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Motivation: We explore the “Implicit Data Crime” of datasets whose subsampled k-space is filled using parallel imaging. These datasets are treated as fully-sampled, but their points derive from (1)prospective sampling, and (2)reconstruction of un-sampled points, creating artificial data correlations given low SNR or high acceleration. Goal(s): How will downstream tasks, including reconstruction algorithm comparison and optimal trajectory design, be biased by effects of parallel imaging on a prospectively undersampled dataset? Approach: Comparing reconstruction performance using data that are fully sampled with data that are completed using the SENSE algorithm. Results: Utilizing parallel imaging filled k-space results in biased downstream perception of algorithm performance. Impact: This study demonstrates evidence of overly-optimistic bias resulting from the use of k-space filled in with parallel imaging as ground truth data. Researchers should be aware of this possibility and carefully examine the computational pipeline behind datasets they use. 

Abstract PurposeThe aim of this work is to develop a method to solve the ill‐posed inverse problem of accelerated image reconstruction while correcting forward model imperfections in the context of subject motion during MRI examinations. MethodsThe proposed solution uses a Bayesian framework based on deep generative diffusion models to jointly estimate a motion‐free image and rigid motion estimates from subsampled and motion‐corrupt two‐dimensional (2D) k‐space data. ResultsWe demonstrate the ability to reconstruct motion‐free images from accelerated two‐dimensional (2D) Cartesian and non‐Cartesian scans without any external reference signal. We show that our method improves over existing correction techniques on both simulated and prospectively accelerated data. ConclusionWe propose a flexible framework for retrospective motion correction of accelerated MRI based on deep generative diffusion models, with potential application to other forward model corruptions. 

Motivation: Publicly available k-space data used for training are inherently noisy with no available ground truth. Goal(s): To denoise k-space data in an unsupervised manner for downstream applications. Approach: We use Generalized Stein’s Unbiased Risk Estimate (GSURE) applied to multi-coil MRI to denoise images without access to ground truth. Subsequently, we train a generative model to show improved accelerated MRI reconstruction. Results: We demonstrate: (1) GSURE can successfully remove noise from k-space; (2) generative priors learned on GSURE-denoised samples produce realistic synthetic samples; and (3) reconstruction performance on subsampled MRI improves using priors trained on denoised images in comparison to training on noisy samples. Impact: This abstract shows that we can denoise multi-coil data without ground truth and train deep generative models directly on noisy k-space in an unsupervised manner, for improved accelerated reconstruction. 

Abstract The Institute for Foundations of Machine Learning (IFML) focuses on core foundational tools to power the next generation of machine learning models. Its research underpins the algorithms and data sets that make generative artificial intelligence (AI) more accurate and reliable. Headquartered at The University of Texas at Austin, IFML researchers collaborate across an ecosystem that spans University of Washington, Stanford, UCLA, Microsoft Research, the Santa Fe Institute, and Wichita State University. Over the past year, we have witnessed incredible breakthroughs in AI on topics that are at the heart of IFML's agenda, such as foundation models, LLMs, fine‐tuning, and diffusion with game‐changing applications influencing almost every area of science and technology. In this article, we seek to highlight seek to highlight the application of foundational machine learning research on key use‐inspired topics:Fairness in Imaging with Deep Learning: designing the correct metrics and algorithms to make deep networks less biased.Deep proteins: using foundational machine learning techniques to advance protein engineering and launch a biomanufacturing revolution.Sounds and Space for Audio‐Visual Learning: building agents capable of audio‐visual navigation in complex 3D environments via new data augmentations.Improving Speed and Robustness of Magnetic Resonance Imaging: using deep learning algorithms to develop fast and robust MRI methods for clinical diagnostic imaging.IFML is also responding to explosive industry demand for an AI‐capable workforce. We have launched an accessible, affordable, and scalable new degree program—the MSAI—that looks to wholly reshape the AI/ML workforce pipeline. 

Image reconstruction is the process of recovering an image from raw, under-sampled signal measurements, and is a critical step in diagnostic medical imaging, such as magnetic resonance imaging (MRI). Recently, data-driven methods have led to improved image quality in MRI reconstruction using a limited number of measurements, but these methods typically rely on the existence of a large, centralized database of fully sampled scans for training. In this work, we investigate federated learning for MRI reconstruction using end-to-end unrolled deep learning models as a means of training global models across multiple clients (data sites), while keeping individual scans local. We empirically identify a low-data regime across a large number of heterogeneous scans, where a small number of training samples per client are available and non-collaborative models lead to performance drops. In this regime, we investigate the performance of adaptive federated optimization algorithms as a function of client data distribution and communication budget. Experimental results show that adaptive optimization algorithms are well suited for the federated learning of unrolled models, even in a limited-data regime (50 slices per data site), and that client-sided personalization can improve reconstruction quality for clients that did not participate in training. 

Although open databases are an important resource in the current deep learning (DL) era, they are sometimes used “off label”: Data published for one task are used to train algorithms for a different one. This work aims to highlight that this common practice may lead to biased, overly optimistic results. We demonstrate this phenomenon for inverse problem solvers and show how their biased performance stems from hidden data-processing pipelines. We describe two processing pipelines typical of open-access databases and study their effects on three well-established algorithms developed for MRI reconstruction: compressed sensing, dictionary learning, and DL. Our results demonstrate that all these algorithms yield systematically biased results when they are naively trained on seemingly appropriate data: The normalized rms error improves consistently with the extent of data processing, showing an artificial improvement of 25 to 48% in some cases. Because this phenomenon is not widely known, biased results sometimes are published as state of the art; we refer to that as implicit “data crimes.” This work hence aims to raise awareness regarding naive off-label usage of big data and reveal the vulnerability of modern inverse problem solvers to the resulting bias.