SU-E-J-92: Validating Dose Uncertainty Estimates Produced by AUTODIRECT, An Automated Program to Evaluate Deformable Image Registration Accuracy

2015 ◽  
Vol 42 (6Part8) ◽  
pp. 3285-3285
Author(s):  
H Kim ◽  
J Chen ◽  
J Pouliot ◽  
J Pukala ◽  
N Kirby
Keyword(s):  
Image Registration ◽  
Deformable Image Registration ◽  
Registration Accuracy ◽  
Uncertainty Estimates ◽  
Dose Uncertainty

scholarly journals Detection of vessel bifurcations in CT scans for automatic objective assessment of deformable image registration accuracy

2021 ◽  
Author(s):  
Guillaume Cazoulat ◽  
Brian M Anderson ◽  
Molly M McCulloch ◽  
Bastien Rigaud ◽  
Eugene J Koay ◽  
...  
Keyword(s):  
Image Registration ◽  
Objective Assessment ◽  
Deformable Image Registration ◽  
Ct Scans ◽  
Registration Accuracy ◽  
Vessel Bifurcations

Multi‐atlas–based auto‐segmentation for prostatic urethra using novel prediction of deformable image registration accuracy

2020 ◽  
Vol 47 (7) ◽  
pp. 3023-3031
Author(s):  
Hisamichi Takagi ◽  
Noriyuki Kadoya ◽  
Tomohiro Kajikawa ◽  
Shohei Tanaka ◽  
Yoshiki Takayama ◽  
...  
Keyword(s):  
Image Registration ◽  
Deformable Image Registration ◽  
Prostatic Urethra ◽  
Registration Accuracy

Impact of deformable image registration accuracy on thoracic images with different regularization weight parameter settings

2017 ◽  
Vol 42 ◽  
pp. 108-111 ◽  
Author(s):  
Hideharu Miura ◽  
Shuichi Ozawa ◽  
Minoru Nakao ◽  
Kengo Furukawa ◽  
Yoshiko Doi ◽  
...  
Keyword(s):  
Image Registration ◽  
Deformable Image Registration ◽  
Registration Accuracy ◽  
Weight Parameter

scholarly journals Evaluation of Intensity- and Contour-Based Deformable Image Registration Accuracy in Pancreatic Cancer Patients

Cancers ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1447 ◽  
Author(s):  
Yoshiki Kubota ◽  
Masahiko Okamoto ◽  
Yang Li ◽  
Shintaro Shiba ◽  
Shohei Okazaki ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Image Registration ◽  
Prone Position ◽  
Supine Position ◽  
Deformable Image Registration ◽  
Pancreatic Cancer Patient ◽  
Target Volume ◽  
Ct Images ◽  
Registration Accuracy ◽  
Computed Tomography Images

We aimed to clarify the accuracy of rigid image registration and deformable image registration (DIR) in carbon-ion radiotherapy (CIRT) for pancreatic cancer. Six patients with pancreatic cancer who were treated with passive irradiation CIRT were enrolled. Three registration patterns were evaluated: treatment planning computed tomography images (TPCT) to CT images acquired in the treatment room (IRCT) in the supine position, TPCT to IRCT in the prone position, and TPCT in the supine position to the prone position. After warping the contours of the original CT images to the destination CT images using deformation matrices from the registration, the warped delineated contours on the destination CT images were compared with the original ones using mean displacement to agreement (MDA). Four contours (clinical target volume (CTV), gross tumor volume (GTV), stomach, duodenum) and four registration algorithms (rigid image registration [RIR], intensity-based DIR [iDIR], contour-based DIR [cDIR], and a hybrid iDIR-cDIR ([hDIR]) were evaluated. The means ± standard deviation of the MDAs of all contours for RIR, iDIR, cDIR, and hDIR were 3.40 ± 3.30, 2.2 1± 2.48, 1.46 ± 1.49, and 1.46 ± 1.37 mm, respectively. There were significant differences between RIR and iDIR, and between RIR/iDIR and cDIR/hDIR. For the pancreatic cancer patient images, cDIR and hDIR had better accuracy than RIR and iDIR.

scholarly journals Effect of Deformation Methods on the Accuracy of Deformable Image Registration From Kilovoltage CT to Tomotherapy Megavoltage CT

2019 ◽  
Vol 18 ◽  
pp. 153303381882118 ◽  
Author(s):  
Wannapha Nobnop ◽  
Imjai Chitapanarux ◽  
Somsak Wanwilairat ◽  
Ekkasit Tharavichitkul ◽  
Vicharn Lorvidhaya ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Nasopharyngeal Carcinoma ◽  
Image Registration ◽  
Deformable Image Registration ◽  
Adaptive Radiotherapy ◽  
Deformation Field ◽  
Registration Accuracy ◽  
Deformation Method ◽  
Computed Tomography Images ◽  
Significant Difference

Introduction: The registration accuracy of megavoltage computed tomography images is limited by low image contrast when compared to that of kilovoltage computed tomography images. Such issues may degrade the deformable image registration accuracy. This study evaluates the deformable image registration from kilovoltage to megavoltage images when using different deformation methods and assessing nasopharyngeal carcinoma patient images. Methods: The kilovoltage and the megavoltage images from the first day and the 20th fractions of the treatment day of 12 patients with nasopharyngeal carcinoma were used to evaluate the deformable image registration application. The deformable image registration image procedures were classified into 3 groups, including kilovoltage to kilovoltage, megavoltage to megavoltage, and kilovoltage to megavoltage. Three deformable image registration methods were employed using the deformable image registration and adaptive radiotherapy software. The validation was compared by volume-based, intensity-based, and deformation field analyses. Results: The use of different deformation methods greatly affected the deformable image registration accuracy from kilovoltage to megavoltage. The asymmetric transformation with the demon method was significantly better than other methods and illustrated satisfactory value for adaptive applications. The deformable image registration accuracy from kilovoltage to megavoltage showed no significant difference from the kilovoltage to kilovoltage images when using the appropriate method of registration. Conclusions: The choice of deformation method should be considered when applying the deformable image registration from kilovoltage to megavoltage images. The deformable image registration accuracy from kilovoltage to megavoltage revealed a good agreement in terms of intensity-based, volume-based, and deformation field analyses and showed clinically useful methods for nasopharyngeal carcinoma adaptive radiotherapy in tomotherapy applications.

scholarly journals Evaluation of Deformable Image Registration Methods for Dose Monitoring in Head and Neck Radiotherapy

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Bastien Rigaud ◽  
Antoine Simon ◽  
Joël Castelli ◽  
Maxime Gobeli ◽  
Juan-David Ospina Arango ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Image Registration ◽  
Head And Neck ◽  
Intensity Modulated Radiation Therapy ◽  
Deformable Image Registration ◽  
Free Form ◽  
Registration Accuracy ◽  
Dose Accumulation ◽  
Dose Monitoring ◽  
Dose Accuracy

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).

scholarly journals Analysis of deformable image registration accuracy using computational modeling

2010 ◽  
Vol 37 (3) ◽  
pp. 970-979 ◽  
Author(s):  
Hualiang Zhong ◽  
Jinkoo Kim ◽  
Indrin J. Chetty
Keyword(s):  
Image Registration ◽  
Computational Modeling ◽  
Deformable Image Registration ◽  
Registration Accuracy
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