6 citations · 18 across the 9 of their papers we have counts for
10 papers
NexOP: Joint Optimization of NEX-Aware k-space Sampling and Image Reconstruction for Low-Field MRI
Tal Oved, Efrat Shimron
Modern low-field magnetic resonance imaging (MRI) technology offers a compelling alternative to standard high-field MRI, with portable, low-cost systems. However, its clinical util…
Toward Artificial Palpation: Representation Learning of Touch on Soft Bodies
Zohar Rimon, Elisei Shafer, Tal Tepper +2
Palpation, the use of touch in medical examination, is almost exclusively performed by humans. We investigate a proof of concept for an artificial palpation method based on self-su…
Deep learning of personalized priors from past MRI scans enables fast, quality-enhanced point-of-care MRI with low-cost systems
Tal Oved, Beatrice Lena, Chloé F. Najac +4
Magnetic resonance imaging (MRI) offers superb-quality images, but its accessibility is limited by high costs, posing challenges for patients requiring longitudinal care. Low-field…
Accelerating Low-field MRI: From Compressed Sensing to Deep Learning Reconstruction with CNNs and Transformers
Efrat Shimron, Shanshan Shan, James Grover +7
Portable, low-field Magnetic Resonance Imaging (MRI) scanners are increasingly being deployed in clinical settings. However, key barriers to their widespread use include low signal…
Reference-Free Image Quality Metric for Degradation and Reconstruction Artifacts
Han Cui, Alfredo De Goyeneche, Efrat Shimron +2
Image Quality Assessment (IQA) is essential in various Computer Vision tasks such as image deblurring and super-resolution. However, most IQA methods require reference images, whic…
Deep Learning for Accelerated and Robust MRI Reconstruction: a Review
Reinhard Heckel, Mathews Jacob, Akshay Chaudhari +2
Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides…
K-band: Self-supervised MRI Reconstruction via Stochastic Gradient Descent over K-space Subsets
Frederic Wang, Han Qi, Alfredo De Goyeneche +3
Although deep learning (DL) methods are powerful for solving inverse problems, their reliance on high-quality training data is a major hurdle. This is significant in high-dimension…
Subtle Data Crimes: Naively training machine learning algorithms could lead to overly-optimistic results
Efrat Shimron, Jonathan I. Tamir, Ke Wang +1
While open databases are an important resource in the Deep Learning (DL) era, they are sometimes used "off-label": data published for one task are used for training algorithms for…
CORE-Deblur: Parallel MRI Reconstruction by Deblurring Using Compressed Sensing
Efrat Shimron, Andrew G. Webb, Haim Azhari
In this work we introduce a new method that combines Parallel MRI and Compressed Sensing (CS) for accelerated image reconstruction from subsampled k-space data. The method first co…
Utilizing Bochners Theorem for Constrained Evaluation of Missing Fourier Data
Jonathan Nemirovsky, Efrat Shimron
A method is presented for estimating unknown Fourier domain (k-space) data using a small number of samples in that space. The method is derived from Bochners Theorem, and is termed…