AIMC Topic: Tomography, Emission-Computed, Single-Photon
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GeSeNet: A General Semantic-Guided Network With Couple Mask Ensemble for Medical Image Fusion.
IEEE transactions on neural networks and learning systems
Oct 29, 2024
At present, multimodal medical image fusion technology has become an essential means for researchers and doctors to predict diseases and study pathology. Nevertheless, how to reserve more unique features from different modal source images on the prem...
Deep learning-based approach for acquisition time reduction in ventilation SPECT in patients after lung transplantation.
Radiological physics and technology
Oct 23, 2024
We aimed to evaluate the image quality and diagnostic performance of chronic lung allograft dysfunction (CLAD) with lung ventilation single-photon emission computed tomography (SPECT) images acquired briefly using a convolutional neural network (CNN)...
Generative AI and large language models in nuclear medicine: current status and future prospects.
Annals of nuclear medicine
Sep 25, 2024
This review explores the potential applications of Large Language Models (LLMs) in nuclear medicine, especially nuclear medicine examinations such as PET and SPECT, reviewing recent advancements in both fields. Despite the rapid adoption of LLMs in v...
Development and validation of a machine learning model to predict myocardial blood flow and clinical outcomes from patients' electrocardiograms.
Cell reports. Medicine
Sep 25, 2024
We develop a machine learning (ML) model using electrocardiography (ECG) to predict myocardial blood flow reserve (MFR) and assess its prognostic value for major adverse cardiovascular events (MACEs). Using 3,639 ECG-positron emission tomography (PET...
Clinical performance of deep learning-enhanced ultrafast whole-body scintigraphy in patients with suspected malignancy.
BMC medical imaging
Sep 9, 2024
BACKGROUND: To evaluate the clinical performance of two deep learning methods, one utilizing real clinical pairs and the other utilizing simulated datasets, in enhancing image quality for two-dimensional (2D) fast whole-body scintigraphy (WBS).
Bone metastasis scintigram generation using generative adversarial learning with multi-receptive field learning and two-stage training.
Medical physics
Sep 3, 2024
BACKGROUND: Deep learning is the primary method for conducting automated analysis of SPECT bone scintigrams. The lack of available large-scale data significantly hinders the development of well-performing deep learning models, as the performance of a...
Unrealistic Data Augmentation Improves the Robustness of Deep Learning-Based Classification of Dopamine Transporter SPECT Against Variability Between Sites and Between Cameras.
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Sep 3, 2024
We propose strongly unrealistic data augmentation to improve the robustness of convolutional neural networks (CNNs) for automatic classification of dopamine transporter SPECT against the variability between sites and between cameras. A CNN was train...
A novel algorithm developed using machine learning and a J-ACCESS database can estimate defect scores from myocardial perfusion single-photon emission tomography images.
Annals of nuclear medicine
Aug 29, 2024
BACKGROUND: Stress myocardial perfusion single-photon emission computed tomography (SPECT) imaging (MPI) has been used to diagnose and predict the prognoses of patients with coronary artery disease (CAD). An ongoing multicenter collaboration establis...
Gated SPECT-Derived Myocardial Strain Estimated From Deep-Learning Image Translation Validated From N-13 Ammonia PET.
Academic radiology
Aug 2, 2024
RATIONALE AND OBJECTIVES: This study investigated the use of deep learning-generated virtual positron emission tomography (PET)-like gated single-photon emission tomography (SPECT) for assessing myocardial strain, overcoming limitations of convention...
Development and Validation of an Explainable Machine Learning Model for Identification of Hyper-Functioning Parathyroid Glands from High-Frequency Ultrasonographic Images.
Ultrasound in medicine & biology
Jul 25, 2024
OBJECTIVE: To develop and validate a machine learning (ML) model based on high-frequency ultrasound (HFUS) images with the aim to identify the functional status of parathyroid glands (PTGs) in secondary hyper-parathyroidism (SHPT) patients.
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