Information for research, scientific, and educational purposes. All statements, data and results presented in these publications should be considered for investigational use.
- Artificial intelligence-based PET denoising could allow a two-fold reduction in 18F FDG PET acquisition time in digital PET/CT. EJNMMI Physics, 2022
- Clinical and phantom validation of a deep learning based denoising algorithm for 18F FDG PET images from lower detection counting in comparison with the standard acquisition. EJNMMI Physics, 2022
- Clinical Assessment of Deep Learning-based Super-Resolution for 3D Volumetric Brain MRI. Radiology: AI Journal, 2022
- Deep Learning Enables 60% Accelerated Volumetric Brain MRI While Preserving Quantitative Performance: A Prospective, Multicenter, Multireader Trial. AJNR, 2021
- The Impact of Artificial Intelligence CNN Based Denoising on FDG PET Radiomics. Frontiers in Oncology, 2021
- Applying Deep Learning to Accelerated Clinical Brain Magnetic Resonance Imaging for Multiple Sclerosis. Frontiers in Neurology, 2021
- Deep Learning Image Processing Enables 40% Faster Spinal MR Scans Which Match or Exceed Quality of Standard of Care. Clinical Neuroradiology, 2021
- Validation of Deep Learning–based Augmentation for Reduced 18F FDG Dose for PET/MRI in Children and Young Adults with Lymphoma. Clinical Neuroradiology, 2021
- Low-count whole-body PET with deep learning in a multicenter and externally validated study. Nature npj Digital Medicine, 2021
- Deep learning–based methods may minimize GBCA dosage in brain MRI. European Radiology, 2021
Artificial intelligence for reduced dose 18F-FDG PET examinations: a real-world deployment through a standardized framework and business case assessment. EJNMMI, 2021
Next generation research applications for hybrid PET/MR and PET/CT imaging using deep learning. EJNMMI, 2019
Applications of Deep Learning to Neuro-Imaging Techniques. Frontiers in Neurology, 2019
- Deep Learning in Neuroradiology: American Journal of Neuroradiology, 2018
- A generic deep learning model for reduced gadolinium dose in contrast-enhanced brain MRI, Magnetic Resonance in Medicine, 2021
- Deep learning enables reduced gadolinium dose for contrast‐enhanced brain MRI. Journal of Magnetic Resonance Imaging, 2018
- ASNR: Deep Learning Enables Accurate Quantitative Volumetric Brain MRI with 2x Faster Scan Times.
- ECR: Evaluating the Capability of Deep Learning (DL) Based Image Processing of Brain MRI to Improve Quality While Reducing Acquisition Times.
- ECR: Evaluating the Performance of Deep Learning AI (DLAI) to Match Routine Lumbar Spine MRI Image Quality at Highly Reduced Scan Times.
- RSNA: Accelerating Whole-Body PET Acquisitions Using Deep Learning: External Validation on Foreign Country Data, Quantitative
- RSNA: Standardized Uptake Value (SUV) Evaluation of 4x Faster PET Scans Enhanced Using Deep Learning
- ISMRM: Deep Learning Enables Accurate Quantitative Volumetric Brain MRI with 2x Faster Scan.
- ISMRM: From 2D Thick Slices to 3D Isotropic Volumetric Brain MRI – A Deep Learning Approach.
- ISMRM: Toward a Site and Scanner-Generic Deep Learning Model for Reduced Gadolinium Dose in Contrast-enhanced Brain MRI
- ISMRM: Clinical Performance of Reduced Gadolinium Dose for Contrast-Enhanced Brain MRI Using Deep Learning
- NeurIPS: Noise-aware PET image Enhancement with Adaptive Deep Learning.
- 200x Low-dose PET Reconstruction using Deep Learning
Additional Research from Subtle Medical Founders
- Deep Generative Adversarial Neural Networks for Compressive Sensing (GANCS) MRI.
Mardani, Morteza, Enhao Gong, Joseph Y. Cheng, Shreyas S. Vasanawala, Greg Zaharchuk, Lei Xing, and John M. Pauly.
IEEE transactions on medical imaging. 2018 Jul 23.
- Quantitative Susceptibility Mapping using Deep Neural Network: QSMnet
Jaeyeon Yoon*, Enhao Gong*, Itthi Chatnuntawech, Berkin Bilgic, Jingu Lee, Woojin Jung, Jingyu Ko, Hosan Jung, Kawin Setsompop, Greg Zaharchuk, Eung Yeop Kim, John Pauly, Jongho Lee
NeuroImage 2018 Jun 15.
- Deep Generative Adversarial Networks for Compressed Sensing Automates MRI
Morteza Mardani, Enhao Gong, Joseph Y. Cheng, Shreyas Vasanawala, Greg Zaharchuk, Marcus Alley, Neil Thakur, Song Han, William Dally, John M. Pauly, Lei Xing
NIPS Workshop 2017, ISMRM 2017, 2018
- Improved Prediction of the Final Infarct From Acute Stroke Neuroimaging Using Deep Learning
Yilin Niu*, Enhao Gong*, Junshen Xu, John Pauly, Greg Zaharchuk
International Stroke Conference (ISC) 2018, ISMRM 2018
- Coil compression for accelerated imaging with Cartesian sampling
T Zhang, JM Pauly, SS Vasanawala, M Lustig
Magnetic Resonance in Medicine 2013