Fast block flow tracking of atrial septal defects in 4D echocardiography

Linguraru, Marius George; Vasilyev, Nikolay V.; Marx, Gerald R.; Tworetzky, Wayne; Nido, Pedro J. del; Howe, Robert D.

doi:10.1016/j.media.2007.12.005

Cited by 21 publications

(12 citation statements)

References 40 publications

(66 reference statements)

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“…Because optical flow can accurately represent image motion between two images, it has been widely applied to medical image registration (Dawood et al, 2008; Keeling and Ring, 2005), tracking (Linguraru et al, 2008; Liu et al, 2008), and colorectal polyp detection (Acar et al, 2001). Our TSMF method also exploited this beneficial property to identify metastasis-caused shape variance.…”

Section: Related Workmentioning

confidence: 99%

Tumor sensitive matching flow: A variational method to detecting and segmenting perihepatic and perisplenic ovarian cancer metastases on contrast-enhanced abdominal CT

Liu

Wang

Linguraru

et al. 2014

Medical Image Analysis

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Accurate automated segmentation and detection of ovarian cancer metastases may improve the diagnosis and prognosis of women with ovarian cancer. In this paper, we focus on an important subset of ovarian cancer metastases that spread to the surface of the liver and spleen. Automated ovarian cancer metastasis detection and segmentation are very challenging problems to solve. These metastases have a wide variety of shapes and intensity values similar to that of the liver, spleen and adjacent soft tissues. To address these challenges, this paper presents a variational approach, called tumor sensitive matching flow (TSMF), to detect and segment perihepatic and perisplenic ovarian cancer metastases. TSMF is an image motion field that only highlights metastasis-caused deformation on the surface of liver and spleen while dampening all other image motion between the patient image and the atlas image. It provides several benefits: 1) juxtaposing the roles of image matching and metastasis classification within a variational framework; 2) only requiring a small set of features from a few patient images to train a metastasis-likelihood function for classification; and 3) dynamically creating shape priors for geodesic active contour (GAC) to prevent inaccurate metastasis segmentation. We compared the TSMF to an organ surface partition (OSP) baseline approach. At a false positive rate of 2 per patient, the sensitivities of TSMF and OSP were 87% and 17% (p < 0.001), respectively. In a comparison of the segmentations conducted using TSMF-constrained GAC and conventional GAC, the volume overlap rates were 73±9% and 46±26% (p < 0.001) and average surface distances were 2.4±1.2mm and 7.0±6.0mm (p < 0.001), respectively. These encouraging results demonstrate that TSMF could accurately detect and segment ovarian cancer metastases.

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Section: Related Workmentioning

confidence: 99%

Tumor sensitive matching flow: A variational method to detecting and segmenting perihepatic and perisplenic ovarian cancer metastases on contrast-enhanced abdominal CT

Liu

Wang

Linguraru

et al. 2014

Medical Image Analysis

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“…Cohen and Dinstein derived this similarity function and called it CD 2 similarity measure 2 . This similarity measure showed superior performance comparing to other similarity measure for ultrasound image tracking, and was widely used in tracing and block matching algorithms [15,16,17,18].…”

Section: Independent Rayleigh Noise Assumptionmentioning

confidence: 99%

Maximum Likelihood Motion Estimation in 3D Echocardiography through Non-rigid Registration in Spherical Coordinates

Myronenko

Song

Sahn

2009

Functional Imaging and Modeling of the Heart

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Abstract. Automated motion tracking of the myocardium from 3D echocardiography provides insight into heart's architecture and function. We present a method for 3D cardiac motion tracking using nonrigid image registration. Our contribution is two-fold. We introduce a new similarity measure derived from a maximum likelihood perspective taking into account physical properties of ultrasound image acquisition and formation. Second, we use envelope-detected 3D echo images in the raw spherical coordinates format, which preserves speckle statistics and represents a compromise between signal detail and data complexity. We derive mechanical measures such as strain and twist, and validate using sonomicrometry in open-chest piglets. The results demonstrate the accuracy and feasibility of our method for studying cardiac motion.

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“…With the full scale commercialization of 3-D clinical scanners, the last few years have seen many additional studies employing 3-D speckle tracking on detected echo data [45]–[47]. Although these methods have been successful, they have used detected data, which eliminates one of ultrasound’s greatest strengths: axial phase information.…”

Section: Introductionmentioning

confidence: 99%

3-D phantom and in vivo cardiac speckle tracking using a matrix array and raw echo data

Byram

Holley

Giannantonio

et al. 2010

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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Cardiac motion has been tracked using various methods, which vary in their invasiveness and dimensionality. One such noninvasive modality for cardiac motion tracking is ultrasound. Three-dimensional ultrasound motion tracking has been demonstrated using detected data at low volume rates. However, the effects of volume rate, kernel size, and data type (raw and detected) have not been sufficiently explored. First comparisons are made within the stated variables for 3-D speckle tracking. Volumetric data were obtained in a raw, baseband format using a matrix array attached to a high parallel receive beam count scanner. The scanner was used to acquire phantom and human in vivo cardiac volumetric data at 1000-Hz volume rates. Motion was tracked using phase-sensitive normalized cross-correlation. Subsample estimation in the lateral and elevational dimensions used the grid-slopes algorithm. The effects of frame rate, kernel size, and data type on 3-D tracking are shown. In general, the results show improvement of motion estimates at volume rates up to 200 Hz, above which they become stable. However, peak and pixel hopping continue to decrease at volume rates higher than 200 Hz. The tracking method and data show, qualitatively, good temporal and spatial stability (for independent kernels) at high volume rates.

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Fast block flow tracking of atrial septal defects in 4D echocardiography

Cited by 21 publications

References 40 publications

Tumor sensitive matching flow: A variational method to detecting and segmenting perihepatic and perisplenic ovarian cancer metastases on contrast-enhanced abdominal CT

Tumor sensitive matching flow: A variational method to detecting and segmenting perihepatic and perisplenic ovarian cancer metastases on contrast-enhanced abdominal CT

Maximum Likelihood Motion Estimation in 3D Echocardiography through Non-rigid Registration in Spherical Coordinates

3-D phantom and in vivo cardiac speckle tracking using a matrix array and raw echo data

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