mri

Welcome to the blog on Artificial Intelligence of
the European Society of Radiology

This blog aims at bringing educational and critical perspectives on AI to readers. It should help imaging professionals to learn and keep up to date with the technologies being developed in this rapidly evolving field.

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Latest posts

Deep learning and compressed sensing for reconstruction of 3D knee MRI

This study explores combining compressed sensing (CS) and artificial intelligence (AI), particularly deep learning (DL), to accelerate three-dimensional (3D) magnetic resonance imaging (MRI) of the knee. Twenty healthy volunteers were examined using a 3-T scanner with a fat-saturated 3D proton density sequence at various acceleration levels. Two reconstruction methods were compared: conventional CS and a new DL-based algorithm (CS-AI). Two

Read More →

Public data homogenization for AI model development in breast cancer

Developing reliable AI models for clinical applications, especially in breast cancer, requires access to both clinical and imaging data. While The Cancer Imaging Archive (TCIA) offers a large collection of publicly available medical images and clinical data, these existing datasets are often too heterogeneous for direct use in AI model development. This study aimed to harmonize these datasets to create

Read More →

AI can generate a scientific paper from scratch that can survive the peer-review process

In our latest exploration, we embarked on a journey to test the resilience of the peer-review systemagainst the ever-growing influence of AI in scientific literature. Alongside my colleague, weconceived a purely fictional MRI technique—Magnetic Resonance Audiometry (MRA)—and askedan AI model to generate an entire manuscript around it. The result? A complete, technically robustresearch paper, complete with equations, references, and even

Read More →

Automatic segmentation of fat metaplasia on sacroiliac joint MRI using deep learning

The authors of this study developed a deep learning model used for segmenting fat metaplasia (FM) on sacroiliac joint (SIJ) MRI with the additional aim of utilizing the deep learning model to classify axial spondyloarthritis (axSpA) and non-axSpA. They were able to conclude that the deep learning model could automatically and accurately segment FM on SIJ MRI, helping to increase

Read More →

Enhancing recurrence risk prediction for bladder cancer using multi-sequence MRI radiomics

This study aimed to develop a radiomics-clinical nomogram using multi-sequence MRI to predict recurrence-free survival (RFS) in patients with bladder cancer (BCa). Using a retrospective cohort of 229 BCa patients, the authors determined that the radiomics-clinical nomogram was able to effectively assess BCa recurrence risk, outperforming both the radiomics model and the clinical model. Key points: Article: Enhancing recurrence risk

Read More →

AI support in MR imaging of incidental renal masses

Our study explores the integration of artificial intelligence (AI) into magnetic resonance (MR) imaging to enhance the differentiation between benign and malignant renal lesions. The findings suggest that AI can significantly improve diagnostic accuracy and cost-effectiveness, addressing a crucial need in radiology. AI has the potential to alleviate pressures on healthcare systems by improving diagnostic efficiency and accuracy. By incorporating

Read More →

AI applied to MRI reliably detects the presence of meniscus tears

It is known that meniscus tears are difficult to diagnose on knee MRIs. Therefore, this study reviews and compares the accuracy of convolutional neural networks (CNNs). The authors assessed databases including PubMed, MEDLINE, EMBASE, and Cochrane, finding eleven articles to include in the final review, consisting of over 13,000 patients and over 57,000 images. They concluded that CNN is accurate

Read More →

AI-based algorithm offers reliable Cobb angle measurement on routine MRI for degenerative scoliosis patients

Due to how the severity of degenerative scoliosis is assessed, this retrospective study aimed to develop and evaluate the reliability of a novel automatic method that measured Cobb angles on lumbar MRI in degenerative scoliosis (DS) patients. The authors developed a 3D artificial intelligence algorithm that was trained on 447 lumbar MRI. The study concluded that the AI-based algorithm offered

Read More →

Knee landmarks detection via deep learning

A deep learning-based approach was developed and validated in this study which aimed to automatically measure the patellofemoral instability (PFI) indices related to patellar height and trochlear dysplasia in knee MRI scans. The authors included a total of 763 knee MRI slices from 95 patients, annotating 3,393 anatomical landmarks. The results indicated that the developed models achieved good accuracy in

Read More →

A deep learning framework for intracranial aneurysms automatic segmentation and detection on magnetic resonance T1 images

This study featured a design of a deep learning-based framework for the automatic segmentation of intracranial aneurysms (IAs) on MR T1 images while also testing the robustness and performance of the framework. The authors were able to conclude that their deep learning framework could effectively detect and segment IAs using clinical routine T1 sequences, which offers potential in improving the

Read More →

Deep learning and compressed sensing for reconstruction of 3D knee MRI

This study explores combining compressed sensing (CS) and artificial intelligence (AI), particularly deep learning (DL), to accelerate three-dimensional (3D) magnetic resonance imaging (MRI) of the knee. Twenty healthy volunteers were examined using a 3-T scanner with a fat-saturated 3D proton density sequence at various acceleration levels. Two reconstruction methods were compared: conventional CS and a new DL-based algorithm (CS-AI). Two

Read More →

Public data homogenization for AI model development in breast cancer

Developing reliable AI models for clinical applications, especially in breast cancer, requires access to both clinical and imaging data. While The Cancer Imaging Archive (TCIA) offers a large collection of publicly available medical images and clinical data, these existing datasets are often too heterogeneous for direct use in AI model development. This study aimed to harmonize these datasets to create

Read More →

AI can generate a scientific paper from scratch that can survive the peer-review process

In our latest exploration, we embarked on a journey to test the resilience of the peer-review systemagainst the ever-growing influence of AI in scientific literature. Alongside my colleague, weconceived a purely fictional MRI technique—Magnetic Resonance Audiometry (MRA)—and askedan AI model to generate an entire manuscript around it. The result? A complete, technically robustresearch paper, complete with equations, references, and even

Read More →

Automatic segmentation of fat metaplasia on sacroiliac joint MRI using deep learning

The authors of this study developed a deep learning model used for segmenting fat metaplasia (FM) on sacroiliac joint (SIJ) MRI with the additional aim of utilizing the deep learning model to classify axial spondyloarthritis (axSpA) and non-axSpA. They were able to conclude that the deep learning model could automatically and accurately segment FM on SIJ MRI, helping to increase

Read More →

Enhancing recurrence risk prediction for bladder cancer using multi-sequence MRI radiomics

This study aimed to develop a radiomics-clinical nomogram using multi-sequence MRI to predict recurrence-free survival (RFS) in patients with bladder cancer (BCa). Using a retrospective cohort of 229 BCa patients, the authors determined that the radiomics-clinical nomogram was able to effectively assess BCa recurrence risk, outperforming both the radiomics model and the clinical model. Key points: Article: Enhancing recurrence risk

Read More →

AI support in MR imaging of incidental renal masses

Our study explores the integration of artificial intelligence (AI) into magnetic resonance (MR) imaging to enhance the differentiation between benign and malignant renal lesions. The findings suggest that AI can significantly improve diagnostic accuracy and cost-effectiveness, addressing a crucial need in radiology. AI has the potential to alleviate pressures on healthcare systems by improving diagnostic efficiency and accuracy. By incorporating

Read More →

AI applied to MRI reliably detects the presence of meniscus tears

It is known that meniscus tears are difficult to diagnose on knee MRIs. Therefore, this study reviews and compares the accuracy of convolutional neural networks (CNNs). The authors assessed databases including PubMed, MEDLINE, EMBASE, and Cochrane, finding eleven articles to include in the final review, consisting of over 13,000 patients and over 57,000 images. They concluded that CNN is accurate

Read More →

AI-based algorithm offers reliable Cobb angle measurement on routine MRI for degenerative scoliosis patients

Due to how the severity of degenerative scoliosis is assessed, this retrospective study aimed to develop and evaluate the reliability of a novel automatic method that measured Cobb angles on lumbar MRI in degenerative scoliosis (DS) patients. The authors developed a 3D artificial intelligence algorithm that was trained on 447 lumbar MRI. The study concluded that the AI-based algorithm offered

Read More →

Knee landmarks detection via deep learning

A deep learning-based approach was developed and validated in this study which aimed to automatically measure the patellofemoral instability (PFI) indices related to patellar height and trochlear dysplasia in knee MRI scans. The authors included a total of 763 knee MRI slices from 95 patients, annotating 3,393 anatomical landmarks. The results indicated that the developed models achieved good accuracy in

Read More →

A deep learning framework for intracranial aneurysms automatic segmentation and detection on magnetic resonance T1 images

This study featured a design of a deep learning-based framework for the automatic segmentation of intracranial aneurysms (IAs) on MR T1 images while also testing the robustness and performance of the framework. The authors were able to conclude that their deep learning framework could effectively detect and segment IAs using clinical routine T1 sequences, which offers potential in improving the

Read More →

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  • Option to participate in the European Diploma. 3
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  • Updates on offers & events through our newsletters
  • Exclusive access to the ESR feed in Juisci

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Footnotes:

01

Reduced registration fees for ECR 2025:
Provided that ESR 2024 membership is activated and approved by August 31, 2024.

Reduced registration fees for ECR 2026:
Provided that ESR 2025 membership is activated and approved by August 31, 2025.

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04
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