AIMC Topic: Tomography, Emission-Computed, Single-Photon
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dSPIC: a deep SPECT image classification network for automated multi-disease, multi-lesion diagnosis.
BMC medical imaging
Aug 11, 2021
BACKGROUND: Functional imaging especially the SPECT bone scintigraphy has been accepted as the effective clinical tool for diagnosis, treatment, evaluation, and prevention of various diseases including metastasis. However, SPECT imaging is brightly c...
Artificial Intelligence-Based Data Corrections for Attenuation and Scatter in Position Emission Tomography and Single-Photon Emission Computed Tomography.
PET clinics
Aug 5, 2021
Recent developments in artificial intelligence (AI) technology have enabled new developments that can improve attenuation and scatter correction in PET and single-photon emission computed tomography (SPECT). These technologies will enable the use of ...
Improving detection accuracy of perfusion defect in standard dose SPECT-myocardial perfusion imaging by deep-learning denoising.
Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
Jul 19, 2021
BACKGROUND: We previously developed a deep-learning (DL) network for image denoising in SPECT-myocardial perfusion imaging (MPI). Here we investigate whether this DL network can be utilized for improving detection of perfusion defects in standard-dos...
Hahn-PCNN-CNN: an end-to-end multi-modal brain medical image fusion framework useful for clinical diagnosis.
BMC medical imaging
Jul 14, 2021
BACKGROUND: In medical diagnosis of brain, the role of multi-modal medical image fusion is becoming more prominent. Among them, there is no lack of filtering layered fusion and newly emerging deep learning algorithms. The former has a fast fusion spe...
Detecting lumbar lesions in Tc-MDP SPECT by deep learning: Comparison with physicians.
Medical physics
Jul 11, 2021
PURPOSE: Tc-MDP single-photon emission computed tomography (SPECT) is an established tool for diagnosing lumbar stress, a common cause of low back pain (LBP) in pediatric patients. However, detection of small stress lesions is complicated by the low...
Development of attenuation correction methods using deep learning in brain-perfusion single-photon emission computed tomography.
Medical physics
Jun 28, 2021
PURPOSE: Computed tomography (CT)-based attenuation correction (CTAC) in single-photon emission computed tomography (SPECT) is highly accurate, but it requires hybrid SPECT/CT instruments and additional radiation exposure. To obtain attenuation corre...
A novel deep-learning-based approach for automatic reorientation of 3D cardiac SPECT images.
European journal of nuclear medicine and molecular imaging
Apr 2, 2021
PURPOSE: Reconstructed transaxial cardiac SPECT images need to be reoriented into standard short-axis slices for subsequent accurate processing and analysis. We proposed a novel deep-learning-based method for fully automatic reorientation of cardiac ...
The promise of artificial intelligence and deep learning in PET and SPECT imaging.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Mar 22, 2021
This review sets out to discuss the foremost applications of artificial intelligence (AI), particularly deep learning (DL) algorithms, in single-photon emission computed tomography (SPECT) and positron emission tomography (PET) imaging. To this end, ...
Attenuation correction using deep learning for brain perfusion SPECT images.
Annals of nuclear medicine
Mar 9, 2021
OBJECTIVE: Non-uniform attenuation correction using computed tomography (CT) improves the image quality and quantification of single-photon emission computed tomography (SPECT). However, it is not widely used because it requires a SPECT/CT scanner. T...
Deep Learning-Based Computed Tomography Perfusion Mapping (DL-CTPM) for Pulmonary CT-to-Perfusion Translation.
International journal of radiation oncology, biology, physics
Mar 6, 2021
PURPOSE: Our purpose was to develop a deep learning-based computed tomography (CT) perfusion mapping (DL-CTPM) method that synthesizes lung perfusion images from CT images.
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