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Bryant JM, Cruz-Chamorro RJ, Gan A, Liveringhouse C, Weygand J, Nguyen A, Keit E, Sandoval ML, Sim AJ, Perez BA, Dilling TJ, Redler G, Andreozzi J, Nardella L, Naghavi AO, Feygelman V, Latifi K, Rosenberg SA. Structure-specific rigid dose accumulation dosimetric analysis of ablative stereotactic MRI-guided adaptive radiation therapy in ultracentral lung lesions. COMMUNICATIONS MEDICINE 2024; 4:96. [PMID: 38778215 PMCID: PMC11111790 DOI: 10.1038/s43856-024-00526-7] [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: 09/05/2023] [Accepted: 05/08/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND Definitive local therapy with stereotactic ablative radiation therapy (SABR) for ultracentral lung lesions is associated with a high risk of toxicity, including treatment related death. Stereotactic MR-guided adaptive radiation therapy (SMART) can overcome many of the challenges associated with SABR treatment of ultracentral lesions. METHODS We retrospectively identified 14 consecutive patients who received SMART to ultracentral lung lesions from 10/2019 to 01/2021. Patients had a median distance from the proximal bronchial tree (PBT) of 0.38 cm. Tumors were most often lung primary (64.3%) and HILUS group A (85.7%). A structure-specific rigid registration approach was used for cumulative dose analysis. Kaplan-Meier log-rank analysis was used for clinical outcome data and the Wilcoxon Signed Rank test was used for dosimetric data. RESULTS Here we show that SMART dosimetric improvements in favor of delivered plans over predicted non-adapted plans for PBT, with improvements in proximal bronchial tree DMax of 5.7 Gy (p = 0.002) and gross tumor 100% prescription coverage of 7.3% (p = 0.002). The mean estimated follow-up is 17.2 months and 2-year local control and local failure free survival rates are 92.9% and 85.7%, respectively. There are no grade ≥ 3 toxicities. CONCLUSIONS SMART has dosimetric advantages and excellent clinical outcomes for ultracentral lung tumors. Daily plan adaptation reliably improves target coverage while simultaneously reducing doses to the proximal airways. These results further characterize the therapeutic window improvements for SMART. Structure-specific rigid dose accumulation dosimetric analysis provides insights that elucidate the dosimetric advantages of SMART more so than per fractional analysis alone.
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Affiliation(s)
- J M Bryant
- Department of Radiation Oncology; H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
| | - Ruben J Cruz-Chamorro
- Department of Radiation Oncology; H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Alberic Gan
- University of South Florida Health Morsani College of Medicine, Tampa, FL, USA
| | - Casey Liveringhouse
- Department of Radiation Oncology; H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Joseph Weygand
- Department of Radiation Oncology; H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Ann Nguyen
- University of South Florida Health Morsani College of Medicine, Tampa, FL, USA
| | - Emily Keit
- Department of Radiation Oncology; H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Maria L Sandoval
- Department of Radiation Oncology; H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Austin J Sim
- Department of Radiation Oncology; James Cancer Hospital, Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Bradford A Perez
- Department of Radiation Oncology; H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Thomas J Dilling
- Department of Radiation Oncology; H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Gage Redler
- Department of Radiation Oncology; H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Jacqueline Andreozzi
- Department of Radiation Oncology; H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Louis Nardella
- Department of Radiation Oncology; H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Arash O Naghavi
- Department of Radiation Oncology; H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Vladimir Feygelman
- Department of Radiation Oncology; H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Kujtim Latifi
- Department of Radiation Oncology; H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Stephen A Rosenberg
- Department of Radiation Oncology; H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
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Lallement A, Noblet V, Antoni D, Meyer P. Detecting and quantifying spatial misalignment between longitudinal kilovoltage computed tomography (kVCT) scans of the head and neck by using convolutional neural networks (CNNs). Technol Health Care 2023:THC220519. [PMID: 36776082 DOI: 10.3233/thc-220519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
BACKGROUND Adaptive radiotherapy (ART) aims to address anatomical modifications appearing during the treatment of patients by modifying the planning treatment according to the daily positioning image. Clinical implementation of ART relies on the quality of the deformable image registration (DIR) algorithms included in the ART workflow. To translate ART into clinical practice, automatic DIR assessment is needed. OBJECTIVE This article aims to estimate spatial misalignment between two head and neck kilovoltage computed tomography (kVCT) images by using two convolutional neural networks (CNNs). METHODS The first CNN quantifies misalignments between 0 mm and 15 mm and the second CNN detects and classifies misalignments into two classes (poor alignment and good alignment). Both networks take pairs of patches of 33x33x33 mm3 as inputs and use only the image intensity information. The training dataset was built by deforming kVCT images with basis splines (B-splines) to simulate DIR error maps. The test dataset was built using 2500 landmarks, consisting of hard and soft landmark tissues annotated by 6 clinicians at 10 locations. RESULTS The quantification CNN reaches a mean error of 1.26 mm (± 1.75 mm) on the landmark set which, depending on the location, has annotation errors between 1 mm and 2 mm. The errors obtained for the quantification network fit the computed interoperator error. The classification network achieves an overall accuracy of 79.32%, and although the classification network overdetects poor alignments, it performs well (i.e., it achieves a rate of 90.4%) in detecting poor alignments when given one. CONCLUSION The performances of the networks indicate the feasibility of using CNNs for an agnostic and generic approach to misalignment quantification and detection.
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Affiliation(s)
| | | | - Delphine Antoni
- Department of Radiation Therapy, Institut de Cancérologie de Strasbourg, Strasbourg, France
| | - Philippe Meyer
- ICube-UMR 7357, Strasbourg, France.,Department of Medical Physics, Institut de Cancérologie de Strasbourg, Strasbourg, France
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3
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A novel edge gradient distance metric for automated evaluation of deformable image registration quality. Phys Med 2022; 103:26-36. [DOI: 10.1016/j.ejmp.2022.09.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 07/28/2022] [Accepted: 09/26/2022] [Indexed: 11/17/2022] Open
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Dossun C, Niederst C, Noel G, Meyer P. Evaluation of DIR algorithm performance in real patients for radiotherapy treatments: A systematic review of operator-dependent strategies. Phys Med 2022; 101:137-157. [PMID: 36007403 DOI: 10.1016/j.ejmp.2022.08.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 07/21/2022] [Accepted: 08/16/2022] [Indexed: 11/15/2022] Open
Abstract
PURPOSE The performance of deformable medical image registration (DIR) algorithms has become a major concern. METHODS We aimed to obtain updated information on DIR algorithm performance quantification through a literature review of articles published between 2010 and 2022. We focused only on studies using operator-based methods to treat real patients. The PubMed, Google Scholar and Embase databases were searched following PRISMA guidelines. RESULTS One hundred and seven articles were identified. The mean number of patients and registrations per publication was 20 and 63, respectively. We found 23 different geometric metrics appearing at least twice, and the dosimetric impact of DIR was quantified in 32 articles. Forty-eight different at-risk organs were described, and target volumes were studied in 43 publications. Prostate, head-and-neck and thoracic locations represented more than ¾ of the studied locations. We summarized the type of DIR and the images used, and other key elements. Intra/interobserver variability, threshold values and the correlation between metrics were also discussed. CONCLUSIONS This literature review covers the past decade and should facilitate the implementation of DIR algorithms in clinical practice by providing practical and pertinent information to quantify their performance on real patients.
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Affiliation(s)
- C Dossun
- Department of Radiotherapy, Institut de Cancerologie Strasbourg Europe (ICANS), Strasbourg, France
| | - C Niederst
- Department of Radiotherapy, Institut de Cancerologie Strasbourg Europe (ICANS), Strasbourg, France
| | - G Noel
- Department of Radiotherapy, Institut de Cancerologie Strasbourg Europe (ICANS), Strasbourg, France
| | - P Meyer
- Department of Radiotherapy, Institut de Cancerologie Strasbourg Europe (ICANS), Strasbourg, France; ICUBE, CNRS UMR 7357, Team IMAGES, Strasbourg, France.
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Adaptive radiation therapy: When, how and what are the benefits that literature provides? Cancer Radiother 2021; 26:622-636. [PMID: 34688548 DOI: 10.1016/j.canrad.2021.08.023] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 08/21/2021] [Accepted: 08/24/2021] [Indexed: 11/21/2022]
Abstract
PURPOSE To identify from the current literature when is the right time to replan and to assign thresholds for the optimum process of replanning. Nowadays, adaptive radiotherapy (ART) for head and neck cancer plays an exceptional role consisting of an evaluation procedure of the prominent anatomical and dosimetric variations. By performing complex radiotherapy methods, the credibility of the therapeutic result is crucial. Image guided radiotherapy (IGRT) was developed to ensure locoregional control and thus changes that might occur during radiotherapy be dealt with. MATERIALS AND METHODS An electronic research of articles published in PubMed/MEDLINE and Science Direct databases from January 2004 to October 2020 was performed. Among a total of 127 studies assessed for eligibility, 85 articles were ultimately retained for the review. RESULTS The most noticeable changes have been reported in the middle fraction of the treatment. Therefore, the suggested optimal time to replan is between the third and the fourth week. Anatomical deviations>1cm in the external contour, average weight loss>10%, violation in the dose coverage of the targets>5%, and violation in the dose of the peripherals were some of the thresholds that are currently used, and which lead to replanning. CONCLUSION ART may decrease toxicity and improve local-control. Whether it is beneficial or not, depends ultimately on each patient. However, more investigation of the changes should be performed in future prospective studies to obtain more accurate results.
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Cazoulat G, Anderson BM, McCulloch MM, Rigaud B, Koay EJ, Brock KK. Detection of vessel bifurcations in CT scans for automatic objective assessment of deformable image registration accuracy. Med Phys 2021; 48:5935-5946. [PMID: 34390007 PMCID: PMC9132059 DOI: 10.1002/mp.15163] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 07/28/2021] [Accepted: 07/29/2021] [Indexed: 12/28/2022] Open
Abstract
PURPOSE Objective assessment of deformable image registration (DIR) accuracy often relies on the identification of anatomical landmarks in image pairs, a manual process known to be extremely time-expensive. The goal of this study is to propose a method to automatically detect vessel bifurcations in images and assess their use for the computation of target registration errors (TREs). MATERIALS AND METHODS Three image datasets were retrospectively analyzed. The first dataset included 10 pairs of inhale/exhale phases from lung 4DCTs and full inhale and exhale breath-hold CT scans from 10 patients presenting with chronic obstructive pulmonary disease, with 300 corresponding landmarks available for each case (DIR-Lab). The second dataset included six pairs of inhale/exhale phases from lung 4DCTs (POPI dataset), with 100 pairs of landmarks for each case. The third dataset included 28 pairs of pre/post-radiotherapy liver contrast-enhanced CT scans, each with five manually picked vessel bifurcation correspondences. For all images, the vasculature was autosegmented by computing and thresholding a vesselness image. Images of the vasculature centerline were computed, and bifurcations were detected based on centerline voxel neighbors' count. The vasculature segmentations were independently registered using a Demons algorithm between representations of their surface with distance maps. Detected bifurcations were considered as corresponding when distant by less than 5 mm after vasculature DIR. The selected pairs of bifurcations were used to calculate TRE after registration of the images considering three algorithms: rigid registration, Anaconda, and a Demons algorithm. For comparison with the ground truth, TRE values calculated using the automatically detected correspondences were interpolated in the whole organs to generate TRE maps. The performance of the method in automatically calculating TRE after image registration was quantified by measuring the correlation with the TRE obtained when using the ground truth landmarks. RESULTS The median Pearson correlation coefficients between ground truth TRE and corresponding values in the generated TRE maps were r = 0.81 and r = 0.67 for the lung and liver cases, respectively. The correlation coefficients between mean TRE for each case were r = 0.99 and r = 0.64 for the lung and liver cases, respectively. CONCLUSION For lungs or liver CT scans DIR, a strong correlation was obtained between TRE calculated using manually picked or landmarks automatically detected with the proposed method. This tool should be particularly useful in studies requiring assessing the reliability of a high number of DIRs.
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Affiliation(s)
- Guillaume Cazoulat
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Brian M Anderson
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Molly M McCulloch
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Bastien Rigaud
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Eugene J Koay
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Kristy K Brock
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
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Amstutz F, Nenoff L, Albertini F, Ribeiro CO, Knopf AC, Unkelbach J, Weber DC, Lomax AJ, Zhang Y. An approach for estimating dosimetric uncertainties in deformable dose accumulation in pencil beam scanning proton therapy for lung cancer. Phys Med Biol 2021; 66. [PMID: 33862616 DOI: 10.1088/1361-6560/abf8f5] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 04/16/2021] [Indexed: 11/11/2022]
Abstract
Deformable image registration (DIR) is an important component for dose accumulation and associated clinical outcome evaluation in radiotherapy. However, the resulting deformation vector field (DVF) is subject to unavoidable discrepancies when different algorithms are applied, leading to dosimetric uncertainties of the accumulated dose. We propose here an approach for proton therapy to estimate dosimetric uncertainties as a consequence of modeled or estimated DVF uncertainties. A patient-specific DVF uncertainty model was built on the first treatment fraction, by correlating the magnitude differences of five DIR results at each voxel to the magnitude of any single reference DIR. In the following fractions, only the reference DIR needs to be applied, and DVF geometric uncertainties were estimated by this model. The associated dosimetric uncertainties were then derived by considering the estimated geometric DVF uncertainty, the dose gradient of fractional recalculated dose distribution and the direction factor from the applied reference DIR of this fraction. This estimated dose uncertainty was respectively compared to the reference dose uncertainty when different DIRs were applied individually for each dose warping. This approach was validated on seven NSCLC patients, each with nine repeated CTs. The proposed model-based method is able to achieve dose uncertainty distribution on a conservative voxel-to-voxel comparison within ±5% of the prescribed dose to the 'reference' dosimetric uncertainty, for 77% of the voxels in the body and 66%-98% of voxels in investigated structures. We propose a method to estimate DIR induced uncertainties in dose accumulation for proton therapy of lung tumor treatments.
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Affiliation(s)
- Florian Amstutz
- Paul Scherrer Institute, Center for Proton Therapy, Switzerland.,Department of Physics, ETH Zurich, Switzerland
| | - Lena Nenoff
- Paul Scherrer Institute, Center for Proton Therapy, Switzerland.,Department of Physics, ETH Zurich, Switzerland
| | | | - Cássia O Ribeiro
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, The Netherlands
| | - Antje C Knopf
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, The Netherlands.,Division for Medical Radiation Physics, Carl von Ossietzky University Oldenburg, Germany
| | - Jan Unkelbach
- Department of Radiation Oncology, University Hospital Zurich, Switzerland
| | - Damien C Weber
- Paul Scherrer Institute, Center for Proton Therapy, Switzerland.,Department of Radiation Oncology, University Hospital Zurich, Switzerland.,Department of Radiation Oncology, University Hospital Bern, Switzerland
| | - Antony J Lomax
- Paul Scherrer Institute, Center for Proton Therapy, Switzerland.,Department of Physics, ETH Zurich, Switzerland
| | - Ye Zhang
- Paul Scherrer Institute, Center for Proton Therapy, Switzerland
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Meschini G, Paganelli C, Vai A, Fontana G, Molinelli S, Pella A, Vitolo V, Barcellini A, Orlandi E, Ciocca M, Riboldi M, Baroni G. An MRI framework for respiratory motion modelling validation. J Med Imaging Radiat Oncol 2021; 65:337-344. [PMID: 33773081 PMCID: PMC8251859 DOI: 10.1111/1754-9485.13175] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 01/27/2021] [Accepted: 03/07/2021] [Indexed: 12/20/2022]
Abstract
Introduction Respiratory motion models establish a correspondence between respiratory‐correlated (RC) 4‐dimensional (4D) imaging and respiratory surrogates, to estimate time‐resolved (TR) 3D breathing motion. To evaluate the performance of motion models on real patient data, a validation framework based on magnetic resonance imaging (MRI) is proposed, entailing the use of RC 4DMRI to build the model, and on both (i) TR 2D cine‐MRI and (ii) additional 4DMRI data for testing intra‐/inter‐fraction breathing motion variability. Methods Repeated MRI data were acquired in 7 patients with abdominal lesions. The considered model relied on deformable image registration (DIR) for building the model and compensating for inter‐fraction baseline variations. Both 2D and 3D validation were performed, by comparing model estimations with the ground truth 2D cine‐MRI and 4DMRI respiratory phases, respectively. Results The median DIR error was comparable to the voxel size (1.33 × 1.33 × 5 mm3), with higher values in the presence of large inter‐fraction motion (median value: 2.97 mm). In the 2D validation, the median estimation error on anatomical landmarks’ position resulted below 4 mm in every scenario, whereas in the 3D validation it was 1.33 mm and 4.21 mm when testing intra‐ and inter‐fraction motion, respectively. The range of motion described in the cine‐MRI was comparable to the motion of the building 4DMRI, being always above the estimation error. Overall, the model performance was dependent on DIR error, presenting reduced accuracy when inter‐fraction baseline variations occurred. Conclusions Results suggest the potential of the proposed framework in evaluating global motion models for organ motion management in MRI‐guided radiotherapy.
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Affiliation(s)
- Giorgia Meschini
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Alessandro Vai
- National Centre for Oncological Hadrontherapy (CNAO), Pavia, Italy
| | - Giulia Fontana
- National Centre for Oncological Hadrontherapy (CNAO), Pavia, Italy
| | - Silvia Molinelli
- National Centre for Oncological Hadrontherapy (CNAO), Pavia, Italy
| | - Andrea Pella
- National Centre for Oncological Hadrontherapy (CNAO), Pavia, Italy
| | - Viviana Vitolo
- National Centre for Oncological Hadrontherapy (CNAO), Pavia, Italy
| | | | - Ester Orlandi
- National Centre for Oncological Hadrontherapy (CNAO), Pavia, Italy
| | - Mario Ciocca
- National Centre for Oncological Hadrontherapy (CNAO), Pavia, Italy
| | - Marco Riboldi
- Department of Medical Physics, Ludwig-Maximilians-Universität (LMU), Garching bei München, Germany
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.,National Centre for Oncological Hadrontherapy (CNAO), Pavia, Italy
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Denis de Senneville B, Manjón JV, Coupé P. RegQCNET: Deep quality control for image-to-template brain MRI affine registration. Phys Med Biol 2020; 65:225022. [PMID: 32906089 DOI: 10.1088/1361-6560/abb6be] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Affine registration of one or several brain image(s) onto a common reference space is a necessary prerequisite for many image processing tasks, such as brain segmentation or functional analysis. Manual assessment of registration quality is a tedious and time-consuming task, especially in studies comprising a large amount of data. Automated and reliable quality control (QC) becomes mandatory. Moreover, the computation time of the QC must be also compatible with the processing of massive datasets. Therefore, automated deep neural network approaches have emerged as a method of choice to automatically assess registration quality. In the current study, a compact 3D convolutional neural network, referred to as RegQCNET, is introduced to quantitatively predict the amplitude of an affine registration mismatch between a registered image and a reference template. This quantitative estimation of registration error is expressed using the metric unit system. Therefore, a meaningful task-specific threshold can be manually or automatically defined in order to distinguish between usable and non-usable images. The robustness of the proposed RegQCNET is first analyzed on lifespan brain images undergoing various simulated spatial transformations and intensity variations between training and testing. Secondly, the potential of RegQCNET to classify images as usable or non-usable is evaluated using both manual and automatic thresholds. During our experiments, automatic thresholds are estimated using several computer-assisted classification models (logistic regression, support vector machine, Naive Bayes and random forest) through cross-validation. To this end we use an expert's visual QC estimated on a lifespan cohort of 3953 brains. Finally, the RegQCNET accuracy is compared to usual image features such as image correlation coefficient and mutual information. The results show that the proposed deep learning QC is robust, fast and accurate at estimating affine registration error in the processing pipeline.
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Meschini G, Vai A, Paganelli C, Molinelli S, Maestri D, Fontana G, Pella A, Vitolo V, Valvo F, Ciocca M, Baroni G. Investigating the use of virtual 4DCT from 4DMRI in gated carbon ion radiation therapy of abdominal tumors. Z Med Phys 2020; 32:98-108. [PMID: 33069586 PMCID: PMC9948849 DOI: 10.1016/j.zemedi.2020.08.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 07/27/2020] [Accepted: 08/31/2020] [Indexed: 12/22/2022]
Abstract
PURPOSE To generate virtual 4DCT from 4DMRI with field of view (FOV) extended to the entire involved patient anatomy, in order to evaluate its use in carbon ion radiation therapy (CIRT) of the abdominal site in a clinical scenario. MATERIALS AND METHODS The virtual 4DCT was generated by deforming a reference CT in order to (1) match the anatomy depicted in the 4DMRI within its FOV, by calculating deformation fields with deformable image registration to describe inter-fractional and breathing motion, and (2) obtain physically plausible deformation outside of the 4DMRI FOV, by propagating and modulating the previously obtained deformation fields. The implemented method was validated on a digital anthropomorphic phantom, for which a ground truth (GT) 4DCT was available. A CIRT treatment plan was optimized at the end-exhale reference CT and the RBE-weighted dose distribution was recalculated on both the virtual and GT 4DCTs. The method estimation error was quantified by comparing the virtual and GT 4DCTs and the corresponding recomputed doses. The method was then evaluated on 8 patients with pancreas or liver tumors treated with CIRT using respiratory gating at end-exhale. The clinical treatment plans adopted at the National Center for Oncological Hadrontherapy (CNAO, Pavia, Italy) were considered and the dose distribution was recomputed on all respiratory phases of the planning and virtual 4DCTs. By comparing the two datasets and the corresponding dose distributions, the geometrical and dosimetric impact of organ motion was assessed. RESULTS For the phantom, the error outside of the 4DMRI FOV was up to 4.5mm, but it remained sub-millimetric in correspondence to the target within the 4DMRI FOV. Although the impact of motion on the target D95% resulted in variations ranging from 22% to 90% between the planned dose and the doses recomputed on the GT 4DCT phases, the corresponding estimation error was ≤2.2%. In the patient cases, the variation of the baseline tumor position between the planning and the virtual end-exhale CTs presented a median (interquartile range) value of 6.0 (4.9) mm. For baseline variations larger than 5mm, the tumor D95% variation between the plan and the dose recomputed on the end-exhale virtual CT resulted larger than 10%. Median variations higher than 10% in the target D95% and gastro-intestinal OARs D2% were quantified at the end-inhale, whereas close to the end-exhale phase, limited variations of relevant dose metrics were found for both tumor and OARs. CONCLUSIONS The negligible impact of the geometrical inaccuracy in the estimated anatomy outside of the 4DMRI FOV on the overall dosimetric accuracy suggests the feasibility of virtual 4DCT with extended FOV in CIRT of the abdominal site. In the analyzed patient group, inter-fractional variations such as baseline variation and breathing variability were quantified, demonstrating the method capability to support treatment planning in gated CIRT of the abdominal site.
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Affiliation(s)
- Giorgia Meschini
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano 20133, Italy.
| | - Alessandro Vai
- Centro Nazionale di Adroterapia Oncologica, Pavia 27100, Italy
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano 20133, Italy
| | | | - Davide Maestri
- Centro Nazionale di Adroterapia Oncologica, Pavia 27100, Italy
| | - Giulia Fontana
- Centro Nazionale di Adroterapia Oncologica, Pavia 27100, Italy
| | - Andrea Pella
- Centro Nazionale di Adroterapia Oncologica, Pavia 27100, Italy
| | - Viviana Vitolo
- Centro Nazionale di Adroterapia Oncologica, Pavia 27100, Italy
| | - Francesca Valvo
- Centro Nazionale di Adroterapia Oncologica, Pavia 27100, Italy
| | - Mario Ciocca
- Centro Nazionale di Adroterapia Oncologica, Pavia 27100, Italy
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano 20133, Italy,Centro Nazionale di Adroterapia Oncologica, Pavia 27100, Italy
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Meschini G, Vai A, Paganelli C, Molinelli S, Fontana G, Pella A, Preda L, Vitolo V, Valvo F, Ciocca M, Riboldi M, Baroni G. Virtual 4DCT from 4DMRI for the management of respiratory motion in carbon ion therapy of abdominal tumors. Med Phys 2020; 47:909-916. [PMID: 31880819 DOI: 10.1002/mp.13992] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 12/17/2019] [Accepted: 12/17/2019] [Indexed: 12/12/2022] Open
Abstract
PURPOSE To evaluate a method for generating virtual four-dimensional computed tomography (4DCT) from four-dimensional magnetic resonance imaging (4DMRI) data in carbon ion radiotherapy with pencil beam scanning for abdominal tumors. METHODS Deformable image registration is used to: (a) register each respiratory phase of the 4DMRI to the end-exhale MRI; (b) register the reference end-exhale CT to the end-exhale MRI volume; (c) generate the virtual 4DCT by warping the registered CT according to the obtained deformation fields. A respiratory-gated carbon ion treatment plan is optimized on the planning 4DCT and the corresponding dose distribution is recalculated on the virtual 4DCT. The method was validated on a digital anthropomorphic phantom and tested on eight patients (18 acquisitions). For the phantom, a ground truth dataset was available to assess the method performances from the geometrical and dosimetric standpoints. For the patients, the virtual 4DCT was compared with the planning 4DCT. RESULTS In the phantom, the method exhibits a geometrical accuracy within the voxel size and Dose Volume Histograms deviations up to 3.3% for target V95% (mean dose difference ≤ 0.2% of the prescription dose, gamma pass rate > 98%). For patients, the virtual and the planning 4DCTs show good agreement at end-exhale (3% median D95% difference), whereas other respiratory phases exhibit moderate motion variability with consequent dose discrepancies, confirming the need for motion mitigation strategies during treatment. CONCLUSIONS The virtual 4DCT approach is feasible to evaluate treatment plan robustness against intra- and interfraction motion in carbon ion therapy delivered at the abdominal site.
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Affiliation(s)
- Giorgia Meschini
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, 20133, Italy
| | - Alessandro Vai
- Centro Nazionale di Adroterapia Oncologica, Pavia, 27100, Italy
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, 20133, Italy
| | | | - Giulia Fontana
- Centro Nazionale di Adroterapia Oncologica, Pavia, 27100, Italy
| | - Andrea Pella
- Centro Nazionale di Adroterapia Oncologica, Pavia, 27100, Italy
| | - Lorenzo Preda
- Centro Nazionale di Adroterapia Oncologica, Pavia, 27100, Italy.,Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, 27100, Italy
| | - Viviana Vitolo
- Centro Nazionale di Adroterapia Oncologica, Pavia, 27100, Italy
| | - Francesca Valvo
- Centro Nazionale di Adroterapia Oncologica, Pavia, 27100, Italy
| | - Mario Ciocca
- Centro Nazionale di Adroterapia Oncologica, Pavia, 27100, Italy
| | - Marco Riboldi
- Chair of Experimental Physics - Medical Physics, Ludwig-Maximilians-Universität (LMU), Munich, 80539, Germany
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, 20133, Italy.,Centro Nazionale di Adroterapia Oncologica, Pavia, 27100, Italy
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Fu Y, Wu X, Thomas AM, Li HH, Yang D. Automatic large quantity landmark pairs detection in 4DCT lung images. Med Phys 2019; 46:4490-4501. [PMID: 31318989 PMCID: PMC8311742 DOI: 10.1002/mp.13726] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 06/20/2019] [Accepted: 07/11/2019] [Indexed: 11/11/2022] Open
Abstract
PURPOSE To automatically and precisely detect a large quantity of landmark pairs between two lung computed tomography (CT) images to support evaluation of deformable image registration (DIR). We expect that the generated landmark pairs will significantly augment the current lung CT benchmark datasets in both quantity and positional accuracy. METHODS A large number of landmark pairs were detected within the lung between the end-exhalation (EE) and end-inhalation (EI) phases of the lung four-dimensional computed tomography (4DCT) datasets. Thousands of landmarks were detected by applying the Harris-Stephens corner detection algorithm on the probability maps of the lung vasculature tree. A parametric image registration method (pTVreg) was used to establish initial landmark correspondence by registering the images at EE and EI phases. A multi-stream pseudo-siamese (MSPS) network was then developed to further improve the landmark pair positional accuracy by directly predicting three-dimensional (3D) shifts to optimally align the landmarks in EE to their counterparts in EI. Positional accuracies of the detected landmark pairs were evaluated using both digital phantoms and publicly available landmark pairs. RESULTS Dense sets of landmark pairs were detected for 10 4DCT lung datasets, with an average of 1886 landmark pairs per case. The mean and standard deviation of target registration error (TRE) were 0.47 ± 0.45 mm with 98% of landmark pairs having a TRE smaller than 2 mm for 10 digital phantom cases. Tests using 300 manually labeled landmark pairs in 10 lung 4DCT benchmark datasets (DIRLAB) produced TRE results of 0.73 ± 0.53 mm with 97% of landmark pairs having a TRE smaller than 2 mm. CONCLUSION A new method was developed to automatically and precisely detect a large quantity of landmark pairs between lung CT image pairs. The detected landmark pairs could be used as benchmark datasets for more accurate and informative quantitative evaluation of DIR algorithms.
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Affiliation(s)
- Yabo Fu
- Department of Radiation Oncology, Washington University in Saint Louis, St. Louis, MO, USA
| | - Xue Wu
- Department of Radiation Oncology, Washington University in Saint Louis, St. Louis, MO, USA
| | - Allan M. Thomas
- Department of Radiation Oncology, Washington University in Saint Louis, St. Louis, MO, USA
| | - Harold H. Li
- Department of Radiation Oncology, Washington University in Saint Louis, St. Louis, MO, USA
| | - Deshan Yang
- Department of Radiation Oncology, Washington University in Saint Louis, St. Louis, MO, USA
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13
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Huang KT. Integrating computer vision and non-linear optimization for automated deformable registration of 3D medical images. Phys Med Biol 2019; 64:135014. [DOI: 10.1088/1361-6560/ab2202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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14
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Cassetta R, Piersimoni P, Riboldi M, Giacometti V, Bashkirov V, Baroni G, Ordonez C, Coutrakon G, Schulte R. Accuracy of low-dose proton CT image registration for pretreatment alignment verification in reference to planning proton CT. J Appl Clin Med Phys 2019; 20:83-90. [PMID: 30933433 PMCID: PMC6448157 DOI: 10.1002/acm2.12565] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 02/21/2019] [Accepted: 02/26/2019] [Indexed: 12/22/2022] Open
Abstract
Purpose Proton CT (pCT) has the ability to reduce inherent uncertainties in proton treatment by directly measuring the relative proton stopping power with respect to water, thereby avoiding the uncertain conversion of X‐ray CT Hounsfield unit to relative stopping power and the deleterious effect of X‐ ray CT artifacts. The purpose of this work was to further evaluate the potential of pCT for pretreatment positioning using experimental pCT data of a head phantom. Methods The performance of a 3D image registration algorithm was tested with pCT reconstructions of a pediatric head phantom. A planning pCT simulation scan of the phantom was obtained with 200 MeV protons and reconstructed with a 3D filtered back projection (FBP) algorithm followed by iterative reconstruction and a representative pretreatment pCT scan was reconstructed with FBP only to save reconstruction time. The pretreatment pCT scan was rigidly transformed by prescribing random errors with six degrees of freedom or deformed by the deformation field derived from a head and neck cancer patient to the pretreatment pCT reconstruction, respectively. After applying the rigid or deformable image registration algorithm to retrieve the original pCT image before transformation, the accuracy of the registration was assessed. To simulate very low‐dose imaging for patient setup, the proton CT images were reconstructed with 100%, 50%, 25%, and 12.5% of the total number of histories of the original planning pCT simulation scan, respectively. Results The residual errors in image registration were lower than 1 mm and 1° of magnitude regardless of the anatomic directions and imaging dose. The mean residual errors ranges found for rigid image registration were from −0.29 ± 0.09 to 0.51 ± 0.50 mm for translations and from −0.05 ± 0.13 to 0.08 ± 0.08 degrees for rotations. The percentages of sub‐millimetric errors found, for deformable image registration, were between 63.5% and 100%. Conclusion This experimental head phantom study demonstrated the potential of low‐dose pCT imaging for 3D image registration. Further work is needed to confirm the value pCT for pretreatment image‐guided proton therapy.
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Affiliation(s)
- Roberto Cassetta
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano University, 20133, Milan, Italy
| | - Pierluigi Piersimoni
- Department of Biomedical Physics in Radiation Oncology, German Cancer Research Center, 69120, Heidelberg, Germany
| | - Marco Riboldi
- Department of Medical Physics, Ludwig-Maximilians-Universität München, 85748, Garching, Germany
| | | | - Vladmir Bashkirov
- Department of Basic Sciences, Loma Linda University, Loma Linda, CA, 92350, USA
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano University, 20133, Milan, Italy
| | - Caesar Ordonez
- Center for Research Computing and Data, Northern Illinois University, DeKalb, IL, 60115, USA
| | - George Coutrakon
- Department of Physics, Northern Illinois University, DeKalb, IL, 60115, USA
| | - Reinhard Schulte
- Department of Basic Sciences, Loma Linda University, Loma Linda, CA, 92350, USA
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15
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Meschini G, Paganelli C, Gianoli C, Summers P, Bellomi M, Baroni G, Riboldi M. A clustering approach to 4D MRI retrospective sorting for the investigation of different surrogates. Phys Med 2019; 58:107-113. [PMID: 30824141 DOI: 10.1016/j.ejmp.2019.02.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Revised: 01/19/2019] [Accepted: 02/06/2019] [Indexed: 12/25/2022] Open
Abstract
PURPOSE In retrospective 4-Dimensional Magnetic Resonance Imaging (4D MRI) sorting, respiratory surrogate selection affects the image quality of reconstructed volumes. We propose a method for retrospective 4D MRI sorting based on clustering, which allowed us to compare the performance of single or multiple internal surrogates vs. a conventional external signal. METHODS A k-medoids clustering algorithm was exploited for sorting 2D MRI into 4D MRI, relying on (A) multiple or (B) single automatically tracked internal landmarks or (C) respiratory belt signal. 4D MRI reconstructions for seven liver cancer patients were compared to those of the state-of-the-art mutual information (MI) approach. Sorting artifacts were measured by the root mean square error (RMSE) between the diaphragm profile and a fitted second order curve. Diaphragm and tumor motions were evaluated. RESULTS The median RMSEs ranged 0.97-1.66 mm, 1.24-1.89 mm, 1.43-2.27 mm, 1.74-3.72 mm for the MI, (A), (B) and (C) methods, respectively. Significant differences (Friedman, α = 5%) were found between (C) and all other methods, and between (B) and MI approaches. The discrepancies between (A) and MI approaches ranged 1.1-6.2 mm and 0.7-5.3 mm respectively in diaphragm and tumor motions. Methods (A) and (B) showed similar ranges of motion. CONCLUSION With multiple internal points, our method yielded the description of a higher range of motion and similar image quality with respect to the MI approach. The single point method led to more artifacts, suggesting the superior suitability of multiple internal surrogates for retrospective 4D MRI sorting. Considering internal rather than external information favored superior performance.
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Affiliation(s)
- Giorgia Meschini
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci, 32, 20133 Milan, Italy.
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci, 32, 20133 Milan, Italy
| | - Chiara Gianoli
- Chair of Experimental Physics - Medical Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748 Garching bei München, Germany
| | - Paul Summers
- Department of Imaging and Radiological Science, European Institute of Oncology, Via Giuseppe Ripamonti, 435, 20141 Milan, Italy
| | - Massimo Bellomi
- Department of Imaging and Radiological Science, European Institute of Oncology, Via Giuseppe Ripamonti, 435, 20141 Milan, Italy; Department of Oncology and Emato-oncology, University of Milan, Via Festa del Perdono, 7, 20122, Italy
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci, 32, 20133 Milan, Italy; Bioengineering Unit, CNAO Foundation, Str. Campeggi, 53, 27100 Pavia, Italy
| | - Marco Riboldi
- Chair of Experimental Physics - Medical Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748 Garching bei München, Germany
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16
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Kipritidis J, Tahir BA, Cazoulat G, Hofman MS, Siva S, Callahan J, Hardcastle N, Yamamoto T, Christensen GE, Reinhardt JM, Kadoya N, Patton TJ, Gerard SE, Duarte I, Archibald-Heeren B, Byrne M, Sims R, Ramsay S, Booth JT, Eslick E, Hegi-Johnson F, Woodruff HC, Ireland RH, Wild JM, Cai J, Bayouth JE, Brock K, Keall PJ. The VAMPIRE challenge: A multi-institutional validation study of CT ventilation imaging. Med Phys 2019; 46:1198-1217. [PMID: 30575051 DOI: 10.1002/mp.13346] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Revised: 11/18/2018] [Accepted: 11/23/2018] [Indexed: 01/31/2023] Open
Abstract
PURPOSE CT ventilation imaging (CTVI) is being used to achieve functional avoidance lung cancer radiation therapy in three clinical trials (NCT02528942, NCT02308709, NCT02843568). To address the need for common CTVI validation tools, we have built the Ventilation And Medical Pulmonary Image Registration Evaluation (VAMPIRE) Dataset, and present the results of the first VAMPIRE Challenge to compare relative ventilation distributions between different CTVI algorithms and other established ventilation imaging modalities. METHODS The VAMPIRE Dataset includes 50 pairs of 4DCT scans and corresponding clinical or experimental ventilation scans, referred to as reference ventilation images (RefVIs). The dataset includes 25 humans imaged with Galligas 4DPET/CT, 21 humans imaged with DTPA-SPECT, and 4 sheep imaged with Xenon-CT. For the VAMPIRE Challenge, 16 subjects were allocated to a training group (with RefVI provided) and 34 subjects were allocated to a validation group (with RefVI blinded). Seven research groups downloaded the Challenge dataset and uploaded CTVIs based on deformable image registration (DIR) between the 4DCT inhale/exhale phases. Participants used DIR methods broadly classified into B-splines, Free-form, Diffeomorphisms, or Biomechanical modeling, with CT ventilation metrics based on the DIR evaluation of volume change, Hounsfield Unit change, or various hybrid approaches. All CTVIs were evaluated against the corresponding RefVI using the voxel-wise Spearman coefficient r S , and Dice similarity coefficients evaluated for low function lung ( DSC low ) and high function lung ( DSC high ). RESULTS A total of 37 unique combinations of DIR method and CT ventilation metric were either submitted by participants directly or derived from participant-submitted DIR motion fields using the in-house software, VESPIR. The r S and DSC results reveal a high degree of inter-algorithm and intersubject variability among the validation subjects, with algorithm rankings changing by up to ten positions depending on the choice of evaluation metric. The algorithm with the highest overall cross-modality correlations used a biomechanical model-based DIR with a hybrid ventilation metric, achieving a median (range) of 0.49 (0.27-0.73) for r S , 0.52 (0.36-0.67) for DSC low , and 0.45 (0.28-0.62) for DSC high . All other algorithms exhibited at least one negative r S value, and/or one DSC value less than 0.5. CONCLUSIONS The VAMPIRE Challenge results demonstrate that the cross-modality correlation between CTVIs and the RefVIs varies not only with the choice of CTVI algorithm but also with the choice of RefVI modality, imaging subject, and the evaluation metric used to compare relative ventilation distributions. This variability may arise from the fact that each of the different CTVI algorithms and RefVI modalities provides a distinct physiologic measurement. Ultimately this variability, coupled with the lack of a "gold standard," highlights the ongoing importance of further validation studies before CTVI can be widely translated from academic centers to the clinic. It is hoped that the information gleaned from the VAMPIRE Challenge can help inform future validation efforts.
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Affiliation(s)
- John Kipritidis
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, NSW, Australia.,Sydney Medical School, University of Sydney, Sydney, NSW, Australia
| | - Bilal A Tahir
- Academic Unit of Clinical Oncology, University of Sheffield, Sheffield, UK.,Academic Radiology, POLARIS, University of Sheffield, Sheffield, UK
| | - Guillaume Cazoulat
- The University of Texas MD Anderson Cancer Center, Department of Imaging Physics, Houston, TX, USA
| | | | - Shankar Siva
- Peter MacCallum Cancer Centre, Melbourne, Vic., Australia
| | - Jason Callahan
- Peter MacCallum Cancer Centre, Melbourne, Vic., Australia
| | | | - Tokihiro Yamamoto
- University of California Davis School of Medicine, Sacramento, CA, USA
| | | | | | - Noriyuki Kadoya
- Tohoku University Graduate School of Medicine, Sendai, Japan
| | | | | | | | - Ben Archibald-Heeren
- Radiation Oncology Centres, Sydney Adventist Hospital, Sydney, NSW, Australia.,University of Wollongong, Wollongong, NSW, Australia
| | - Mikel Byrne
- Radiation Oncology Centres, Sydney Adventist Hospital, Sydney, NSW, Australia
| | - Rick Sims
- Auckland Radiation Oncology, Auckland, New Zealand
| | - Scott Ramsay
- Auckland Radiation Oncology, Auckland, New Zealand
| | - Jeremy T Booth
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, NSW, Australia.,School of Physics, University of Sydney, Sydney, NSW, Australia
| | - Enid Eslick
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, NSW, Australia.,Sydney Medical School, University of Sydney, Sydney, NSW, Australia
| | - Fiona Hegi-Johnson
- Sydney Medical School, University of Sydney, Sydney, NSW, Australia.,Peter MacCallum Cancer Centre, Melbourne, Vic., Australia
| | - Henry C Woodruff
- The D-Lab: Decision Support for Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Rob H Ireland
- Academic Unit of Clinical Oncology, University of Sheffield, Sheffield, UK
| | - Jim M Wild
- Academic Radiology, POLARIS, University of Sheffield, Sheffield, UK
| | - Jing Cai
- Duke University Medical Center, Durham, NC, USA.,Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong
| | | | - Kristy Brock
- The University of Texas MD Anderson Cancer Center, Department of Imaging Physics, Houston, TX, USA
| | - Paul J Keall
- Sydney Medical School, University of Sydney, Sydney, NSW, Australia
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Performance of a feature-based algorithm for 3D-3D registration of CT angiography to cone-beam CT for endovascular repair of complex abdominal aortic aneurysms. BMC Med Imaging 2018; 18:42. [PMID: 30409129 PMCID: PMC6225564 DOI: 10.1186/s12880-018-0285-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2018] [Accepted: 10/24/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A crucial step in image fusion for intraoperative guidance during endovascular procedures is the registration of preoperative computed tomography angiography (CTA) with intraoperative Cone Beam CT (CBCT). Automatic tools for image registration facilitate the 3D image guidance workflow. However their performance is not always satisfactory. The aim of this study is to assess the accuracy of a new fully automatic, feature-based algorithm for 3D3D registration of CTA to CBCT. METHODS The feature-based algorithm was tested on clinical image datasets from 14 patients undergoing complex endovascular aortic repair. Deviations in Euclidian distances between vascular as well as bony landmarks were measured and compared to an intensity-based, normalized mutual information algorithm. RESULTS The results for the feature-based algorithm showed that the median 3D registration error between the anatomical landmarks of CBCT and CT images was less than 3 mm. The feature-based algorithm showed significantly better accuracy compared to the intensity-based algorithm (p < 0.001). CONCLUSION A feature-based algorithm for 3D image registration is presented.
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18
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Paganelli C, Meschini G, Molinelli S, Riboldi M, Baroni G. “Patient-specific validation of deformable image registration in radiation therapy: Overview and caveats”. Med Phys 2018; 45:e908-e922. [DOI: 10.1002/mp.13162] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Revised: 07/30/2018] [Accepted: 08/24/2018] [Indexed: 12/26/2022] Open
Affiliation(s)
- Chiara Paganelli
- Dipartimento di Elettronica, Informazione e Bioingegneria; Politecnico di Milano; Milano 20133 Italy
| | - Giorgia Meschini
- Dipartimento di Elettronica, Informazione e Bioingegneria; Politecnico di Milano; Milano 20133 Italy
| | | | - Marco Riboldi
- Department of Medical Physics; Ludwig-Maximilians-Universitat Munchen; Munich 80539 Germany
| | - Guido Baroni
- Dipartimento di Elettronica, Informazione e Bioingegneria; Politecnico di Milano; Milano 20133 Italy
- Centro Nazionale di Adroterapia Oncologica; Pavia 27100 Italy
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Vickress JR, Battista J, Barnett R, Yartsev S. Online daily assessment of dose change in head and neck radiotherapy without dose-recalculation. J Appl Clin Med Phys 2018; 19:659-665. [PMID: 30084159 PMCID: PMC6123138 DOI: 10.1002/acm2.12432] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2018] [Revised: 06/21/2018] [Accepted: 07/17/2018] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND Head and neck cancers are commonly treated with radiation therapy, but due to possible volume changes, plan adaptation may be required during the course of treatment. Currently, plan adaptations consume significant clinical resources. Existing methods to evaluate the need for plan adaptation requires deformable image registration (DIR) to a new CT simulation or daily cone beam CT (CBCT) images and the recalculation of the dose distribution. In this study, we explore a tool to assist the decision for plan adaptation using a CBCT without re-computation of dose, allowing for rapid online assessment. METHODS This study involved 18 head and neck cancer patients treated with CBCT image guidance who had their treatment plan modified based on a new CT simulation (ReCT). Dose changes were estimated using different methods and compared to the current gold standard of using DIR between the planning CT scan (PCT) and ReCT with recomputed dose. The first and second methods used DIR between the PCT and daily CBCT with the planned dose or recalculated dose from the ReCT respectively, with the dose transferred to the CBCT using rigid registration. The necessity of plan adaptation was assessed by the change in dose to 95% of the planning target volume (D95) and mean dose to the parotids. RESULTS The treatment plans were adapted clinically for all 18 patients but only 7 actually needed an adaptation yielding 11 unnecessary adaptations. Applying a method using the daily CBCT with the planned dose distribution would have yielded only four unnecessary adaptations and no missed adaptations: a significant improvement from that done clinically. CONCLUSION Using the DIR between the planning CT and daily CBCT can flag cases for plan adaptation before every fraction while not requiring a new re-planning CT scan and dose recalculation.
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Affiliation(s)
| | - Jerry Battista
- Department of Medical BiophysicsWestern UniversityLondonONCanada
- Department of OncologyWestern UniversityLondonONCanada
- London Regional Cancer ProgramLondon Health Sciences CentreLondonONCanada
| | - Rob Barnett
- Department of Medical BiophysicsWestern UniversityLondonONCanada
- Department of OncologyWestern UniversityLondonONCanada
- London Regional Cancer ProgramLondon Health Sciences CentreLondonONCanada
| | - Slav Yartsev
- Department of Medical BiophysicsWestern UniversityLondonONCanada
- Department of OncologyWestern UniversityLondonONCanada
- London Regional Cancer ProgramLondon Health Sciences CentreLondonONCanada
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20
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Muller M, Paganelli C, Keall P. A phantom study to create synthetic CT from orthogonal twodimensional cine MRI and evaluate the effect of irregular breathing. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:4162-4165. [PMID: 30441272 DOI: 10.1109/embc.2018.8513236] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
An exciting innovation in radiotherapy is the use of real-time MRI for treatment adaptation. This study proposes an in-silico framework for the generation of 3D synthetic CT (sCT) from orthogonal interleaved 2D cine MRI data to overcome the lack of electron density information in MR images. The method uses pre-treatment data to build a patient breathing motion model. This model is then driven by surrogates extracted from cine MR images during the treatment. The effect of irregular breathing on the motion model is also evaluated by simulating different motion components related to uncorrelated diaphragm, chest and tumor motion. 3D sCT were successfully created for each of the 512 cine MRI pairs in the digital phantom study. The analysis showed that the diaphragm position was a good surrogate to rescale the 3D breathing motion for the current regular breathing phase. However, respiratory and tumor motion were correlated in only 59% of the phases, resulting in tumor position uncertainties of up to 3mm. The inclusion of additional chest and tumor motion information improved the accuracy for irregular changes in breathing pattern and enhanced the tumor position uncertainties to less than 1mm. This study successfully demonstrated a proof-ofprinciple for a digital phantom dataset based on patient parameters, providing a way to create real-time 3D electron density volumes and enhancing the need to account for irregular breathing pattern.
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Paganelli C, Lee D, Kipritidis J, Whelan B, Greer PB, Baroni G, Riboldi M, Keall P. Feasibility study on 3D image reconstruction from 2D orthogonal cine-MRI for MRI-guided radiotherapy. J Med Imaging Radiat Oncol 2018; 62:389-400. [DOI: 10.1111/1754-9485.12713] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2017] [Accepted: 01/12/2018] [Indexed: 12/25/2022]
Affiliation(s)
- Chiara Paganelli
- Dipartimento di Elettronica, Informazione e Bioingegneria; Politecnico di Milano; Milano Italy
| | - Danny Lee
- Department of Radiation Oncology; Calvary Mater Newcastle; Newcastle New South Wales Australia
| | - John Kipritidis
- Northern Sydney Cancer Centre; Royal North Shore Hospital; Sydney New South Wales Australia
- ACRF Image X Institute; Sydney Medical School; University of Sydney; Sydney New South Wales Australia
| | - Brendan Whelan
- ACRF Image X Institute; Sydney Medical School; University of Sydney; Sydney New South Wales Australia
| | - Peter B Greer
- Department of Radiation Oncology; Calvary Mater Newcastle; Newcastle New South Wales Australia
- School of Mathematical and Physical Sciences; University of Newcastle; Newcastle New South Wales Australia
| | - Guido Baroni
- Dipartimento di Elettronica, Informazione e Bioingegneria; Politecnico di Milano; Milano Italy
- Bioengineering Unit; Centro Nazionale di Adroterapia Oncologica; Pavia Italy
| | - Marco Riboldi
- Department of Medical Physics; Ludwig-Maximilians-Universität München; Munich Germany
| | - Paul Keall
- ACRF Image X Institute; Sydney Medical School; University of Sydney; Sydney New South Wales Australia
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22
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Seregni M, Paganelli C, Summers P, Bellomi M, Baroni G, Riboldi M. A Hybrid Image Registration and Matching Framework for Real-Time Motion Tracking in MRI-Guided Radiotherapy. IEEE Trans Biomed Eng 2018; 65:131-139. [DOI: 10.1109/tbme.2017.2696361] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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23
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Schaly B, Kempe J, Venkatesan V, Mitchell S, Battista JJ. Using gamma index to flag changes in anatomy during image-guided radiation therapy of head and neck cancer. J Appl Clin Med Phys 2017; 18:79-87. [PMID: 28901659 PMCID: PMC5689936 DOI: 10.1002/acm2.12180] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Revised: 08/04/2017] [Accepted: 08/09/2017] [Indexed: 11/16/2022] Open
Abstract
During radiation therapy of head and neck cancer, the decision to consider replanning a treatment because of anatomical changes has significant resource implications. We developed an algorithm that compares cone‐beam computed tomography (CBCT) image pairs and provides an automatic alert as to when remedial action may be required. Retrospective CBCT data from ten head and neck cancer patients that were replanned during their treatment was used to train the algorithm on when to recommend a repeat CT simulation (re‐CT). An additional 20 patients (replanned and not replanned) were used to validate the predictive power of the algorithm. CBCT images were compared in 3D using the gamma index, combining Hounsfield Unit (HU) difference with distance‐to‐agreement (DTA), where the CBCT study acquired on the first fraction is used as the reference. We defined the match quality parameter (MQPx) as a difference between the xth percentiles of the failed‐pixel histograms calculated from the reference gamma comparison and subsequent comparisons, where the reference gamma comparison is taken from the first two CBCT images acquired during treatment. The decision to consider re‐CT was based on three consecutive MQP values being less than or equal to a threshold value, such that re‐CT recommendations were within ±3 fractions of the actual re‐CT order date for the training cases. Receiver‐operator characteristic analysis showed that the best trade‐off in sensitivity and specificity was achieved using gamma criteria of 3 mm DTA and 30 HU difference, and the 80th percentile of the failed‐pixel histogram. A sensitivity of 82% and 100% was achieved in the training and validation cases, respectively, with a false positive rate of ~30%. We have demonstrated that gamma analysis of CBCT‐acquired anatomy can be used to flag patients for possible replanning in a manner consistent with local clinical practice guidelines.
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Affiliation(s)
- Bryan Schaly
- Departments of Oncology and Medical Biophysics, Western University, London, ON, Canada.,Physics and Engineering, London Regional Cancer Program, London, ON, Canada
| | - Jeff Kempe
- Physics and Engineering, London Regional Cancer Program, London, ON, Canada
| | - Varagur Venkatesan
- Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.,Department of Radiation Oncology, London Regional Cancer Program, London, ON, Canada
| | - Sylvia Mitchell
- Department of Radiation Therapy, London Regional Cancer Program, London, ON, Canada
| | - Jerry J Battista
- Departments of Oncology and Medical Biophysics, Western University, London, ON, Canada.,Physics and Engineering, London Regional Cancer Program, London, ON, Canada
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Yang D, Zhang M, Chang X, Fu Y, Liu S, Li HH, Mutic S, Duan Y. A method to detect landmark pairs accurately between intra-patient volumetric medical images. Med Phys 2017; 44:5859-5872. [PMID: 28834555 DOI: 10.1002/mp.12526] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Revised: 06/14/2017] [Accepted: 08/14/2017] [Indexed: 01/26/2023] Open
Abstract
PURPOSES An image processing procedure was developed in this study to detect large quantity of landmark pairs accurately in pairs of volumetric medical images. The detected landmark pairs can be used to evaluate of deformable image registration (DIR) methods quantitatively. METHODS Landmark detection and pair matching were implemented in a Gaussian pyramid multi-resolution scheme. A 3D scale-invariant feature transform (SIFT) feature detection method and a 3D Harris-Laplacian corner detection method were employed to detect feature points, i.e., landmarks. A novel feature matching algorithm, Multi-Resolution Inverse-Consistent Guided Matching or MRICGM, was developed to allow accurate feature pairs matching. MRICGM performs feature matching using guidance by the feature pairs detected at the lower resolution stage and the higher confidence feature pairs already detected at the same resolution stage, while enforces inverse consistency. RESULTS The proposed feature detection and feature pair matching algorithms were optimized to process 3D CT and MRI images. They were successfully applied between the inter-phase abdomen 4DCT images of three patients, between the original and the re-scanned radiation therapy simulation CT images of two head-neck patients, and between inter-fractional treatment MRIs of two patients. The proposed procedure was able to successfully detect and match over 6300 feature pairs on average. The automatically detected landmark pairs were manually verified and the mismatched pairs were rejected. The automatic feature matching accuracy before manual error rejection was 99.4%. Performance of MRICGM was also evaluated using seven digital phantom datasets with known ground truth of tissue deformation. On average, 11855 feature pairs were detected per digital phantom dataset with TRE = 0.77 ± 0.72 mm. CONCLUSION A procedure was developed in this study to detect large number of landmark pairs accurately between two volumetric medical images. It allows a semi-automatic way to generate the ground truth landmark datasets that allow quantitatively evaluation of DIR algorithms for radiation therapy applications.
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Affiliation(s)
- Deshan Yang
- Department of Radiation Oncology; Washington University in Saint Louis; Saint Louis MO USA
| | - Miao Zhang
- Department of Physics and Astronomy; University of Missouri; Columbia MO USA
| | - Xiao Chang
- Department of Radiation Oncology; Washington University in Saint Louis; Saint Louis MO USA
| | - Yabo Fu
- Department of Radiation Oncology; Washington University in Saint Louis; Saint Louis MO USA
| | - Shi Liu
- Department of Radiation Oncology; Washington University in Saint Louis; Saint Louis MO USA
| | - Harold H. Li
- Department of Radiation Oncology; Washington University in Saint Louis; Saint Louis MO USA
| | - Sasa Mutic
- Department of Radiation Oncology; Washington University in Saint Louis; Saint Louis MO USA
| | - Ye Duan
- Department of Computer Science & IT; University of Missouri; Columbia MO USA
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Hegi-Johnson F, Keall P, Barber J, Bui C, Kipritidis J. Evaluating the accuracy of 4D-CT ventilation imaging: First comparison with Technegas SPECT ventilation. Med Phys 2017; 44:4045-4055. [PMID: 28477378 DOI: 10.1002/mp.12317] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2017] [Revised: 03/21/2017] [Accepted: 04/05/2017] [Indexed: 02/03/2023] Open
Abstract
PURPOSE Computed tomography ventilation imaging (CTVI) is a highly accessible functional lung imaging modality that can unlock the potential for functional avoidance in lung cancer radiation therapy. Previous attempts to validate CTVI against clinical ventilation single-photon emission computed tomography (V-SPECT) have been hindered by radioaerosol clumping artifacts. This work builds on those studies by performing the first comparison of CTVI with 99m Tc-carbon ('Technegas'), a clinical V-SPECT modality featuring smaller radioaerosol particles with less clumping. METHODS Eleven lung cancer radiotherapy patients with early stage (T1/T2N0) disease received treatment planning four-dimensional CT (4DCT) scans paired with Technegas V/Q-SPECT/CT. For each patient, we applied three different CTVI methods. Two of these used deformable image registration (DIR) to quantify breathing-induced lung density changes (CTVIDIR-HU ), or breathing-induced lung volume changes (CTVIDIR-Jac ) between the 4DCT exhale/inhale phases. A third method calculated the regional product of air-tissue densities (CTVIHU ) and did not involve DIR. Corresponding CTVI and V-SPECT scans were compared using the Dice similarity coefficient (DSC) for functional defect and nondefect regions, as well as the Spearman's correlation r computed over the whole lung. The DIR target registration error (TRE) was quantified using both manual and computer-selected anatomic landmarks. RESULTS Interestingly, the overall best performing method (CTVIHU ) did not involve DIR. For nondefect regions, the CTVIHU , CTVIDIR-HU , and CTVIDIR-Jac methods achieved mean DSC values of 0.69, 0.68, and 0.54, respectively. For defect regions, the respective DSC values were moderate: 0.39, 0.33, and 0.44. The Spearman r-values were generally weak: 0.26 for CTVIHU , 0.18 for CTVIDIR-HU , and -0.02 for CTVIDIR-Jac . The spatial accuracy of CTVI was not significantly correlated with TRE, however the DIR accuracy itself was poor with TRE > 3.6 mm on average, potentially indicative of poor quality 4DCT. Q-SPECT scans achieved good correlations with V-SPECT (mean r > 0.6), suggesting that the image quality of Technegas V-SPECT was not a limiting factor in this study. CONCLUSIONS We performed a validation of CTVI using clinically available 4DCT and Technegas V/Q-SPECT for 11 lung cancer patients. The results reinforce earlier findings that the spatial accuracy of CTVI exhibits significant interpatient and intermethod variability. We propose that the most likely factor affecting CTVI accuracy was poor image quality of clinical 4DCT.
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Affiliation(s)
- Fiona Hegi-Johnson
- Radiation Physics Laboratory, Faculty of Medicine, Sydney University, Camperdown, NSW, 2006, Australia.,Department of Medical Physics, School of Mathematical and Physical Sciences, University of Newcastle, Newcastle, NSW, 2300, Australia.,Radiation Oncology Centre, Seventh Day Adventist Hospital, Wahroonga, NSW 2076, Australia.,Department of Radiation Oncology, Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Vic., 3000, Australia
| | - Paul Keall
- Radiation Physics Laboratory, Faculty of Medicine, Sydney University, Camperdown, NSW, 2006, Australia
| | - Jeff Barber
- Crown Princess Mary Cancer Care Centre, Blacktown Hospital, Blacktown, NSW, 2148, Australia
| | - Chuong Bui
- Department of Nuclear Medicine, Nepean Hospital, Nepean, NSW, 2750, Australia
| | - John Kipritidis
- Radiation Physics Laboratory, Faculty of Medicine, Sydney University, Camperdown, NSW, 2006, Australia.,Department of Radiotherapy, Royal North Shore Hospital, St Leonards, NSW, 2065, Australia
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Wölfelschneider J, Seregni M, Fassi A, Ziegler M, Baroni G, Fietkau R, Riboldi M, Bert C. Examination of a deformable motion model for respiratory movements and 4D dose calculations using different driving surrogates. Med Phys 2017; 44:2066-2076. [PMID: 28369900 DOI: 10.1002/mp.12243] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2016] [Revised: 03/13/2017] [Accepted: 03/16/2017] [Indexed: 11/11/2022] Open
Abstract
PURPOSE The aim of this study was to evaluate a surrogate-driven motion model based on four-dimensional computed tomography that is able to predict CT volumes corresponding to arbitrary respiratory phases. Furthermore, the comparison of three different driving surrogates is examined and the feasibility of using the model for 4D dose re-calculation will be discussed. METHODS The study is based on repeated 4DCTs of twenty patients treated for bronchial carcinoma and metastasis. The motion model was estimated from the planning 4DCT through deformable image registration. To predict a certain phase of a follow-up 4DCT, the model considers inter-fractional variations (baseline correction) and intra-fractional respiratory parameters (amplitude and phase) derived from surrogates. The estimated volumes resulting from the model were compared to ground-truth clinical 4DCTs using absolute HU differences in the lung region and landmarks localized using the Scale Invariant Feature Transform. Finally, the γ-index was used to evaluate the dosimetric effects of the intensity differences measured between the estimated and the ground-truth CT volumes. RESULTS The results show absolute HU differences between estimated and ground-truth images with median value (± standard deviation) of (61.3 ± 16.7) HU. Median 3D distances, measured on about 400 matching landmarks in each volume, were (2.9 ± 3.0) mm. 3D errors up to 28.2 mm were found for CT images with artifacts or reduced quality. Pass rates for all surrogate approaches were above 98.9% with a γ-criterion of 2%/2 mm. CONCLUSION The results depend mainly on the image quality of the initial 4DCT and the deformable image registration. All investigated surrogates can be used to estimate follow-up 4DCT phases, however, uncertainties decrease for volumetric approaches. Application of the model for 4D dose calculations is feasible.
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Affiliation(s)
- Jens Wölfelschneider
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054, Erlangen, Germany
| | - Matteo Seregni
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133, Milan, Italy
| | - Aurora Fassi
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133, Milan, Italy
| | - Marc Ziegler
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054, Erlangen, Germany
| | - Guido Baroni
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133, Milan, Italy
| | - Rainer Fietkau
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054, Erlangen, Germany
| | - Marco Riboldi
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133, Milan, Italy
| | - Christoph Bert
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054, Erlangen, Germany
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Woodruff HC, Shieh CC, Hegi-Johnson F, Keall PJ, Kipritidis J. Quantifying the reproducibility of lung ventilation images between 4-Dimensional Cone Beam CT and 4-Dimensional CT. Med Phys 2017; 44:1771-1781. [DOI: 10.1002/mp.12199] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2016] [Revised: 02/17/2017] [Accepted: 02/17/2017] [Indexed: 02/01/2023] Open
Affiliation(s)
- Henry C. Woodruff
- Radiation Physics Laboratory; School of Medicine; University of Sydney; Sydney NSW 2006 Australia
| | - Chun-Chien Shieh
- Radiation Physics Laboratory; School of Medicine; University of Sydney; Sydney NSW 2006 Australia
| | - Fiona Hegi-Johnson
- Radiation Physics Laboratory; School of Medicine; University of Sydney; Sydney NSW 2006 Australia
- Department of Medical Physics; School of Mathematical and Physical Sciences; University of Newcastle; Newcastle NSW 2300 Australia
- Radiation Oncology Centre; Seventh Day Adventist Hospital; Wahroonga NSW Australia
| | - Paul J. Keall
- Radiation Physics Laboratory; School of Medicine; University of Sydney; Sydney NSW 2006 Australia
| | - John Kipritidis
- Radiation Physics Laboratory; School of Medicine; University of Sydney; Sydney NSW 2006 Australia
- Northern Sydney Cancer Center; Royal North Shore Hospital; Sydney NSW 2065 Australia
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Paganelli C, Summers P, Gianoli C, Bellomi M, Baroni G, Riboldi M. A tool for validating MRI-guided strategies: a digital breathing CT/MRI phantom of the abdominal site. Med Biol Eng Comput 2017; 55:2001-2014. [DOI: 10.1007/s11517-017-1646-6] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2016] [Accepted: 03/25/2017] [Indexed: 12/18/2022]
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Park S, Plishker W, Quon H, Wong J, Shekhar R, Lee J. Deformable registration of CT and cone-beam CT with local intensity matching. Phys Med Biol 2017; 62:927-947. [PMID: 28074785 DOI: 10.1088/1361-6560/aa4f6d] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Cone-beam CT (CBCT) is a widely used intra-operative imaging modality in image-guided radiotherapy and surgery. A short scan followed by a filtered-backprojection is typically used for CBCT reconstruction. While data on the mid-plane (plane of source-detector rotation) is complete, off-mid-planes undergo different information deficiency and the computed reconstructions are approximate. This causes different reconstruction artifacts at off-mid-planes depending on slice locations, and therefore impedes accurate registration between CT and CBCT. In this paper, we propose a method to accurately register CT and CBCT by iteratively matching local CT and CBCT intensities. We correct CBCT intensities by matching local intensity histograms slice by slice in conjunction with intensity-based deformable registration. The correction-registration steps are repeated in an alternating way until the result image converges. We integrate the intensity matching into three different deformable registration methods, B-spline, demons, and optical flow that are widely used for CT-CBCT registration. All three registration methods were implemented on a graphics processing unit for efficient parallel computation. We tested the proposed methods on twenty five head and neck cancer cases and compared the performance with state-of-the-art registration methods. Normalized cross correlation (NCC), structural similarity index (SSIM), and target registration error (TRE) were computed to evaluate the registration performance. Our method produced overall NCC of 0.96, SSIM of 0.94, and TRE of 2.26 → 2.27 mm, outperforming existing methods by 9%, 12%, and 27%, respectively. Experimental results also show that our method performs consistently and is more accurate than existing algorithms, and also computationally efficient.
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Affiliation(s)
- Seyoun Park
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD, USA
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Vickress J, Battista J, Barnett R, Morgan J, Yartsev S. Automatic landmark generation for deformable image registration evaluation for 4D CT images of lung. Phys Med Biol 2016; 61:7236-7245. [DOI: 10.1088/0031-9155/61/20/7236] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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31
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Hauler F, Furtado H, Jurisic M, Polanec SH, Spick C, Laprie A, Nestle U, Sabatini U, Birkfellner W. Automatic quantification of multi-modal rigid registration accuracy using feature detectors. Phys Med Biol 2016; 61:5198-214. [DOI: 10.1088/0031-9155/61/14/5198] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Landry G, Nijhuis R, Dedes G, Handrack J, Thieke C, Janssens G, Orban de Xivry J, Reiner M, Kamp F, Wilkens JJ, Paganelli C, Riboldi M, Baroni G, Ganswindt U, Belka C, Parodi K. Investigating CT to CBCT image registration for head and neck proton therapy as a tool for daily dose recalculation. Med Phys 2016; 42:1354-66. [PMID: 25735290 DOI: 10.1118/1.4908223] [Citation(s) in RCA: 103] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
PURPOSE Intensity modulated proton therapy (IMPT) of head and neck (H&N) cancer patients may be improved by plan adaptation. The decision to adapt the treatment plan based on a dose recalculation on the current anatomy requires a diagnostic quality computed tomography (CT) scan of the patient. As gantry-mounted cone beam CT (CBCT) scanners are currently being offered by vendors, they may offer daily or weekly updates of patient anatomy. CBCT image quality may not be sufficient for accurate proton dose calculation and it is likely necessary to perform CBCT CT number correction. In this work, the authors investigated deformable image registration (DIR) of the planning CT (pCT) to the CBCT to generate a virtual CT (vCT) to be used for proton dose recalculation. METHODS Datasets of six H&N cancer patients undergoing photon intensity modulated radiation therapy were used in this study to validate the vCT approach. Each dataset contained a CBCT acquired within 3 days of a replanning CT (rpCT), in addition to a pCT. The pCT and rpCT were delineated by a physician. A Morphons algorithm was employed in this work to perform DIR of the pCT to CBCT following a rigid registration of the two images. The contours from the pCT were deformed using the vector field resulting from DIR to yield a contoured vCT. The DIR accuracy was evaluated with a scale invariant feature transform (SIFT) algorithm comparing automatically identified matching features between vCT and CBCT. The rpCT was used as reference for evaluation of the vCT. The vCT and rpCT CT numbers were converted to stopping power ratio and the water equivalent thickness (WET) was calculated. IMPT dose distributions from treatment plans optimized on the pCT were recalculated with a Monte Carlo algorithm on the rpCT and vCT for comparison in terms of gamma index, dose volume histogram (DVH) statistics as well as proton range. The DIR generated contours on the vCT were compared to physician-drawn contours on the rpCT. RESULTS The DIR accuracy was better than 1.4 mm according to the SIFT evaluation. The mean WET differences between vCT (pCT) and rpCT were below 1 mm (2.6 mm). The amount of voxels passing 3%/3 mm gamma criteria were above 95% for the vCT vs rpCT. When using the rpCT contour set to derive DVH statistics from dose distributions calculated on the rpCT and vCT the differences, expressed in terms of 30 fractions of 2 Gy, were within [-4, 2 Gy] for parotid glands (D(mean)), spinal cord (D(2%)), brainstem (D(2%)), and CTV (D(95%)). When using DIR generated contours for the vCT, those differences ranged within [-8, 11 Gy]. CONCLUSIONS In this work, the authors generated CBCT based stopping power distributions using DIR of the pCT to a CBCT scan. DIR accuracy was below 1.4 mm as evaluated by the SIFT algorithm. Dose distributions calculated on the vCT agreed well to those calculated on the rpCT when using gamma index evaluation as well as DVH statistics based on the same contours. The use of DIR generated contours introduced variability in DVH statistics.
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Affiliation(s)
- Guillaume Landry
- Department of Medical Physics, Ludwig-Maximilians-University, Munich D85748, Germany and Department of Radiation Oncology, Ludwig-Maximilians-University, Munich D81377, Germany
| | - Reinoud Nijhuis
- Department of Radiation Oncology, Ludwig-Maximilians-University, Munich D81377, Germany
| | - George Dedes
- Department of Medical Physics, Ludwig-Maximilians-University, Munich D85748, Germany
| | - Josefine Handrack
- Department of Medical Physics, Ludwig-Maximilians-University, Munich D85748, Germany
| | - Christian Thieke
- Department of Radiation Oncology, Ludwig-Maximilians-University, Munich D81377, Germany
| | - Guillaume Janssens
- ICTEAM, Université Catholique de Louvain, Louvain-La-Neuve B1348, Belgium
| | | | - Michael Reiner
- Department of Radiation Oncology, Ludwig-Maximilians-University, Munich D81377, Germany
| | - Florian Kamp
- Department of Radiation Oncology, Technische Universität München, Klinikum rechts der Isar, Munich D81675, Germany and Physik-Department, Technische Universität München, Garching D85748, Germany
| | - Jan J Wilkens
- Department of Radiation Oncology, Technische Universität München, Klinikum rechts der Isar, Munich D81675, Germany and Physik-Department, Technische Universität München, Garching D85748, Germany
| | - Chiara Paganelli
- Dipartimento di Elettronica Informazione e Bioingegneria, Politecnico di Milano, Milan 20133, Italy
| | - Marco Riboldi
- Dipartimento di Elettronica Informazione e Bioingegneria, Politecnico di Milano, Milan 20133, Italy
| | - Guido Baroni
- Dipartimento di Elettronica Informazione e Bioingegneria, Politecnico di Milano, Milan 20133, Italy
| | - Ute Ganswindt
- Department of Radiation Oncology, Ludwig-Maximilians-University, Munich D81377, Germany
| | - Claus Belka
- Department of Radiation Oncology, Ludwig-Maximilians-University, Munich D81377, Germany
| | - Katia Parodi
- Department of Medical Physics, Ludwig-Maximilians-University, Munich D85748, Germany
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Lee DH, Lee DW, Han BS. Possibility Study of Scale Invariant Feature Transform (SIFT) Algorithm Application to Spine Magnetic Resonance Imaging. PLoS One 2016; 11:e0153043. [PMID: 27064404 PMCID: PMC4827852 DOI: 10.1371/journal.pone.0153043] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2015] [Accepted: 03/22/2016] [Indexed: 12/04/2022] Open
Abstract
The purpose of this study is an application of scale invariant feature transform (SIFT) algorithm to stitch the cervical-thoracic-lumbar (C-T-L) spine magnetic resonance (MR) images to provide a view of the entire spine in a single image. All MR images were acquired with fast spin echo (FSE) pulse sequence using two MR scanners (1.5 T and 3.0 T). The stitching procedures for each part of spine MR image were performed and implemented on a graphic user interface (GUI) configuration. Moreover, the stitching process is performed in two categories; manual point-to-point (mPTP) selection that performed by user specified corresponding matching points, and automated point-to-point (aPTP) selection that performed by SIFT algorithm. The stitched images using SIFT algorithm showed fine registered results and quantitatively acquired values also indicated little errors compared with commercially mounted stitching algorithm in MRI systems. Our study presented a preliminary validation of the SIFT algorithm application to MRI spine images, and the results indicated that the proposed approach can be performed well for the improvement of diagnosis. We believe that our approach can be helpful for the clinical application and extension of other medical imaging modalities for image stitching.
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Affiliation(s)
- Dong-Hoon Lee
- Division of MR Research, Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Do-Wan Lee
- Division of MR Research, Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Bong-Soo Han
- Department of Radiological Science, College of Health Science, Yonsei University, Wonju, Rep. of Korea
- * E-mail:
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Riboldi M, Baroni G. Challenges and opportunities in image guided particle therapy. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2015:5227-30. [PMID: 26737470 DOI: 10.1109/embc.2015.7319570] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The application of biomedical imaging and image processing to radiation therapy with accelerated particles has unique challenges. The potential of particle therapy to precisely tailor the dose distribution around the target volume needs to account for the intrinsic sensitivity to uncertainties in dose deposition. These peculiar features motivate the use of image guided methods to consistently verify the accuracy in dose delivery. Dedicated imaging and image processing methods are required, from treatment planning to treatment verification phases, in order to reduce the effects of uncertainties. The scenario is also complicated by the lack of standardized layouts of treatment bunkers, which implies the relatively increased use of custom solutions. Conversely, imaging can be applied to verify the actual delivered dose, representing a valuable opportunity to validate specific protocols and visualize the efficacy of the intended treatment. In this contribution, challenges and opportunities in image guided particle therapy are overviewed, with a clear focus on research perspectives in biomedical imaging and image processing.
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Mazur TR, Fischer-Valuck BW, Wang Y, Yang D, Mutic S, Li HH. SIFT-based dense pixel tracking on 0.35 T cine-MR images acquired during image-guided radiation therapy with application to gating optimization. Med Phys 2015; 43:279. [DOI: 10.1118/1.4938096] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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Paganelli C, Summers P, Bellomi M, Baroni G, Riboldi M. Liver 4DMRI: A retrospective image-based sorting method. Med Phys 2015; 42:4814-21. [DOI: 10.1118/1.4927252] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
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Kurz C, Dedes G, Resch A, Reiner M, Ganswindt U, Nijhuis R, Thieke C, Belka C, Parodi K, Landry G. Comparing cone-beam CT intensity correction methods for dose recalculation in adaptive intensity-modulated photon and proton therapy for head and neck cancer. Acta Oncol 2015. [PMID: 26198654 DOI: 10.3109/0284186x.2015.1061206] [Citation(s) in RCA: 77] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
BACKGROUND Adaptive intensity-modulated photon and proton radiotherapy (IMRT and IMPT) of head and neck (H&N) cancer requires frequent three-dimensional (3D) dose calculation. We compared two approaches for dose recalculation on the basis of intensity-corrected cone-beam (CB) x-ray computed tomography (CT) images. MATERIAL AND METHODS For nine H&N tumor patients, virtual CTs (vCT) were generated by deformable image registration of the planning CT (pCT) to the CBCT. The second intensity correction approach used population-based lookup tables for scaling CBCT intensities to the pCT HU range (CBCTLUT). IMRT and IMPT plans were generated with a commercial treatment planning system. Dose recalculations on vCT and CBCTLUT were analyzed using a (3%, 3 mm) gamma-index analysis and comparison of normal tissue and tumor dose/volume parameters. A replanning CT (rpCT) acquired within three days of the CBCT served as reference. Single field uniform dose (SFUD) proton plans were created and recalculated on vCT and CBCTLUT for proton range comparison. RESULTS Dose/volume parameters showed minor differences between rpCT, vCT and CBCTLUT in IMRT, but clinically relevant deviations between CBCTLUT and rpCT in the spinal cord for IMPT. Gamma-index pass-rates were found increased for vCT with respect to CBCTLUT in IMPT (by up to 21 percentage points) and IMRT (by up to 9 percentage points) for most cases. The SFUD-based proton range assessment showed improved agreement of vCT and rpCT, with 88-99% of the depth dose profiles in beam's eye view agreeing within 3 mm. For CBCTLUT, only 80-94% of the profiles fulfilled this criterion. CONCLUSION vCT and CBCTLUT are suitable options for dose recalculation in adaptive IMRT. In the scope of IMPT, the vCT approach is preferable.
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Affiliation(s)
- Christopher Kurz
- a Department of Radiation Oncology , Ludwig-Maximilians-University , Munich , Germany
- b Department of Medical Physics , Ludwig-Maximilians-University , Munich , Germany
| | - George Dedes
- b Department of Medical Physics , Ludwig-Maximilians-University , Munich , Germany
| | - Andreas Resch
- b Department of Medical Physics , Ludwig-Maximilians-University , Munich , Germany
| | - Michael Reiner
- a Department of Radiation Oncology , Ludwig-Maximilians-University , Munich , Germany
| | - Ute Ganswindt
- a Department of Radiation Oncology , Ludwig-Maximilians-University , Munich , Germany
| | - Reinoud Nijhuis
- a Department of Radiation Oncology , Ludwig-Maximilians-University , Munich , Germany
| | - Christian Thieke
- a Department of Radiation Oncology , Ludwig-Maximilians-University , Munich , Germany
| | - Claus Belka
- a Department of Radiation Oncology , Ludwig-Maximilians-University , Munich , Germany
| | - Katia Parodi
- b Department of Medical Physics , Ludwig-Maximilians-University , Munich , Germany
| | - Guillaume Landry
- a Department of Radiation Oncology , Ludwig-Maximilians-University , Munich , Germany
- b Department of Medical Physics , Ludwig-Maximilians-University , Munich , Germany
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Magnetic Resonance Imaging–Guided versus Surrogate-Based Motion Tracking in Liver Radiation Therapy: A Prospective Comparative Study. Int J Radiat Oncol Biol Phys 2015; 91:840-8. [DOI: 10.1016/j.ijrobp.2014.12.013] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2014] [Revised: 10/14/2014] [Accepted: 12/08/2014] [Indexed: 12/25/2022]
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Rigaud B, Simon A, Castelli J, Gobeli M, Ospina Arango JD, Cazoulat G, Henry O, Haigron P, De Crevoisier R. Evaluation of deformable image registration methods for dose monitoring in head and neck radiotherapy. BIOMED RESEARCH INTERNATIONAL 2015; 2015:726268. [PMID: 25759821 PMCID: PMC4339705 DOI: 10.1155/2015/726268] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2014] [Revised: 01/16/2015] [Accepted: 01/16/2015] [Indexed: 11/18/2022]
Abstract
In the context of head and neck cancer (HNC) adaptive radiation therapy (ART), the two purposes of the study were to compare the performance of multiple deformable image registration (DIR) methods and to quantify their impact for dose accumulation, in healthy structures. Fifteen HNC patients had a planning computed tomography (CT0) and weekly CTs during the 7 weeks of intensity-modulated radiation therapy (IMRT). Ten DIR approaches using different registration methods (demons or B-spline free form deformation (FFD)), preprocessing, and similarity metrics were tested. Two observers identified 14 landmarks (LM) on each CT-scan to compute LM registration error. The cumulated doses estimated by each method were compared. The two most effective DIR methods were the demons and the FFD, with both the mutual information (MI) metric and the filtered CTs. The corresponding LM registration accuracy (precision) was 2.44 mm (1.30 mm) and 2.54 mm (1.33 mm), respectively. The corresponding LM estimated cumulated dose accuracy (dose precision) was 0.85 Gy (0.93 Gy) and 0.88 Gy (0.95 Gy), respectively. The mean uncertainty (difference between maximal and minimal dose considering all the 10 methods) to estimate the cumulated mean dose to the parotid gland (PG) was 4.03 Gy (SD = 2.27 Gy, range: 1.06-8.91 Gy).
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Affiliation(s)
- Bastien Rigaud
- Université de Rennes 1, LTSI, Campus de Beaulieu, 35000 Rennes, France
- INSERM, U1099, Campus de Beaulieu, 35000 Rennes, France
| | - Antoine Simon
- Université de Rennes 1, LTSI, Campus de Beaulieu, 35000 Rennes, France
- INSERM, U1099, Campus de Beaulieu, 35000 Rennes, France
| | - Joël Castelli
- Université de Rennes 1, LTSI, Campus de Beaulieu, 35000 Rennes, France
- INSERM, U1099, Campus de Beaulieu, 35000 Rennes, France
- Centre Eugene Marquis, Radiotherapy Department, 35000 Rennes, France
| | - Maxime Gobeli
- Centre Eugene Marquis, Radiotherapy Department, 35000 Rennes, France
| | - Juan-David Ospina Arango
- Université de Rennes 1, LTSI, Campus de Beaulieu, 35000 Rennes, France
- INSERM, U1099, Campus de Beaulieu, 35000 Rennes, France
| | - Guillaume Cazoulat
- Université de Rennes 1, LTSI, Campus de Beaulieu, 35000 Rennes, France
- INSERM, U1099, Campus de Beaulieu, 35000 Rennes, France
| | - Olivier Henry
- Centre Eugene Marquis, Radiotherapy Department, 35000 Rennes, France
| | - Pascal Haigron
- Université de Rennes 1, LTSI, Campus de Beaulieu, 35000 Rennes, France
- INSERM, U1099, Campus de Beaulieu, 35000 Rennes, France
| | - Renaud De Crevoisier
- Université de Rennes 1, LTSI, Campus de Beaulieu, 35000 Rennes, France
- INSERM, U1099, Campus de Beaulieu, 35000 Rennes, France
- Centre Eugene Marquis, Radiotherapy Department, 35000 Rennes, France
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Landry G, Dedes G, Zöllner C, Handrack J, Janssens G, Orban de Xivry J, Reiner M, Paganelli C, Riboldi M, Kamp F, Söhn M, Wilkens JJ, Baroni G, Belka C, Parodi K. Phantom based evaluation of CT to CBCT image registration for proton therapy dose recalculation. Phys Med Biol 2014; 60:595-613. [PMID: 25548912 DOI: 10.1088/0031-9155/60/2/595] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
The ability to perform dose recalculation on the anatomy of the day is important in the context of adaptive proton therapy. The objective of this study was to investigate the use of deformable image registration (DIR) and cone beam CT (CBCT) imaging to generate the daily stopping power distribution of the patient. We investigated the deformation of the planning CT scan (pCT) onto daily CBCT images to generate a virtual CT (vCT) using a deformable phantom designed for the head and neck (H & N) region. The phantom was imaged at a planning CT scanner in planning configuration, yielding a pCT and in deformed, treatment day configuration, yielding a reference CT (refCT). The treatment day configuration was additionally scanned at a CBCT scanner. A Morphons DIR algorithm was used to generate a vCT. The accuracy of the vCT was evaluated by comparison to the refCT in terms of corresponding features as identified by an adaptive scale invariant feature transform (aSIFT) algorithm. Additionally, the vCT CT numbers were compared to those of the refCT using both profiles and regions of interest and the volumes and overlap (DICE coefficients) of various phantom structures were compared. The water equivalent thickness (WET) of the vCT, refCT and pCT were also compared to evaluate proton range differences. Proton dose distributions from the same initial fluence were calculated on the refCT, vCT and pCT and compared in terms of proton range. The method was tested on a clinical dataset using a replanning CT scan acquired close in time to a CBCT scan as reference using the WET evaluation. Results from the aSIFT investigation suggest a deformation accuracy of 2-3 mm. The use of the Morphon algorithm did not distort CT number intensity in uniform regions and WET differences between vCT and refCT were of the order of 2% of the proton range. This result was confirmed by proton dose calculations. The patient results were consistent with phantom observations. In conclusion, our phantom study suggests the vCT approach is adequate for proton dose recalculation on the basis of CBCT imaging.
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Affiliation(s)
- Guillaume Landry
- Department of Physics, Ludwig-Maximilians-University, Munich, Germany. Department of Radiation Oncology, Ludwig-Maximilians-University, Munich, Germany
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Bert C, Graeff C, Riboldi M, Nill S, Baroni G, Knopf AC. Advances in 4D treatment planning for scanned particle beam therapy - report of dedicated workshops. Technol Cancer Res Treat 2014; 13:485-95. [PMID: 24354749 PMCID: PMC4527425 DOI: 10.7785/tcrtexpress.2013.600274] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2013] [Revised: 09/27/2013] [Accepted: 10/01/2013] [Indexed: 11/25/2022] Open
Abstract
We report on recent progress in the field of mobile tumor treatment with scanned particle beams, as discussed in the latest editions of the 4D treatment planning workshop. The workshop series started in 2009, with about 20 people from 4 research institutes involved, all actively working on particle therapy delivery and development. The first workshop resulted in a summary of recommendations for the treatment of mobile targets, along with a list of requirements to apply these guidelines clinically. The increased interest in the treatment of mobile tumors led to a continuously growing number of attendees: the 2012 edition counted more than 60 participants from 20 institutions and commercial vendors. The focus of research discussions among workshop participants progressively moved from 4D treatment planning to complete 4D treatments, aiming at effective and safe treatment delivery. Current research perspectives on 4D treatments include all critical aspects of time resolved delivery, such as in-room imaging, motion detection, beam application, and quality assurance techniques. This was motivated by the start of first clinical treatments of hepato cellular tumors with a scanned particle beam, relying on gating or abdominal compression for motion mitigation. Up to date research activities emphasize significant efforts in investigating advanced motion mitigation techniques, with a specific interest in the development of dedicated tools for experimental validation. Potential improvements will be made possible in the near future through 4D optimized treatment plans that require upgrades of the currently established therapy control systems for time resolved delivery. But since also these novel optimization techniques rely on the validity of the 4DCT, research focusing on alternative 4D imaging technique, such as MRI based 4DCT generation will continue.
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Affiliation(s)
- Christoph Bert
- University Clinic Erlangen, Radiation Oncology, Universitatsstrasse 27, 91054 Erlangen, Germany.
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Uncertainties encountered in implementation of adaptive planning with in vivo dosimeters. Radiol Phys Technol 2014; 8:81-7. [PMID: 25236778 DOI: 10.1007/s12194-014-0291-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2014] [Revised: 09/08/2014] [Accepted: 09/10/2014] [Indexed: 10/24/2022]
Abstract
Under a previously approved institutional review board protocol for prostate cancer patients, implanted metal-oxide semiconductor field-effect transistor dosimeters (dose verification system, Sicel Technologies) were used for measurement of the in vivo delivered daily dose. This dosimetric information provided the ability to adapt the plan if the measured doses did not match the dose expected from the planning system. Due to the inherent uncertainty in the dosimeters, the decision to adapt the treatment plan was made only if readings differed by more than 7 % for three consecutive days. To validate this method, we acquired daily cone beam computed tomography images for five patients, and the dose delivered to the dosimeters was calculated by use of (1) an automated procedure (MIM Maestro, MIM Software) and (2) the treatment planning system (XIO, Elekta). 72 % of the doses calculated automatically fell within 1 % of the doses calculated in the planning system, and 99 % agreed within 2 %. When compared to the calculated dose, 53 % of the in vivo measurements fell within 3 % of the calculated dose, 80 % fell within 5 %, and 9.8 % were greater than 7 %, but never on three consecutive days. The measured doses agreed reasonably well with the calculated doses, supporting the decision to adapt the plan only if there were discrepancies of more than 7 % over three consecutive days. Even with the inherent uncertainty in the dosimeters, this adaptive planning method can detect delivery inaccuracies that would not otherwise be caught with the use of only daily image guidance or other dose calculation surrogates.
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An adaptive radiotherapy planning strategy for bladder cancer using deformation vector fields. Radiother Oncol 2014; 112:371-5. [DOI: 10.1016/j.radonc.2014.07.012] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2014] [Revised: 06/03/2014] [Accepted: 07/15/2014] [Indexed: 11/18/2022]
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Stoll M, Giske K, Debus J, Bendl R, Stoiber EM. The frequency of re-planning and its variability dependent on the modification of the re-planning criteria and IGRT correction strategy in head and neck IMRT. Radiat Oncol 2014; 9:175. [PMID: 25112458 PMCID: PMC4251689 DOI: 10.1186/1748-717x-9-175] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2014] [Accepted: 07/31/2014] [Indexed: 12/03/2022] Open
Abstract
Background To analyse the frequency of re-planning and its variability dependent on the IGRT correction strategy and on the modification of the dosimetric criteria for re-planning for the spinal cord in head and neck IG-IMRT. Methods Daily kV-control-CTs of six head and neck patients (=175 CTs) were analysed. All volumes of interest were re-contoured using deformable image registration. Three IGRT correction strategies were simulated and the resulting dose distributions were computed for all fractions. Different sets of criteria with varying dose thresholds for re-planning were investigated. All sets of criteria ensure equivalent target coverage of both CTVs, but vary in the tolerance threshold of the spinal cord. Results The variations of the D95 and D2 in respect to the planned values ranged from -7% to +3% for both CTVs, and -2% to +6% for the spinal cord. Despite different correction vectors of the three IGRT strategies, the dosimetric differences were small. The number of fractions not requiring re-planning varied between 0% and 11% dependent on the applied IGRT correction strategy. In contrast, this number ranged between 32% and 70% dependent on the dosimetric thresholds, even though these thresholds were only gently modified. Conclusions The more precise the planned dose needs to be maintained over the treatment course, the more frequently re-planning is required. The influence of different IGRT correction strategies, even though geometrically notable, was found to be of only limited relevance for the re-planning frequency. In contrast, the definition and modification of thresholds for re-planning have a major impact on the re-planning frequency.
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Affiliation(s)
- Markus Stoll
- Department of Medical Physics in Radiation Oncology, DKFZ, INF 280, 69120 Heidelberg, Germany.
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Efficient iris recognition based on optimal subfeature selection and weighted subregion fusion. ScientificWorldJournal 2014; 2014:157173. [PMID: 24683317 PMCID: PMC3934576 DOI: 10.1155/2014/157173] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2013] [Accepted: 11/10/2013] [Indexed: 11/23/2022] Open
Abstract
In this paper, we propose three discriminative feature selection strategies and weighted subregion matching method to improve the performance of iris recognition system. Firstly, we introduce the process of feature extraction and representation based on scale invariant feature transformation (SIFT) in detail. Secondly, three strategies are described, which are orientation probability distribution function (OPDF) based strategy to delete some redundant feature keypoints, magnitude probability distribution function (MPDF) based strategy to reduce dimensionality of feature element, and compounded strategy combined OPDF and MPDF to further select optimal subfeature. Thirdly, to make matching more effective, this paper proposes a novel matching method based on weighted sub-region matching fusion. Particle swarm optimization is utilized to accelerate achieve different sub-region's weights and then weighted different subregions' matching scores to generate the final decision. The experimental results, on three public and renowned iris databases (CASIA-V3 Interval, Lamp, andMMU-V1), demonstrate that our proposed methods outperform some of the existing methods in terms of correct recognition rate, equal error rate, and computation complexity.
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Stoiber EM, Schwarz M, Debus J, Bendl R, Giske K. An optimised IGRT correction vector determined from a displacement vector field: a proof of principle of a decision-making aid for re-planning. Acta Oncol 2014; 53:33-9. [PMID: 23614778 DOI: 10.3109/0284186x.2013.790559] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
BACKGROUND To present a new method that determines an optimised IGRT couch correction vector from a displacement vector field (DVF). The DVF is computed by a deformable image registration (DIR) method. The proposed method can improve the quality of volume-of-interest (VOI) alignment in image guided radiation therapy (IGRT), and can serve as a decision-making aid for re-planning. MATERIAL AND METHODS The proposed method was demonstrated using the CT data sets of 11 head-and-neck cancer patients with daily kilovoltage control-CTs. A DVF was computed for each control-CT using a DIR method. The DVF was used for voxel tracking and re-contouring of the VOIs in the control-CTs. Then a rigid body transformation, which could be used as couch correction vector, was optimised. The aim of the optimisation process was to find a vector and rotations that map the deformed VOIs into a specified territory. This territory was defined by a margin extension of the VOIs at the time of the planning process. Within this extension, VOI motion and deformation was tolerated. The objective function in the optimisation process was the sum of all volume fractions outside the defined territories. RESULTS The proposed method was able to find a correction vector, which resulted in a coverage of the target volumes of at least 98% in 52.3% of all fractions. In contrast, a standard IGRT correction using a rigid registration method only fulfilled this criterion in 22.6% of all fractions. The optimisation process took an average of 1.5 minutes per fraction. CONCLUSION The knowledge of the deformation of the anatomy allows the determination of an optimised rigid correction vector using our method. The method ensures controlled mapping of the VOIs despite small deformations. If no optimised vector can be determined, re-planning should be considered. Thus, our method can also serve as a decision-making aid for re-planning.
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Affiliation(s)
- Eva Maria Stoiber
- Department of Medical Physics in Radiation Oncology, DKFZ , Heidelberg , Germany
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Zhang J, Wang J, Wang X, Feng D. The adaptive FEM elastic model for medical image registration. Phys Med Biol 2013; 59:97-118. [PMID: 24334618 DOI: 10.1088/0031-9155/59/1/97] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
This paper proposes an adaptive mesh refinement strategy for the finite element method (FEM) based elastic registration model. The signature matrix for mesh refinement takes into account the regional intensity variance and the local deformation displacement. The regional intensity variance reflects detailed information for improving registration accuracy and the deformation displacement fine-tunes the mesh refinement for a more efficient algorithm. The gradient flows of two different similarity metrics, the sum of the squared difference and the spatially encoded mutual information for the mono-modal and multi-modal registrations, are used to derive external forces to drive the model to the equilibrium state. We compared our approach to three other models: (1) the conventional multi-resolution FEM registration algorithm; (2) the FEM elastic method that uses variation information for mesh refinement; and (3) the robust block matching based registration. Comparisons among different methods in a dataset with 20 CT image pairs upon artificial deformation demonstrate that our registration method achieved significant improvement in accuracies. Experimental results in another dataset of 40 real medical image pairs for both mono-modal and multi-modal registrations also show that our model outperforms the other three models in its accuracy.
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Affiliation(s)
- Jingya Zhang
- School of Electronic and Information Engineering, Soochow University, Suzhou 215006, People's Republic of China. Dept Phys, Changshu Inst Technol, Changshu 215500, People's Republic of China
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Peroni M, Spadea MF, Riboldi M, Falcone S, Vaccaro C, Sharp GC, Baroni G. Validation of Automatic Contour Propagation for 4D Treatment Planning Using Multiple Metrics. Technol Cancer Res Treat 2013; 12:501-10. [DOI: 10.7785/tcrt.2012.500347] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
The aim of this work is to provide insights into multiple metrics clinical validation of deformable image registration and contour propagation methods in 4D lung radiotherapy planning. The following indices were analyzed and compared: Volume Difference (VD), Dice Similarity Coefficient (DSC), Positive Predictive Value (PPV) and Surface Distances (SD). The analysis was performed on three patient datasets, using as reference a ground-truth volume generated by means of Simultaneous Truth And Performance Level Estimation (STAPLE) algorithm from the outlines of five experts. Significant discrepancies in the quality assessment provided by the different metrics in all the examined cases were found. Metrics sensitivity was more evident in presence of image artifacts and particularly for tubular anatomical structures, such as esophagus or spinal cord. Volume Differences did not account for position and DSC exhibited criticalities due to its intrinsic symmetry ( i.e. over- and under-estimation of the reference contours cannot be discriminated) and dependency on the total volume of the structure. PPV analysis showed more robust performance, as each voxel concurs to the classification of the propagation, but was not able to detect inclusion of propagated and ground-truth volumes. Mesh distances could interpret the actual shape of the structures, but might report higher mismatches in case of large local differences in the contour surfaces. According to our study, the combination of VD and SD for the validation of contour propagation algorithms in 4D could provide the necessary failure detection accuracy.
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Affiliation(s)
- M. Peroni
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, via Golgi 39, 20133 Milano, Italy
| | - M. F. Spadea
- Department of Experimental and Clinical Medicine, Università degli Studi Magna Graecia, Catanzaro, Italy
| | - M. Riboldi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, via Golgi 39, 20133 Milano, Italy
- Bioengineering Unit, Centro Nazionale di Adroterapia Oncologica, Strada Campeggi 53, 27100 Pavia, Italy
| | - S. Falcone
- Radiation Oncology Department, Policlinico Mater Domini, Catanzaro, Italy
| | - C. Vaccaro
- Radiation Oncology Department, Policlinico Mater Domini, Catanzaro, Italy
| | - G. C. Sharp
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
| | - G. Baroni
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, via Golgi 39, 20133 Milano, Italy
- Bioengineering Unit, Centro Nazionale di Adroterapia Oncologica, Strada Campeggi 53, 27100 Pavia, Italy
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Paganelli C, Peroni M, Baroni G, Riboldi M. Quantification of organ motion based on an adaptive image-based scale invariant feature method. Med Phys 2013; 40:111701. [DOI: 10.1118/1.4822486] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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Fabri D, Zambrano V, Bhatia A, Furtado H, Bergmann H, Stock M, Bloch C, Lütgendorf-Caucig C, Pawiro S, Georg D, Birkfellner W, Figl M. A quantitative comparison of the performance of three deformable registration algorithms in radiotherapy. Z Med Phys 2013; 23:279-90. [PMID: 23969092 PMCID: PMC3865361 DOI: 10.1016/j.zemedi.2013.07.006] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2012] [Revised: 07/24/2013] [Accepted: 07/25/2013] [Indexed: 11/17/2022]
Abstract
We present an evaluation of various non-rigid registration algorithms for the purpose of compensating interfractional motion of the target volume and organs at risk areas when acquiring CBCT image data prior to irradiation. Three different deformable registration (DR) methods were used: the Demons algorithm implemented in the iPlan Software (BrainLAB AG, Feldkirchen, Germany) and two custom-developed piecewise methods using either a Normalized Correlation or a Mutual Information metric (featureletNC and featureletMI). These methods were tested on data acquired using a novel purpose-built phantom for deformable registration and clinical CT/CBCT data of prostate and lung cancer patients. The Dice similarity coefficient (DSC) between manually drawn contours and the contours generated by a derived deformation field of the structures in question was compared to the result obtained with rigid registration (RR). For the phantom, the piecewise methods were slightly superior, the featureletNC for the intramodality and the featureletMI for the intermodality registrations. For the prostate cases in less than 50% of the images studied the DSC was improved over RR. Deformable registration methods improved the outcome over a rigid registration for lung cases and in the phantom study, but not in a significant way for the prostate study. A significantly superior deformation method could not be identified.
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Affiliation(s)
- Daniella Fabri
- Center of Medical Physics and Biomedical Engineering, Medical University of Vienna, AKH-4L, Waehringer Guertel 18-20, A-1090 Vienna, Austria
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