Dataset Information

A fast hybrid algorithm combining regularized motion tracking and predictive search for reducing the occurrence of large displacement errors.

ABSTRACT: A hybrid approach that inherits both the robustness of the regularized motion tracking approach and the efficiency of the predictive search approach is reported. The basic idea is to use regularized speckle tracking to obtain high-quality seeds in an explorative search that can be used in the subsequent intelligent predictive search. The performance of the hybrid speckle-tracking algorithm was compared with three published speckle-tracking methods using in vivo breast lesion data. We found that the hybrid algorithm provided higher displacement quality metric values, lower root mean squared errors compared with a locally smoothed displacement field, and higher improvement ratios compared with the classic block-matching algorithm. On the basis of these comparisons, we concluded that the hybrid method can further enhance the accuracy of speckle tracking compared with its real-time counterparts, at the expense of slightly higher computational demands.

SUBMITTER: Jiang J

PROVIDER: S-EPMC3385873 | biostudies-literature | 2011 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A fast hybrid algorithm combining regularized motion tracking and predictive search for reducing the occurrence of large displacement errors.

Jiang Jingfeng J Hall Timothy J TJ

IEEE transactions on ultrasonics, ferroelectrics, and frequency control 20110401 4

A hybrid approach that inherits both the robustness of the regularized motion tracking approach and the efficiency of the predictive search approach is reported. The basic idea is to use regularized speckle tracking to obtain high-quality seeds in an explorative search that can be used in the subsequent intelligent predictive search. The performance of the hybrid speckle-tracking algorithm was compared with three published speckle-tracking methods using in vivo breast lesion data. We found that ...[more]

PMID: 21507750

Similar Datasets

Project description:The accuracy of the CyberKnife Synchrony Respiratory Tracking System (SRTS) is considered to be patient-dependent because the SRTS relies on an individual correlation between the internal tumor position (ITP) and the external marker position (EMP), as well as a prediction method to compensate for the delay incurred to adjust the position of the linear accelerator (linac). We aimed to develop a system for obtaining pretreatment statistical measurements of the SRTS tracking error by using beam's eye view (BEV) images, to enable the prediction of the patient-specific accuracy. The respiratory motion data for the ITP and the EMP were derived from cine MR images obtained from 23 patients. The dynamic motion phantom was used to reproduce both the ITP and EMP motions. The CyberKnife was subsequently operated with the SRTS, with a CCD camera mounted on the head of the linac. BEV images from the CCD camera were recorded during the tracking of a ball target by the linac. The tracking error was measured at 15 Hz using in-house software. To assess the precision of the position detection using an MR image, the positions of test tubes (determined from MR images) were compared with their actual positions. To assess the precision of the position detection of the ball, ball positions measured from BEV images were compared with values measured using a Vernier caliper. The SRTS accuracy was evaluated by determining the tracking error that could be identified with a probability of more than 95% (Ep95). The detection precision of the tumor position (determined from cine MR images) was < 0.2 mm. The detection precision of the tracking error when using the BEV images was < 0.2mm. These two detection precisions were derived from our measurement system and were not obtained from the SRTS. The median of Ep95 was found to be 1.5 (range, 1.0-3.5) mm. The difference between the minimum and maximum Ep95 was 2.5mm, indicating that this provides a better means of evaluating patient-specific SRTS accuracy. A suitable margin, based on the predicted patient-specific SRTS accuracy, can be added to the clinical target volume.

Dataset Information

A fast hybrid algorithm combining regularized motion tracking and predictive search for reducing the occurrence of large displacement errors.

Publications

A fast hybrid algorithm combining regularized motion tracking and predictive search for reducing the occurrence of large displacement errors.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets