minor and 4 requiring major corrections. CONCLUSIONS We trained 3D U-Nets on clinical MRI planning data which produced accurate delineations in the thoracic region. DLAS contours were preferred over the clinical contours.
Collapse
Affiliation(s)
- Marvin F Ribeiro
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Sebastian Marschner
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Maria Kawula
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Moritz Rabe
- 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, Munich, Germany
- Bavarian Cancer Research Center (BZKF), Munich, Germany
| | - Marco Riboldi
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Garching, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany.
| |
Collapse
|
15
|
Schmitz H, Rabe M, Janssens G, Rit S, Parodi K, Belka C, Kamp F, Landry G, Kurz C. Scatter correction of 4D cone beam computed tomography to detect dosimetric effects due to anatomical changes in proton therapy for lung cancer. Med Phys 2023; 50:4981-4992. [PMID: 36847184 DOI: 10.1002/mp.16335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 02/01/2023] [Accepted: 02/14/2023] [Indexed: 03/01/2023] Open
Abstract
BACKGROUND The treatment of moving tumor entities is expected to have superior clinical outcomes, using image-guided adaptive intensity-modulated proton therapy (IMPT). PURPOSE For 21 lung cancer patients, IMPT dose calculations were performed on scatter-corrected 4D cone beam CTs (4DCBCTcor ) to evaluate their potential for triggering treatment adaptation. Additional dose calculations were performed on corresponding planning 4DCTs and day-of-treatment 4D virtual CTs (4DvCTs). METHODS A 4DCBCT correction workflow, previously validated on a phantom, generates 4DvCT (CT-to-CBCT deformable registration) and 4DCBCTcor images (projection-based correction using 4DvCT as a prior) with 10 phase bins, using day-of-treatment free-breathing CBCT projections and planning 4DCT images as input. Using a research planning system, robust IMPT plans administering eight fractions of 7.5 Gy were created on a free-breathing planning CT (pCT) contoured by a physician. The internal target volume (ITV) was overridden with muscle tissue. Robustness settings for range and setup uncertainties were 3% and 6 mm, and a Monte Carlo dose engine was used. On every phase of planning 4DCT, day-of-treatment 4DvCT, and 4DCBCTcor , the dose was recalculated. For evaluation, image analysis as well as dose analysis were performed using mean error (ME) and mean absolute error (MAE) analysis, dose-volume histogram (DVH) parameters, and 2%/2-mm gamma pass rate analysis. Action levels (1.6% ITV D98 and 90% gamma pass rate) based on our previous phantom validation study were set to determine which patients had a loss of dosimetric coverage. RESULTS Quality enhancements of 4DvCT and 4DCBCTcor over 4DCBCT were observed. ITV D98% and bronchi D2% had its largest agreement for 4DCBCTcor -4DvCT, and the largest gamma pass rates (>94%, median 98%) were found for 4DCBCTcor -4DvCT. Deviations were larger and gamma pass rates were smaller for 4DvCT-4DCT and 4DCBCTcor -4DCT. For five patients, deviations were larger than the action levels, suggesting substantial anatomical changes between pCT and CBCT projections acquisition. CONCLUSIONS This retrospective study shows the feasibility of daily proton dose calculation on 4DCBCTcor for lung tumor patients. The applied method is of clinical interest as it generates up-to-date in-room images, accounting for breathing motion and anatomical changes. This information could be used to trigger replanning.
Collapse
Affiliation(s)
- Henning Schmitz
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Bavaria, Germany
| | - Moritz Rabe
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Bavaria, Germany
| | | | - Simon Rit
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294, F-69373, Lyon, France
| | - Katia Parodi
- Department of Medical Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching (Munich), Germany
| | - Claus Belka
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Bavaria, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
| | - Florian Kamp
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Bavaria, Germany
- Department of Radiation Oncology, University Hospital Cologne, Cologne, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Bavaria, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Bavaria, Germany
| |
Collapse
|
16
|
Schmitz H, Thummerer A, Kawula M, Lombardo E, Parodi K, Belka C, Kamp F, Kurz C, Landry G. ScatterNet for projection-based 4D cone-beam computed tomography intensity correction of lung cancer patients. Phys Imaging Radiat Oncol 2023; 27:100482. [PMID: 37680905 PMCID: PMC10480315 DOI: 10.1016/j.phro.2023.100482] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 08/04/2023] [Accepted: 08/11/2023] [Indexed: 09/09/2023] Open
Abstract
Background and purpose: In radiotherapy, dose calculations based on 4D cone beam CTs (4DCBCTs) require image intensity corrections. This retrospective study compared the dose calculation accuracy of a deep learning, projection-based scatter correction workflow (ScatterNet), to slower workflows: conventional 4D projection-based scatter correction (CBCTcor) and a deformable image registration (DIR)-based method (4DvCT). Materials and methods: For 26 lung cancer patients, planning CTs (pCTs), 4DCTs and CBCT projections were available. ScatterNet was trained with pairs of raw and corrected CBCT projections. Corrected projections from ScatterNet and the conventional workflow were reconstructed using MA-ROOSTER, yielding 4DCBCTSN and 4DCBCTcor. The 4DvCT was generated by 4DCT to 4DCBCT DIR, as part of the 4DCBCTcor workflow. Robust intensity modulated proton therapy treatment plans were created on free-breathing pCTs. 4DCBCTSN was compared to 4DCBCTcor and the 4DvCT in terms of image quality and dose calculation accuracy (dose-volume-histogram parameters and 3 % /3 mm gamma analysis). Results: 4DCBCTSN resulted in an average mean absolute error of 87 HU and 102 HU when compared to 4DCBCTcor and 4DvCT respectively. High agreement was observed in targets with median dose differences of 0.4 Gy (4DCBCTSN-4DCBCTcor) and 0.3 Gy (4DCBCTSN-4DvCT). The gamma analysis showed high average 3 % /3 mm pass rates of 96 % for both 4DCBCTSN vs. 4DCBCTcor and 4DCBCTSN vs. 4DvCT. Conclusions: Accurate 4D dose calculations are feasible for lung cancer patients using ScatterNet for 4DCBCT correction. Average scatter correction times could be reduced from 10 min (4DCBCTcor) to 3.9 s , showing the clinical suitability of the proposed deep learning-based method.
Collapse
Affiliation(s)
- Henning Schmitz
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Adrian Thummerer
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Maria Kawula
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Elia Lombardo
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Katia Parodi
- Department of Medical Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching (Munich), Germany
| | - Claus Belka
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
- Bavarian Cancer Research Center (BZKF), Munich, Germany
| | - Florian Kamp
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
- Department of Radiation Oncology, University Hospital Cologne, Cologne, 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
| |
Collapse
|
17
|
Annunziata S, Rabe M, Vai A, Molinelli S, Nakas A, Meschini G, Pella A, Vitolo V, Barcellini A, Imparato S, Ciocca M, Orlandi E, Landry G, Kamp F, Kurz C, Baroni G, Riboldi M, Paganelli C. Virtual 4DCT generated from 4DMRI in gated particle therapy: phantom validation and application to lung cancer patients. Phys Med Biol 2023. [PMID: 37321258 DOI: 10.1088/1361-6560/acdec5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
OBJECTIVE Respiration negatively affects the outcome of a radiation therapy treatment, with potentially severe effects especially in particle therapy (PT). If compensation strategies are not applied, accuracy cannot be achieved. To support the clinical practice based on 4D Computed Tomography (CT), 4D Magnetic Resonance Imaging (MRI) acquisitions can be exploited. The purpose of this study was to validate a method for virtual 4DCT generation from 4DMRI data for lung cancers on a porcine lung phantom, and to apply it to lung cancer patients in PT.
Approach: Deformable image registration was used to register each respiratory phase of the 4DMRI to a reference phase. Then, a static 3DCT was registered to this reference MR image set, and the virtual 4DCT was generated by warping the registered CT according to previously obtained deformation fields. The method was validated on a physical phantom for which a ground truth 4DCT was available and tested on lung tumor patients, treated with gated PT at end-exhale, by comparing the virtual 4DCT with a re-evaluation 4DCT. The geometric and dosimetric evaluation was performed for both proton and carbon ion treatment plans.
Main results: The phantom validation exhibited a geometrical accuracy within the maximum resolution of the MRI and mean dose deviations, with respect to the prescription dose, up to 3.2% for target D95%, with a mean gamma pass rate of 98%. For patients, the virtual and re-evaluation 4DCTs showed good correspondence, with errors on target D95% up to 2% within the gating window. For one patient, dose variations up to 10% at end-exhale were observed due to relevant inter-fraction anatomo-pathological changes that occurred between the planning and re-evaluation CTs. 
Significance: Results obtained on phantom data showed that the virtual 4DCT method was accurate, allowing its application on patient data for testing within a clinical scenario.

.
Collapse
Affiliation(s)
- Simone Annunziata
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Via G. Colombo 40, Milano, 20133, ITALY
| | - Moritz Rabe
- Radiation Oncology, Klinikum der Universität München, Ziemssenstraße 1, Munchen, 80336, GERMANY
| | - Alessandro Vai
- National Centre of Oncological Hadrontherapy, Strada Campeggi, Pavia, Lombardia, 27100, ITALY
| | - Silvia Molinelli
- National Centre of Oncological Hadrontherapy, Strada Campeggi, Pavia, Lombardia, 27100, ITALY
| | - Anestis Nakas
- Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Via Giuseppe Colombo 40, Milano, Lombardia, 20133, ITALY
| | - Giorgia Meschini
- Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Via Giuseppe Colombo 40, Milano, Lombardia, 20133, ITALY
| | - Andrea Pella
- Bioengineering, Clinical Department, National Centre of Oncological Hadrontherapy, Strada Campeggi, Pavia, Lombardia, 27100, ITALY
| | - Viviana Vitolo
- National Centre of Oncological Hadrontherapy, Strada Campeggi, Pavia, Lombardia, 27100, ITALY
| | - Amelia Barcellini
- National Centre of Oncological Hadrontherapy, Strada Campeggi, Pavia, Lombardia, 27100, ITALY
| | - Sara Imparato
- National Centre of Oncological Hadrontherapy, Strada Campeggi, Pavia, Lombardia, 27100, ITALY
| | - Mario Ciocca
- Medical Physics, National Centre of Oncological Hadrontherapy, Strada Privata Campeggi, 53, Pavia, Lombardia, 27100, ITALY
| | - Ester Orlandi
- National Centre of Oncological Hadrontherapy, Strada Campeggi, Pavia, Lombardia, 27100, ITALY
| | - Guillaume Landry
- Radiation Oncology, University Hospital Munich Campus Grosshadern, Marchioninistr. 15, Munchen, 81377, GERMANY
| | - Florian Kamp
- Klinik und Poliklinik für Radioonkologie, Cyberknife und Strahlentherapie, Uniklinik Köln, Kerpener Str. 62, Koln, Nordrhein-Westfalen, 50937, GERMANY
| | - Christopher Kurz
- Radiation Oncology, University Hospital Munich, Ziemssenstraße 1, Munich, Bavaria, 80336, GERMANY
| | - Guido Baroni
- Department of Electronics Information and Bioengineering, Politecnico di Milano, P.zza Leonardo da Vinci 32,, Milano, Lombardia, 20133, ITALY
| | - Marco Riboldi
- Ludwig-Maximilians-Universität München, Geschwister-Scholl-Platz 1, Munchen, Bayern, 80539, GERMANY
| | - Chiara Paganelli
- Dipartimento di Elettronica Informazione e Bioingegneria, Politecnico di Milano, via G. Colombo 40, Milano, Lombardia, 20133, ITALY
| |
Collapse
|
18
|
Chan YCI, Li M, Parodi K, Belka C, Landry G, Kurz C. Feasibility of CycleGAN enhanced low dose CBCT imaging for prostate radiotherapy dose calculation. Phys Med Biol 2023; 68. [PMID: 37054740 DOI: 10.1088/1361-6560/acccce] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 04/13/2023] [Indexed: 04/15/2023]
Abstract
OBJECTIVE Daily CBCT imaging during the course of fractionated radiotherapy treatment can enable online adaptive radiotherapy but also expose patients to a non-negligible amount of radiation dose. This work investigates the feasibility of low dose CBCT imaging capable of enabling accurate prostate radiotherapy dose calculation with only 25% projections by overcoming under-sampling artifacts and correcting CT numbers by employing cycle-consistent generative adversarial networks (cycleGAN).

Approach: Uncorrected CBCTs of 41 prostate cancer patients, acquired with ∼350 projections (CBCTorg), were retrospectively under-sampled to 25% dose images (CBCTLD) with only ∼90 projections and reconstructed using FDK. We adapted a cycleGAN including shape loss to translate CBCTLDinto planning CT (pCT) equivalent images (CBCTLD_GAN). An alternative cycleGAN with a generator residual connection was implemented to improve anatomical fidelity (CBCTLD_ResGAN).

Unpaired 4-fold cross-validation (33 patients) was performed to allow using the median of 4 models as output. Deformable image registration was used to generate virtual CTs (vCT) for Hounsfield units (HU) accuracy evaluation on 8 additional test patients. Volumetric modulated arc therapy (VMAT) plans were optimized on vCT, and recalculated on CBCTLD_GANand CBCTLD_ResGANto determine dose calculation accuracy. CBCTLD_GAN, CBCTLD_ResGANand CBCTorgwere registered to pCT and residual shifts were analyzed. Bladder and rectum were manually contoured on CBCTLD_GAN, CBCTLD_ResGANand CBCTorgand compared in terms of dice similarity coefficient (DSC), average and 95th percentile Hausdorff distance (HDavg, HD95).

Main results: The mean absolute error decreased from 126 HU for CBCTLDto 55 HU for CBCTLD_GANand 44 HU for CBCTLD_ResGAN. For PTV, the median differences of D98%, D50%and D2%comparing CBCTLD_GANto vCT were 0.3%, 0.3% and 0.3%, and comparing CBCTLD_ResGANto vCT were 0.4%, 0.3% and 0.4%. Dose accuracy was high with both 2% dose difference pass rates of 99% (10% dose threshold). Compared to CBCTorg-to-pCT registration, the majority of mean absolute differences of rigid transformation parameters were less than 0.20 mm/ 0.20°. For bladder and rectum, the DSC were 0.88 and 0.77 for CBCTLD_GANand 0.92 and 0.87 for CBCTLD_ResGANcompared to CBCTorg, and HDavgwere 1.34 mm and 1.93 mm for CBCTLD_GAN, and 0.90 mm and 1.05 mm for CBCTLD_ResGAN. The computational time was ∼2 s per patient.

Significance: This study investigated the feasibility of adapting two cycleGAN models to simultaneously remove under-sampling artifacts and correct image intensities of 25% dose CBCT images. High accuracy on dose calculation, HU and patient alignment were achieved. CBCTLD_ResGANachieved better anatomical fidelity.
Collapse
Affiliation(s)
- Yan Chi Ivy Chan
- Radiation Oncology, University Hospital Munich, Marchioninistr. 15, Munchen, 81377, GERMANY
| | - Minglun Li
- Radiation Oncology, University Hospital Munich, Marchioninistr. 15, Munchen, Bayern, 81377, GERMANY
| | - Katia Parodi
- Medical Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, Garching b. München, 85748 Garching, GERMANY
| | - Claus Belka
- Radiation Oncology, University Hospital Munich, Marchioninistr. 15, Munchen, Bayern, 81377, GERMANY
| | - Guillaume Landry
- Radiation Oncology, University Hospital Munich, Marchioninistr. 15, Munchen, Bayern, 81377, GERMANY
| | - Christopher Kurz
- Radiation Oncology, University Hospital Munich, Marchioninistr. 15, Munich, Bavaria, 81377, GERMANY
| |
Collapse
|
19
|
Klaar R, Rabe M, Gaass T, Schneider MJ, Benlala I, Eze C, Corradini S, Belka C, Landry G, Kurz C, Dinkel J. Ventilation and perfusion MRI at a 0.35 T MR-Linac: feasibility and reproducibility study. Radiat Oncol 2023; 18:58. [PMID: 37013541 PMCID: PMC10069152 DOI: 10.1186/s13014-023-02244-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 03/07/2023] [Indexed: 04/05/2023] Open
Abstract
BACKGROUND Hybrid devices that combine radiation therapy and MR-imaging have been introduced in the clinical routine for the treatment of lung cancer. This opened up not only possibilities in terms of accurate tumor tracking, dose delivery and adapted treatment planning, but also functional lung imaging. The aim of this study was to show the feasibility of Non-uniform Fourier Decomposition (NuFD) MRI at a 0.35 T MR-Linac as a potential treatment response assessment tool, and propose two signal normalization strategies for enhancing the reproducibility of the results. METHODS Ten healthy volunteers (median age 28 ± 8 years, five female, five male) were repeatedly scanned at a 0.35 T MR-Linac using an optimized 2D+t balanced steady-state free precession (bSSFP) sequence for two coronal slice positions. Image series were acquired in normal free breathing with breaks inside and outside the scanner as well as deep and shallow breathing. Ventilation- and perfusion-weighted maps were generated for each image series using NuFD. For intra-volunteer ventilation map reproducibility, a normalization factor was defined based on the linear correlation of the ventilation signal and diaphragm position of each scan as well as the diaphragm motion amplitude of a reference scan. This allowed for the correction of signal dependency on the diaphragm motion amplitude, which varies with breathing patterns. The second strategy, which can be used for ventilation and perfusion, eliminates the dependency on the signal amplitude by normalizing the ventilation/perfusion maps with the average ventilation/perfusion signal within a selected region-of-interest (ROI). The position and size dependency of this ROI was analyzed. To evaluate the performance of both approaches, the normalized ventilation/perfusion-weighted maps were compared and the deviation of the mean ventilation/perfusion signal from the reference was calculated for each scan. Wilcoxon signed-rank tests were performed to test whether the normalization methods can significantly improve the reproducibility of the ventilation/perfusion maps. RESULTS The ventilation- and perfusion-weighted maps generated with the NuFD algorithm demonstrated a mostly homogenous distribution of signal intensity as expected for healthy volunteers regardless of the breathing maneuver and slice position. Evaluation of the ROI's size and position dependency showed small differences in the performance. Applying both normalization strategies improved the reproducibility of the ventilation by reducing the median deviation of all scans to 9.1%, 5.7% and 8.6% for the diaphragm-based, the best and worst performing ROI-based normalization, respectively, compared to 29.5% for the non-normalized scans. The significance of this improvement was confirmed by the Wilcoxon signed rank test with [Formula: see text] at [Formula: see text]. A comparison of the techniques against each other revealed a significant difference in the performance between best ROI-based normalization and worst ROI ([Formula: see text]) and between best ROI-based normalization and scaling factor ([Formula: see text]), but not between scaling factor and worst ROI ([Formula: see text]). Using the ROI-based approach for the perfusion-maps, the uncorrected deviation of 10.2% was reduced to 5.3%, which was shown to be significant ([Formula: see text]). CONCLUSIONS Using NuFD for non-contrast enhanced functional lung MRI at a 0.35 T MR-Linac is feasible and produces plausible ventilation- and perfusion-weighted maps for volunteers without history of chronic pulmonary diseases utilizing different breathing patterns. The reproducibility of the results in repeated scans significantly benefits from the introduction of the two normalization strategies, making NuFD a potential candidate for fast and robust early treatment response assessment of lung cancer patients during MR-guided radiotherapy.
Collapse
Affiliation(s)
- Rabea Klaar
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
- Comprehensive Pneumology Center (CPC-M), Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Moritz Rabe
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Thomas Gaass
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
- Comprehensive Pneumology Center (CPC-M), Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Moritz J. Schneider
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
- Comprehensive Pneumology Center (CPC-M), Member of the German Center for Lung Research (DZL), Munich, Germany
- Antaros Medical AB, BioVenture Hub, Mölndal, Sweden
| | - Ilyes Benlala
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
- Comprehensive Pneumology Center (CPC-M), Member of the German Center for Lung Research (DZL), Munich, Germany
- Univ. Bordeaux, Centre de Recherche Cardio-thoracique de Bordeaux, F-33600 Pessac, France
- CHU Bordeaux, Service d’Imagerie Thoracique et Cardiovasculaire, Service des Maladies Respiratoires, Service d’Exploration Fonctionnelle Respiratoire, Unité de Pneumologie Pédiatrique, CIC 1401, F-33600 Pessac, France
- INSERM, U1045, Centre de Recherche Cardio-thoracique de Bordeaux, F-33600 Pessac, France
| | - Chukwuka Eze
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Stefanie Corradini
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Claus Belka
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), Munich, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Julien Dinkel
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
- Comprehensive Pneumology Center (CPC-M), Member of the German Center for Lung Research (DZL), Munich, Germany
| |
Collapse
|
20
|
Kawula M, Hadi I, Nierer L, Vagni M, Cusumano D, Boldrini L, Placidi L, Corradini S, Belka C, Landry G, Kurz C. Patient-specific transfer learning for auto-segmentation in adaptive 0.35 T MRgRT of prostate cancer: a bi-centric evaluation. Med Phys 2023; 50:1573-1585. [PMID: 36259384 DOI: 10.1002/mp.16056] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 09/23/2022] [Accepted: 09/25/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Online adaptive radiation therapy (RT) using hybrid magnetic resonance linear accelerators (MR-Linacs) can administer a tailored radiation dose at each treatment fraction. Daily MR imaging followed by organ and target segmentation adjustments allow to capture anatomical changes, improve target volume coverage, and reduce the risk of side effects. The introduction of automatic segmentation techniques could help to further improve the online adaptive workflow by shortening the re-contouring time and reducing intra- and inter-observer variability. In fractionated RT, prior knowledge, such as planning images and manual expert contours, is usually available before irradiation, but not used by current artificial intelligence-based autocontouring approaches. PURPOSE The goal of this study was to train convolutional neural networks (CNNs) for automatic segmentation of bladder, rectum (organs at risk, OARs), and clinical target volume (CTV) for prostate cancer patients treated at 0.35 T MR-Linacs. Furthermore, we tested the CNNs generalization on data from independent facilities and compared them with the MR-Linac treatment planning system (TPS) propagated structures currently used in clinics. Finally, expert planning delineations were utilized for patient- (PS) and facility-specific (FS) transfer learning to improve auto-segmentation of CTV and OARs on fraction images. METHODS In this study, data from fractionated treatments at 0.35 T MR-Linacs were leveraged to develop a 3D U-Net-based automatic segmentation. Cohort C1 had 73 planning images and cohort C2 had 19 planning and 240 fraction images. The baseline models (BMs) were trained solely on C1 planning data using 53 MRIs for training and 10 for validation. To assess their accuracy, the models were tested on three data subsets: (i) 10 C1 planning images not used for training, (ii) 19 C2 planning, and (iii) 240 C2 fraction images. BMs also served as a starting point for FS and PS transfer learning, where the planning images from C2 were used for network parameter fine tuning. The segmentation output of the different trained models was compared against expert ground truth by means of geometric metrics. Moreover, a trained physician graded the network segmentations as well as the segmentations propagated by the clinical TPS. RESULTS The BMs showed dice similarity coefficients (DSC) of 0.88(4) and 0.93(3) for the rectum and the bladder, respectively, independent of the facility. CTV segmentation with the BM was the best for intermediate- and high-risk cancer patients from C1 with DSC=0.84(5) and worst for C2 with DSC=0.74(7). The PS transfer learning brought a significant improvement in the CTV segmentation, yielding DSC=0.72(4) for post-prostatectomy and low-risk patients and DSC=0.88(5) for intermediate- and high-risk patients. The FS training did not improve the segmentation accuracy considerably. The physician's assessment of the TPS-propagated versus network-generated structures showed a clear advantage of the latter. CONCLUSIONS The obtained results showed that the presented segmentation technique has potential to improve automatic segmentation for MR-guided RT.
Collapse
Affiliation(s)
- Maria Kawula
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Indrawati Hadi
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Lukas Nierer
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Marica Vagni
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy
| | - Davide Cusumano
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy
| | - Luca Boldrini
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy
| | - Lorenzo Placidi
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy
| | - Stefanie Corradini
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Claus Belka
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), Munich, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| |
Collapse
|
21
|
Rabe M, Palacios MA, van Sörnsen de Koste JR, Eze C, Hillbrand M, Belka C, Landry G, Senan S, Kurz C. Comparison of MR-guided radiotherapy accumulated doses for central lung tumors with non-adaptive and online adaptive proton therapy. Med Phys 2023; 50:2625-2636. [PMID: 36810708 DOI: 10.1002/mp.16319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 01/18/2023] [Accepted: 02/07/2023] [Indexed: 02/24/2023] Open
Abstract
BACKGROUND Stereotactic body radiation therapy (SBRT) of central lung tumors with photon or proton therapy has a risk of increased toxicity. Treatment planning studies comparing accumulated doses for state-of-the-art treatment techniques, such as MR-guided radiotherapy (MRgRT) and intensity modulated proton therapy (IMPT), are currently lacking. PURPOSE We conducted a comparison of accumulated doses for MRgRT, robustly optimized non-adaptive IMPT, and online adaptive IMPT for central lung tumors. A special focus was set on analyzing the accumulated doses to the bronchial tree, a parameter linked to high-grade toxicities. METHODS Data of 18 early-stage central lung tumor patients, treated at a 0.35 T MR-linac in eight or five fractions, were analyzed. Three gated treatment scenarios were compared: (S1) online adaptive MRgRT, (S2) non-adaptive IMPT, and (S3) online adaptive IMPT. The treatment plans were recalculated or reoptimized on the daily imaging data acquired during MRgRT, and accumulated over all treatment fractions. Accumulated dose-volume histogram (DVH) parameters of the gross tumor volume (GTV), lung, heart, and organs-at-risk (OARs) within 2 cm of the planning target volume (PTV) were extracted for each scenario and compared in Wilcoxon signed-rank tests between S1 & S2, and S1 & S3. RESULTS The accumulated GTV D98% was above the prescribed dose for all patients and scenarios. Significant reductions (p < 0.05) of the mean ipsilateral lung dose (S2: -8%; S3: -23%) and mean heart dose (S2: -79%; S3: -83%) were observed for both proton scenarios compared to S1. The bronchial tree D0.1cc was significantly lower for S3 (S1: 48.1 Gy; S3: 39.2 Gy; p = 0.005), but not significantly different for S2 (S2: 45.0 Gy; p = 0.094), compared to S1. The D0.1cc for S2 and S3 compared to S1 was significantly (p < 0.05) smaller for OARs within 1-2 cm of the PTV (S1: 30.2 Gy; S2: 24.6 Gy; S3: 23.1 Gy), but not significantly different for OARs within 1 cm of the PTV. CONCLUSIONS A significant dose sparing potential of non-adaptive and online adaptive proton therapy compared to MRgRT for OARs in close, but not direct proximity of central lung tumors was identified. The near-maximum dose to the bronchial tree was not significantly different for MRgRT and non-adaptive IMPT. Online adaptive IMPT achieved significantly lower doses to the bronchial tree compared to MRgRT.
Collapse
Affiliation(s)
- Moritz Rabe
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Miguel A Palacios
- Department of Radiation Oncology, Amsterdam University Medical Centers, location VUmc, Amsterdam, The Netherlands
| | - John R van Sörnsen de Koste
- Department of Radiation Oncology, Amsterdam University Medical Centers, location VUmc, Amsterdam, The Netherlands
| | - Chukwuka Eze
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Martin Hillbrand
- Institut für Radio-Onkologie, Kantonsspital Graubünden, Chur, Switzerland
| | - Claus Belka
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Suresh Senan
- Department of Radiation Oncology, Amsterdam University Medical Centers, location VUmc, Amsterdam, The Netherlands
| | - Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| |
Collapse
|
22
|
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] [What about the content of this article? (0)] [Affiliation(s)] [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.
Collapse
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.
| |
Collapse
|
23
|
Graham P, Kurz C, Batamula C. Finding Vygotsky in Early Childhood Deaf Education: Sociocultural Bodies and Conversations. Am Ann Deaf 2023; 168:80-101. [PMID: 38588087 DOI: 10.1353/aad.2023.a904168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/10/2024]
Abstract
Children, including those who are deaf, become aware of and learn about their environments through playing and social and cultural interactions. For most deaf children, preschool classrooms are optimal spaces for these interactions to occur, but only if they can fully engage with this environment. We discuss the need for and constituent aspects of full access to learning in these environments for deaf children. Vygotsky's sociocultural theory is employed chiefly as the basis for exploring and analyzing useful strategies for educators and families of deaf children. Our analysis focuses on processes in which individuals create knowledge through interacting with other people and the environment, a core emphasis of our work. We also discuss that, concomitant with full access to linguistic and social opportunities, deaf preschoolers develop a stronger sense of self, which leads to the development of cultures and languages in and out of their families.
Collapse
|
24
|
Hu G, Niepel K, Risch F, Kurz C, Würl M, Kröncke T, Schwarz F, Parodi K, Landry G. Assessment of quantitative information for radiation therapy at a first-generation clinical photon-counting computed tomography scanner. Front Oncol 2022; 12:970299. [PMID: 36185297 PMCID: PMC9515409 DOI: 10.3389/fonc.2022.970299] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 08/17/2022] [Indexed: 11/28/2022] Open
Abstract
As one of the latest developments in X-ray computed tomography (CT), photon-counting technology allows spectral detection, demonstrating considerable advantages as compared to conventional CT. In this study, we investigated the use of a first-generation clinical photon-counting computed tomography (PCCT) scanner and estimated proton relative (to water) stopping power (RSP) of tissue-equivalent materials from virtual monoenergetic reconstructions provided by the scanner. A set of calibration and evaluation tissue-equivalent inserts were scanned at 120 kVp. Maps of relative electron density (RED) and effective atomic number (EAN) were estimated from the reconstructed virtual monoenergetic images (VMI) using an approach previously applied to a spectral CT scanner with dual-layer detector technology, which allows direct calculation of RSP using the Bethe-Bloch formula. The accuracy of RED, EAN, and RSP was evaluated by root-mean-square errors (RMSE) averaged over the phantom inserts. The reference RSP values were obtained experimentally using a water column in an ion beam. For RED and EAN, the reference values were calculated based on the mass density and the chemical composition of the inserts. Different combinations of low- and high-energy VMIs were investigated in this study, ranging from 40 to 190 keV. The overall lowest error was achieved using VMIs at 60 and 180 keV, with an RSP accuracy of 1.27% and 0.71% for the calibration and the evaluation phantom, respectively.
Collapse
Affiliation(s)
- Guyue Hu
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU), Garching bei München, Germany
- *Correspondence: Guyue Hu,
| | - Katharina Niepel
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU), Garching bei München, Germany
| | - Franka Risch
- Department of Diagnostic and Interventional Radiology, Universitätsklinikum Augsburg, Augsburg, Germany
| | | | - Matthias Würl
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU), Garching bei München, Germany
| | - Thomas Kröncke
- Department of Diagnostic and Interventional Radiology, Universitätsklinikum Augsburg, Augsburg, Germany
| | - Florian Schwarz
- Department of Diagnostic and Interventional Radiology, Universitätsklinikum Augsburg, Augsburg, Germany
- Medical Faculty, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Katia Parodi
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU), Garching bei München, Germany
| | - Guillaume Landry
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU), Garching bei München, Germany
- Department of Radiation Oncology, LMU Klinikum, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
| |
Collapse
|
25
|
Da Silva Mendes V, Reiner M, Huang L, Reitz D, Straub K, Corradini S, Niyazi M, Belka C, Kurz C, Landry G, Freislederer P. ExacTrac Dynamic workflow evaluation: Combined surface optical/thermal imaging and X-ray positioning. J Appl Clin Med Phys 2022; 23:e13754. [PMID: 36001389 DOI: 10.1002/acm2.13754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 07/07/2022] [Accepted: 07/19/2022] [Indexed: 11/09/2022] Open
Abstract
In modern radiotherapy (RT), especially for stereotactic radiotherapy or stereotactic radiosurgery treatments, image guidance is essential. Recently, the ExacTrac Dynamic (EXTD) system, a new combined surface-guided RT and image-guided RT (IGRT) system for patient positioning, monitoring, and tumor targeting, was introduced in clinical practice. The purpose of this study was to provide more information about the geometric accuracy of EXTD and its workflow in a clinical environment. The surface optical/thermal- and the stereoscopic X-ray imaging positioning systems of EXTD was evaluated and compared to cone-beam computed tomography (CBCT). Additionally, the congruence with the radiation isocenter was tested. A Winston Lutz test was executed several times over 1 year, and repeated end-to-end positioning tests were performed. The magnitude of the displacements between all systems, CBCT, stereoscopic X-ray, optical-surface imaging, and MV portal imaging was within the submillimeter range, suggesting that the image guidance provided by EXTD is accurate at any couch angle. Additionally, results from the evaluation of 14 patients with intracranial tumors treated with open-face masks are reported, and limited differences with a maximum of 0.02 mm between optical/thermal- and stereoscopic X-ray imaging were found. As the optical/thermal positioning system showed a comparable accuracy to other IGRT systems, and due to its constant monitoring capability, it can be an efficient tool for detecting intra-fractional motion and for real-time tracking of the surface position during RT.
Collapse
Affiliation(s)
| | - Michael Reiner
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Lili Huang
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Daniel Reitz
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Katrin Straub
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Stefanie Corradini
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Maximilian Niyazi
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Claus Belka
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Philipp Freislederer
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| |
Collapse
|
26
|
Wang Y, Lombardo E, Avanzo M, Zschaek S, Weingärtner J, Holzgreve A, Albert NL, Marschner S, Fanetti G, Franchin G, Stancanello J, Walter F, Corradini S, Niyazi M, Lang J, Belka C, Riboldi M, Kurz C, Landry G. Deep learning based time-to-event analysis with PET, CT and joint PET/CT for head and neck cancer prognosis. Comput Methods Programs Biomed 2022; 222:106948. [PMID: 35752119 DOI: 10.1016/j.cmpb.2022.106948] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 06/07/2022] [Accepted: 06/08/2022] [Indexed: 05/02/2023]
Abstract
OBJECTIVES Recent studies have shown that deep learning based on pre-treatment positron emission tomography (PET) or computed tomography (CT) is promising for distant metastasis (DM) and overall survival (OS) prognosis in head and neck cancer (HNC). However, lesion segmentation is typically required, resulting in a predictive power susceptible to variations in primary and lymph node gross tumor volume (GTV) segmentation. This study aimed at achieving prognosis without GTV segmentation, and extending single modality prognosis to joint PET/CT to allow investigating the predictive performance of combined- compared to single-modality inputs. METHODS We employed a 3D-Resnet combined with a time-to-event outcome model to incorporate censoring information. We focused on the prognosis of DM and OS for HNC patients. For each clinical endpoint, five models with PET and/or CT images as input were compared: PET-GTV, PET-only, CT-GTV, CT-only, and PET/CT-GTV models, where -GTV indicates that the corresponding images were masked using the GTV contour. Publicly available delineated CT and PET scans from 4 different Canadian hospitals (293) and the MAASTRO clinic (74) were used for training by 3-fold cross-validation (CV). For independent testing, we used 110 patients from a collaborating institution. The predictive performance was evaluated via Harrell's Concordance Index (HCI) and Kaplan-Meier curves. RESULTS In a 5-year time-to-event analysis, all models could produce CV HCIs with median values around 0.8 for DM and 0.7 for OS. The best performance was obtained with the PET-only model, achieving a median testing HCI of 0.82 for DM and 0.69 for OS. Compared with the PET/CT-GTV model, the PET-only still had advantages of up to 0.07 in terms of testing HCI. The Kaplan-Meier curves and corresponding log-rank test results also demonstrated significant stratification capability of our models for the testing cohort. CONCLUSION Deep learning-based DM and OS time-to-event models showed predictive capability and could provide indications for personalized RT. The best predictive performance achieved by the PET-only model suggested GTV segmentation might be less relevant for PET-based prognosis.
Collapse
Affiliation(s)
- Yiling Wang
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany; Sichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, China
| | - Elia Lombardo
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Michele Avanzo
- Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Medical Physics, Aviano, Italy
| | - Sebastian Zschaek
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Radiation Oncology, Berlin, Germany
| | - Julian Weingärtner
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Radiation Oncology, Berlin, Germany
| | - Adrien Holzgreve
- University Hospital, LMU Munich, Nuclear Medicine, Munich, Germany
| | | | - Sebastian Marschner
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Giuseppe Fanetti
- Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Radiation Oncology, Aviano, Italy
| | - Giovanni Franchin
- Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Radiation Oncology, Aviano, Italy
| | - Joseph Stancanello
- ELEKTA SAS, Clinical Applications Development, Boulogne-Billancourt, France
| | - Franziska Walter
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Stefanie Corradini
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Maximilian Niyazi
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Jinyi Lang
- Sichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, China
| | - Claus Belka
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany; German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
| | - Marco Riboldi
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Garching, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany.
| |
Collapse
|
27
|
Crane LD, Decker RA, Flaaen A, Hamins-Puertolas A, Kurz C. Business exit during the COVID-19 pandemic: Non-traditional measures in historical context. J Macroecon 2022; 72:103419. [PMID: 35342211 PMCID: PMC8938302 DOI: 10.1016/j.jmacro.2022.103419] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 03/02/2022] [Accepted: 03/04/2022] [Indexed: 05/05/2023]
Abstract
Lags in official data releases have forced economists and policymakers to leverage "alternative" or "non-traditional" data to measure business exit resulting from the COVID-19 pandemic. We first review official data on business exit in recent decades to place the alternative measures of exit within historical context. For the U.S., business exit is fairly common, with about 7.5 percent of firms exiting annually in recent years. The high level of exit is driven by very small firms and establishments. We then explore a range of alternative measures of business exit, including novel measures based on paycheck issuance and phone-tracking data, which indicate exit was elevated in certain sectors during the first year of the pandemic. That said, we find many industries have likely seen lower-than-usual exit rates, and exiting businesses do not appear to represent a large share of U.S. employment. As a result, exit appears lower than widespread expectations from early in the pandemic.
Collapse
|
28
|
Lombardo E, Rabe M, Xiong Y, Nierer L, Cusumano D, Placidi L, Boldrini L, Corradini S, Niyazi M, Belka C, Riboldi M, Kurz C, Landry G. Offline and online LSTM networks for respiratory motion prediction in MR-guided radiotherapy. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac60b7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 03/24/2022] [Indexed: 11/11/2022]
Abstract
Abstract
Objective. Gated beam delivery is the current clinical practice for respiratory motion compensation in MR-guided radiotherapy, and further research is ongoing to implement tracking. To manage intra-fractional motion using multileaf collimator tracking the total system latency needs to be accounted for in real-time. In this study, long short-term memory (LSTM) networks were optimized for the prediction of superior–inferior tumor centroid positions extracted from clinically acquired 2D cine MRIs. Approach. We used 88 patients treated at the University Hospital of the LMU Munich for training and validation (70 patients, 13.1 h), and for testing (18 patients, 3.0 h). Three patients treated at Fondazione Policlinico Universitario Agostino Gemelli were used as a second testing set (1.5 h). The performance of the LSTMs in terms of root mean square error (RMSE) was compared to baseline linear regression (LR) models for forecasted time spans of 250 ms, 500 ms and 750 ms. Both the LSTM and the LR were trained with offline (offline LSTM and offline LR) and online schemes (offline+online LSTM and online LR), the latter to allow for continuous adaptation to recent respiratory patterns. Main results. We found the offline+online LSTM to perform best for all investigated forecasts. Specifically, when predicting 500 ms ahead it achieved a mean RMSE of 1.20 mm and 1.00 mm, while the best performing LR model achieved a mean RMSE of 1.42 mm and 1.22 mm for the LMU and Gemelli testing set, respectively. Significance. This indicates that LSTM networks have potential as respiratory motion predictors and that continuous online re-optimization can enhance their performance.
Collapse
|
29
|
Lombardo E, Xiong Y, Rabe M, Nierer L, Cusumano D, Placidi L, Boldrini L, Corradini S, Belka C, Riboldi M, Kurz C, Landry G. OC-0043 LSTM networks for real-time respiratory motion prediction for a 0.35 T MR-linac. Radiother Oncol 2022. [DOI: 10.1016/s0167-8140(22)02462-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
30
|
Kawula M, Hadi I, Cusumano D, Boldrini L, Placidi L, Corradini S, Belka C, Landry G, Kurz C. PD-0067 AI auto-segmentation for MRgRT of prostate cancer: evaluating 269 MR images from two institutes. Radiother Oncol 2022. [DOI: 10.1016/s0167-8140(22)02737-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
31
|
Xiong Y, Rabe M, Nierer L, Corradini S, Belka C, Riboldi M, Landry G, Kurz C. PD-0227 reconstructing the dosimetric impact of intra-fractional prostate motion in MR-guided radiotherapy. Radiother Oncol 2022. [DOI: 10.1016/s0167-8140(22)02782-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
32
|
Wang Y, Lombardo E, Zschaek S, Weingärtner J, Holzgreve A, Albert N, Marschner S, Avanzo M, Fanetti G, Franchin G, Stancanello J, Walter F, Corradini S, Niyazi M, Belka C, Riboldi M, Kurz C, Landry G. OC-0460 Deep learning based time to event analysis with PET, CT and joint PET/CT for H&N cancer prognosis. Radiother Oncol 2022. [DOI: 10.1016/s0167-8140(22)02596-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
33
|
Rabe M, Palacios M, van Sörnsen de Koste J, Eze C, Hillbrand M, Belka C, Landry G, Senan S, Kurz C. PD-0398 Accumulated dose comparison of stereotactic MRgRT and proton therapy for central lung tumors. Radiother Oncol 2022. [DOI: 10.1016/s0167-8140(22)02833-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
34
|
Nierer L, Kamp F, Reiner M, Corradini S, Rabe M, Dietrich O, Parodi K, Belka C, Kurz C, Landry G. Evaluation of an anthropomorphic ion chamber and 3D gel dosimetry head phantom at a 0.35 T MR-linac using separate 1.5 T MR-scanners for gel readout. Z Med Phys 2022; 32:312-325. [PMID: 35305857 PMCID: PMC9948847 DOI: 10.1016/j.zemedi.2022.01.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 01/27/2022] [Accepted: 01/31/2022] [Indexed: 12/22/2022]
Abstract
PURPOSE To date, no universally accepted technique for the evaluation of the overall dosimetric performance of hybrid integrated magnetic resonance imaging (MR) - linear accelerators (linacs) is available. We report on the suitability and reliability of a novel phantom with modular inserts for combined polymer gel (PG) and ionisation chamber (IC) measurements at a 0.35 T MR-linac. METHODS Three 3D-printed, modular head phantoms, based on real patient anatomy, were used for repeated (2 times) PG irradiations of cranial treatment plans on a 0.35 T MR-linac. The PG readout was performed on two 1.5 T diagnostic MR-scanners to reduce scanning time. The PG dose volumes were normalised to the IC dose (normalised dose N1) and to the median planning target volume dose (normalised dose N2). Linearity of the PG dose response was validated and dose profiles, centres of mass (COM) of the 95% isodoses and dose volume histograms (DVH) were compared between planned and measured dose distributions and a 3D gamma analysis was performed. RESULTS Dose linearity of the PG was good (R2> 0.99 for all linear fit functions). High agreement was found between planned and measured dose volumes in the dose profiles and DVHs. The largest dose deviation was found in the intermediate dose region (mean dose deviation 0.2Gy; 5.6%). A mean COM offset of 1.2mm indicated high spatial accuracy. Mean 3D gamma passing rates (2%, 2mm) of 83.3% for N1 and 91.6% for N2 dose distributions were determined. When comparing repeated PG measurements to each other, a mean gamma passing rate of 95.7% was found. CONCLUSION The new modular phantom was found practical for use at a 0.35 T MR-linac. In contrast to the high dose region, larger mean deviations were found in the mid dose range. The PG measurements showed high reproducibility. The MR-linac performed well in a non-adaptive setting in terms of spatial and dosimetric accuracy.
Collapse
Affiliation(s)
- Lukas Nierer
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany.
| | - Florian Kamp
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany; Department of Radiation Oncology, University Hospital Cologne, Kerpener Str. 62, 50937 Cologne, Germany
| | - Michael Reiner
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany
| | - Stefanie Corradini
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany
| | - Moritz Rabe
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany
| | - Olaf Dietrich
- Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany
| | - Katia Parodi
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, 85748 Garching, Germany
| | - Claus Belka
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany; German Cancer Consortium (DKTK), partner site Munich, Munich, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany
| |
Collapse
|
35
|
Nierer L, Eze C, da Silva Mendes V, Braun J, Thum P, von Bestenbostel R, Kurz C, Landry G, Reiner M, Niyazi M, Belka C, Corradini S. Dosimetric benefit of MR-guided online adaptive radiotherapy in different tumor entities: liver, lung, abdominal lymph nodes, pancreas and prostate. Radiat Oncol 2022; 17:53. [PMID: 35279185 PMCID: PMC8917666 DOI: 10.1186/s13014-022-02021-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 02/27/2022] [Indexed: 01/18/2023] Open
Abstract
Background Hybrid magnetic resonance (MR)-Linac systems have recently been introduced into clinical practice. The systems allow online adaption of the treatment plan with the aim of compensating for interfractional anatomical changes. The aim of this study was to evaluate the dose volume histogram (DVH)-based dosimetric benefits of online adaptive MR-guided radiotherapy (oMRgRT) across different tumor entities and to investigate which subgroup of plans improved the most from adaption. Methods Fifty patients treated with oMRgRT for five different tumor entities (liver, lung, multiple abdominal lymph nodes, pancreas, and prostate) were included in this retrospective analysis. Various target volume (gross tumor volume GTV, clinical target volume CTV, and planning target volume PTV) and organs at risk (OAR) related DVH parameters were compared between the dose distributions before and after plan adaption. Results All subgroups clearly benefited from online plan adaption in terms of improved PTV coverage. For the liver, lung and abdominal lymph nodes cases, a consistent improvement in GTV coverage was found, while many fractions of the prostate subgroup showed acceptable CTV coverage even before plan adaption. The largest median improvements in GTV near-minimum dose (D98%) were found for the liver (6.3%, p < 0.001), lung (3.9%, p < 0.001), and abdominal lymph nodes (6.8%, p < 0.001) subgroups. Regarding OAR sparing, the largest median OAR dose reduction during plan adaption was found for the pancreas subgroup (-87.0%). However, in the pancreas subgroup an optimal GTV coverage was not always achieved because sparing of OARs was prioritized. Conclusion With online plan adaptation, it was possible to achieve significant improvements in target volume coverage and OAR sparing for various tumor entities and account for interfractional anatomical changes.
Collapse
|
36
|
Kawula M, Purice D, Li M, Vivar G, Ahmadi SA, Parodi K, Belka C, Landry G, Kurz C. Dosimetric impact of deep learning-based CT auto-segmentation on radiation therapy treatment planning for prostate cancer. Radiat Oncol 2022; 17:21. [PMID: 35101068 PMCID: PMC8805311 DOI: 10.1186/s13014-022-01985-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 01/10/2022] [Indexed: 12/19/2022] Open
Abstract
Background The evaluation of automatic segmentation algorithms is commonly performed using geometric metrics. An analysis based on dosimetric parameters might be more relevant in clinical practice but is often lacking in the literature. The aim of this study was to investigate the impact of state-of-the-art 3D U-Net-generated organ delineations on dose optimization in radiation therapy (RT) for prostate cancer patients. Methods A database of 69 computed tomography images with prostate, bladder, and rectum delineations was used for single-label 3D U-Net training with dice similarity coefficient (DSC)-based loss. Volumetric modulated arc therapy (VMAT) plans have been generated for both manual and automatic segmentations with the same optimization settings. These were chosen to give consistent plans when applying perturbations to the manual segmentations. Contours were evaluated in terms of DSC, average and 95% Hausdorff distance (HD). Dose distributions were evaluated with the manual segmentation as reference using dose volume histogram (DVH) parameters and a 3%/3 mm gamma-criterion with 10% dose cut-off. A Pearson correlation coefficient between DSC and dosimetric metrics, i.e. gamma index and DVH parameters, has been calculated. Results 3D U-Net-based segmentation achieved a DSC of 0.87 (0.03) for prostate, 0.97 (0.01) for bladder and 0.89 (0.04) for rectum. The mean and 95% HD were below 1.6 (0.4) and below 5 (4) mm, respectively. The DVH parameters, V\documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$_{60/65/70\,{\mathrm{Gy}}}$$\end{document}60/65/70Gy for the bladder and V\documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$_{50/65/70\,{\mathrm{Gy}}}$$\end{document}50/65/70Gy for the rectum, showed agreement between dose distributions within \documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$\pm \, 5\%$$\end{document}±5% and \documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$\pm \,2\%$$\end{document}±2%, respectively. The D\documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$_{98/2\%}$$\end{document}98/2% and V\documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$_{95\%}$$\end{document}95%, for prostate and its 3 mm expansion (surrogate clinical target volume) showed agreement with the reference dose distribution within 2% and 3 Gy with the exception of one case. The average gamma pass-rate was 85%. The comparison between geometric and dosimetric metrics showed no strong statistically significant correlation. Conclusions The 3D U-Net developed for this work achieved state-of-the-art geometrical performance. Analysis based on clinically relevant DVH parameters of VMAT plans demonstrated neither excessive dose increase to OARs nor substantial under/over-dosage of the target in all but one case. Yet the gamma analysis indicated several cases with low pass rates. The study highlighted the importance of adding dosimetric analysis to the standard geometric evaluation.
Collapse
|
37
|
Marschner SN, Lombardo E, Minibek L, Holzgreve A, Kaiser L, Albert NL, Kurz C, Riboldi M, Späth R, Baumeister P, Niyazi M, Belka C, Corradini S, Landry G, Walter F. Risk Stratification Using 18F-FDG PET/CT and Artificial Neural Networks in Head and Neck Cancer Patients Undergoing Radiotherapy. Diagnostics (Basel) 2021; 11:diagnostics11091581. [PMID: 34573924 PMCID: PMC8468242 DOI: 10.3390/diagnostics11091581] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 08/27/2021] [Accepted: 08/28/2021] [Indexed: 12/24/2022] Open
Abstract
This study retrospectively analyzed the performance of artificial neural networks (ANN) to predict overall survival (OS) or locoregional failure (LRF) in HNSCC patients undergoing radiotherapy, based on 2-[18F]FDG PET/CT and clinical covariates. We compared predictions relying on three different sets of features, extracted from 230 patients. Specifically, (i) an automated feature selection method independent of expert rating was compared with (ii) clinical variables with proven influence on OS or LRF and (iii) clinical data plus expert-selected SUV metrics. The three sets were given as input to an artificial neural network for outcome prediction, evaluated by Harrell’s concordance index (HCI) and by testing stratification capability. For OS and LRF, the best performance was achieved with expert-based PET-features (0.71 HCI) and clinical variables (0.70 HCI), respectively. For OS stratification, all three feature sets were significant, whereas for LRF only expert-based PET-features successfully classified low vs. high-risk patients. Based on 2-[18F]FDG PET/CT features, stratification into risk groups using ANN for OS and LRF is possible. Differences in the results for different feature sets confirm the relevance of feature selection, and the key importance of expert knowledge vs. automated selection.
Collapse
Affiliation(s)
- Sebastian N. Marschner
- Department of Radiation Oncology, University Hospital, LMU Munich, 81377 Munich, Germany; (L.M.); (R.S.); (M.N.); (C.B.); (S.C.); (F.W.)
- Correspondence:
| | - Elia Lombardo
- Department of Radiation Oncology, University Hospital, LMU Munich, 81377 Munich, Germany; (L.M.); (R.S.); (M.N.); (C.B.); (S.C.); (F.W.)
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748 Garching, Germany; (E.L.); (C.K.); (M.R.); (G.L.)
| | - Lena Minibek
- Department of Radiation Oncology, University Hospital, LMU Munich, 81377 Munich, Germany; (L.M.); (R.S.); (M.N.); (C.B.); (S.C.); (F.W.)
| | - Adrien Holzgreve
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany; (A.H.); (L.K.); (N.L.A.)
| | - Lena Kaiser
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany; (A.H.); (L.K.); (N.L.A.)
| | - Nathalie L. Albert
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany; (A.H.); (L.K.); (N.L.A.)
| | - Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, 81377 Munich, Germany; (L.M.); (R.S.); (M.N.); (C.B.); (S.C.); (F.W.)
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748 Garching, Germany; (E.L.); (C.K.); (M.R.); (G.L.)
| | - Marco Riboldi
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748 Garching, Germany; (E.L.); (C.K.); (M.R.); (G.L.)
| | - Richard Späth
- Department of Radiation Oncology, University Hospital, LMU Munich, 81377 Munich, Germany; (L.M.); (R.S.); (M.N.); (C.B.); (S.C.); (F.W.)
| | - Philipp Baumeister
- Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital, LMU Munich, 81377 Munich, Germany;
| | - Maximilian Niyazi
- Department of Radiation Oncology, University Hospital, LMU Munich, 81377 Munich, Germany; (L.M.); (R.S.); (M.N.); (C.B.); (S.C.); (F.W.)
| | - Claus Belka
- Department of Radiation Oncology, University Hospital, LMU Munich, 81377 Munich, Germany; (L.M.); (R.S.); (M.N.); (C.B.); (S.C.); (F.W.)
- German Cancer Consortium (DKTK), Partner Site Munich, 81377 Munich, Germany
| | - Stefanie Corradini
- Department of Radiation Oncology, University Hospital, LMU Munich, 81377 Munich, Germany; (L.M.); (R.S.); (M.N.); (C.B.); (S.C.); (F.W.)
| | - Guillaume Landry
- Department of Radiation Oncology, University Hospital, LMU Munich, 81377 Munich, Germany; (L.M.); (R.S.); (M.N.); (C.B.); (S.C.); (F.W.)
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748 Garching, Germany; (E.L.); (C.K.); (M.R.); (G.L.)
| | - Franziska Walter
- Department of Radiation Oncology, University Hospital, LMU Munich, 81377 Munich, Germany; (L.M.); (R.S.); (M.N.); (C.B.); (S.C.); (F.W.)
| |
Collapse
|
38
|
Schmitz H, Rabe M, Janssens G, Bondesson D, Rit S, Parodi K, Belka C, Dinkel J, Kurz C, Kamp F, Landry G. Validation of proton dose calculation on scatter corrected 4D cone beam computed tomography using a porcine lung phantom. Phys Med Biol 2021; 66. [PMID: 34293737 DOI: 10.1088/1361-6560/ac16e9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 07/22/2021] [Indexed: 12/25/2022]
Abstract
Proton therapy treatment for lungs remains challenging as images enabling the detection of inter- and intra-fractional motion, which could be used for proton dose adaptation, are not readily available. 4D computed tomography (4DCT) provides high image quality but is rarely available in-room, while in-room 4D cone beam computed tomography (4DCBCT) suffers from image quality limitations stemming mostly from scatter detection. This study investigated the feasibility of using virtual 4D computed tomography (4DvCT) as a prior for a phase-per-phase scatter correction algorithm yielding a 4D scatter corrected cone beam computed tomography image (4DCBCTcor), which can be used for proton dose calculation. 4DCT and 4DCBCT scans of a porcine lung phantom, which generated reproducible ventilation, were acquired with matching breathing patterns. Diffeomorphic Morphons, a deformable image registration algorithm, was used to register the mid-position 4DCT to the mid-position 4DCBCT and yield a 4DvCT. The 4DCBCT was reconstructed using motion-aware reconstruction based on spatial and temporal regularization (MA-ROOSTER). Successively for each phase, digitally reconstructed radiographs of the 4DvCT, simulated without scatter, were exploited to correct scatter in the corresponding CBCT projections. The 4DCBCTcorwas then reconstructed with MA-ROOSTER using the corrected CBCT projections and the same settings and deformation vector fields as those already used for reconstructing the 4DCBCT. The 4DCBCTcorand the 4DvCT were evaluated phase-by-phase, performing proton dose calculations and comparison to those of a ground truth 4DCT by means of dose-volume-histograms (DVH) and gamma pass-rates (PR). For accumulated doses, DVH parameters deviated by at most 1.7% in the 4DvCT and 2.0% in the 4DCBCTcorcase. The gamma PR for a (2%, 2 mm) criterion with 10% threshold were at least 93.2% (4DvCT) and 94.2% (4DCBCTcor), respectively. The 4DCBCTcortechnique enabled accurate proton dose calculation, which indicates the potential for applicability to clinical 4DCBCT scans.
Collapse
Affiliation(s)
- Henning Schmitz
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Moritz Rabe
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | | | - David Bondesson
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Simon Rit
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69373, LYON, France
| | - Katia Parodi
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching (Munich), Germany
| | - Claus Belka
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
| | - Julien Dinkel
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany.,Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching (Munich), Germany
| | - Florian Kamp
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany.,Department of Radiation Oncology, University Hospital Cologne, Cologne, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany.,Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching (Munich), Germany
| |
Collapse
|
39
|
Kurz C. SP-0582 Online CBCT-based proton range and dose calculation. Radiother Oncol 2021. [DOI: 10.1016/s0167-8140(21)08633-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
40
|
Rogowski P, von Bestenbostel R, Walter F, Straub K, Nierer L, Landry G, Reiner M, Kurz C, Auernhammer C, Belka C, Niyazi M, Corradini S. PO-1235 Feasibility and early clinical experience of online adaptive MR-guided radiotherapy of liver tumors. Radiother Oncol 2021. [DOI: 10.1016/s0167-8140(21)07686-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
41
|
Da Silva Mendes V, Nierer L, Li M, Corradini S, Reiner M, Kamp F, Niyazi M, Kurz C, Landry G, Belka C. Dosimetric comparison of MR-linac-based IMRT and conventional VMAT treatment plans for prostate cancer. Radiat Oncol 2021; 16:133. [PMID: 34289868 PMCID: PMC8296626 DOI: 10.1186/s13014-021-01858-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 07/12/2021] [Indexed: 02/07/2023] Open
Abstract
Background The aim of this study was to evaluate and compare the performance of intensity modulated radiation therapy (IMRT) plans, planned for low-field strength magnetic resonance (MR) guided linear accelerator (linac) delivery (labelled IMRT MRL plans), and clinical conventional volumetric modulated arc therapy (VMAT) plans, for the treatment of prostate cancer (PCa). Both plans used the original planning target volume (PTV) margins. Additionally, the potential dosimetric benefits of MR-guidance were estimated, by creating IMRT MRL plans using smaller PTV margins. Materials and methods 20 PCa patients previously treated with conventional VMAT were considered. For each patient, two different IMRT MRL plans using the low-field MR-linac treatment planning system were created: one with original (orig.) PTV margins and the other with reduced (red.) PTV margins. Dose indices related to target coverage, as well as dose-volume histogram (DVH) parameters for the target and organs at risk (OAR) were compared. Additionally, the estimated treatment delivery times and the number of monitor units (MU) of each plan were evaluated. Results The dose distribution in the high dose region and the target volume DVH parameters (D98%, D50%, D2% and V95%) were similar for all three types of treatment plans, with deviations below 1% in most cases. Both IMRT MRL plans (orig. and red. PTV margins) showed similar homogeneity indices (HI), however worse values for the conformity index (CI) were also found when compared to VMAT. The IMRT MRL plans showed similar OAR sparing when the orig. PTV margins were used but a significantly better sparing was feasible when red. PTV margins were applied. Higher number of MU and longer predicted treatment delivery times were seen for both IMRT MRL plans. Conclusions A comparable plan quality between VMAT and IMRT MRL plans was achieved, when applying the same PTV margin. However, online MR-guided adaptive radiotherapy allows for a reduction of PTV margins. With a red. PTV margin, better sparing of the surrounding tissues can be achieved, while maintaining adequate target coverage. Nonetheless, longer treatment delivery times, characteristic for the IMRT technique, have to be expected.
Collapse
Affiliation(s)
- Vanessa Da Silva Mendes
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany.
| | - Lukas Nierer
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Minglun Li
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Stefanie Corradini
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Michael Reiner
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Florian Kamp
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany.,Department of Radiation Oncology, Cologne University Hospital, Cologne, Germany
| | - Maximilian Niyazi
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Claus Belka
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
| |
Collapse
|
42
|
Neppl S, Kurz C, Köpl D, Yohannes I, Schneider M, Bondesson D, Rabe M, Belka C, Dietrich O, Landry G, Parodi K, Kamp F. Measurement-based range evaluation for quality assurance of CBCT-based dose calculations in adaptive proton therapy. Med Phys 2021; 48:4148-4159. [PMID: 34032301 DOI: 10.1002/mp.14995] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 04/08/2021] [Accepted: 05/10/2021] [Indexed: 12/30/2022] Open
Abstract
PURPOSE The implementation of volumetric in-room imaging for online adaptive radiotherapy makes extensive testing of this image data for treatment planning necessary. Especially for proton beams the higher sensitivity to stopping power properties of the tissue results in more stringent requirements. Current approaches mainly focus on recalculation of the plans on the new image data, lacking experimental verification, and ignoring the impact on the plan re-optimization process. The aim of this study was to use gel and film dosimetry coupled with a three-dimensional (3D) printed head phantom (based on the planning CT of the patient) for 3D range verification of intensity-corrected cone beam computed tomography (CBCT) image data for adaptive proton therapy. METHODS Single field uniform dose pencil beam scanning proton plans were optimized for three different patients on the patients' planning CT (planCT) and the patients' intensity-corrected CBCT (scCBCT) for the same target volume using the same optimization constraints. The CBCTs were corrected on projection level using the planCT as a prior. The dose optimized on planCT and recalculated on scCBCT was compared in terms of proton range differences (80% distal fall-off, recalculation). Moreover, the dose distribution resulting from recalculation of the scCBCT-optimized plan on the planCT and the original planCT dose distribution were compared (simulation). Finally, the two plans of each patient were irradiated on the corresponding patient-specific 3D printed head phantom using gel dosimetry inserts for one patient and film dosimetry for all three patients. Range differences were extracted from the measured dose distributions. The measured and the simulated range differences were corrected for range differences originating from the initial plans and evaluated. RESULTS The simulation approach showed high agreement with the standard recalculation approach. The median values of the range differences of these two methods agreed within 0.1 mm and the interquartile ranges (IQRs) within 0.3 mm for all three patients. The range differences of the film measurement were accurately matching with the simulation approach in the film plane. The median values of these range differences deviated less than 0.1 mm and the IQRs less than 0.4 mm. For the full 3D evaluation of the gel range differences, the median value and IQR matched those of the simulation approach within 0.7 and 0.5 mm, respectively. scCBCT- and planCT-based dose distributions were found to have a range agreement better than 3 mm (median and IQR) for all considered scenarios (recalculation, simulation, and measurement). CONCLUSIONS The results of this initial study indicate that an online adaptive proton workflow based on scatter-corrected CBCT image data for head irradiations is feasible. The novel presented measurement- and simulation-based method was shown to be equivalent to the standard literature recalculation approach. Additionally, it has the capability to catch effects of image differences on the treatment plan optimization. This makes the measurement-based approach particularly interesting for quality assurance of CBCT-based online adaptive proton therapy. The observed uncertainties could be kept within those of the registration and positioning. The proposed validation could also be applied for other alternative in-room images, e.g. for MR-based pseudoCTs.
Collapse
Affiliation(s)
- Sebastian Neppl
- Department of Radiation Oncology, University Hospital, LMU Munich, 81377, Munich, Germany.,Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), 85748, Garching bei München, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, 81377, Munich, Germany.,Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), 85748, Garching bei München, Germany
| | - Daniel Köpl
- Rinecker Proton Therapy Center, 81371, Munich, Germany
| | | | - Moritz Schneider
- Department of Radiology, University Hospital, LMU Munich, 81377, Munich, Germany.,Comprehensive Pneumology Center Munich (CPC-M), German Center for Lung Research (DZL), 81377, Munich, Germany
| | - David Bondesson
- Department of Radiology, University Hospital, LMU Munich, 81377, Munich, Germany.,Comprehensive Pneumology Center Munich (CPC-M), German Center for Lung Research (DZL), 81377, Munich, Germany
| | - Moritz Rabe
- Department of Radiation Oncology, University Hospital, LMU Munich, 81377, Munich, Germany.,Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), 85748, Garching bei München, Germany
| | - Claus Belka
- Department of Radiation Oncology, University Hospital, LMU Munich, 81377, Munich, Germany.,German Cancer Consortium (DKTK), Partner site Munich, 81377, Munich, Germany
| | - Olaf Dietrich
- Department of Radiology, University Hospital, LMU Munich, 81377, Munich, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, University Hospital, LMU Munich, 81377, Munich, Germany.,Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), 85748, Garching bei München, Germany
| | - Katia Parodi
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), 85748, Garching bei München, Germany
| | - Florian Kamp
- Department of Radiation Oncology, University Hospital, LMU Munich, 81377, Munich, Germany
| |
Collapse
|
43
|
Rabe M, Paganelli C, Riboldi M, Bondesson D, Jörg Schneider M, Chmielewski T, Baroni G, Dinkel J, Reiner M, Landry G, Parodi K, Belka C, Kamp F, Kurz C. Porcine lung phantom-based validation of estimated 4D-MRI using orthogonal cine imaging for low-field MR-Linacs. Phys Med Biol 2021; 66:055006. [PMID: 33171458 DOI: 10.1088/1361-6560/abc937] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Real-time motion monitoring of lung tumors with low-field magnetic resonance imaging-guided linear accelerators (MR-Linacs) is currently limited to sagittal 2D cine magnetic resonance imaging (MRI). To provide input data for improved intrafractional and interfractional adaptive radiotherapy, the 4D anatomy has to be inferred from data with lower dimensionality. The purpose of this study was to experimentally validate a previously proposed propagation method that provides continuous time-resolved estimated 4D-MRI based on orthogonal cine MRI for a low-field MR-Linac. Ex vivo porcine lungs were injected with artificial nodules and mounted in a dedicated phantom that allows for the simulation of periodic and reproducible breathing motion. The phantom was scanned with a research version of a commercial 0.35 T MR-Linac. Respiratory-correlated 4D-MRI were reconstructed and served as ground truth images. Series of interleaved orthogonal slices in sagittal and coronal orientation, intersecting the injected targets, were acquired at 7.3 Hz. Estimated 4D-MRI at 3.65 Hz were created in post-processing using the propagation method and compared to the ground truth 4D-MRI. Eight datasets at different breathing frequencies and motion amplitudes were acquired for three porcine lungs. The overall median (95[Formula: see text] percentile) deviation between ground truth and estimated deformation vector fields was 2.3 mm (5.7 mm), corresponding to 0.7 (1.6) times the in-plane imaging resolution (3.5 × 3.5 mm2). Median (95[Formula: see text] percentile) estimated nodule position errors were 1.5 mm (3.8 mm) for nodules intersected by orthogonal slices and 2.1 mm (7.1 mm) for nodules located more than 2 cm away from either of the orthogonal slices. The estimation error depended on the breathing phase, the motion amplitude and the location of the estimated position with respect to the orthogonal slices. By using the propagation method, the 4D motion within the porcine lung phantom could be accurately and robustly estimated. The method could provide valuable information for treatment planning, real-time motion monitoring, treatment adaptation, and post-treatment evaluation of MR-guided radiotherapy treatments.
Collapse
Affiliation(s)
- Moritz Rabe
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Chiara Paganelli
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy
| | - Marco Riboldi
- Department of Medical Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching (Munich), Germany
| | - David Bondesson
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany.,Comprehensive Pneumology Center, German Center for Lung Research (DZL), Munich, Germany
| | - Moritz Jörg Schneider
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany.,Comprehensive Pneumology Center, German Center for Lung Research (DZL), Munich, Germany
| | | | - Guido Baroni
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy.,Bioengineering Unit, Centro Nazionale di Adroterapia Oncologica, Pavia, Italy
| | - Julien Dinkel
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany.,Comprehensive Pneumology Center, German Center for Lung Research (DZL), Munich, Germany
| | - Michael Reiner
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany.,Department of Medical Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching (Munich), Germany
| | - Katia Parodi
- Department of Medical Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching (Munich), Germany
| | - Claus Belka
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
| | - Florian Kamp
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany.,Department of Medical Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching (Munich), Germany
| |
Collapse
|
44
|
Dumlu HS, Meschini G, Kurz C, Kamp F, Baroni G, Belka C, Paganelli C, Riboldi M. Dosimetric impact of geometric distortions in an MRI-only proton therapy workflow for lung, liver and pancreas. Z Med Phys 2020; 32:85-97. [PMID: 33168274 PMCID: PMC9948883 DOI: 10.1016/j.zemedi.2020.10.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 09/02/2020] [Accepted: 10/01/2020] [Indexed: 12/25/2022]
Abstract
In a radiation therapy workflow based on Magnetic Resonance Imaging (MRI), dosimetric errors may arise due to geometric distortions introduced by MRI. The aim of this study was to quantify the dosimetric effect of system-dependent geometric distortions in an MRI-only workflow for proton therapy applied at extra-cranial sites. An approach was developed, in which computed tomography (CT) images were distorted using an MRI displacement map, which represented the MR distortions in a spoiled gradient-echo sequence due to gradient nonlinearities and static magnetic field inhomogeneities. A retrospective study was conducted on 4DCT/MRI digital phantoms and 18 4DCT clinical datasets of the thoraco-abdominal site. The treatment plans were designed and separately optimized for each beam in a beam specific Planning Target Volume on the distorted CT, and the final dose distribution was obtained as the average. The dose was then recalculated in undistorted CT using the same beam geometry and beam weights. The analysis was performed in terms of Dose Volume Histogram (DVH) parameters. No clinically relevant dosimetric impact was observed on organs at risk, whereas in the target structure, geometric distortions caused statistically significant variations in the planned dose DVH parameters and dose homogeneity index (DHI). The dosimetric variations in the target structure were smaller in abdominal cases (ΔD2%, ΔD98%, and ΔDmean all below 0.1% and ΔDHI below 0.003) compared to the lung cases. Indeed, lung patients with tumors isolated inside lung parenchyma exhibited higher dosimetric variations (ΔD2%≥0.3%, ΔD98%≥15.9%, ΔDmean≥3.3% and ΔDHI≥0.102) than lung patients with tumor close to soft tissue (ΔD2%≤0.4%, ΔD98%≤5.6%, ΔDmean≤0.9% and ΔDHI≤0.027) potentially due to higher density variations along the beam path. Results suggest the potential applicability of MRI-only proton therapy, provided that specific analysis is applied for isolated lung tumors.
Collapse
Affiliation(s)
- Hatice Selcen Dumlu
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, Italy; Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748 Garching bei München, Germany
| | - Giorgia Meschini
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, Italy
| | - Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377 München, Germany
| | - Florian Kamp
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377 München, Germany
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, Italy; Centro Nazionale di Adroterapia Oncologica, Strada Campeggi 53, 27100 Pavia, Italy
| | - Claus Belka
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377 München, Germany; German Cancer Consortium (DKTK) partner site Munich, Germany and German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, Italy
| | - Marco Riboldi
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748 Garching bei München, Germany.
| |
Collapse
|
45
|
Landry G, Kurz C. SP-0511: Improving image quality of CBCT using AI. Radiother Oncol 2020. [DOI: 10.1016/s0167-8140(21)00533-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
46
|
Meijers A, Seller OC, Free J, Bondesson D, Seller Oria C, Rabe M, Parodi K, Landry G, Langendijk JA, Both S, Kurz C, Knopf AC. Assessment of range uncertainty in lung-like tissue using a porcine lung phantom and proton radiography. ACTA ACUST UNITED AC 2020; 65:155014. [DOI: 10.1088/1361-6560/ab91db] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
|
47
|
Hager M, Kurz C, Parry JP, Holzer I, Marschalek J, Ott J. Die hysteroskopische Beurteilung der Tubendurchgängigkeit: Ein prospektiver, randomisierter Vergleich zwischen der „Flow“- und der „Parryscope“-Technik. Geburtshilfe Frauenheilkd 2020. [DOI: 10.1055/s-0040-1713193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
Affiliation(s)
- M Hager
- Medizinische Universität Wien, Universitätsklinik für Frauenheilkunde, Klinische Abteilung für Gynäkologische Endokrinologie und Reproduktionsmedizin
| | - C Kurz
- Medizinische Universität Wien, Universitätsklinik für Frauenheilkunde, Klinische Abteilung für Gynäkologische Endokrinologie und Reproduktionsmedizin
| | - J P Parry
- Medizinische Universität Wien, Universitätsklinik für Frauenheilkunde, Klinische Abteilung für Gynäkologische Endokrinologie und Reproduktionsmedizin
| | - I Holzer
- Medizinische Universität Wien, Universitätsklinik für Frauenheilkunde, Klinische Abteilung für Gynäkologische Endokrinologie und Reproduktionsmedizin
| | - J Marschalek
- Medizinische Universität Wien, Universitätsklinik für Frauenheilkunde, Klinische Abteilung für Gynäkologische Endokrinologie und Reproduktionsmedizin
| | - J Ott
- Medizinische Universität Wien, Universitätsklinik für Frauenheilkunde, Klinische Abteilung für Gynäkologische Endokrinologie und Reproduktionsmedizin
| |
Collapse
|
48
|
Hager M, Wenzl R, Riesenhuber S, Marschalek J, Kuessel L, Mayrhofer D, Ristl R, Kurz C, Ott J. Die Prävalenz der inzidentiellen Endometriose bei Frauen, die ein laparoskopisches Ovarian Drilling (LOD) wegen Clomiphen-Resistenz bei polyzystischem Ovarsyndrom (PCOS) erhalten: eine retrospektive Kohortenstudie und Meta-Analyse. Geburtshilfe Frauenheilkd 2020. [DOI: 10.1055/s-0040-1713194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
Affiliation(s)
- M Hager
- Medizinische Universität Wien, Universitätsklinik für Frauenheilkunde, Klinische Abteilung für Gynäkologische Endokrinologie und Reproduktionsmedizin
| | - R Wenzl
- Medizinische Universität Wien, Universitätsklinik für Frauenheilkunde, Klinische Abteilung für Gynäkologische Endokrinologie und Reproduktionsmedizin
| | - S Riesenhuber
- Medizinische Universität Wien, Universitätsklinik für Frauenheilkunde, Klinische Abteilung für Gynäkologische Endokrinologie und Reproduktionsmedizin
| | - J Marschalek
- Medizinische Universität Wien, Universitätsklinik für Frauenheilkunde, Klinische Abteilung für Gynäkologische Endokrinologie und Reproduktionsmedizin
| | - L Kuessel
- Medizinische Universität Wien, Universitätsklinik für Frauenheilkunde, Klinische Abteilung für Gynäkologische Endokrinologie und Reproduktionsmedizin
| | - D Mayrhofer
- Medizinische Universität Wien, Universitätsklinik für Frauenheilkunde, Klinische Abteilung für Gynäkologische Endokrinologie und Reproduktionsmedizin
| | - R Ristl
- Medizinische Universität Wien, Universitätsklinik für Frauenheilkunde, Klinische Abteilung für Gynäkologische Endokrinologie und Reproduktionsmedizin
| | - C Kurz
- Medizinische Universität Wien, Universitätsklinik für Frauenheilkunde, Klinische Abteilung für Gynäkologische Endokrinologie und Reproduktionsmedizin
| | - J Ott
- Medizinische Universität Wien, Universitätsklinik für Frauenheilkunde, Klinische Abteilung für Gynäkologische Endokrinologie und Reproduktionsmedizin
| |
Collapse
|
49
|
Hoffmann A, Oborn B, Moteabbed M, Yan S, Bortfeld T, Knopf A, Fuchs H, Georg D, Seco J, Spadea MF, Jäkel O, Kurz C, Parodi K. MR-guided proton therapy: a review and a preview. Radiat Oncol 2020; 15:129. [PMID: 32471500 PMCID: PMC7260752 DOI: 10.1186/s13014-020-01571-x] [Citation(s) in RCA: 69] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 05/17/2020] [Indexed: 02/14/2023] Open
Abstract
Background The targeting accuracy of proton therapy (PT) for moving soft-tissue tumours is expected to greatly improve by real-time magnetic resonance imaging (MRI) guidance. The integration of MRI and PT at the treatment isocenter would offer the opportunity of combining the unparalleled soft-tissue contrast and real-time imaging capabilities of MRI with the most conformal dose distribution and best dose steering capability provided by modern PT. However, hybrid systems for MR-integrated PT (MRiPT) have not been realized so far due to a number of hitherto open technological challenges. In recent years, various research groups have started addressing these challenges and exploring the technical feasibility and clinical potential of MRiPT. The aim of this contribution is to review the different aspects of MRiPT, to report on the status quo and to identify important future research topics. Methods Four aspects currently under study and their future directions are discussed: modelling and experimental investigations of electromagnetic interactions between the MRI and PT systems, integration of MRiPT workflows in clinical facilities, proton dose calculation algorithms in magnetic fields, and MRI-only based proton treatment planning approaches. Conclusions Although MRiPT is still in its infancy, significant progress on all four aspects has been made, showing promising results that justify further efforts for research and development to be undertaken. First non-clinical research solutions have recently been realized and are being thoroughly characterized. The prospect that first prototype MRiPT systems for clinical use will likely exist within the next 5 to 10 years seems realistic, but requires significant work to be performed by collaborative efforts of research groups and industrial partners.
Collapse
Affiliation(s)
- Aswin Hoffmann
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.,Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiooncology-OncoRay, Dresden, Germany.,Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Bradley Oborn
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia.,Illawarra Cancer Care Centre, Wollongong Hospital, Wollongong, Australia
| | - Maryam Moteabbed
- Division of Radiation Biophysics, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | - Susu Yan
- Division of Radiation Biophysics, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | - Thomas Bortfeld
- Division of Radiation Biophysics, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | - Antje Knopf
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Herman Fuchs
- Department of Radiation Oncology, Medical University of Vienna/AKH, Vienna, Austria.,Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Dietmar Georg
- Department of Radiation Oncology, Medical University of Vienna/AKH, Vienna, Austria.,Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Joao Seco
- Biomedical Physics in Radiation Oncology, Deutsches Krebsforschungszentrum DKFZ, Heidelberg, Germany.,Department of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
| | - Maria Francesca Spadea
- Biomedical Physics in Radiation Oncology, Deutsches Krebsforschungszentrum DKFZ, Heidelberg, Germany.,Department of Experimental and Clinical Medicine, Magna Graecia University, Catanzaro, Italy
| | - Oliver Jäkel
- Medical Physics in Radiation Oncology, Deutsches Krebsforschungszentrum DKFZ and Heidelberg Ion-Beam Therapy Center at the University Medical Center, Heidelberg, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany.,Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Garching, Germany
| | - Katia Parodi
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Garching, Germany.
| |
Collapse
|
50
|
Kurz C, Buizza G, Landry G, Kamp F, Rabe M, Paganelli C, Baroni G, Reiner M, Keall PJ, van den Berg CAT, Riboldi M. Medical physics challenges in clinical MR-guided radiotherapy. Radiat Oncol 2020; 15:93. [PMID: 32370788 PMCID: PMC7201982 DOI: 10.1186/s13014-020-01524-4] [Citation(s) in RCA: 77] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Accepted: 03/24/2020] [Indexed: 12/18/2022] Open
Abstract
The integration of magnetic resonance imaging (MRI) for guidance in external beam radiotherapy has faced significant research and development efforts in recent years. The current availability of linear accelerators with an embedded MRI unit, providing volumetric imaging at excellent soft tissue contrast, is expected to provide novel possibilities in the implementation of image-guided adaptive radiotherapy (IGART) protocols. This study reviews open medical physics issues in MR-guided radiotherapy (MRgRT) implementation, with a focus on current approaches and on the potential for innovation in IGART.Daily imaging in MRgRT provides the ability to visualize the static anatomy, to capture internal tumor motion and to extract quantitative image features for treatment verification and monitoring. Those capabilities enable the use of treatment adaptation, with potential benefits in terms of personalized medicine. The use of online MRI requires dedicated efforts to perform accurate dose measurements and calculations, due to the presence of magnetic fields. Likewise, MRgRT requires dedicated quality assurance (QA) protocols for safe clinical implementation.Reaction to anatomical changes in MRgRT, as visualized on daily images, demands for treatment adaptation concepts, with stringent requirements in terms of fast and accurate validation before the treatment fraction can be delivered. This entails specific challenges in terms of treatment workflow optimization, QA, and verification of the expected delivered dose while the patient is in treatment position. Those challenges require specialized medical physics developments towards the aim of fully exploiting MRI capabilities. Conversely, the use of MRgRT allows for higher confidence in tumor targeting and organs-at-risk (OAR) sparing.The systematic use of MRgRT brings the possibility of leveraging IGART methods for the optimization of tumor targeting and quantitative treatment verification. Although several challenges exist, the intrinsic benefits of MRgRT will provide a deeper understanding of dose delivery effects on an individual basis, with the potential for further treatment personalization.
Collapse
Affiliation(s)
- Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748, Garching, Germany
| | - Giulia Buizza
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, P.za Leonardo da Vinci 32, 20133, Milano, Italy
| | - Guillaume Landry
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748, Garching, Germany
- German Cancer Consortium (DKTK), 81377, Munich, Germany
| | - Florian Kamp
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Moritz Rabe
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, P.za Leonardo da Vinci 32, 20133, Milano, Italy
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, P.za Leonardo da Vinci 32, 20133, Milano, Italy
- Bioengineering Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Privata Campeggi 53, 27100, Pavia, Italy
| | - Michael Reiner
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Paul J Keall
- ACRF Image X Institute, University of Sydney, Sydney, NSW, 2006, Australia
| | - Cornelis A T van den Berg
- Department of Radiotherapy, University Medical Centre Utrecht, PO box 85500, 3508 GA, Utrecht, The Netherlands
| | - Marco Riboldi
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748, Garching, Germany.
| |
Collapse
|