AIMC Topic: Radionuclide Imaging
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Acquisition time reduction in pediatric Tc-DMSA planar imaging using deep learning.
Journal of applied clinical medical physics
Apr 5, 2023
PURPOSE: Given the potential risk of motion artifacts, acquisition time reduction is desirable in pediatric Tc-dimercaptosuccinic acid (DMSA) scintigraphy. The aim of this study was to evaluate the performance of predicted full-acquisition-time imag...
Artificial intelligence-based analysis of whole-body bone scintigraphy: The quest for the optimal deep learning algorithm and comparison with human observer performance.
Zeitschrift fur medizinische Physik
Mar 15, 2023
PURPOSE: Whole-body bone scintigraphy (WBS) is one of the most widely used modalities in diagnosing malignant bone diseases during the early stages. However, the procedure is time-consuming and requires vigour and experience. Moreover, interpretation...
Deep learning for improving PET/CT attenuation correction by elastic registration of anatomical data.
European journal of nuclear medicine and molecular imaging
Mar 8, 2023
BACKGROUND: For PET/CT, the CT transmission data are used to correct the PET emission data for attenuation. However, subject motion between the consecutive scans can cause problems for the PET reconstruction. A method to match the CT to the PET would...
Deep learning based identification of bone scintigraphies containing metastatic bone disease foci.
Cancer imaging : the official publication of the International Cancer Imaging Society
Jan 25, 2023
PURPOSE: Metastatic bone disease (MBD) is the most common form of metastases, most frequently deriving from prostate cancer. MBD is screened with bone scintigraphy (BS), which have high sensitivity but low specificity for the diagnosis of MBD, often ...
MobileNet-SVM: A Lightweight Deep Transfer Learning Model to Diagnose BCH Scans for IoMT-Based Imaging Sensors.
Sensors (Basel, Switzerland)
Jan 6, 2023
Many individuals worldwide pass away as a result of inadequate procedures for prompt illness identification and subsequent treatment. A valuable life can be saved or at least extended with the early identification of serious illnesses, such as variou...
Artificial Intelligence in Nuclear Medicine: Opportunities, Challenges, and Responsibilities Toward a Trustworthy Ecosystem.
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Dec 15, 2022
Trustworthiness is a core tenet of medicine. The patient-physician relationship is evolving from a dyad to a broader ecosystem of health care. With the emergence of artificial intelligence (AI) in medicine, the elements of trust must be revisited. We...
Transparency in Artificial Intelligence Research: a Systematic Review of Availability Items Related to Open Science in Radiology and Nuclear Medicine.
Academic radiology
Dec 14, 2022
RATIONALE AND OBJECTIVES: Reproducibility of artificial intelligence (AI) research has become a growing concern. One of the fundamental reasons is the lack of transparency in data, code, and model. In this work, we aimed to systematically review the ...
CT image quality evaluation in the age of deep learning: trade-off between functionality and fidelity.
European radiology
Nov 9, 2022
OBJECTIVE: To quantitatively compare DLIR and ASiR-V with realistic anatomical images.
Comparison of deep learning segmentation and multigrader-annotated mandibular canals of multicenter CBCT scans.
Scientific reports
Nov 3, 2022
Deep learning approach has been demonstrated to automatically segment the bilateral mandibular canals from CBCT scans, yet systematic studies of its clinical and technical validation are scarce. To validate the mandibular canal localization accuracy ...
The transformational potential of molecular radiomics.
Journal of medical radiation sciences
Oct 13, 2022
Conventional radiomics in nuclear medicine involve hand-crafted and computer-assisted regions of interest. Recent developments in artificial intelligence (AI) have seen the emergence of AI-augmented segmentation and extraction of lower order traditio...
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