Objective: Image post-processing corrects for cardiac and respiratory motion (MoCo) during cardiovascular magnetic resonance (CMR) stress perfusion. The study analyzed its influence on visual image evaluation.
Materials And Methods: Sixty-two patients with (suspected) coronary artery disease underwent a standard CMR stress perfusion exam during free-breathing. Image post-processing was performed without (non-MoCo) and with MoCo (image intensity normalization; motion extraction with iterative non-rigid registration; motion warping with the combined displacement field). Images were evaluated regarding the perfusion pattern (perfusion deficit, dark rim artifact, uncertain signal loss, and normal perfusion), the general image quality (non-diagnostic, imperfect, good, and excellent), and the reader's subjective confidence to assess the images (not confident, confident, very confident).
Results: Fifty-three (non-MoCo) and 52 (MoCo) myocardial segments were rated as 'perfusion deficit', 113 vs. 109 as 'dark rim artifacts', 9 vs. 7 as 'uncertain signal loss', and 817 vs. 824 as 'normal'. Agreement between non-MoCo and MoCo was high with no diagnostic difference per-patient. The image quality of MoCo was rated more often as 'good' or 'excellent' (92 vs. 63%), and the diagnostic confidence more often as "very confident" (71 vs. 45%) compared to non-MoCo.
Conclusions: The comparison of perfusion images acquired during free-breathing and post-processed with and without motion correction demonstrated that both methods led to a consistent evaluation of the perfusion pattern, while the image quality and the reader's subjective confidence to assess the images were rated more favorably for MoCo.
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http://dx.doi.org/10.1007/s10334-021-00923-2 | DOI Listing |
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Breast cancer cells sense shear stresses in response to interstitial fluid flow in bone and induce specific biological responses. Computational fluid dynamics models have been instrumental in estimating these shear stresses to relate the cell mechanoresponse to exact mechanical signals, better informing experiment design. Most computational models greatly simplify the experimental and cell mechanical environments for ease of computation, but these simplifications may overlook complex cell-substrate mechanical interactions that significantly change shear stresses experienced by cells.
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