Image Processing

Automatic organ segmentation model now available for testing with pediatric CTs
By Liz Carey
Cincinnati Children's Hospital Medical Center researchers have made their algorithm available as an open-source application for further CT research.
May 2, 2024
This image shows ground-truth manual organ segmentations (yellow) vs. DynUNet-TL (green) and TotalSegmentator (red) predictions. Photo courtesy of the American Journal of Roentgenology.
ECR: AI algorithm quantifies fatty tissue on chest CT for lung cancer prognosis
By Kate Madden Yee
A deep-learning algorithm used with chest CT could help clinicians quantify patients' subcutaneous fat tissue levels on lung cancer screening.
March 3, 2024
Fat Tissue Adipocytes
Can new image processing methods deliver reproducible radiomics?
By Erik L. Ridley
A multinational team of researchers has developed standardized image processing techniques that show promise for significantly improving the reproducibility of AI-based radiomics analysis.
February 7, 2024
Image
AI predicts cardiovascular disease mortality risk on chest CT
By Erik L. Ridley
A deep-learning algorithm could predict a patient’s risk of atherosclerotic cardiovascular disease from analysis of noncontrast-enhanced chest CT exams.
December 19, 2023
Heart Attack
MRI muscle volumes linked to risk of TKRs
By Will Morton
Having stronger thigh muscles may lower the odds of total knee replacement (TKR) surgery in patients with osteoarthritis.
November 27, 2023
MRI images of a knee joint of a patient showing (left) severe cartilage defects compared to a patient with an intact knee joint. Image courtesy of Upadhyay Bharadwaj, MD
Morphological markers culled from MRI exams help diagnose autism
By Kate Madden Yee
Morphological markers culled from diffusion tensor MRI brain scans using an AI algorithm can help accurately diagnose autism in young children.
November 21, 2023
The top five white matter features (region pairs) in a single image. The color map is: yellow = superior cerebellar peduncle (R)/uncinate fasciculus (R), orange = column and body of fornix/posterior corona radiata (L), purple = splenium/retrolenticular internal capsule (L), blue = dorsal cingulum (L)/cres of fornix (R), green = splenium/external capsule (R). Image and caption courtesy of the RSNA.
3D CT reconstruction shows ties to malignant pulmonary nodule growth
By Amerigo Allegretto
3D reconstruction of cardiac CT images could aid in follow-up strategies for pulmonary nodule screening.
November 20, 2023
Ct Scanner 400
Deep learning helps categorize acute appendicitis on pelvic CT
By Kate Madden Yee
A combined deep learning and radiomics model helps clinicians differentiate complicated from uncomplicated acute appendicitis on pelvic CT.
September 29, 2023
Appendix Blue 400
PET radiomics model identifies high-risk lymphoma patients
By Will Morton
A group in the Netherlands has built a radiomics model using FDG-PET scans that predicts disease-free survival odds in patients with relapsed lymphoma.
September 25, 2023
Lymphoma Scan
ISCT 2023: Quantitative CT: Not just for bone scanning
By Kate Madden Yee
Quantitative CT is increasingly being used in applications beyond diagnosing and tracking osteoporosis, according to a presentation on September 8 at the International Society of Computed Tomography (ISCT) 2023 meeting in San Diego.
September 12, 2023
2022 07 21 18 40 3375 Ct Scan Patient2 400
RSNA changes tack on quantitative imaging efforts
By Will Morton
The RSNA is planning to launch a new Quantitative Imaging Committee with the goal of expanding its quantitative imaging initiatives into areas not currently reached by the Quantitative Imaging Biomarkers Alliance.
September 11, 2023
2022 11 28 16 17 2543 Rsna 2022 Welcome 400
MRI is effective for assessing body composition
By Kate Madden Yee
MRI is just as effective as CT for assessing body composition in obese individuals at risk of conditions such as type 2 diabetes mellitus and nonalcoholic fatty liver and cardiovascular disease, according to a team of researchers from Spain.
September 4, 2023
2021 10 07 21 13 3594 Computer Display Monitor Lab Mri V1 400
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