Ulrik Kristensen, PhD

AI
From lab to clinic: How AI can help
Clinical trials are an important checkpoint in moving therapies from the lab to the clinic, and artificial intelligence (AI) software can help researchers and clinicians reach their goals in several ways, according to a new article by U.K. market research firm Signify Research.
March 2, 2020
2020 01 30 18 30 5972 Data Network Hand 400
IS
Radiology business intelligence grows in demand
Commercial activity surrounding radiology business intelligence software has picked up recently with a new generation of applications entering the market. Why are imaging IT vendors focusing on adding these tools to their portfolios? Ulrik Kristensen, PhD, from Signify Research reviews the key forces driving growth.
August 6, 2019
2018 02 13 23 24 2339 Kristensen Ulrik 20190806201409
Advanced Visualization
Advanced visualization and the cloud that never formed
Recent hype about cloud technology has yet to materialize in clinical and diagnostic healthcare IT, especially when it comes to advanced visualization (AV). Why haven't more healthcare providers moved their AV solutions to the cloud? Ulrik Kristensen, PhD, from Signify Research examines this question.
May 13, 2018
2018 05 11 20 13 2288 Cloud 400
Advanced Visualization
Is virtual reality in radiology becoming a reality?
Virtual reality is by no means a new technology, but recent developments have given it new life, enabling it to spread from gaming and consumer markets into industries such as manufacturing and healthcare. Will the healthcare move be hard? Ulrik Kristensen of Signify Research offers a perspective.
February 13, 2018
2018 02 13 22 31 4830 Virtual Reality Girl 200
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Hinton acknowledges mistake in predicting AI replacement of radiologists
AI pioneer Geoffrey Hinton, PhD, has a new prediction for AI in radiology.
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ISMRM 2025
ISMRM: White-matter biomarkers help diagnose Alzheimer's earlier
2025 05 14 Images Large Radiol 243463 fig4
CT
AI-assisted chest CT can measure ALS respiratory function
Research presented at ISMRM 2025 found that adding dobutamine stress imaging to CMR can separate athletic hearts from hearts with early DCM. The stress CMR findings correlated well with CPET results, highlighting the feasability of adding dobutamine stress imaging to CMR exams.
ISMRM 2025
ISMRM: Adding dobutamine stress imaging to CMR feasible
AI models can generate synthetic equivalents to PET images from routinely acquired T1W MRI scans.
ISMRM 2025
ISMRM: Barriers face synthetic amyloid PET image development
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Hinton acknowledges mistake in predicting AI replacement of radiologists
By AuntMinnie.com staff writers
AI pioneer Geoffrey Hinton, PhD, has a new prediction for AI in radiology.
May 14, 2025
Chat Gpt Artificial Intelligence Ai Head
ISMRM: White-matter biomarkers help diagnose Alzheimer's earlier
By Kate Madden Yee
May 14, 2025
2025 05 14 White Matter Thumbnail
AI-assisted chest CT can measure ALS respiratory function
By Maryam Payne
Deep learning-assisted chest CT can be used to assess respiratory function in patients with amyotrophic lateral sclerosis (ALS).
May 14, 2025
2025 05 14 Images Large Radiol 243463 fig4
ISMRM: Adding dobutamine stress imaging to CMR feasible
By Amerigo Allegretto
Adding dobutamine stress imaging during clinical cardiovascular MR exams is feasible for separating athletic hearts from hearts with early DCM.
May 14, 2025
Research presented at ISMRM 2025 found that adding dobutamine stress imaging to CMR can separate athletic hearts from hearts with early DCM. The stress CMR findings correlated well with CPET results, highlighting the feasability of adding dobutamine stress imaging to CMR exams.
ISMRM: Barriers face synthetic amyloid PET image development
By Will Morton
Work on developing synthetic amyloid PET images from MRI scans is in its early stages.
May 14, 2025
AI models can generate synthetic equivalents to PET images from routinely acquired T1W MRI scans.
ISMRM: AI spots ischemic heart disease on low GBCA dose images
By Erik L. Ridley
An AI-based framework was highly accurate for detecting myocardial ischemia, even on images acquired with 90% less gadolinium.
May 14, 2025
Representative stress perfusion CMR case with 80% contrast dose reduction. Pixel-wise MBF maps and AHA 16-segment bull's eye plots are shown for full-dose and reduced-dose acquisitions. When analyzed using the proposed MST deep learning method, the reduced-dose data closely matches the full-dose data across basal, mid, and apical slices. In contrast, Fermi-deconvolution shows good agreement in apical and mid slices but overestimates MBF in some basal segments, indicating reduced accuracy under low-dose conditions. Image courtesy of the ISMRM.
Report highlights LLM cybersecurity threats
By Will Morton
The use of LLMs in radiology and healthcare as a whole is expanding, making it crucial to start understanding their potential vulnerabilities.
May 14, 2025
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ISMRM: Synthesized MR images mitigate patient exposure to gadolinium
By Kate Madden Yee
Using synthesized "contrast-enhanced" images can address gadolinium-related health concerns and cut exam time.
May 13, 2025
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ISMRM: MRI postoperative imaging useful for DBS treatment planning
By Liz Carey
Researchers are getting closer to understanding how deep brain stimulation alters the medication burden of patients with Parkinson's.
May 13, 2025
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ISMRM: DMI metabolic modeling provides insight for neuro diseases
By Amerigo Allegretto
Using deuterium metabolic imaging data for metabolic modeling shows differences in brain tissue between healthy, epilepsy, and glioblastoma patients.
May 13, 2025
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ISMRM: AI reconstruction can improve sustainability in MRI
By Erik L. Ridley
Researchers reported substantial energy and time savings from adoption of AI-based MR image reconstruction.
May 13, 2025
Judith Herrmann, MD, PD, of the University Hospital Tübingen in Germany.
Breast texture patterns on mammo may predict cancer risk in dense breasts
By Amerigo Allegretto
Radiomic phenotypes found on mammography may be linked to elevated breast cancer risk in women with dense breasts.
May 13, 2025
Full-field digital mammography (FFDM) images in a 52-year-old woman show a high-risk phenotype (top), and FFDM images in a 58-year-old woman show a low-risk phenotype (bottom). The high-risk phenotype was defined by having high values of the first principal component (PC1) and was assigned to cluster 3 (top row), and the low-risk phenotype was defined by having low values of the PC1 and was assigned to cluster 1 (bottom row). The index images (left side; before any diagnosis of cancer) are provided as well as follow-up images obtained at either the time of cancer diagnosis (for the high-risk phenotype, top right) or at the last follow-up (low-risk phenotype, bottom right). The red circle indicates the location of subsequent breast cancer. Both women had a BI-RADS breast density of C. The woman at high risk for breast cancer was Black and the woman at low risk for breast cancer was white.
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