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Title: SU-C-BRA-07: Variability of Patient-Specific Motion Models Derived Using Different Deformable Image Registration Algorithms for Lung Cancer Stereotactic Body Radiotherapy (SBRT) Patients

Abstract

Purpose: To study the variability of patient-specific motion models derived from 4-dimensional CT (4DCT) images using different deformable image registration (DIR) algorithms for lung cancer stereotactic body radiotherapy (SBRT) patients. Methods: Motion models are derived by 1) applying DIR between each 4DCT image and a reference image, resulting in a set of displacement vector fields (DVFs), and 2) performing principal component analysis (PCA) on the DVFs, resulting in a motion model (a set of eigenvectors capturing the variations in the DVFs). Three DIR algorithms were used: 1) Demons, 2) Horn-Schunck, and 3) iterative optical flow. The motion models derived were compared using patient 4DCT scans. Results: Motion models were derived and the variations were evaluated according to three criteria: 1) the average root mean square (RMS) difference which measures the absolute difference between the components of the eigenvectors, 2) the dot product between the eigenvectors which measures the angular difference between the eigenvectors in space, and 3) the Euclidean Model Norm (EMN), which is calculated by summing the dot products of an eigenvector with the first three eigenvectors from the reference motion model in quadrature. EMN measures how well an eigenvector can be reconstructed using another motion model derived usingmore » a different DIR algorithm. Results showed that comparing to a reference motion model (derived using the Demons algorithm), the eigenvectors of the motion model derived using the iterative optical flow algorithm has smaller RMS, larger dot product, and larger EMN values than those of the motion model derived using Horn-Schunck algorithm. Conclusion: The study showed that motion models vary depending on which DIR algorithms were used to derive them. The choice of a DIR algorithm may affect the accuracy of the resulting model, and it is important to assess the suitability of the algorithm chosen for a particular application. This project was supported, in part, through a Master Research Agreement with Varian Medical Systems, Inc, Palo Alto, CA.« less

Authors:
;  [1];  [2];  [3]
  1. Brigham and Women’s Hospital / Harvard Medical School, Boston, MA (United States)
  2. William Beaumont Hospital, Royal Oak, MI (United States)
  3. University of California at Los Angeles, Los Angeles, CA (United States)
Publication Date:
OSTI Identifier:
22624329
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 43; Journal Issue: 6; Other Information: (c) 2016 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 61 RADIATION PROTECTION AND DOSIMETRY; ACCURACY; ALGORITHMS; COMPUTERIZED TOMOGRAPHY; EIGENVECTORS; IMAGES; ITERATIVE METHODS; LUNGS; NEOPLASMS; PATIENTS; QUADRATURES; RADIOTHERAPY

Citation Formats

Dhou, S, Williams, C, Ionascu, D, and Lewis, J. SU-C-BRA-07: Variability of Patient-Specific Motion Models Derived Using Different Deformable Image Registration Algorithms for Lung Cancer Stereotactic Body Radiotherapy (SBRT) Patients. United States: N. p., 2016. Web. doi:10.1118/1.4955568.
Dhou, S, Williams, C, Ionascu, D, & Lewis, J. SU-C-BRA-07: Variability of Patient-Specific Motion Models Derived Using Different Deformable Image Registration Algorithms for Lung Cancer Stereotactic Body Radiotherapy (SBRT) Patients. United States. doi:10.1118/1.4955568.
Dhou, S, Williams, C, Ionascu, D, and Lewis, J. Wed . "SU-C-BRA-07: Variability of Patient-Specific Motion Models Derived Using Different Deformable Image Registration Algorithms for Lung Cancer Stereotactic Body Radiotherapy (SBRT) Patients". United States. doi:10.1118/1.4955568.
@article{osti_22624329,
title = {SU-C-BRA-07: Variability of Patient-Specific Motion Models Derived Using Different Deformable Image Registration Algorithms for Lung Cancer Stereotactic Body Radiotherapy (SBRT) Patients},
author = {Dhou, S and Williams, C and Ionascu, D and Lewis, J},
abstractNote = {Purpose: To study the variability of patient-specific motion models derived from 4-dimensional CT (4DCT) images using different deformable image registration (DIR) algorithms for lung cancer stereotactic body radiotherapy (SBRT) patients. Methods: Motion models are derived by 1) applying DIR between each 4DCT image and a reference image, resulting in a set of displacement vector fields (DVFs), and 2) performing principal component analysis (PCA) on the DVFs, resulting in a motion model (a set of eigenvectors capturing the variations in the DVFs). Three DIR algorithms were used: 1) Demons, 2) Horn-Schunck, and 3) iterative optical flow. The motion models derived were compared using patient 4DCT scans. Results: Motion models were derived and the variations were evaluated according to three criteria: 1) the average root mean square (RMS) difference which measures the absolute difference between the components of the eigenvectors, 2) the dot product between the eigenvectors which measures the angular difference between the eigenvectors in space, and 3) the Euclidean Model Norm (EMN), which is calculated by summing the dot products of an eigenvector with the first three eigenvectors from the reference motion model in quadrature. EMN measures how well an eigenvector can be reconstructed using another motion model derived using a different DIR algorithm. Results showed that comparing to a reference motion model (derived using the Demons algorithm), the eigenvectors of the motion model derived using the iterative optical flow algorithm has smaller RMS, larger dot product, and larger EMN values than those of the motion model derived using Horn-Schunck algorithm. Conclusion: The study showed that motion models vary depending on which DIR algorithms were used to derive them. The choice of a DIR algorithm may affect the accuracy of the resulting model, and it is important to assess the suitability of the algorithm chosen for a particular application. This project was supported, in part, through a Master Research Agreement with Varian Medical Systems, Inc, Palo Alto, CA.},
doi = {10.1118/1.4955568},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = {Wed Jun 15 00:00:00 EDT 2016},
month = {Wed Jun 15 00:00:00 EDT 2016}
}
  • Purpose: Quantification of volume changes on CBCT during SBRT for NSCLC may provide a useful radiological marker for radiation response and adaptive treatment planning, but the reproducibility of CBCT volume delineation is a concern. This study is to quantify inter-scan/inter-observer variability in tumor volume delineation on CBCT. Methods: Twenty earlystage (stage I and II) NSCLC patients were included in this analysis. All patients were treated with SBRT with a median dose of 54 Gy in 3 to 5 fractions. Two physicians independently manually contoured the primary gross tumor volume on CBCTs taken immediately before SBRT treatment (Pre) and after themore » same SBRT treatment (Post). Absolute volume differences (AVD) were calculated between the Pre and Post CBCTs for a given treatment to quantify inter-scan variability, and then between the two observers for a given CBCT to quantify inter-observer variability. AVD was also normalized with respect to average volume to obtain relative volume differences (RVD). Bland-Altman approach was used to evaluate variability. All statistics were calculated with SAS version 9.4. Results: The 95% limit of agreement (mean ± 2SD) on AVD and RVD measurements between Pre and Post scans were −0.32cc to 0.32cc and −0.5% to 0.5% versus −1.9 cc to 1.8 cc and −15.9% to 15.3% for the two observers respectively. The 95% limit of agreement of AVD and RVD between the two observers were −3.3 cc to 2.3 cc and −42.4% to 28.2% respectively. The greatest variability in inter-scan RVD was observed with very small tumors (< 5 cc). Conclusion: Inter-scan variability in RVD is greatest with small tumors. Inter-observer variability was larger than inter-scan variability. The 95% limit of agreement for inter-observer and inter-scan variability (∼15–30%) helps define a threshold for clinically meaningful change in tumor volume to assess SBRT response, with larger thresholds needed for very small tumors. Part of the work was funded by a Kaye award; Disclosure/Conflict of interest: Raymond H. Mak: Stock ownership: Celgene, Inc. Consulting: Boehringer-Ingelheim, Inc.« less
  • Purpose: To investigate the effectiveness of employing abdominal compression (AC) in reducing motion for the target region and sub-regions of the lung as part of the planning process for radiation therapy. Methods: Fourteen patients with early lung cancer were scanned with 4DCT and it was determined that target motion exceeded our institutional limit of > 8 mm motion and received a repeat 4DCT with AC. For each 4DCT, deformable image registration (DIR) was used to map the max inhale to the max exhale phase to determine the deformation vector fields (DVF). DIR was performed with Morphons and Demons algorithms. Themore » mean DVF was used to represent that sub-region for each patient. The magnitudes of the mean DVF were quantified for the target and 12 sub-regions in the AP, LR SI directions. The sub-regions were contoured on each lung as (add prefix R or L for lung): Upper-Anterior (UA), Upper-Posterior (UP), Mid-Anterior (MA), Mid-Posterior (MP), Lower-Anterior (LA) and Lower-Posterior (LP). Results: The min/max SI motion for the target on the uncompressed 4DCT was 8mm/24.5 mm. The magnitude of decrease in SI was greatest in the RLP region (3.7±4.0mm) followed by target region (3.3±2.2mm) and finally the LLP region (3.0±3.5mm). The magnitude of decrease in 3D vector followed the same trend; RLP (3.5±2.2mm) then GTV (3.5±2.6mm) then LLP (2.7±3.8mm). 79% of the cases had a SI decrease of >12.5%, 43% had a SI decrease of >25% and 21% had a SI decrease of >50% as compared to the motion on the uncompressed 4DCT. Conclusion: AC is useful in reducing motion with the largest decreases observed in the lower posterior regions of the lungs. However, it should be noted that AC will not greatly decrease motion for all cases as 21% of cases did not reduce SI motion more than 12.5% of initial motion.« less
  • Purpose: To study breathing related tumor motion amplitudes by lung lobe location under controlled breathing conditions used in Stereotactic Body Radiation Therapy (SBRT) for NSCLC. Methods: Sixty-five NSCLC SBRT patients since 2009 were investigated. Patients were categorized based on tumor anatomic location (RUL-17, RML-7, RLL-18, LUL-14, LLL-9). A 16-slice CT scanner [GE RT16 Pro] along with Varian Realtime Position Management (RPM) software was used to acquire the 4DCT data set using 1.25 mm slice width. Images were binned in 10 phases, T00 being at maximum inspiration ' T50 at maximum expiration phase. Tumor volume was segmented in T50 using themore » CT-lung window and its displacement were measured from phase to phase in all three axes; superiorinferior, anterior-posterior ' medial-lateral at the centroid level of the tumor. Results: The median tumor movement in each lobe was as follows: RUL= 3.8±2.0 mm (mean ITV: 9.5 cm{sup 3}), RML= 4.7±2.8 mm (mean ITV: 9.2 cm{sup 3}), RLL=6.6±2.6 mm (mean ITV: 12.3 cm{sup 3}), LUL=3.8±2.4 mm (mean ITV: 18.5 cm{sup 3}), ' LLL=4.7±2.5 mm (mean ITV: 11.9 cm{sup 3}). The median respiratory cycle for all patients was found to be 3.81 ± 1.08 seconds [minimum 2.50 seconds, maximum 7.07 seconds]. The tumor mobility incorporating breathing cycle was RUL = 0.95±0.49 mm/s, RML = 1.35±0.62 mm/s, RLL = 1.83±0.71 mm/s, LUL = 0.98 ±0.50 mm/s, and LLL = 1.15 ±0.53 mm/s. Conclusion: Our results show that tumor displacement is location dependent. The range of motion and mobility increases as the location of the tumor nears the diaphragm. Under abdominal compression, the magnitude of tumor motion is reduced by as much as a factor of 2 in comparison to reported tumor magnitudes under conventional free breathing conditions. This study demonstrates the utility of abdominal compression in reducing the tumor motion leading to reduced ITV and planning tumor volumes (PTV)« less
  • Purpose: Tomotherapy has unique challenges in handling intrafractional motion compared to conventional LINAC. In this study, we analyzed the impact of intrafractional motion on cumulative dosimetry using actual patient motion data and investigated real time jaw/MLC compensation approaches to minimize the motion-induced dose discrepancy in Tomotherapy SBRT treatment. Methods: Intrafractional motion data recorded in two CyberKnife lung treatment cases through fiducial tracking and two LINAC prostate cases through Calypso tracking were used in this study. For each treatment site, one representative case has an average motion (6mm) and one has a large motion (10mm for lung and 15mm for prostate).more » The cases were re-planned on Tomotherapy for SBRT. Each case was planned with 3 different jaw settings: 1cm static, 2.5cm dynamic, and 5cm dynamic. 4D dose accumulation software was developed to compute dose with the recorded motions and theoretically compensate motions by modifying original jaw and MLC to track the trajectory of the tumor. Results: PTV coverage in Tomotherapy SBRT for patients with intrafractional motion depends on motion type, amplitude and plan settings. For the prostate patient with large motion, PTV coverage changed from 97.2% (motion-free) to 47.1% (target motion-included), 96.6% to 58.5% and 96.3% to 97.8% for the 1cm static jaw, 2.5cm dynamic jaw and 5cm dynamic jaw setting, respectively. For the lung patient with large motion, PTV coverage discrepancies showed a similar trend of change. When the jaw and MLC compensation program was engaged, the motion compromised PTV coverage was recovered back to >95% for all cases and plans. All organs at risk (OAR) were spared with < 5% increase from original motion-free plans. Conclusion: Tomotherapy SBRT is less motion-impacted when 5cm dynamic jaw is used. Once the motion pattern is known, the jaw and MLC compensation program can largely minimize the compromised target coverage and OAR sparing.« less
  • Purpose: To correlate dose distributions computed using six algorithms for recurrent early stage non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiotherapy (SBRT), with outcome (local failure). Methods: Of 270 NSCLC patients treated with 12Gyx4, 20 were found to have local recurrence prior to the 2-year time point. These patients were originally planned with 1-D pencil beam (1-D PB) algorithm. 4D imaging was performed to manage tumor motion. Regions of local failures were determined from follow-up PET-CT scans. Follow-up CT images were rigidly fused to the planning CT (pCT), and recurrent tumor volumes (Vrecur) were mapped to themore » pCT. Dose was recomputed, retrospectively, using five algorithms: 3-D PB, collapsed cone convolution (CCC), anisotropic analytical algorithm (AAA), AcurosXB, and Monte Carlo (MC). Tumor control probability (TCP) was computed using the Marsden model (1,2). Patterns of failure were classified as central, in-field, marginal, and distant for Vrecur ≥95% of prescribed dose, 95–80%, 80–20%, and ≤20%, respectively (3). Results: Average PTV D95 (dose covering 95% of the PTV) for 3-D PB, CCC, AAA, AcurosXB, and MC relative to 1-D PB were 95.3±2.1%, 84.1±7.5%, 84.9±5.7%, 86.3±6.0%, and 85.1±7.0%, respectively. TCP values for 1-D PB, 3-D PB, CCC, AAA, AcurosXB, and MC were 98.5±1.2%, 95.7±3.0, 79.6±16.1%, 79.7±16.5%, 81.1±17.5%, and 78.1±20%, respectively. Patterns of local failures were similar for 1-D and 3D PB plans, which predicted that the majority of failures occur in centraldistal regions, with only ∼15% occurring distantly. However, with convolution/superposition and MC type algorithms, the majority of failures (65%) were predicted to be distant, consistent with the literature. Conclusion: Based on MC and convolution/superposition type algorithms, average PTV D95 and TCP were ∼15% lower than the planned 1-D PB dose calculation. Patterns of failure results suggest that MC and convolution/superposition type algorithms predict different outcomes for patterns of failure relative to PB algorithms. Work supported in part by Varian Medical Systems, Palo Alto, CA.« less