Semi-Automatic Tracking of Myocardial Motion in MR Tagged Images

Dara L. Kraitchman; Alistair A. Young; Cheng Ning Chang; Leon Axel

doi:10.1109/42.414606

Semi-Automatic Tracking of Myocardial Motion in MR Tagged Images

Dara L. Kraitchman, Alistair A. Young, Cheng Ning Chang, Leon Axel

Research output: Contribution to journal › Article › peer-review

72 Scopus citations

Abstract

Tissue tagging using magnetic resonance (MR) imaging has enabled quantitative noninvasive analysis of motion and deformation in vivo. One method for MR tissue tagging is Spatial Modulation of Magnetization (SPAMM). Manual detection and tracking of tissue tags by visual inspection remains a time-consuming and tedious process. We have developed an interactively guided semi-automated method of detecting and tracking tag intersections in cardiac MR images. A template matching approach combined with a novel adaptation of active contour modeling permits rapid analysis of MR images. We have validated our technique using MR SPAMM images of a silicone gel phantom with controlled deformations. Average discrepancy between theoretically predicted and semi-automatically selected tag intersections was 0.30 mm ± 0.17 [mean ± SD, NS (P < 0.05)]. Cardiac SPAMM images of normal volunteers and diseased patients also have been evaluated using our technique.

Original language	English (US)
Pages (from-to)	422-433
Number of pages	12
Journal	IEEE transactions on medical imaging
Volume	14
Issue number	3
DOIs	https://doi.org/10.1109/42.414606
State	Published - Sep 1995
Externally published	Yes

ASJC Scopus subject areas

Software
Radiological and Ultrasound Technology
Computer Science Applications
Electrical and Electronic Engineering

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@article{e10caa24a64548f19d96f9498016306d,

title = "Semi-Automatic Tracking of Myocardial Motion in MR Tagged Images",

abstract = "Tissue tagging using magnetic resonance (MR) imaging has enabled quantitative noninvasive analysis of motion and deformation in vivo. One method for MR tissue tagging is Spatial Modulation of Magnetization (SPAMM). Manual detection and tracking of tissue tags by visual inspection remains a time-consuming and tedious process. We have developed an interactively guided semi-automated method of detecting and tracking tag intersections in cardiac MR images. A template matching approach combined with a novel adaptation of active contour modeling permits rapid analysis of MR images. We have validated our technique using MR SPAMM images of a silicone gel phantom with controlled deformations. Average discrepancy between theoretically predicted and semi-automatically selected tag intersections was 0.30 mm ± 0.17 [mean ± SD, NS (P < 0.05)]. Cardiac SPAMM images of normal volunteers and diseased patients also have been evaluated using our technique.",

author = "Kraitchman, {Dara L.} and Young, {Alistair A.} and Chang, {Cheng Ning} and Leon Axel",

note = "Funding Information: Manuscript received December 23, 1993; revised April 25. 1995. This work was supported by NIH Grant HL 43014 of the National Heart, Lung, and Blood Institute and the NIH Medical Scientist Training Program. The Associate Editor responsible for coordinating the review of this paper and recommending its publication was E. R. McVeigh. The authors are with the Pendergrass Radiology Research Laboratory, Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104-6021 USA; e-mail: dara@mipgsun.mipg.upenn.edu. IEEE Log Number 9413556.",

year = "1995",

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doi = "10.1109/42.414606",

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pages = "422--433",

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AU - Kraitchman, Dara L.

AU - Young, Alistair A.

AU - Chang, Cheng Ning

AU - Axel, Leon

N1 - Funding Information: Manuscript received December 23, 1993; revised April 25. 1995. This work was supported by NIH Grant HL 43014 of the National Heart, Lung, and Blood Institute and the NIH Medical Scientist Training Program. The Associate Editor responsible for coordinating the review of this paper and recommending its publication was E. R. McVeigh. The authors are with the Pendergrass Radiology Research Laboratory, Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104-6021 USA; e-mail: dara@mipgsun.mipg.upenn.edu. IEEE Log Number 9413556.

PY - 1995/9

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N2 - Tissue tagging using magnetic resonance (MR) imaging has enabled quantitative noninvasive analysis of motion and deformation in vivo. One method for MR tissue tagging is Spatial Modulation of Magnetization (SPAMM). Manual detection and tracking of tissue tags by visual inspection remains a time-consuming and tedious process. We have developed an interactively guided semi-automated method of detecting and tracking tag intersections in cardiac MR images. A template matching approach combined with a novel adaptation of active contour modeling permits rapid analysis of MR images. We have validated our technique using MR SPAMM images of a silicone gel phantom with controlled deformations. Average discrepancy between theoretically predicted and semi-automatically selected tag intersections was 0.30 mm ± 0.17 [mean ± SD, NS (P < 0.05)]. Cardiac SPAMM images of normal volunteers and diseased patients also have been evaluated using our technique.

AB - Tissue tagging using magnetic resonance (MR) imaging has enabled quantitative noninvasive analysis of motion and deformation in vivo. One method for MR tissue tagging is Spatial Modulation of Magnetization (SPAMM). Manual detection and tracking of tissue tags by visual inspection remains a time-consuming and tedious process. We have developed an interactively guided semi-automated method of detecting and tracking tag intersections in cardiac MR images. A template matching approach combined with a novel adaptation of active contour modeling permits rapid analysis of MR images. We have validated our technique using MR SPAMM images of a silicone gel phantom with controlled deformations. Average discrepancy between theoretically predicted and semi-automatically selected tag intersections was 0.30 mm ± 0.17 [mean ± SD, NS (P < 0.05)]. Cardiac SPAMM images of normal volunteers and diseased patients also have been evaluated using our technique.

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Semi-Automatic Tracking of Myocardial Motion in MR Tagged Images

Abstract

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