Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
25480110
OBJECTIVE: We aimed to investigate if early revascularization in patients with suspected coronary artery disease can be effectively predicted by integrating clinical data and quantitative image features derived from perfusion SPECT (MPS) by machine l...
OBJECTIVES: The study evaluated the automatic prediction of obstructive disease from myocardial perfusion imaging (MPI) by deep learning as compared with total perfusion deficit (TPD).
OBJECTIVES: The patient-based diagnosis with an artificial neural network (ANN) has shown potential utility for the detection of coronary artery disease; however, the region-based accuracy of the detected regions has not been fully evaluated. The aim...
INTRODUCTION: The objective of the study was to use fuzzy logic-based moving average filters for reducing noise from Tc-99m-sestamibi parathyroid images and to compare its performance with classical moving average filters.
OBJECTIVE: It is important to detect parathyroid adenomas by parathyroid scintigraphy with 99m-technetium sestamibi (Tc-MIBI) before surgery. This study aimed to develop and validate deep learning (DL)-based models to detect parathyroid adenoma in pa...
Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
35982208
BACKGROUND: Low-dose (LD) myocardial perfusion (MP) SPECT suffers from high noise level, leading to compromised diagnostic accuracy. Here we investigated the denoising performance for MP-SPECT using a conditional generative adversarial network (cGAN)...
Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
36097242
BACKGROUND: Deep learning (DL)-based attenuation correction (AC) is promising to improve myocardial perfusion (MP) SPECT. We aimed to optimize and compare the DL-based direct and indirect AC methods, with and without SPECT and CT mismatch.
PURPOSE: To train and validate machine learning-derived clinical decision algorithm (CDA) for the diagnosis of hyperfunctioning parathyroid glands using preoperative variables to facilitate surgical planning.