Even if cardiovascular magnetic resonance (CMR) perfusion imaging has proven its relevance for visual detection of ischemia, myocardial blood flow (MBF) quantification at the voxel observation scale remains challenging. Integration of an automated segmentation step, prior to perfusion index estimation, might be a significant reconstruction component that could allow sustainable assumptions and constraint enlargement prior to advanced modeling. Current clustering techniques, such as bullseye representation or manual delineation, are not designed to discriminate voxels belonging to the lesion from healthy areas. Hence, the resulting average time-intensity curve, which is assumed to represent the dynamic contrast enhancement inside of a lesion, might be contaminated by voxels with perfectly healthy microcirculation. This study introduces a hierarchical lesion segmentation approach based on time-intensity curve features that considers the spatial particularities of CMR myocardial perfusion. A first kmeans clustering approach enables this method to perform coarse clustering, which is refined by a novel spatiotemporal region-growing (STRG) segmentation, thus ensuring spatial and time-intensity curve homogeneity. Over a cohort of 30 patients, myocardial blood flow (MBF) measured in voxels of lesion regions detected with STRG was significantly lower than in regions drawn manually (mean difference = 0.14, 95% CI [0.07, 0.2]) and defined with the bullseye template (mean difference = 0.25, 95% CI [0.17, 0.36]). Over the 90 analyzed slices, he median Dice scores calculated against the ground truth ranged between 0.62 and 0.67, the inclusion coefficients ranged between 0.62 and 0.76 and the centroid distances ranged between 0.97 3.88 mm. Therefore, though these metrics highlight spatial differences, they could not be used as an index to evaluate the accuracy and performance of the method, which can only be attested by the variability of the MBF clinical index.
Cardiac magnetic resonance myocardial perfusion imaging can detect coronary artery disease and is an alternative to single-photon emission computed tomography or positron emission tomography. However, the complex, non-linear MR signal and the lack of robust quantification of myocardial blood flow have hindered its widespread clinical application thus far. Recently, a new Bayesian approach was developed for brain imaging and evaluation of perfusion indexes (Kudo et al., 2014). In addition to providing accurate perfusion measurements, this probabilistic approach appears more robust than previous approaches, particularly due to its insensitivity to bolus arrival delays. We assessed the performance of this approach against a well-known and commonly deployed model-independent method based on the Fermi function for cardiac magnetic resonance myocardial perfusion imaging. The methods were first evaluated for accuracy and precision using a digital phantom to test them against the ground truth; next, they were applied in a group of coronary artery disease patients. The Bayesian method can be considered an appropriate model-independent method with which to estimate myocardial blood flow and delays. The digital phantom comprised a set of synthetic time-concentration curve combinations generated with a 2-compartment exchange model and a realistic combination of perfusion indexes, arterial input dynamics, noise and delays collected from the clinical dataset. The myocardial blood flow values estimated with the two methods showed an excellent correlation coefficient (r2 > 0.9) under all noise and delay conditions. The Bayesian approach showed excellent robustness to bolus arrival delays, with a similar performance to Fermi modeling when delays were considered. Delays were better estimated with the Bayesian approach than with Fermi modeling. An in vivo analysis of coronary artery disease patients revealed that the Bayesian approach had an excellent ability to distinguish between abnormal and normal myocardium. The Bayesian approach was able to discriminate not only flows but also delays with increased sensitivity by offering a clearly enlarged range of distribution for the physiologic parameters.
Two-year minimum clinical outcomes were collected on anatomic and reverse total shoulder arthroplasty patients enrolled in a single implant global registry that were performed using an intraoperative computer navigated surgery system. Age, gender, and follow-up matched cohorts were created from the same registry for comparison purposes for both anatomic and reverse total shoulder arthroplasty. The navigated cohorts exhibited as good or better clinical outcomes compared to the non-navigated cohorts as well as reductions in postoperative complications, revision rates, and adverse events.
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