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Table 2 Tagging analysis techniques

From: Myocardial tagging by Cardiovascular Magnetic Resonance: evolution of techniques–pulse sequences, analysis algorithms, and applications

Method Characteristics Advantages Disadvantages Ref #
Active contour Uses spline curves that are fitted to the tag lines using multiple constraints. Intuitive approach; parametric continuity; local control of the curve shape. Long processing time; sensitive to weights of different constraint forces. 93, 96, 97, 101, 102.
Optical flow Tracks tag lines intersections based on tagging contrast. Possibility for automatic processing; reduced processing time. Sensitive to image quality, especially tagging contrast. 103-105, 107-110.
Template matching Cross-correlates a pre-defined tagging pattern with the resulting images. Reduced processing time. Pre-defined assumptions must be met. 95.
Sinusoidal analysis Data are analyzed into different frequency components. Decreased sensitivity to noise; high accuracy. Complicated data analysis. 111-113.
Volumetric modeling Analyzes a stack of parallel tagged images. 3-D tagging analysis; more automatic processing. Long processing time. 134-139.
Finite-element modeling Creates model tags, which define the tag lines in the images. 3-D tagging analysis; reduced processing time. Measurements are not directly related to clinical understanding. 140-142.
Statistical modeling Uses statistical methods for estimating tag lines deformation. 3-D capability; more intuitive and understandable parameters. Predefined assumptions; complicated processing. 141, 143.
3-D active contour modeling Uses 3-D spline curves that are fitted to tag lines from a set of parallel images. 3-D capability; high resolution; parametric continuity. Long processing time. 144-148.