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Title: SU-G-BRA-02: Development of a Learning Based Block Matching Algorithm for Ultrasound Tracking in Radiotherapy

Abstract

Purpose: To develop an ultrasound learning-based tracking algorithm with the potential to provide real-time motion traces of anatomy-based fiducials that may aid in the effective delivery of external beam radiation. Methods: The algorithm was developed in Matlab R2015a and consists of two main stages: reference frame selection, and localized block matching. Immediately following frame acquisition, a normalized cross-correlation (NCC) similarity metric is used to determine a reference frame most similar to the current frame from a series of training set images that were acquired during a pretreatment scan. Segmented features in the reference frame provide the basis for the localized block matching to determine the feature locations in the current frame. The boundary points of the reference frame segmentation are used as the initial locations for the block matching and NCC is used to find the most similar block in the current frame. The best matched block locations in the current frame comprise the updated feature boundary. The algorithm was tested using five features from two sets of ultrasound patient data obtained from MICCAI 2014 CLUST. Due to the lack of a training set associated with the image sequences, the first 200 frames of the image sets were considered amore » valid training set for preliminary testing, and tracking was performed over the remaining frames. Results: Tracking of the five vessel features resulted in an average tracking error of 1.21 mm relative to predefined annotations. The average analysis rate was 15.7 FPS with analysis for one of the two patients reaching real-time speeds. Computations were performed on an i5-3230M at 2.60 GHz. Conclusion: Preliminary tests show tracking errors comparable with similar algorithms at close to real-time speeds. Extension of the work onto a GPU platform has the potential to achieve real-time performance, making tracking for therapy applications a feasible option. This work is partially funded by NIH grant R01CA190298.« less

Authors:
;  [1]
  1. University of Wisconsin, Madison, WI (United States)
Publication Date:
OSTI Identifier:
22649290
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; ALGORITHMS; CALCULATION METHODS; GHZ RANGE 01-100; IMAGES; LEARNING; PERFORMANCE; RADIOTHERAPY; TESTING; TRAINING

Citation Formats

Shepard, A, and Bednarz, B. SU-G-BRA-02: Development of a Learning Based Block Matching Algorithm for Ultrasound Tracking in Radiotherapy. United States: N. p., 2016. Web. doi:10.1118/1.4956926.
Shepard, A, & Bednarz, B. SU-G-BRA-02: Development of a Learning Based Block Matching Algorithm for Ultrasound Tracking in Radiotherapy. United States. doi:10.1118/1.4956926.
Shepard, A, and Bednarz, B. 2016. "SU-G-BRA-02: Development of a Learning Based Block Matching Algorithm for Ultrasound Tracking in Radiotherapy". United States. doi:10.1118/1.4956926.
@article{osti_22649290,
title = {SU-G-BRA-02: Development of a Learning Based Block Matching Algorithm for Ultrasound Tracking in Radiotherapy},
author = {Shepard, A and Bednarz, B},
abstractNote = {Purpose: To develop an ultrasound learning-based tracking algorithm with the potential to provide real-time motion traces of anatomy-based fiducials that may aid in the effective delivery of external beam radiation. Methods: The algorithm was developed in Matlab R2015a and consists of two main stages: reference frame selection, and localized block matching. Immediately following frame acquisition, a normalized cross-correlation (NCC) similarity metric is used to determine a reference frame most similar to the current frame from a series of training set images that were acquired during a pretreatment scan. Segmented features in the reference frame provide the basis for the localized block matching to determine the feature locations in the current frame. The boundary points of the reference frame segmentation are used as the initial locations for the block matching and NCC is used to find the most similar block in the current frame. The best matched block locations in the current frame comprise the updated feature boundary. The algorithm was tested using five features from two sets of ultrasound patient data obtained from MICCAI 2014 CLUST. Due to the lack of a training set associated with the image sequences, the first 200 frames of the image sets were considered a valid training set for preliminary testing, and tracking was performed over the remaining frames. Results: Tracking of the five vessel features resulted in an average tracking error of 1.21 mm relative to predefined annotations. The average analysis rate was 15.7 FPS with analysis for one of the two patients reaching real-time speeds. Computations were performed on an i5-3230M at 2.60 GHz. Conclusion: Preliminary tests show tracking errors comparable with similar algorithms at close to real-time speeds. Extension of the work onto a GPU platform has the potential to achieve real-time performance, making tracking for therapy applications a feasible option. This work is partially funded by NIH grant R01CA190298.},
doi = {10.1118/1.4956926},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = 2016,
month = 6
}
  • Purpose: To implement and evaluate a block matching-based registration (BMR) algorithm for locally advanced lung tumor localization during image-guided radiotherapy. Methods: Small (1 cm{sup 3}), nonoverlapping image subvolumes (“blocks”) were automatically identified on the planning image to cover the tumor surface using a measure of the local intensity gradient. Blocks were independently and automatically registered to the on-treatment image using a rigid transform. To improve speed and robustness, registrations were performed iteratively from coarse to fine image resolution. At each resolution, all block displacements having a near-maximum similarity score were stored. From this list, a single displacement vector for eachmore » block was iteratively selected which maximized the consistency of displacement vectors across immediately neighboring blocks. These selected displacements were regularized using a median filter before proceeding to registrations at finer image resolutions. After evaluating all image resolutions, the global rigid transform of the on-treatment image was computed using a Procrustes analysis, providing the couch shift for patient setup correction. This algorithm was evaluated for 18 locally advanced lung cancer patients, each with 4–7 weekly on-treatment computed tomography scans having physician-delineated gross tumor volumes. Volume overlap (VO) and border displacement errors (BDE) were calculated relative to the nominal physician-identified targets to establish residual error after registration. Results: Implementation of multiresolution registration improved block matching accuracy by 39% compared to registration using only the full resolution images. By also considering multiple potential displacements per block, initial errors were reduced by 65%. Using the final implementation of the BMR algorithm, VO was significantly improved from 77% ± 21% (range: 0%–100%) in the initial bony alignment to 91% ± 8% (range: 56%–100%;p < 0.001). Left-right, anterior-posterior, and superior-inferior systematic BDE were 3.2, 2.4, and 4.4 mm, respectively, with random BDE of 2.4, 2.1, and 2.7 mm. Margins required to include both localization and delineation uncertainties ranged from 5.0 to 11.7 mm, an average of 40% less than required for bony alignment. Conclusions: BMR is a promising approach for automatic lung tumor localization. Further evaluation is warranted to assess the accuracy and robustness of BMR against other potential localization strategies.« less
  • Purpose: To evaluate the accuracy of measuring volumes using three-dimensional ultrasound (3D US), and to verify the feasibility of the replacement of CT-MR fusion images with CT-3D US in radiotherapy treatment planning. Methods: Phantoms, consisting of water, contrast agent, and agarose, were manufactured. The volume was measured using 3D US, CT, and MR devices. A CT-3D US and MR-3D US image fusion software was developed using the Insight Toolkit library in order to acquire three-dimensional fusion images. The quality of the image fusion was evaluated using metric value and fusion images. Results: Volume measurement, using 3D US, shows a 2.8more » {+-} 1.5% error, 4.4 {+-} 3.0% error for CT, and 3.1 {+-} 2.0% error for MR. The results imply that volume measurement using the 3D US devices has a similar accuracy level to that of CT and MR. Three-dimensional image fusion of CT-3D US and MR-3D US was successfully performed using phantom images. Moreover, MR-3D US image fusion was performed using human bladder images. Conclusions: 3D US could be used in the volume measurement of human bladders and prostates. CT-3D US image fusion could be used in monitoring the target position in each fraction of external beam radiation therapy. Moreover, the feasibility of replacing the CT-MR image fusion to the CT-3D US in radiotherapy treatment planning was verified.« less
  • Purpose: Robust matching of ultrasound images is a challenging problem as images of the same anatomy often present non-trivial differences. This poses an obstacle for ultrasound guidance in radiotherapy. Thus our objective is to overcome this obstacle by designing and evaluating an image blocks matching framework based on a two channel deep convolutional neural network. Methods: We extend to 3D an algorithmic structure previously introduced for 2D image feature learning [1]. To obtain the similarity between two 3D image blocks A and B, the 3D image blocks are divided into 2D patches Ai and Bi. The similarity is then calculatedmore » as the average similarity score of Ai and Bi. The neural network was then trained with public non-medical image pairs, and subsequently evaluated on ultrasound image blocks for the following scenarios: (S1) same image blocks with/without shifts (A and A-shift-x); (S2) non-related random block pairs; (S3) ground truth registration matched pairs of different ultrasound images with/without shifts (A-i and A-reg-i-shift-x). Results: For S1 the similarity scores of A and A-shift-x were 32.63, 18.38, 12.95, 9.23, 2.15 and 0.43 for x=ranging from 0 mm to 10 mm in 2 mm increments. For S2 the average similarity score for non-related block pairs was −1.15. For S3 the average similarity score of ground truth registration matched blocks A-i and A-reg-i-shift-0 (1≤i≤5) was 12.37. After translating A-reg-i-shift-0 by 0 mm, 2 mm, 4 mm, 6 mm, 8 mm, and 10 mm, the average similarity scores of A-i and A-reg-i-shift-x were 11.04, 8.42, 4.56, 2.27, and 0.29 respectively. Conclusion: The proposed method correctly assigns highest similarity to corresponding 3D ultrasound image blocks despite differences in image content and thus can form the basis for ultrasound image registration and tracking.[1] Zagoruyko, Komodakis, “Learning to compare image patches via convolutional neural networks', IEEE CVPR 2015,pp.4353–4361.« less
  • This study was designed to examine the feasibility of utilizing transabdominal ultrasound for real-time monitoring of target motion during a radiotherapy fraction. A clinical Acuson 128/XP ultrasound scanner was used to image various stationary and moving phantoms while an Elekta SL25 linear accelerator radiotherapy treatment machine was operating. The ultrasound transducer was positioned to image from the outer edge of the treatment field at all times. Images were acquired to videotape and analyzed using in-house motion tracking algorithms to determine the effect of the SL25 on the quality of the displacement measurements. To determine the effect on the dosimetry ofmore » the presence of the transducer, dose distributions were examined using thermoluminescent dosimeters loaded into an Alderson Rando phantom and exposed to a 10x10 cm{sup 2} treatment field with and without the ultrasound transducer mounted 2.5 cm outside the field edge. The ultrasound images acquired a periodic noise that was shown to occur at the pulsing frequency of the treatment machine. Images of moving tissue were analyzed and the standard deviation on the displacement estimates within the tissue was identical with the SL25 on and off. This implies that the periodic noise did not significantly degrade the precision of the tracking algorithm (which was better than 0.01 mm). The presence of the transducer at the surface of the phantom presented only a 2.6% change to the dose distribution to the volume of the phantom. The feasibility of ultrasonic motion tracking during radiotherapy treatment is demonstrated. This presents the possibility of developing a noninvasive, real-time and low-cost method of tracking target motion during a treatment fraction.« less
  • Purpose: Prostate SABR is emerging as a clinically viable, potentially cost effective alternative to prostate IMRT but its adoption is contingent on providing solutions for accurate tracking during beam delivery. Our goal is to evaluate the performance of the Clarity Autoscan ultrasound monitoring system for inter-fractional prostate motion tracking in both phantoms and in-vivo. Methods: In-vivo evaluation was performed under IRB protocol to allow data collection in prostate patients treated with VMAT whereby prostate was imaged through the acoustic window of the perineum. The probe was placed before KV imaging and real-time tracking was started and continued until the endmore » of treatment. Initial absolute 3D positions of fiducials were estimated from KV images. Fiducial positions in MV images subsequently acquired during beam delivery were compared with predicted positions based on Clarity estimated motion. Results: Phantom studies with motion amplitudes of ±1.5, ±3, ±6 mm in lateral direction and ±2 mm in longitudinal direction resulted in tracking errors of −0.03 ± 0.3, −0.04 ± 0.6, −0.2 ± 0.9 mm, respectively, in lateral direction and −0.05 ± 0.30 mm in longitudinal direction. In phantom, measured and predicted fiducial positions in MV images were within 0.1 ± 0.6 mm. Four patients consented to participate in the study and data was acquired over a total of 140 fractions. MV imaging tracking was possible in about 75% of the time (due to occlusion of fiducials) compared to 100% with Clarity. Overall range of estimated motion by Clarity was 0 to 4.0 mm. In-vivo fiducial localization error was 1.2 ± 1.0 mm compared to 1.8 ± 1.9 mm if not taking Clarity estimated motion into account. Conclusion: Real-time transperineal ultrasound tracking reduces uncertainty in prostate position due to intrafractional motion. Research was supported by Elekta.« less