Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis

In patients with coronary artery stenoses of intermediate severity, the functional significance needs to be determined. Fractional flow reserve (FFR) measurement, performed during invasive coronary angiography (ICA), is most often used in clinical practice. To reduce the number of ICA procedures, we present a method for automatic identification of patients with functionally significant coronary artery stenoses, employing deep learning analysis of the left ventricle (LV) myocardium in rest coronary CT angiography (CCTA). The study includes consecutively acquired CCTA scans of 166 patients with FFR measurements. To identify patients with a functionally significant coronary artery stenosis, analysis is performed in several stages. First, the LV myocardium is segmented using a multiscale convolutional neural network (CNN). To characterize the segmented LV myocardium, it is subsequently encoded using unsupervised convolutional autoencoder (CAE). Thereafter, patients are classified according to the presence of functionally significant stenosis using an SVM classifier based on the extracted and clustered encodings. Quantitative evaluation of LV myocardium segmentation in 20 images resulted in an average Dice coefficient of 0.91 and an average mean absolute distance between the segmented and reference LV boundaries of 0.7 mm. Classification of patients was evaluated in the remaining 126 CCTA scans in 50 10-fold cross-validation experiments and resulted in an area under the receiver operating characteristic curve of 0.74 +- 0.02. At sensitivity levels 0.60, 0.70 and 0.80, the corresponding specificity was 0.77, 0.71 and 0.59, respectively. The results demonstrate that automatic analysis of the LV myocardium in a single CCTA scan acquired at rest, without assessment of the anatomy of the coronary arteries, can be used to identify patients with functionally significant coronary artery stenosis.Comment: This paper was submitted in April 2017 and accepted in November 2017 for publication in Medical Image Analysis. Please cite as: Zreik et al., Medical Image Analysis, 2018, vol. 44, pp. 72-8

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

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

Cardiac hybrid imaging

Computed tomography coronary angiography (CTCA) and myocardial perfusion imaging techniques (single photon emission computed tomography, SPECT, or positron emission tomography, PET) are established non-invasive modalities for the diagnosis of coronary artery disease (CAD). Cardiac hybrid imaging consists of the combination (or ‘fusion') of both modalities and allows obtaining complementary morphological (coronary anatomy, stenoses) and functional (myocardial perfusion) information in a single setting. However, hybrid cardiac imaging has also generated controversy with regard to which patients should undergo such integrated examinations for clinical effectiveness and minimization of costs and radiation dose. The feasibility and clinical value of hybrid imaging has been documented in small cohort studies and selected series of patients. Hybrid imaging appears to offer superior diagnostic and prognostic information compared with stand-alone or side-by-side interpretation of data sets. Particularly in patients with multivessel disease, the hybrid approach allows identification of flow-limiting coronary lesions and thereby provides useful information for the planning of revascularization procedures. Furthermore, integration of the detailed anatomical information from CTCA with the high molecular sensitivity of SPECT and PET may be useful to evaluate targeted molecular and cellular abnormalities in the future. While currently still restricted to specialized cardiac centres, the ongoing efforts to reduce radiation exposure and the increasing clinical interest will further pave the way for an increasing use of cardiac hybrid imaging in clinical practic

Coronary CT angiography and myocardial perfusion imaging to detect flow-limiting stenoses: a potential gatekeeper for coronary revascularization?

Aims To evaluate the diagnostic accuracy of a combined non-invasive assessment of coronary artery disease with coronary CT angiography (CTA) and myocardial perfusion imaging (MPI) for the detection of flow-limiting coronary stenoses and its potential as a gatekeeper for invasive examination and treatment. Methods and results In 78 patients (mean age 65 ± 9 years) referred for coronary angiography (CA), additional CTA and MPI (using single-photon emission-computed tomography) were performed and the findings not communicated. Detection of flow-limiting stenoses (justifying revascularization) by the combination of CTA and MPI (CTA/MPI) was compared with the combination of quantitative coronary angiography (QCA) plus MPI (QCA/MPI), which served as standard of reference. The findings of both combinations were related to the treatment strategy (revascularization vs. medical treatment) chosen in the catheterization laboratory based on the CA findings. Sensitivity, specificity, positive and negative predictive value, and accuracy of CTA/MPI for the detection of flow-limiting coronary stenoses were 100% each. More than half of revascularization procedures (21/40, 53%) was performed in patients without flow-limiting stenoses and 76% (47/62) of revascularized vessels were not associated with ischaemia on MPI. Conclusion The combined non-invasive approach CTA/MPI has an excellent accuracy to detect flow-limiting coronary stenoses compared with QCA/MPI and its use as a gatekeeper appears to make a substantial part of revascularization procedures redundan

Cardiac hybrid imaging: state-of-the-art

The field of noninvasive cardiac imaging has experienced enormous advances including computerized tomography coronary angiography (CTCA). Invasive angiography remains the anatomic standard of reference but it is associated with a non-negligible peri-procedural morbidity and mortality which suggests confining its use to patients who will benefit from a revascularization procedure. Many factors that are beyond the simple quantification of diameter narrowing and therefore cannot be fully assessed with luminology will eventually determine whether or not a given lesion produces stress-induced ischemia. Myocardial perfusion scintigraphy by single photon emission computerized tomography (SPECT) is one of the most widely used and well established noninvasive tools for the diagnosis of ischemic heart disease. Although positron emission tomography (PET) offers a higher accuracy than SPECT its use is often limited to large centers. This article explains the great potential of cardiac hybrid imaging which allows a comprehensive evaluation of coronary artery disease as it combines both morphological and functional information by fusing either SPECT or PET with CTCA. SPECT/CT and PET/CT hybrid imaging can provide entirely noninvasively unique information which helps improving diagnostic assessment and risk stratification and also impacts decision making with regard to revascularization in patients with coronary artery diseas

A New Approach in Risk Stratification by Coronary CT Angiography.

For a decade, coronary computed tomographic angiography (CCTA) has been used as a promising noninvasive modality for the assessment of coronary artery disease (CAD) as well as cardiovascular risks. CCTA can provide more information incorporating the presence, extent, and severity of CAD; coronary plaque burden; and characteristics that highly correlate with those on invasive coronary angiography. Moreover, recent techniques of CCTA allow assessing hemodynamic significance of CAD. CCTA may be potentially used as a substitute for other invasive or noninvasive modalities. This review summarizes risk stratification by anatomical and hemodynamic information of CAD, coronary plaque characteristics, and burden observed on CCTA