Impact of incomplete ventricular coverage on diagnostic performance of myocardial perfusion imaging.

In the context of myocardial perfusion imaging (MPI) with cardiac magnetic resonance (CMR), there is ongoing debate on the merits of using technically complex acquisition methods to achieve whole-heart spatial coverage, rather than conventional 3-slice acquisition. An adequately powered comparative study is difficult to achieve given the requirement for two separate stress CMR studies in each patient. The aim of this work is to draw relevant conclusions from SPECT MPI by comparing whole-heart versus simulated 3-slice coverage in a large existing dataset. SPECT data from 651 patients with suspected coronary artery disease who underwent invasive angiography were analyzed. A computational approach was designed to model 3-slice MPI by retrospective subsampling of whole- heart data. For both whole-heart and 3-slice approaches, the diagnostic performance and the stress total perfusion deficit (TPD) score-a measure of ischemia extent/severity-were quantified and compared. Diagnostic accuracy for the 3-slice and whole-heart approaches were similar (area under the curve: 0.843 vs. 0.855, respectively; P = 0.07). The majority (54%) of cases missed by 3-slice imaging had primarily apical ischemia. Whole-heart and 3-slice TPD scores were strongly correlated (R2 = 0.93, P < 0.001) but 3-slice TPD showed a small yet significant bias compared to whole-heart TPD (- 1.19%; P < 0.0001) and the 95% limits of agreement were relatively wide (- 6.65% to 4.27%). Incomplete ventricular coverage typically acquired in 3-slice CMR MPI does not significantly affect the diagnostic accuracy. However, 3-slice MPI may fail to detect severe apical ischemia and underestimate the extent/severity of perfusion defects. Our results suggest that caution is required when comparing the ischemic burden between 3-slice and whole-heart datasets, and corroborate the need to establish prognostic thresholds specific to each approach

Assessment of poststress left ventricular ejection fraction by gated SPECT: comparison with equilibrium radionuclide angiocardiography

PURPOSE: We compared left ventricular (LV) ejection fraction obtained by gated SPECT with that obtained by equilibrium radionuclide angiocardiography in a large cohort of patients. METHODS: Within 1 week, 514 subjects with suspected or known coronary artery disease underwent same-day stress-rest (99m)Tc-sestamibi gated SPECT and radionuclide angiocardiography. For both studies, data were acquired 30 min after completion of exercise and after 3 h rest. RESULTS: In the overall study population, a good correlation between ejection fraction measured by gated SPECT and by radionuclide angiocardiography was observed at rest (r=0.82, p<0.0001) and after stress (r=0.83, p<0.0001). In Bland-Altman analysis, the mean differences in ejection fraction (radionuclide angiocardiography minus gated SPECT) were -0.6% at rest and 1.7% after stress. In subjects with normal perfusion (n=362), a good correlation between ejection fraction measured by gated SPECT and by radionuclide angiocardiography was observed at rest (r=0.72, p<0.0001) and after stress (r=0.70, p<0.0001) and the mean differences in ejection fraction were -0.9% at rest and 1.4% after stress. Also in patients with abnormal perfusion (n=152), a good correlation between the two techniques was observed both at rest (r=0.89, p<0.0001) and after stress (r=0.90, p<0.0001) and the mean differences in ejection fraction were 0.1% at rest and 2.5% after stress. CONCLUSION: In a large study population, a good agreement was observed in the evaluation of LV ejection fraction between gated SPECT and radionuclide angiocardiography. However, in patients with perfusion abnormalities, a slight underestimation in poststress LV ejection fraction was observed using gated SPECT as compared to equilibrium radionuclide angiocardiography

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Informative sample generation using class aware generative adversarial networks for classification of chest Xrays

Training robust deep learning (DL) systems for disease detection from medical images is challenging due to limited images covering different disease types and severity. The problem is especially acute, where there is a severe class imbalance. We propose an active learning (AL) framework to select most informative samples for training our model using a Bayesian neural network. Informative samples are then used within a novel class aware generative adversarial network (CAGAN) to generate realistic chest xray images for data augmentation by transferring characteristics from one class label to another. Experiments show our proposed AL framework is able to achieve state-of-the-art performance by using about $35\%$ of the full dataset, thus saving significant time and effort over conventional methods