Position tracking of moving liver lesion based on real‐time registration between 2D ultrasound and 3D preoperative images

Weon, Chijun; Nam, Woo Hyun; Lee, Duhgoon; Lee, Jae Young; Beom, Jong

doi:10.1118/1.4903945

Cited by 16 publications

(12 citation statements)

References 27 publications

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“…A matrix array transducer operating at high volume rates would be the optimal solution to overcome these restrictions (by enabling fast 3D imaging) but currently this technology is underdeveloped 7 . An alternative approach could use a model which relates a single pretreatment (or updated) 3D ultrasound volume or MR/CT data to the 2D imaging plane 29 taking advantage of high frame rate 2D imaging during treatment. A pretreatment imaging stage could also be used to generate a correlation model between the tumor position (in a contrast‐enhanced B‐mode image and/or MR or CT image) and trackable features (e.g., blood vessels surrounding the tumor) to enable accurate internal surrogate tracking.…”

Section: Discussionmentioning

confidence: 99%

Temporal regularization of ultrasound-based liver motion estimation for image-guided radiation therapy

2015

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Purpose:Ultrasound‐based motion estimation is an expanding subfield of image‐guided radiation therapy. Although ultrasound can detect tissue motion that is a fraction of a millimeter, its accuracy is variable. For controlling linear accelerator tracking and gating, ultrasound motion estimates must remain highly accurate throughout the imaging sequence. This study presents a temporal regularization method for correlation‐based template matching which aims to improve the accuracy of motion estimates.Methods:Liver ultrasound sequences (15–23 Hz imaging rate, 2.5–5.5 min length) from ten healthy volunteers under free breathing were used. Anatomical features (blood vessels) in each sequence were manually annotated for comparison with normalized cross‐correlation based template matching. Five sequences from a Siemens Acuson™ scanner were used for algorithm development (training set). Results from incremental tracking (IT) were compared with a temporal regularization method, which included a highly specific similarity metric and state observer, known as the α–β filter/similarity threshold (ABST). A further five sequences from an Elekta Clarity™ system were used for validation, without alteration of the tracking algorithm (validation set).Results:Overall, the ABST method produced marked improvements in vessel tracking accuracy. For the training set, the mean and 95th percentile (95%) errors (defined as the difference from manual annotations) were 1.6 and 1.4 mm, respectively (compared to 6.2 and 9.1 mm, respectively, for IT). For each sequence, the use of the state observer leads to improvement in the 95% error. For the validation set, the mean and 95% errors for the ABST method were 0.8 and 1.5 mm, respectively.Conclusions:Ultrasound‐based motion estimation has potential to monitor liver translation over long time periods with high accuracy. Nonrigid motion (strain) and the quality of the ultrasound data are likely to have an impact on tracking performance. A future study will investigate spatial uniformity of motion and its effect on the motion estimation errors.

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Section: Discussionmentioning

confidence: 99%

Temporal regularization of ultrasound-based liver motion estimation for image-guided radiation therapy

2015

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“…A 4D CT image and a 4D pulmonary vessel image are generated by adopting our previously proposed method . We assign I exp and I ins as a target image and a source image for the registration, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…Using the refined combined 3D motion vector field, we can determine a 4D motion vector field and thereby generate successive multiple 3D CT images along the respiratory axis, which are called a 4D CT image . If d exp→ins denotes the combined 3D motion vector, an intermediate image I n between I exp and I ins can be determined on every integer point x n as

I_{n} (x_{n}) = \frac{1}{Q} \sum_{{\overset{x}{true}}_{n} \in {normalR}_{x_{n}}} exp (- \frac{{()({boldx}_{n} - {\overset{x}{true}}_{n})}^{2}}{2 σ_{2}^{2}}) I_{exp} (x_{exp}),

…”

Section: Methodsmentioning

confidence: 99%

Real‐time respiratory phase matching between 2D fluoroscopic images and 3D CT images for precise percutaneous lung biopsy

et al. 2017

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We propose a real-time matching algorithm to compensate for respiratory motion between a 2D fluoroscopic image and 3D CT images of the lung, regardless of cardiac motion, based on a newly improved matching measure. The proposed algorithm can improve the accuracy of a guiding system for the PLB by providing 3D images precisely registered to 2D fluoroscopic images in real-time, without time-consuming respiratory-gated or cardiac-gated CT images.

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“…This gives the spatial position of the US slice. It is then possible to estimate an approximate position of the US slice in the MR volume [19][20][21]. However, EM trackers are not available in most hospitals in Europe, so that in most of the methods, an expert must search for the best MR slice that matches the corresponding US image.…”

Section: Us-mr Image Registrationmentioning

confidence: 99%

Mapping and characterizing endometrial implants by registering 2D transvaginal ultrasound to 3D pelvic magnetic resonance images

Yavariabdi¹,

Bartoli²,

Samir³

et al. 2015

Computerized Medical Imaging and Graphics

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We propose a new deformable slice-to-volume registration method to register a 2D Transvaginal Ultrasound (TVUS) to a 3D Magnetic Resonance (MR) volume. Our main goal is to find a cross-section of the MR volume such that the endometrial implants and their depth of infiltration can be mapped from TVUS to MR. The proposed TVUS-MR registration method uses contour to surface correspondences through a novel variational one-step deformable Iterative Closest Point (ICP) method. Specifically, we find a smooth deformation field while establishing point correspondences automatically. We demonstrate the accuracy of the proposed method by quantitative and qualitative tests on both semi-synthetic and clinical data. To generate semi-synthetic data sets, 3D surfaces are deformed with 4-40% degrees of deformation and then various intersection curves are obtained at 0-20° cutting angles. Results show an average mean square error of 5.7934±0.4615mm, average Hausdorff distance of 2.493±0.14mm, and average Dice similarity coefficient of 0.9750±0.0030.

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Position tracking of moving liver lesion based on real‐time registration between 2D ultrasound and 3D preoperative images

Cited by 16 publications

References 27 publications

Temporal regularization of ultrasound-based liver motion estimation for image-guided radiation therapy

Temporal regularization of ultrasound-based liver motion estimation for image-guided radiation therapy

Real‐time respiratory phase matching between 2D fluoroscopic images and 3D CT images for precise percutaneous lung biopsy

Mapping and characterizing endometrial implants by registering 2D transvaginal ultrasound to 3D pelvic magnetic resonance images

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