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Lauria M, Miller C, Singhrao K, Lewis J, Lin W, O'Connell D, Naumann L, Stiehl B, Santhanam A, Boyle P, Raldow AC, Goldin J, Barjaktarevic I, Low DA. Motion compensated cone-beam CT reconstruction using an a priorimotion model from CT simulation: a pilot study. Phys Med Biol 2024; 69:075022. [PMID: 38452385 DOI: 10.1088/1361-6560/ad311b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 03/07/2024] [Indexed: 03/09/2024]
Abstract
Objective. To combat the motion artifacts present in traditional 4D-CBCT reconstruction, an iterative technique known as the motion-compensated simultaneous algebraic reconstruction technique (MC-SART) was previously developed. MC-SART employs a 4D-CBCT reconstruction to obtain an initial model, which suffers from a lack of sufficient projections in each bin. The purpose of this study is to demonstrate the feasibility of introducing a motion model acquired during CT simulation to MC-SART, coined model-based CBCT (MB-CBCT).Approach. For each of 5 patients, we acquired 5DCTs during simulation and pre-treatment CBCTs with a simultaneous breathing surrogate. We cross-calibrated the 5DCT and CBCT breathing waveforms by matching the diaphragms and employed the 5DCT motion model parameters for MC-SART. We introduced the Amplitude Reassignment Motion Modeling technique, which measures the ability of the model to control diaphragm sharpness by reassigning projection amplitudes with varying resolution. We evaluated the sharpness of tumors and compared them between MB-CBCT and 4D-CBCT. We quantified sharpness by fitting an error function across anatomical boundaries. Furthermore, we compared our MB-CBCT approach to the traditional MC-SART approach. We evaluated MB-CBCT's robustness over time by reconstructing multiple fractions for each patient and measuring consistency in tumor centroid locations between 4D-CBCT and MB-CBCT.Main results. We found that the diaphragm sharpness rose consistently with increasing amplitude resolution for 4/5 patients. We observed consistently high image quality across multiple fractions, and observed stable tumor centroids with an average 0.74 ± 0.31 mm difference between the 4D-CBCT and MB-CBCT. Overall, vast improvements over 3D-CBCT and 4D-CBCT were demonstrated by our MB-CBCT technique in terms of both diaphragm sharpness and overall image quality.Significance. This work is an important extension of the MC-SART technique. We demonstrated the ability ofa priori5DCT models to provide motion compensation for CBCT reconstruction. We showed improvements in image quality over both 4D-CBCT and the traditional MC-SART approach.
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Affiliation(s)
- Michael Lauria
- UCLA, Department of Radiation Oncology, Los Angeles, CA, United States of America
| | - Claudia Miller
- UCLA, Department of Radiation Oncology, Los Angeles, CA, United States of America
| | - Kamal Singhrao
- Brigham and Women's Hospital, Dana Farber Cancer Institute and Harvard Medical School, Department of Radiation Oncology, Boston, MA, United States of America
| | - John Lewis
- Cedars-Sinai Medical Center, Department of Radiation Oncology, Los Angeles, CA, United States of America
| | - Weicheng Lin
- UCLA, Department of Radiation Oncology, Los Angeles, CA, United States of America
| | - Dylan O'Connell
- UCLA, Department of Radiation Oncology, Los Angeles, CA, United States of America
| | - Louise Naumann
- UCLA, Department of Radiation Oncology, Los Angeles, CA, United States of America
| | - Bradley Stiehl
- Cedars-Sinai Medical Center, Department of Radiation Oncology, Los Angeles, CA, United States of America
| | - Anand Santhanam
- UCLA, Department of Radiation Oncology, Los Angeles, CA, United States of America
| | - Peter Boyle
- UCLA, Department of Radiation Oncology, Los Angeles, CA, United States of America
| | - Ann C Raldow
- UCLA, Department of Radiation Oncology, Los Angeles, CA, United States of America
| | - Jonathan Goldin
- UCLA, Department of Radiological Sciences, Los Angeles, CA, United States of America
| | - Igor Barjaktarevic
- UCLA, Department of Pulmonary and Critical Care Medicine, Los Angeles, CA, United States of America
| | - Daniel A Low
- UCLA, Department of Radiation Oncology, Los Angeles, CA, United States of America
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Lauria M, Stiehl B, Santhanam A, O’Connell D, Naumann L, McNitt-Gray M, Raldow A, Goldin J, Barjaktarevic I, Low DA. An analysis of the regional heterogeneity in tissue elasticity in lung cancer patients with COPD. Front Med (Lausanne) 2023; 10:1151867. [PMID: 37840998 PMCID: PMC10575648 DOI: 10.3389/fmed.2023.1151867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 09/08/2023] [Indexed: 10/17/2023] Open
Abstract
Purpose Recent advancements in obtaining image-based biomarkers from CT images have enabled lung function characterization, which could aid in lung interventional planning. However, the regional heterogeneity in these biomarkers has not been well documented, yet it is critical to several procedures for lung cancer and COPD. The purpose of this paper is to analyze the interlobar and intralobar heterogeneity of tissue elasticity and study their relationship with COPD severity. Methods We retrospectively analyzed a set of 23 lung cancer patients for this study, 14 of whom had COPD. For each patient, we employed a 5DCT scanning protocol to obtain end-exhalation and end-inhalation images and semi-automatically segmented the lobes. We calculated tissue elasticity using a biomechanical property estimation model. To obtain a measure of lobar elasticity, we calculated the mean of the voxel-wise elasticity values within each lobe. To analyze interlobar heterogeneity, we defined an index that represented the properties of the least elastic lobe as compared to the rest of the lobes, termed the Elasticity Heterogeneity Index (EHI). An index of 0 indicated total homogeneity, and higher indices indicated higher heterogeneity. Additionally, we measured intralobar heterogeneity by calculating the coefficient of variation of elasticity within each lobe. Results The mean EHI was 0.223 ± 0.183. The mean coefficient of variation of the elasticity distributions was 51.1% ± 16.6%. For mild COPD patients, the interlobar heterogeneity was low compared to the other categories. For moderate-to-severe COPD patients, the interlobar and intralobar heterogeneities were highest, showing significant differences from the other groups. Conclusion We observed a high level of lung tissue heterogeneity to occur between and within the lobes in all COPD severity cases, especially in moderate-to-severe cases. Heterogeneity results demonstrate the value of a regional, function-guided approach like elasticity for procedures such as surgical decision making and treatment planning.
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Affiliation(s)
- Michael Lauria
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Bradley Stiehl
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Anand Santhanam
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Dylan O’Connell
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Louise Naumann
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Michael McNitt-Gray
- Department of Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, United States
| | - Ann Raldow
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Jonathan Goldin
- Department of Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, United States
| | - Igor Barjaktarevic
- Division of Pulmonary and Critical Care Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Daniel A. Low
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States
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Tryggestad E, Li H, Rong Y. 4DCT is long overdue for improvement. J Appl Clin Med Phys 2023; 24:e13933. [PMID: 36866617 PMCID: PMC10113694 DOI: 10.1002/acm2.13933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 02/06/2023] [Accepted: 02/07/2023] [Indexed: 03/04/2023] Open
Affiliation(s)
- Erik Tryggestad
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota, USA
| | - Heng Li
- Department of Radiation Oncology, John Hopkins University, Baltimore, Maryland, USA
| | - Yi Rong
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
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Low DA, O'Connell D, Lauria M, Stiehl B, Naumann L, Lee P, Hegde J, Barjaktarevic I, Goldin J, Santhanam A. Ventilation measurements using fast-helical free-breathing computed tomography. Med Phys 2021; 48:6094-6105. [PMID: 34410014 DOI: 10.1002/mp.15173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 07/28/2021] [Accepted: 08/01/2021] [Indexed: 11/10/2022] Open
Abstract
PURPOSE To examine the use of multiple fast-helical free breathing computed tomography (FHFBCT) scans for ventilation measurement. METHODS Ten patients were scanned 25 times in alternating directions using a FHFBCT protocol. Simultaneously, an abdominal pneumatic bellows was used as a real-time breathing surrogate. Regions-of-interest (ROIs) were selected from the upper right lungs of each patient for analysis. The ROIs were first registered using a published registration technique (pTV). A subsequent follow-up registration employed an objective function with two terms, a ventilation-adjusted Hounsfield Unit difference and a conservation-of-mass term labeled ΔΓ that denoted the difference between the deformation Jacobian and the tissue density ratio. The ventilations were calculated voxel-by-voxel as the slope of a first-order fit of the Jacobian as a function of the breathing amplitude. RESULTS The ventilations of the 10 patients showed different patterns and magnitudes. The average ventilation calculated from the deformation vector fields (DVFs) of the pTV and secondary registration was nearly identical, but the standard deviation of the voxel-to-voxel differences was approximately 0.1. The mean of the 90th percentile values of ΔΓ was reduced from 0.153 to 0.079 between the pTV and secondary registration, implying first that the secondary registration improved the conservation-of-mass criterion by almost 50% and that on average the correspondence between the Jacobian and density ratios as demonstrated by ΔΓ was less than 0.1. This improvement occurred in spite of the average of the 90th percentile changes in the DVF magnitudes being only 0.58 mm. CONCLUSIONS This work introduces the use of multiple free-breathing CT scans for free-breathing ventilation measurements. The approach has some benefits over the traditional use of 4-dimensional CT (4DCT) or breath-hold scans. The benefit over 4DCT is that FHFBCT does not have sorting artifacts. The benefits over breath-hold scans include the relatively small motion induced by quiet respiration versus deep-inspiration breath hold and the potential for characterizing dynamic breathing processes that disappear during breath hold.
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Affiliation(s)
- Daniel A Low
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, California, USA
| | - Dylan O'Connell
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, California, USA
| | - Michael Lauria
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, California, USA
| | - Bradley Stiehl
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, California, USA
| | - Louise Naumann
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, California, USA
| | - Percy Lee
- Department of Radiation Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA
| | - John Hegde
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, California, USA
| | - Igor Barjaktarevic
- Department of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Jonathan Goldin
- Department of Radiology, University of California, Los Angeles, Los Angeles, California, USA
| | - Anand Santhanam
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, California, USA
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Scalable quorum-based deep neural networks with adversarial learning for automated lung lobe segmentation in fast helical free-breathing CTs. Int J Comput Assist Radiol Surg 2021; 16:1775-1784. [PMID: 34378122 DOI: 10.1007/s11548-021-02454-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 07/06/2021] [Indexed: 10/20/2022]
Abstract
PURPOSE Fast helical free-breathing CT (FHFBCT) scans are widely used for 5DCT and 5D Cone Beam imaging protocols. For quantitative analysis of lung physiology and function, it is important to segment the lung lobes in these scans. Since the 5DCT protocols use up to 25 FHFBCT scans, it is important that this segmentation task be automated. In this paper, we present a deep neural network (DNN) framework for segmenting the lung lobes in near real time. METHODS A total of 22 patient datasets (550 3D CT scans) were used for the study. Each of the lung lobes was manually segmented and considered ground-truth. A supervised and constrained generative adversarial network (CGAN) was employed for learning each set of lobe segmentations for each patient with 12 patients designated for training data. The resulting generator DNNs represented the lobe segmentations for each training dataset. A quorum-based algorithm was then implemented to test validation data consisting of 10 separate patient datasets (250 3D CTs). Each of the DNNs predicted their corresponding lobes for the validation data, and equal weights were given to the 12 generator CGANs. The quorum process worked by selecting the weighted average result of all 12 CGAN results for each lobe. RESULTS When evaluated against ground-truth segmentations, the quorum-based lobe segmentation was observed to have average structural similarity index, normalized cross-correlation coefficient, and dice coefficient values of 0.929, 0.806, and 0.814, respectively, compared to values of 0.911, 0.698, and 0.696, respectively, using a conventional strategy. CONCLUSION The proposed quorum-based approach computed segmentations with clinically acceptable accuracy in near real time using a multi-GPU-based computing setup. This method is scalable as more patient-specific CGANs can be added to the quorum over time.
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Vergalasova I, Cai J. A modern review of the uncertainties in volumetric imaging of respiratory-induced target motion in lung radiotherapy. Med Phys 2020; 47:e988-e1008. [PMID: 32506452 DOI: 10.1002/mp.14312] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 05/15/2020] [Accepted: 05/26/2020] [Indexed: 12/25/2022] Open
Abstract
Radiotherapy has become a critical component for the treatment of all stages and types of lung cancer, often times being the primary gateway to a cure. However, given that radiation can cause harmful side effects depending on how much surrounding healthy tissue is exposed, treatment of the lung can be particularly challenging due to the presence of moving targets. Careful implementation of every step in the radiotherapy process is absolutely integral for attaining optimal clinical outcomes. With the advent and now widespread use of stereotactic body radiation therapy (SBRT), where extremely large doses are delivered, accurate, and precise dose targeting is especially vital to achieve an optimal risk to benefit ratio. This has largely become possible due to the rapid development of image-guided technology. Although imaging is critical to the success of radiotherapy, it can often be plagued with uncertainties due to respiratory-induced target motion. There has and continues to be an immense research effort aimed at acknowledging and addressing these uncertainties to further our abilities to more precisely target radiation treatment. Thus, the goal of this article is to provide a detailed review of the prevailing uncertainties that remain to be investigated across the different imaging modalities, as well as to highlight the more modern solutions to imaging motion and their role in addressing the current challenges.
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Affiliation(s)
- Irina Vergalasova
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ, USA
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong
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Free-to-use DIR solutions in radiotherapy: Benchmark against commercial platforms through a contour-propagation study. Phys Med 2020; 74:110-117. [PMID: 32464468 DOI: 10.1016/j.ejmp.2020.05.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Revised: 05/08/2020] [Accepted: 05/17/2020] [Indexed: 11/22/2022] Open
Abstract
PURPOSE A contour propagation study has been conducted to benchmark three algorithms for Deformable Image Registration (DIR) freely available online against well-established commercial solutions. METHODS ElastiX, BRAINS and Plastimach, available as modules in the open source platform 3DSlicer, were tested as the recent AAPM Task group 132 guidelines proposes. The overlap of the DIR-mapped ROIs in four computational anthropomorphic phantoms was measured. To avoid bias every algorithm was left to run without any human interaction nor particular registration strategy. The accuracy of the algorithms was measured using the Dice Similarity Coefficient (DSC) and Mean Distance to Conformity (MDC) metrics. The registration quality was compared to the recommended geometrical accuracy suggested by AAPM TG132 and to the results of a large population-based study performed with commercial DIR solutions. RESULTS The considered free-to-use DIR solutions generally meet acceptable accuracy and good overlap (DSC > 0.85). Mild failures (DSC < 0.75) were detected only for the smallest structures. In case of extremely severe deformations acceptable accuracy was not met (MDC > 3 mm). The morphing capability of the tested algorithms equals those of commercial systems when the user interaction is avoided. Underperformances were detected only in cases where a specific registration strategy is mandatory to obtain a satisfying match. CONCLUSIONS All of the considered algorithms show performances not inferior to previously published data and have the potential to be good candidates for use in the clinical routine. The results and conclusions only apply to the considered phantoms and should not be considered to be generally applicable and extendable to patient cases.
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Guo M, Chee G, O'Connell D, Dhou S, Fu J, Singhrao K, Ionascu D, Ruan D, Lee P, Low DA, Zhao J, Lewis JH. Reconstruction of a high-quality volumetric image and a respiratory motion model from patient CBCT projections. Med Phys 2019; 46:3627-3639. [PMID: 31087359 DOI: 10.1002/mp.13595] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Revised: 04/10/2019] [Accepted: 05/08/2019] [Indexed: 12/25/2022] Open
Abstract
PURPOSE To develop and evaluate a method of reconstructing a patient- and treatment day- specific volumetric image and motion model from free-breathing cone-beam projections and respiratory surrogate measurements. This Motion-Compensated Simultaneous Algebraic Reconstruction Technique (MC-SART) generates and uses a motion model derived directly from the cone-beam projections, without requiring prior motion measurements from 4DCT, and can compensate for both inter- and intrabin deformations. The motion model can be used to generate images at arbitrary breathing points, which can be used for estimating volumetric images during treatment delivery. METHODS The MC-SART was formulated using simultaneous image reconstruction and motion model estimation. For image reconstruction, projections were first binned according to external surrogate measurements. Projections in each bin were used to reconstruct a set of volumetric images using MC-SART. The motion model was estimated based on deformable image registration between the reconstructed bins, and least squares fitting to model parameters. The model was used to compensate for motion in both projection and backprojection operations in the subsequent image reconstruction iterations. These updated images were then used to update the motion model, and the two steps were alternated between. The final output is a volumetric reference image and a motion model that can be used to generate images at any other time point from surrogate measurements. RESULTS A retrospective patient dataset consisting of eight lung cancer patients was used to evaluate the method. The absolute intensity differences in the lung regions compared to ground truth were 50.8 ± 43.9 HU in peak exhale phases (reference) and 80.8 ± 74.0 in peak inhale phases (generated). The 50th percentile of voxel registration error of all voxels in the lung regions with >5 mm amplitude was 1.3 mm. The MC-SART was also applied to measured patient cone-beam projections acquired with a linac-mounted CBCT system. Results from this patient data demonstrate the feasibility of MC-SART and showed qualitative image quality improvements compared to other state-of-the-art algorithms. CONCLUSION We have developed a simultaneous image reconstruction and motion model estimation method that uses Cone-beam computed tomography (CBCT) projections and respiratory surrogate measurements to reconstruct a high-quality reference image and motion model of a patient in treatment position. The method provided superior performance in both HU accuracy and positional accuracy compared to other existing methods. The resultant reference image and motion model can be combined with respiratory surrogate measurements to generate volumetric images representing patient anatomy at arbitrary time points.
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Affiliation(s)
- Minghao Guo
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.,Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Geraldine Chee
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Dylan O'Connell
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Salam Dhou
- Department of Computer Science and Engineering, American University of Sharjah, Sharjah, 26666, United Arab Emirates
| | - Jie Fu
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Kamal Singhrao
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Dan Ionascu
- Department of Radiation Oncology, College of Medicine, University of Cincinnati, Cincinnati, OH, 45221, USA
| | - Dan Ruan
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Percy Lee
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Daniel A Low
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Jun Zhao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - John H Lewis
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
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O'Connell D, Thomas DH, Lewis JH, Hasse K, Santhanam A, Lamb JM, Cao M, Tenn S, Agazaryan N, Lee PP, Low DA. Safety-oriented design of in-house software for new techniques: A case study using a model-based 4DCT protocol. Med Phys 2019; 46:1523-1532. [PMID: 30656699 DOI: 10.1002/mp.13386] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Revised: 12/05/2018] [Accepted: 12/13/2018] [Indexed: 11/09/2022] Open
Abstract
PURPOSE In-house software is commonly employed to implement new imaging and therapy techniques before commercial solutions are available. Risk analysis methods, as detailed in the TG-100 report of the American Association of Physicists in Medicine, provide a framework for quality management of processes but offer little guidance on software design. In this work, we examine a novel model-based four-dimensional computed tomography (4DCT) protocol using the TG-100 approach and describe two additional methods for promoting safety of the associated in-house software. METHODS To implement a previously published model-based 4DCT protocol, in-house software was necessary for tasks such as synchronizing a respiratory signal to computed tomography images, deformable image registration (DIR), model parameter fitting, and interfacing with a treatment planning system. A process map was generated detailing the workflow. Failure modes and effects analysis (FMEA) was performed to identify critical steps and guide quality interventions. Software system safety was addressed through writing "use cases," narratives that characterize the behavior of the software, for all major operations to elicit safety requirements. Safety requirements were codified using the easy approach to requirements syntax (EARS) to ensure testability and eliminate ambiguity. RESULTS Sixty-one failure modes were identified and assigned risk priority numbers using FMEA. Resultant quality management interventions include integration of a comprehensive reporting and logging system into the software, mandating daily and monthly equipment quality assurance procedures, and a checklist to be completed at image acquisition. Use cases and resulting safety requirements informed the design of needed in-house software as well as a suite of tests performed during the image generation process. CONCLUSIONS TG-100 methods were used to construct a process-level quality management program for a 4DCT imaging protocol. Two supplemental tools from the field of requirements engineering facilitated elicitation and codification of safety requirements that informed the design and testing of in-house software necessary to implement the protocol. These general tools can be applied to promote safety when in-house software is needed to bring new techniques to the clinic.
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Affiliation(s)
- Dylan O'Connell
- Department of Radiation Oncology, University of California, Los Angeles, 200 Medical Plaza Suite B265, Los Angeles, California, 90095, USA
| | - David H Thomas
- Department of Radiation Oncology, University of Colorado School of Medicine, 1665 Aurora Court Anschutz Cancer Pavilion, Aurora, CO, 80045, USA
| | - John H Lewis
- Department of Radiation Oncology, University of California, Los Angeles, 200 Medical Plaza Suite B265, Los Angeles, California, 90095, USA
| | - Katelyn Hasse
- Department of Radiation Oncology, University of California, Los Angeles, 200 Medical Plaza Suite B265, Los Angeles, California, 90095, USA
| | - Anand Santhanam
- Department of Radiation Oncology, University of California, Los Angeles, 200 Medical Plaza Suite B265, Los Angeles, California, 90095, USA
| | - James M Lamb
- Department of Radiation Oncology, University of California, Los Angeles, 200 Medical Plaza Suite B265, Los Angeles, California, 90095, USA
| | - Minsong Cao
- Department of Radiation Oncology, University of California, Los Angeles, 200 Medical Plaza Suite B265, Los Angeles, California, 90095, USA
| | - Stephen Tenn
- Department of Radiation Oncology, University of California, Los Angeles, 200 Medical Plaza Suite B265, Los Angeles, California, 90095, USA
| | - Nzhde Agazaryan
- Department of Radiation Oncology, University of California, Los Angeles, 200 Medical Plaza Suite B265, Los Angeles, California, 90095, USA
| | - Percy P Lee
- Department of Radiation Oncology, University of California, Los Angeles, 200 Medical Plaza Suite B265, Los Angeles, California, 90095, USA
| | - Daniel A Low
- Department of Radiation Oncology, University of California, Los Angeles, 200 Medical Plaza Suite B265, Los Angeles, California, 90095, USA
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Ginn JS, Ruan D, Low DA, Lamb JM. Multislice motion modeling for MRI-guided radiotherapy gating. Med Phys 2019; 46:465-474. [PMID: 30570755 DOI: 10.1002/mp.13350] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Revised: 11/15/2018] [Accepted: 12/13/2018] [Indexed: 12/25/2022] Open
Abstract
PURPOSE On-board magnetic resonance imaging (MRI) greatly enhances real-time target tracking capability during radiotherapy treatments. However, multislice and volumetric MRI techniques are frame rate limited and introduce unacceptable latency between the target moving out of position and the beam being turned off. We present a technique to estimate continuous volumetric tissue motion using motion models built from a repeated acquisition of a stack of MR slices. Applications including multislice target visualization and out-of-slice motion estimation during MRI-guided radiotherapy are demonstrated. METHODS Eight healthy volunteer studies were performed using a 0.35 T MRI-guided radiotherapy system. Images were acquired at three frames per second in an interleaved fashion across ten adjacent sagittal slice positions covering 4.5 cm using a balanced steady-state-free precession sequence. A previously published five-dimensional (5D) linear motion model used for MRI-guided radiotherapy gating was extended to include multiple slices. This model utilizes an external respiratory bellows signal recorded during imaging to simultaneously estimate motion across all imaged slices. For comparison to an image-based approach, the manifold learning technique local linear embedding (LLE) was used to derive a respiratory surrogate for motion modeling. Manifolds for every slice were aligned during LLE in a group-wise fashion, enabling motion estimation outside the current imaged slice using a motion model, a process which we denote as mSGA. Additionally, a method is developed to evaluate out-of-slice motion estimates. The multislice motion model was evaluated in a single slice with each newly acquired image using a leave-one-out approach. Model-generated gating decision accuracy and beam-on positive predictive value (PPV) are reported along with the median and 95th percentile distance between model and ground truth target centroids. RESULTS The average model gating decision accuracy and PPV across all volunteer studies was 93.7% and 92.8% using the 5D model, and 96.8% and 96.1% using the mSGA model, respectively. The median and 95th percentile distance between model and ground truth target centroids was 0.91 and 2.90 mm, respectively, using the 5D model and 0.58 and 1.49 mm using the mSGA model, averaged over all eight subjects. The mSGA motion model provided a statistically significant improvement across all evaluation metrics compared to the external surrogate-based 5D model. CONCLUSION The proposed techniques for out-of-slice target motion estimation demonstrated accuracy likely sufficient for clinical use. Results indicate the mSGA model may provide higher accuracy, however, the external surrogate-based model allows for unbiased in vivo accuracy evaluation.
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Affiliation(s)
- John S Ginn
- Department of Radiation Oncology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Dan Ruan
- Department of Radiation Oncology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Daniel A Low
- Department of Radiation Oncology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - James M Lamb
- Department of Radiation Oncology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90095, USA
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Ginn JS, O'Connell D, Thomas DH, Low DA, Lamb JM. Model-Interpolated Gating for Magnetic Resonance Image-Guided Radiation Therapy. Int J Radiat Oncol Biol Phys 2018; 102:885-894. [PMID: 29970314 PMCID: PMC6542358 DOI: 10.1016/j.ijrobp.2018.05.012] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Revised: 04/03/2018] [Accepted: 05/02/2018] [Indexed: 12/25/2022]
Abstract
PURPOSE To develop and validate a technique for radiation therapy gating using slow (≤1 frame per second) magnetic resonance imaging (MRI) and a motion model. Proposed uses of the technique include radiation therapy gating using T2-weighted images and conducting additional imaging studies during gated treatments. METHODS AND MATERIALS The technique uses a physiologically guided breathing motion model to interpolate deformed target position between 2-dimensional (2D) MRI images acquired every 1 to 3 seconds. The model is parameterized by a 1-dimensional respiratory bellows surrogate and is continuously updated with the most recently acquired 2D images. A phantom and 8 volunteers were imaged with a 0.35T MRI-guided radiation therapy system. A balanced steady-state free precession sequence with a 2D frame rate of 3 frames per second was used to evaluate the technique. The accuracy and beam-on positive predictive value (PPV) of the model-based gating decisions were evaluated using the gating decisions derived from imaging as a ground truth. A T2-weighted gating offline proof-of-concept study using a half-Fourier, single-shot, turbo-spin echo sequence is reported. RESULTS Model-interpolated gating accuracy, beam-on PPV, and median absolute distances between model and image-tracked target centroids were, on average, 98.3%, 98.4%, and 0.33 mm, respectively, in the balanced steady-state free precession phantom studies and 93.7%, 92.1%, and 0.86 mm, respectively, in the volunteer studies. T2 model-interpolated gating in 6 volunteers yielded an average accuracy and PPV of 94.3% and 92.5%, respectively, and the mean absolute median distance between modeled and imaged target centroids was 0.86 mm. CONCLUSIONS This work demonstrates the concept of model-interpolated gating for MRI-guided radiation therapy. The technique was found to be potentially sufficiently accurate for clinical use. Further development is needed to accommodate out-of-plane motion and the use of an internal MR-based respiratory surrogate.
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Affiliation(s)
- John S Ginn
- Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California.
| | - Dylan O'Connell
- Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - David H Thomas
- Department of Radiation Oncology, University of Colorado School of Medicine, University of Colorado, Aurora, Colorado
| | - Daniel A Low
- Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - James M Lamb
- Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
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O'Connell D, Thomas DH, Lamb JM, Lewis JH, Dou T, Sieren JP, Saylor M, Hofmann C, Hoffman EA, Lee PP, Low DA. Dependence of subject-specific parameters for a fast helical CT respiratory motion model on breathing rate: an animal study. Phys Med Biol 2018; 63:04NT04. [PMID: 29360098 DOI: 10.1088/1361-6560/aaaa15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
To determine if the parameters relating lung tissue displacement to a breathing surrogate signal in a previously published respiratory motion model vary with the rate of breathing during image acquisition. An anesthetized pig was imaged using multiple fast helical scans to sample the breathing cycle with simultaneous surrogate monitoring. Three datasets were collected while the animal was mechanically ventilated with different respiratory rates: 12 bpm (breaths per minute), 17 bpm, and 24 bpm. Three sets of motion model parameters describing the correspondences between surrogate signals and tissue displacements were determined. The model error was calculated individually for each dataset, as well asfor pairs of parameters and surrogate signals from different experiments. The values of one model parameter, a vector field denoted [Formula: see text] which related tissue displacement to surrogate amplitude, determined for each experiment were compared. The mean model error of the three datasets was 1.00 ± 0.36 mm with a 95th percentile value of 1.69 mm. The mean error computed from all combinations of parameters and surrogate signals from different datasets was 1.14 ± 0.42 mm with a 95th percentile of 1.95 mm. The mean difference in [Formula: see text] over all pairs of experiments was 4.7% ± 5.4%, and the 95th percentile was 16.8%. The mean angle between pairs of [Formula: see text] was 5.0 ± 4.0 degrees, with a 95th percentile of 13.2 mm. The motion model parameters were largely unaffected by changes in the breathing rate during image acquisition. The mean error associated with mismatched sets of parameters and surrogate signals was 0.14 mm greater than the error achieved when using parameters and surrogate signals acquired with the same breathing rate, while maximum respiratory motion was 23.23 mm on average.
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Affiliation(s)
- Dylan O'Connell
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA 90095, United States of America. Author to whom any correspondence should be addressed
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O'Connell D, Ruan D, Thomas DH, Dou TH, Lewis JH, Santhanam A, Lee P, Low DA. A prospective gating method to acquire a diverse set of free-breathing CT images for model-based 4DCT. Phys Med Biol 2018; 63:04NT03. [PMID: 29350191 DOI: 10.1088/1361-6560/aaa90f] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Breathing motion modeling requires observation of tissues at sufficiently distinct respiratory states for proper 4D characterization. This work proposes a method to improve sampling of the breathing cycle with limited imaging dose. We designed and tested a prospective free-breathing acquisition protocol with a simulation using datasets from five patients imaged with a model-based 4DCT technique. Each dataset contained 25 free-breathing fast helical CT scans with simultaneous breathing surrogate measurements. Tissue displacements were measured using deformable image registration. A correspondence model related tissue displacement to the surrogate. Model residual was computed by comparing predicted displacements to image registration results. To determine a stopping criteria for the prospective protocol, i.e. when the breathing cycle had been sufficiently sampled, subsets of N scans where 5 ⩽ N ⩽ 9 were used to fit reduced models for each patient. A previously published metric was employed to describe the phase coverage, or 'spread', of the respiratory trajectories of each subset. Minimum phase coverage necessary to achieve mean model residual within 0.5 mm of the full 25-scan model was determined and used as the stopping criteria. Using the patient breathing traces, a prospective acquisition protocol was simulated. In all patients, phase coverage greater than the threshold necessary for model accuracy within 0.5 mm of the 25 scan model was achieved in six or fewer scans. The prospectively selected respiratory trajectories ranked in the (97.5 ± 4.2)th percentile among subsets of the originally sampled scans on average. Simulation results suggest that the proposed prospective method provides an effective means to sample the breathing cycle with limited free-breathing scans. One application of the method is to reduce the imaging dose of a previously published model-based 4DCT protocol to 25% of its original value while achieving mean model residual within 0.5 mm.
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Affiliation(s)
- D O'Connell
- Department of Radiation Oncology, University of California, Los Angeles, CA 90095, United States of America
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Comparison of lung tumor motion measured using a model-based 4DCT technique and a commercial protocol. Pract Radiat Oncol 2017; 8:e175-e183. [PMID: 29429921 DOI: 10.1016/j.prro.2017.11.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2017] [Revised: 10/28/2017] [Accepted: 11/08/2017] [Indexed: 11/22/2022]
Abstract
PURPOSE To compare lung tumor motion measured with a model-based technique to commercial 4-dimensional computed tomography (4DCT) scans and describe a workflow for using model-based 4DCT as a clinical simulation protocol. METHODS AND MATERIALS Twenty patients were imaged using a model-based technique and commercial 4DCT. Tumor motion was measured on each commercial 4DCT dataset and was calculated on model-based datasets for 3 breathing amplitude percentile intervals: 5th to 85th, 5th to 95th, and 0th to 100th. Internal target volumes (ITVs) were defined on the 4DCT and 5th to 85th interval datasets and compared using Dice similarity. Images were evaluated for noise and rated by 2 radiation oncologists for artifacts. RESULTS Mean differences in tumor motion magnitude between commercial and model-based images were 0.47 ± 3.0, 1.63 ± 3.17, and 5.16 ± 4.90 mm for the 5th to 85th, 5th to 95th, and 0th to 100th amplitude intervals, respectively. Dice coefficients between ITVs defined on commercial and 5th to 85th model-based images had a mean value of 0.77 ± 0.09. Single standard deviation image noise was 11.6 ± 9.6 HU in the liver and 6.8 ± 4.7 HU in the aorta for the model-based images compared with 57.7 ± 30 and 33.7 ± 15.4 for commercial 4DCT. Mean model error within the ITV regions was 1.71 ± 0.81 mm. Model-based images exhibited reduced presence of artifacts at the tumor compared with commercial images. CONCLUSION Tumor motion measured with the model-based technique using the 5th to 85th percentile breathing amplitude interval corresponded more closely to commercial 4DCT than the 5th to 95th or 0th to 100th intervals, which showed greater motion on average. The model-based technique tended to display increased tumor motion when breathing amplitude intervals wider than 5th to 85th were used because of the influence of unusually deep inhalations. These results suggest that care must be taken in selecting the appropriate interval during image generation when using model-based 4DCT methods.
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Fattori G, Safai S, Carmona PF, Peroni M, Perrin R, Weber DC, Lomax AJ. Monitoring of breathing motion in image-guided PBS proton therapy: comparative analysis of optical and electromagnetic technologies. Radiat Oncol 2017; 12:63. [PMID: 28359341 PMCID: PMC5374699 DOI: 10.1186/s13014-017-0797-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2016] [Accepted: 03/08/2017] [Indexed: 02/07/2023] Open
Abstract
Background Motion monitoring is essential when treating non-static tumours with pencil beam scanned protons. 4D medical imaging typically relies on the detected body surface displacement, considered as a surrogate of the patient's anatomical changes, a concept similarly applied by most motion mitigation techniques. In this study, we investigate benefits and pitfalls of optical and electromagnetic tracking, key technologies for non-invasive surface motion monitoring, in the specific environment of image-guided, gantry-based proton therapy. Methods Polaris SPECTRA optical tracking system and the Aurora V3 electromagnetic tracking system from Northern Digital Inc. (NDI, Waterloo, CA) have been compared both technically, by measuring tracking errors and system latencies under laboratory conditions, and clinically, by assessing their practicalities and sensitivities when used with imaging devices and PBS treatment gantries. Additionally, we investigated the impact of using different surrogate signals, from different systems, on the reconstructed 4D CT images. Results Even though in controlled laboratory conditions both technologies allow for the localization of static fiducials with sub-millimetre jitter and low latency (31.6 ± 1 msec worst case), significant dynamic and environmental distortions limit the potential of the electromagnetic approach in a clinical setting. The measurement error in case of close proximity to a CT scanner is up to 10.5 mm and precludes its use for the monitoring of respiratory motion during 4DCT acquisitions. Similarly, the motion of the treatment gantry distorts up to 22 mm the tracking result. Conclusions Despite the line of sight requirement, the optical solution offers the best potential, being the most robust against environmental factors and providing the highest spatial accuracy. The significant difference in the temporal location of the reconstructed phase points is used to speculate on the need to apply the same monitoring system for imaging and treatment to ensure the consistency of detected phases.
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Affiliation(s)
- Giovanni Fattori
- Center for Proton Therapy, Paul Scherrer Institut, 5232, Villigen, PSI, Switzerland
| | - Sairos Safai
- Center for Proton Therapy, Paul Scherrer Institut, 5232, Villigen, PSI, Switzerland
| | | | - Marta Peroni
- Center for Proton Therapy, Paul Scherrer Institut, 5232, Villigen, PSI, Switzerland
| | - Rosalind Perrin
- Center for Proton Therapy, Paul Scherrer Institut, 5232, Villigen, PSI, Switzerland
| | - Damien Charles Weber
- Center for Proton Therapy, Paul Scherrer Institut, 5232, Villigen, PSI, Switzerland.,Radiation Oncology Department, Inselspital Universitätsspital Bern, 3010, Bern, Switzerland
| | - Antony John Lomax
- Center for Proton Therapy, Paul Scherrer Institut, 5232, Villigen, PSI, Switzerland. .,Department of Physics, ETH-Hönggerberg, 8093, Zurich, Switzerland.
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Low DA, Yang L, Chen J, O'Connel D, Lewis JH, Thomas DH, Lee P. Technical Note: Analysis of motion blurring artifact in fast helical free-breathing thoracic CT scans. Med Phys 2017; 44:1456-1461. [DOI: 10.1002/mp.12153] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2016] [Revised: 01/25/2017] [Accepted: 02/01/2017] [Indexed: 12/25/2022] Open
Affiliation(s)
- Daniel A. Low
- UCLA Department of Radiation Therapy; Los Angeles California 90095 USA
| | - Lisa Yang
- UCLA Department of Radiation Therapy; Los Angeles California 90095 USA
| | - Jingjia Chen
- UCLA Department of Radiation Therapy; Los Angeles California 90095 USA
| | - Dylan O'Connel
- UCLA Department of Radiation Therapy; Los Angeles California 90095 USA
| | - John H. Lewis
- UCLA Department of Radiation Therapy; Los Angeles California 90095 USA
| | - David H. Thomas
- University of Colorado Department of Radiation Oncology; Denver Colorado 80045 USA
| | - Percy Lee
- UCLA Department of Radiation Therapy; Los Angeles California 90095 USA
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Required transition from research to clinical application: Report on the 4D treatment planning workshops 2014 and 2015. Phys Med 2016; 32:874-82. [DOI: 10.1016/j.ejmp.2016.05.064] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2015] [Revised: 05/28/2016] [Accepted: 05/31/2016] [Indexed: 12/25/2022] Open
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