The fastMRI dataset now includes breast data!
Two of the researchers behind the new fastMRI breast dataset—lead author Eddy Solomon & senior author Laura Heacock—explain what makes these data unique:
cai2r.net/fastmri-data...
#AI #ML #innovation #MRI
#BreastCancer
#ScienceWithoutBorders 🧪
1/
Two of the researchers behind the new fastMRI breast dataset—lead author Eddy Solomon & senior author Laura Heacock—explain what makes these data unique:
cai2r.net/fastmri-data...
#AI #ML #innovation #MRI
#BreastCancer
#ScienceWithoutBorders 🧪
1/
FastMRI Dataset Adds Breast Imaging Data to Encourage New Directions in AI Research on MRI • Center for Advanced Imaging Innovation and Research
The fastMRI dataset now includes curated breast MRI data to boost AI innovation in radial, dynamic contrast-enhanced, ultrafast MRI of the breast.
cai2r.net
February 18, 2025 at 8:42 PM
The fastMRI dataset now includes breast data!
Two of the researchers behind the new fastMRI breast dataset—lead author Eddy Solomon & senior author Laura Heacock—explain what makes these data unique:
cai2r.net/fastmri-data...
#AI #ML #innovation #MRI
#BreastCancer
#ScienceWithoutBorders 🧪
1/
Two of the researchers behind the new fastMRI breast dataset—lead author Eddy Solomon & senior author Laura Heacock—explain what makes these data unique:
cai2r.net/fastmri-data...
#AI #ML #innovation #MRI
#BreastCancer
#ScienceWithoutBorders 🧪
1/
FastMRI Breast: A Publicly Available Radial K-space Dataset of Breast Dynamic Contrast-enhanced MRI https://buff.ly/4fT1Son #OncoRad #BreastRad #ML
January 21, 2025 at 3:45 PM
FastMRI Breast: A Publicly Available Radial K-space Dataset of Breast Dynamic Contrast-enhanced MRI https://buff.ly/4fT1Son #OncoRad #BreastRad #ML
FastMRI Breast: A Publicly Available Radial K-space Dataset of Breast Dynamic Contrast-enhanced MRI https://buff.ly/4fT1Son #breast #OncoRad #MachineLearning
January 13, 2025 at 3:45 PM
FastMRI Breast: A Publicly Available Radial K-space Dataset of Breast Dynamic Contrast-enhanced MRI https://buff.ly/4fT1Son #breast #OncoRad #MachineLearning
Check out today's #FreeFriday @radiology-ai.bsky.social article from #PubMedCentral!
FastMRI Breast: A Publicly Available Radial k-Space Dataset of Breast Dynamic Contrast-enhanced MRI
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11791504 #AI #ML #MachineLearning
FastMRI Breast: A Publicly Available Radial k-Space Dataset of Breast Dynamic Contrast-enhanced MRI
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11791504 #AI #ML #MachineLearning
April 3, 2026 at 2:16 PM
Check out today's #FreeFriday @radiology-ai.bsky.social article from #PubMedCentral!
FastMRI Breast: A Publicly Available Radial k-Space Dataset of Breast Dynamic Contrast-enhanced MRI
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11791504 #AI #ML #MachineLearning
FastMRI Breast: A Publicly Available Radial k-Space Dataset of Breast Dynamic Contrast-enhanced MRI
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11791504 #AI #ML #MachineLearning
FastMRI Breast: A Publicly Available Radial K-space Dataset of Breast Dynamic Contrast-enhanced MRI https://doi.org/10.1148/ryai.240345 @ESolomonMRI @WeillCornell @FAU_Germany #BreastCancer #OncoRad #MachineLearning
January 24, 2025 at 10:15 PM
FastMRI Breast: A Publicly Available Radial K-space Dataset of Breast Dynamic Contrast-enhanced MRI https://doi.org/10.1148/ryai.240345 @ESolomonMRI @WeillCornell @FAU_Germany #BreastCancer #OncoRad #MachineLearning
Another FastMRI dataset!
The fastMRI dataset now includes breast data!
Two of the researchers behind the new fastMRI breast dataset—lead author Eddy Solomon & senior author Laura Heacock—explain what makes these data unique:
cai2r.net/fastmri-data...
#AI #ML #innovation #MRI
#BreastCancer
#ScienceWithoutBorders 🧪
1/
Two of the researchers behind the new fastMRI breast dataset—lead author Eddy Solomon & senior author Laura Heacock—explain what makes these data unique:
cai2r.net/fastmri-data...
#AI #ML #innovation #MRI
#BreastCancer
#ScienceWithoutBorders 🧪
1/
FastMRI Dataset Adds Breast Imaging Data to Encourage New Directions in AI Research on MRI • Center for Advanced Imaging Innovation and Research
The fastMRI dataset now includes curated breast MRI data to boost AI innovation in radial, dynamic contrast-enhanced, ultrafast MRI of the breast.
cai2r.net
February 19, 2025 at 1:37 AM
Another FastMRI dataset!
What's in fastMRI breast?
300 anonymized ✳️ radial 🌈 DCE breast #MRI exams w/
💉 pre- & post-contrast data
✨ k-space
🏷️ labels
📦 recon code
Ready for #AI research.
Details in pub. by our scientists & colleagues at @weillcornell.bsky.social & @fau.de @radiology-ai.bsky.social: doi.org/10.1148/ryai...
2/
300 anonymized ✳️ radial 🌈 DCE breast #MRI exams w/
💉 pre- & post-contrast data
✨ k-space
🏷️ labels
📦 recon code
Ready for #AI research.
Details in pub. by our scientists & colleagues at @weillcornell.bsky.social & @fau.de @radiology-ai.bsky.social: doi.org/10.1148/ryai...
2/
FastMRI Breast: A Publicly Available Radial k-Space Dataset of Breast Dynamic Contrast-enhanced MRI | Radiology: Artificial Intelligence
The fastMRI breast dataset is the first large-scale dataset of radial k-space and Digital Imaging and Communications in Medicine data for breast dynamic contrast-enhanced MRI with case-level labels...
doi.org
February 18, 2025 at 8:42 PM
What's in fastMRI breast?
300 anonymized ✳️ radial 🌈 DCE breast #MRI exams w/
💉 pre- & post-contrast data
✨ k-space
🏷️ labels
📦 recon code
Ready for #AI research.
Details in pub. by our scientists & colleagues at @weillcornell.bsky.social & @fau.de @radiology-ai.bsky.social: doi.org/10.1148/ryai...
2/
300 anonymized ✳️ radial 🌈 DCE breast #MRI exams w/
💉 pre- & post-contrast data
✨ k-space
🏷️ labels
📦 recon code
Ready for #AI research.
Details in pub. by our scientists & colleagues at @weillcornell.bsky.social & @fau.de @radiology-ai.bsky.social: doi.org/10.1148/ryai...
2/
“I was so excited to read #FASTMRI found #breastcancers smaller and earlier than mammograms. I wanted to bring it to
Bristol”
9 years on Lyn Jones, the DYAMOND study lead, shares what it has taken to open the trial to patients.
👉 bit.ly/3KkSO1s
Bristol”
9 years on Lyn Jones, the DYAMOND study lead, shares what it has taken to open the trial to patients.
👉 bit.ly/3KkSO1s
The FAST MRI DYAMOND breast cancer screening study – How did we get here? | North Bristol NHS Trust
A new study is investigating whether FAST MRI, a new type of scan, can help detect small but aggressive breast cancers earlier. Now open to patients, the
bit.ly
October 2, 2025 at 9:48 AM
“I was so excited to read #FASTMRI found #breastcancers smaller and earlier than mammograms. I wanted to bring it to
Bristol”
9 years on Lyn Jones, the DYAMOND study lead, shares what it has taken to open the trial to patients.
👉 bit.ly/3KkSO1s
Bristol”
9 years on Lyn Jones, the DYAMOND study lead, shares what it has taken to open the trial to patients.
👉 bit.ly/3KkSO1s
Fast Abdominal #MRI with Deep Learning Image Reconstruction. Cuts scan time, improves image quality & boosts patient comfort. Covers T2WI, #MRCP & DWI.
Read more: marketing.webassets.siemens-healthineers.com/fce0d2bcf600...
#MagnetomWorld #DeepResolve #Radiology #FastMRI #AI #MedSky #OncSky
Read more: marketing.webassets.siemens-healthineers.com/fce0d2bcf600...
#MagnetomWorld #DeepResolve #Radiology #FastMRI #AI #MedSky #OncSky
April 15, 2025 at 7:00 AM
Fast Abdominal #MRI with Deep Learning Image Reconstruction. Cuts scan time, improves image quality & boosts patient comfort. Covers T2WI, #MRCP & DWI.
Read more: marketing.webassets.siemens-healthineers.com/fce0d2bcf600...
#MagnetomWorld #DeepResolve #Radiology #FastMRI #AI #MedSky #OncSky
Read more: marketing.webassets.siemens-healthineers.com/fce0d2bcf600...
#MagnetomWorld #DeepResolve #Radiology #FastMRI #AI #MedSky #OncSky
AI innovation in #MRI requires raw k-space data, but open access to such data is scarce. Our fastMRI dataset—launched in 2018 & expanded since—is the largest open collection of professionally prepared & deidentified raw MRI data for #AI research.
🎁 fastmri.med.nyu.edu
#OpenScience
3/3
🎁 fastmri.med.nyu.edu
#OpenScience
3/3
fastMRI Dataset
fastmri.med.nyu.edu
February 18, 2025 at 8:42 PM
AI innovation in #MRI requires raw k-space data, but open access to such data is scarce. Our fastMRI dataset—launched in 2018 & expanded since—is the largest open collection of professionally prepared & deidentified raw MRI data for #AI research.
🎁 fastmri.med.nyu.edu
#OpenScience
3/3
🎁 fastmri.med.nyu.edu
#OpenScience
3/3
BBC Points West - Dr Lyn Jones, FAST MRI DYAMOND breast cancer screening study lead explains how FAST MRI may help find ‘hard to spot’ cancers earlier
ow.ly/BJxL50Ww3Gx
#breastcancer #screening #FASTMRI #research
ow.ly/BJxL50Ww3Gx
#breastcancer #screening #FASTMRI #research
July 29, 2025 at 10:35 AM
BBC Points West - Dr Lyn Jones, FAST MRI DYAMOND breast cancer screening study lead explains how FAST MRI may help find ‘hard to spot’ cancers earlier
ow.ly/BJxL50Ww3Gx
#breastcancer #screening #FASTMRI #research
ow.ly/BJxL50Ww3Gx
#breastcancer #screening #FASTMRI #research
We had a brilliant turnout at our Breast Screening Research Workshop at the Easton Community Centre on Monday!
Thank you to everyone who shared their experiences to help make breast screening and research more inclusive and accessible.
#DYAMONDStudy #FASTMRI #PPIE #BreastCancerAwareness
Thank you to everyone who shared their experiences to help make breast screening and research more inclusive and accessible.
#DYAMONDStudy #FASTMRI #PPIE #BreastCancerAwareness
November 7, 2025 at 4:37 PM
We had a brilliant turnout at our Breast Screening Research Workshop at the Easton Community Centre on Monday!
Thank you to everyone who shared their experiences to help make breast screening and research more inclusive and accessible.
#DYAMONDStudy #FASTMRI #PPIE #BreastCancerAwareness
Thank you to everyone who shared their experiences to help make breast screening and research more inclusive and accessible.
#DYAMONDStudy #FASTMRI #PPIE #BreastCancerAwareness
for reconstruction. Experimental results on the NYU fastMRI dataset demonstrate that our method outperforms existing approaches in multiple evaluation metrics. Our code is available at https://anonymous.4open.science/r/DDO-IN-A73B. [5/5 of https://arxiv.org/abs/2503.08056v1]
March 12, 2025 at 5:58 AM
for reconstruction. Experimental results on the NYU fastMRI dataset demonstrate that our method outperforms existing approaches in multiple evaluation metrics. Our code is available at https://anonymous.4open.science/r/DDO-IN-A73B. [5/5 of https://arxiv.org/abs/2503.08056v1]
MMRQA combines MRQy metrics and a Qwen LLM model to give scores and explanations for MRI quality. Tested on MR‑ART, FastMRI and MyConnectome, it showed zero‑shot generalisation. https://getnews.me/multimodal-ai-advances-mri-quality-assessment-with-mmrqa-framework/ #mmrqa #mri #ai
October 1, 2025 at 12:53 AM
MMRQA combines MRQy metrics and a Qwen LLM model to give scores and explanations for MRI quality. Tested on MR‑ART, FastMRI and MyConnectome, it showed zero‑shot generalisation. https://getnews.me/multimodal-ai-advances-mri-quality-assessment-with-mmrqa-framework/ #mmrqa #mri #ai
Patch‑based diffusion model PaDIS‑MRI achieves high‑quality reconstructions with only 25 k‑space scans and a 7× undersampling factor, beating FastMRI‑EDM in PSNR, SSIM and NRMSE. https://getnews.me/patch-based-diffusion-improves-mri-reconstruction-with-minimal-data/ #mri #diffusion #ai
September 29, 2025 at 5:30 PM
Patch‑based diffusion model PaDIS‑MRI achieves high‑quality reconstructions with only 25 k‑space scans and a 7× undersampling factor, beating FastMRI‑EDM in PSNR, SSIM and NRMSE. https://getnews.me/patch-based-diffusion-improves-mri-reconstruction-with-minimal-data/ #mri #diffusion #ai
Zero-shot Adaptive Diffusion Sampling (ZADS) optimizes fidelity weights at test time, boosting MRI reconstruction quality on the fastMRI knee dataset without retraining the diffusion model. https://getnews.me/zero-shot-adaptive-diffusion-sampling-improves-mri-reconstruction/ #zads #mri
September 17, 2025 at 7:37 AM
Zero-shot Adaptive Diffusion Sampling (ZADS) optimizes fidelity weights at test time, boosting MRI reconstruction quality on the fastMRI knee dataset without retraining the diffusion model. https://getnews.me/zero-shot-adaptive-diffusion-sampling-improves-mri-reconstruction/ #zads #mri
FastMRI Breast is a large-scale dataset of radial k-space & DICOM data for breast dynamic contrast-enhanced #MRI https://doi.org/10.1148/ryai.240345 @ESolomonMRI @WeillCornell @FAU_Germany #breast #AI #MachineLearning
January 26, 2025 at 4:15 PM
FastMRI Breast is a large-scale dataset of radial k-space & DICOM data for breast dynamic contrast-enhanced #MRI https://doi.org/10.1148/ryai.240345 @ESolomonMRI @WeillCornell @FAU_Germany #breast #AI #MachineLearning
perturbations in a sparse domain, leading to more reliable and artifact-free reconstructions. The results obtained from the fastMRI knee and brain datasets show that the proposed training strategy effectively reduces aliasing artifacts and mitigates [4/5 of https://arxiv.org/abs/2505.24136v1]
June 2, 2025 at 6:01 AM
perturbations in a sparse domain, leading to more reliable and artifact-free reconstructions. The results obtained from the fastMRI knee and brain datasets show that the proposed training strategy effectively reduces aliasing artifacts and mitigates [4/5 of https://arxiv.org/abs/2505.24136v1]
samples, and privacy concerns. The SD model, pre-trained on natural images, was fine-tuned using the 3T fastMRI dataset and the 0.3T M4Raw dataset, with the goal of generating brain T1, T2, and FLAIR images across different magnetic field strengths. [2/5 of https://arxiv.org/abs/2505.22682v1]
May 30, 2025 at 6:01 AM
samples, and privacy concerns. The SD model, pre-trained on natural images, was fine-tuned using the 3T fastMRI dataset and the 0.3T M4Raw dataset, with the goal of generating brain T1, T2, and FLAIR images across different magnetic field strengths. [2/5 of https://arxiv.org/abs/2505.22682v1]
one-step reconstruction by first training a conditional DM and then iteratively distilling this model. Comprehensive experiments were conducted on both publicly available fastMRI images and an in-house multi-echo GRE (QSM) subject. Overall, the [4/6 of https://arxiv.org/abs/2505.08142v1]
May 14, 2025 at 6:01 AM
one-step reconstruction by first training a conditional DM and then iteratively distilling this model. Comprehensive experiments were conducted on both publicly available fastMRI images and an in-house multi-echo GRE (QSM) subject. Overall, the [4/6 of https://arxiv.org/abs/2505.08142v1]
varying noise conditions and MRI contrasts on the M4Raw and fastMRI dataset. [8/8 of https://arxiv.org/abs/2505.05631v1]
May 12, 2025 at 6:01 AM
varying noise conditions and MRI contrasts on the M4Raw and fastMRI dataset. [8/8 of https://arxiv.org/abs/2505.05631v1]
tasks: skull-stripping directly in k-Space and MRI reconstruction using the publicly available FastMRI dataset. Our results show that training with synthetic phase data significantly improves generalization for skull-stripping on real-world data, with [4/7 of https://arxiv.org/abs/2504.07560v1]
April 11, 2025 at 6:02 AM
tasks: skull-stripping directly in k-Space and MRI reconstruction using the publicly available FastMRI dataset. Our results show that training with synthetic phase data significantly improves generalization for skull-stripping on real-world data, with [4/7 of https://arxiv.org/abs/2504.07560v1]
sequentially trained in a supervised manner on the fastMRI dataset and validated for 2D multi-coil MRI in simulation and on real data, targeting highly under-sampled radial k-space sampling. Results suggest that a series with only few DNNs achieves [6/8 of https://arxiv.org/abs/2503.09559v1]
March 13, 2025 at 6:02 AM
sequentially trained in a supervised manner on the fastMRI dataset and validated for 2D multi-coil MRI in simulation and on real data, targeting highly under-sampled radial k-space sampling. Results suggest that a series with only few DNNs achieves [6/8 of https://arxiv.org/abs/2503.09559v1]
maintain high reconstruction quality with approximately 50% fewer parameters compared to existing models. Experimental evaluation on the FastMRI dataset demonstrates competitive PSNR, SSIM, and LPIPS metrics, even at high acceleration factors (8x and [4/6 of https://arxiv.org/abs/2503.05063v1]
March 10, 2025 at 6:12 AM
maintain high reconstruction quality with approximately 50% fewer parameters compared to existing models. Experimental evaluation on the FastMRI dataset demonstrates competitive PSNR, SSIM, and LPIPS metrics, even at high acceleration factors (8x and [4/6 of https://arxiv.org/abs/2503.05063v1]