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

Cardiovascular magnetic resonance (CMR) tagging has been established as an essential technique for measuring regional myocardial function. It allows quantification of local intramyocardial motion measures, e.g. strain and strain rate. The invention of CMR tagging came in the late eighties, where the technique allowed for the first time for visualizing transmural myocardial movement without having to implant physical markers. This new idea opened the door for a series of developments and improvements that continue up to the present time. Different tagging techniques are currently available that are more extensive, improved, and sophisticated than they were twenty years ago. Each of these techniques has different versions for improved resolution, signal-to-noise ratio (SNR), scan time, anatomical coverage, three-dimensional capability, and image quality. The tagging techniques covered in this article can be broadly divided into two main categories: 1) Basic techniques, which include magnetization saturation, spatial modulation of magnetization (SPAMM), delay alternating with nutations for tailored excitation (DANTE), and complementary SPAMM (CSPAMM); and 2) Advanced techniques, which include harmonic phase (HARP), displacement encoding with stimulated echoes (DENSE), and strain encoding (SENC). Although most of these techniques were developed by separate groups and evolved from different backgrounds, they are in fact closely related to each other, and they can be interpreted from more than one perspective. Some of these techniques even followed parallel paths of developments, as illustrated in the article. As each technique has its own advantages, some efforts have been made to combine different techniques together for improved image quality or composite information acquisition. In this review, different developments in pulse sequences and related image processing techniques are described along with the necessities that led to their invention, which makes this article easy to read and the covered techniques easy to follow. Major studies that applied CMR tagging for studying myocardial mechanics are also summarized. Finally, the current article includes a plethora of ideas and techniques with over 300 references that motivate the reader to think about the future of CMR tagging

Confidence measures for assessing the HARP algorithm in tagged magnetic resonance imaging

Cardiac deformation and changes therein have been linked to\u3cbr/\u3epathologies. Both can be extracted in detail from tagged Magnetic Resonance\u3cbr/\u3eImaging (tMRI) using harmonic phase (HARP) images. Although\u3cbr/\u3epoint tracking algorithms have shown to have high accuracies on HARP\u3cbr/\u3eimages, these vary with position. Detecting and discarding areas with\u3cbr/\u3eunreliable results is crucial for use in clinical support systems. This\u3cbr/\u3epaper assesses the capability of two confidence measures (CMs), based on\u3cbr/\u3eenergy and image structure, for detecting locations with reduced accuracy\u3cbr/\u3ein motion tracking results. These CMs were tested on a database\u3cbr/\u3eof simulated tMRI images containing the most common artifacts that\u3cbr/\u3emay affect tracking accuracy. CM performance is assessed based on its\u3cbr/\u3ecapability for HARP tracking error bounding and compared in terms of\u3cbr/\u3esignificant differences detected using a multi comparison analysis of variance\u3cbr/\u3ethat takes into account the most influential factors on HARP tracking\u3cbr/\u3eperformance. Results showed that the CM based on image structure\u3cbr/\u3ewas better suited to detect unreliable optical flow vectors. In addition,\u3cbr/\u3eit was shown that CMs can be used to detect optical flow vectors with\u3cbr/\u3elarge errors in order to improve the optical flow obtained with the HARP\u3cbr/\u3etracking algorithm

Accurate two-dimensional cardiac strain calculation using adaptive windowed Fourier transform and Gabor wavelet transform

10.1007/s11548-012-0689-2International Journal of Computer Assisted Radiology and Surgery81135-14