Content-based image retrieval for the diagnosis of myocardial perfusion imaging using a deep convolutional autoencoder.

BACKGROUND: Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) plays a crucial role in the optimal treatment strategy for patients with coronary heart disease. We tested the feasibility of feature extraction from MPI using a deep convolutional autoencoder (CAE) model. METHODS: Eight hundred and forty-three pairs of stress and rest myocardial perfusion images were collected from consecutive patients who underwent cardiac scintigraphy in our hospital between December 2019 and February 2022. We trained a CAE model to reproduce the input paired image data, so as the encoder to output a 256-dimensional feature vector. The extracted feature vectors were further dimensionally reduced via principal component analysis (PCA) for data visualization. Content-based image retrieval (CBIR) was performed based on the cosine similarity of the feature vectors between the query and reference images. The agreement of the radiologist's finding between the query and retrieved MPI was evaluated using binary accuracy, precision, recall, and F1-score. RESULTS: A three-dimensional scatter plot with PCA revealed that feature vectors retained clinical information such as percent summed difference score, presence of ischemia, and the location of scar reported by radiologists. When CBIR was used as a similarity-based diagnostic tool, the binary accuracy was 81.0%. CONCLUSION: The results indicated the utility of unsupervised feature learning for CBIR in MPI.

Clinical Outcomes and Provoked Epicardial Spasm Phenotypes via Intracoronary Acetylcholine Testing in 680 Patients with Angina and Nonobstructive Coronary Arteries.

Background: Epicardial spasm (ES) phenotypes may be related to the prognosis in patients with coronary spastic angina. Objectives: The purpose of this study was to elucidate the relationship between angiographic coronary vasomotor responses to intracoronary acetylcholine (ACh) injection and prognosis in patients with angina and nonobstructive coronary artery disease (ANOCAD). Methods: This was a retrospective, observational, single-center study of 680 patients with ANOCAD. ACh spasm provocation tests on both coronary arteries were performed without administering nitroglycerine to relieve provoked spasm in a first-attempt artery. ACh was injected in incremental doses of 20/50/100/200 µg into the left coronary artery and 20/50/80 µg into the right coronary artery. Positive ES was defined as ≥90% stenosis and usual chest pain and ischemic ECG changes. Results: Provoked positive ES was observed in 310 patients (46%), including 85 patients (13%) with focal spasm, 150 patients (22%) with diffuse spasm, and 75 patients (11%) with combined spasm (diffuse spasm and focal spasm), whereas the remaining 370 patients (54%) had no provoked spasm. An unclassified ACh test was observed in 186 patients (27%), while 184 patients (27%) had a complete negative ACh test. The clinical outcomes in patients with complete negative ES were satisfactory compared with those with positive ES and unclassified ACh test results. The prognosis in patients with an unclassified ACh test was not different from those with a positive ES. Furthermore, prognosis in patients with ES phenotypes was not different among the three groups. Conclusions: There was no correlation between provoked ES phenotypes via intracoronary ACh testing and prognosis in patients with ANOCAD; however, clinical outcomes in patients with positive ES and unclassified ACh tests were worse compared to those with complete negative ACh tests. We should focus on the treatments in patients with unclassified ACh tests as well as those with ESs.

Image similarity-based cardiac rhythm device identification from X-rays using feature point matching.

AIMS: Identifying the manufacturer and the type of cardiac implantable electronic devices (CIEDs) is important in emergent clinical settings. Recent studies have illustrated that artificial neural network models can successfully recognize CIEDs from chest X-ray images. However, all existing methods require a vast amount of medical data to train the classification model. Here, we have proposed a novel method to retrieve an identical CIED image from an image database by employing the feature point matching algorithm. METHODS AND RESULTS: A total of 653 unique X-ray images from 456 patients who visited our pacemaker clinic between April 2012 and August 2020 were collected. The device images were manually square-shaped, and was thereafter resized to 224 × 224 pixels. A scale-invariant feature transform (SIFT) algorithm was used to extract the keypoints from the query image and reference images. Paired feature points were selected via brute-force matching, and the average Euclidean distance was calculated. The image with the shortest average distance was defined as the most similar image. The classification performance was indicated by accuracy, precision, recall, and F1-score for detecting the manufacturers and model groups, respectively. The average accuracy, precision, recall, and F-1 score for the manufacturer classification were 97.0%, 0.97, 0.96, and 0.96, respectively. For the model classification task, the average accuracy, precision, recall, and F-1 score were 93.2%, 0.94, 0.92, and 0.93, respectively, all of which were higher than those of the previously reported machine learning models. CONCLUSION: Feature point matching is useful for identifying CIEDs from X-ray images.