Accelerated Motion Correction with Deep Generative Diffusion Models

Magnetic Resonance Imaging (MRI) is a powerful medical imaging modality, but unfortunately suffers from long scan times which, aside from increasing operational costs, can lead to image artifacts due to patient motion. Motion during the acquisition leads to inconsistencies in measured data that manifest as blurring and ghosting if unaccounted for in the image reconstruction process. Various deep learning based reconstruction techniques have been proposed which decrease scan time by reducing the number of measurements needed for a high fidelity reconstructed image. Additionally, deep learning has been used to correct motion using end-to-end techniques. This, however, increases susceptibility to distribution shifts at test time (sampling pattern, motion level). In this work we propose a framework for jointly reconstructing highly sub-sampled MRI data while estimating patient motion using diffusion based generative models. Our method does not make specific assumptions on the sampling trajectory or motion pattern at training time and thus can be flexibly applied to various types of measurement models and patient motion. We demonstrate our framework on retrospectively accelerated 2D brain MRI corrupted by rigid motion

Conditional Score-Based Reconstructions for Multi-contrast MRI

Magnetic resonance imaging (MRI) exam protocols consist of multiple contrast-weighted images of the same anatomy to emphasize different tissue properties. Due to the long acquisition times required to collect fully sampled k-space measurements, it is common to only collect a fraction of k-space for some, or all, of the scans and subsequently solve an inverse problem for each contrast to recover the desired image from sub-sampled measurements. Recently, there has been a push to further accelerate MRI exams using data-driven priors, and generative models in particular, to regularize the ill-posed inverse problem of image reconstruction. These methods have shown promising improvements over classical methods. However, many of the approaches neglect the multi-contrast nature of clinical MRI exams and treat each scan as an independent reconstruction. In this work we show that by learning a joint Bayesian prior over multi-contrast data with a score-based generative model we are able to leverage the underlying structure between multi-contrast images and thus improve image reconstruction fidelity over generative models that only reconstruct images of a single contrast

Solving Inverse Problems with Score-Based Generative Priors learned from Noisy Data

We present SURE-Score: an approach for learning score-based generative models using training samples corrupted by additive Gaussian noise. When a large training set of clean samples is available, solving inverse problems via score-based (diffusion) generative models trained on the underlying fully-sampled data distribution has recently been shown to outperform end-to-end supervised deep learning. In practice, such a large collection of training data may be prohibitively expensive to acquire in the first place. In this work, we present an approach for approximately learning a score-based generative model of the clean distribution, from noisy training data. We formulate and justify a novel loss function that leverages Stein's unbiased risk estimate to jointly denoise the data and learn the score function via denoising score matching, while using only the noisy samples. We demonstrate the generality of SURE-Score by learning priors and applying posterior sampling to ill-posed inverse problems in two practical applications from different domains: compressive wireless multiple-input multiple-output channel estimation and accelerated 2D multi-coil magnetic resonance imaging reconstruction, where we demonstrate competitive reconstruction performance when learning at signal-to-noise ratio values of 0 and 10 dB, respectively

An untrained deep learning method for reconstructing dynamic magnetic resonance images from accelerated model-based data

The purpose of this work is to implement physics-based regularization as a stopping condition in tuning an untrained deep neural network for reconstructing MR images from accelerated data. The ConvDecoder neural network was trained with a physics-based regularization term incorporating the spoiled gradient echo equation that describes variable-flip angle (VFA) data. Fully-sampled VFA k-space data were retrospectively accelerated by factors of R={8,12,18,36} and reconstructed with ConvDecoder (CD), ConvDecoder with the proposed regularization (CD+r), locally low-rank (LR) reconstruction, and compressed sensing with L1-wavelet regularization (L1). Final images from CD+r training were evaluated at the \emph{argmin} of the regularization loss; whereas the CD, LR, and L1 reconstructions were chosen optimally based on ground truth data. The performance measures used were the normalized root-mean square error, the concordance correlation coefficient (CCC), and the structural similarity index (SSIM). The CD+r reconstructions, chosen using the stopping condition, yielded SSIMs that were similar to the CD (p=0.47) and LR SSIMs (p=0.95) across R and that were significantly higher than the L1 SSIMs (p=0.04). The CCC values for the CD+r T1 maps across all R and subjects were greater than those corresponding to the L1 (p=0.15) and LR (p=0.13) T1 maps, respectively. For R > 12 (<4.2 minutes scan time), L1 and LR T1 maps exhibit a loss of spatially refined details compared to CD+r. We conclude that the use of an untrained neural network together with a physics-based regularization loss shows promise as a measure for determining the optimal stopping point in training without relying on fully-sampled ground truth data.Comment: 45 pages, 7 figures, 2 Tables, supplementary material included (10 figures, 4 tables

WNT activates the AAK1 kinase to promote clathrin-mediated endocytosis of LRP6 and establish a negative feedback loop

beta-Catenin-dependent WNT signal transduction governs development, tissue homeostasis, and a vast array of human diseases. Signal propagation through a WNT-Frizzled/LRP receptor complex requires proteins necessary for clathrin-mediated endocytosis (CME). Paradoxically, CME also negatively regulates WNT signaling through internalization and degradation of the receptor complex. Here, using a gain-of-function screen of the human kinome, we report that the AP2 associated kinase 1 (AAK1), a known CME enhancer, inhibits WNT signaling. Reciprocally, AAK1 genetic silencing or its pharmacological inhibition using a potent and selective inhibitor activates WNT signaling. Mechanistically, we show that AAK1 promotes clearance of LRP6 from the plasma membrane to suppress the WNT pathway. Time-course experiments support a transcription-uncoupled, WNT-driven negative feedback loop; prolonged WNT treatment drives AAK1-dependent phosphorylation of AP2M1, clathrin-coated pit maturation, and endocytosis of LRP6. We propose that, following WNT receptor activation, increased AAK1 function and CME limits WNT signaling longevity2617993FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP2013/50724-5; 2016/17469-0M.B.M. acknowledges support from the NIH (RO1-CA187799 and U24-DK116204-01). M.J.A. received financial support from NIH T32 Predoctoral Training Grants in Pharmacology (T32-GM007040-43 and T32-GM007040-42), an Initiative for Maximizing Student Diversity Grant (R25-GM055336-16), and the NIH National Cancer Institute (NCI) NRSA Predoctoral Fellowship to Promote Diversity in Health-Related Research (F31CA228289). M.P.W. received support from the Lymphoma Research Foundation (337444) and the NIH (T32-CA009156-35). Y.N. was supported by grants-in-aid from the Japan Society for the Promotion of Science (JSPS) (15KK0356 and 16K11493). T.T. was supported by the Howard Hughes Medical Institute Gilliam Fellowship for Advanced Study. M.V.G. was supported by Cancer Research UK (grants C7379/A15291 and C7379/A24639 to Mariann Bienz). The UNC Flow Cytometry Core Facility is supported in part by Cancer Center Core Support Grant P30 CA016086 to the UNC Lineberger Comprehensive Cancer Center, and research reported in this publication was supported by the Center for AIDS Research (award number 5P30AI050410), and the content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The Structural Genomics Consortium (SGC) is a registered charity (number 1097737) that receives funds from AbbVie, Bayer Pharma AG, Boehringer Ingelheim, the Canada Foundation for Innovation, the Eshelman Institute for Innovation, Genome Canada, the Innovative Medicines Initiative (European Union [EU]/European Federation of Pharmaceutical Industries and Associations [EFPIA]) (ULTRA-DD grant no. 115766), Janssen, Merck & Company, Merck KGaA, Novartis Pharma AG, the Ontario Ministry of Economic Development and Innovation, Pfizer, the São Paulo Research Foundation (FAPESP) (2013/50724-5), Takeda, and the Wellcome Trust (106169/ZZ14/Z). R.R.R. received financial support from FAPESP (2016/17469-0). We would also like to thank Claire Strain-Damerell and Pavel Savitsky for cloning various mutants of AAK1 and BMP2K proteins that were used in the crystallization trials. Additionally, we thank Dr. Sean Conner for providing the AAK1 plasmids, Dr. Stephane Angers for kindly providing the HEK293T DVL TKO cells, and Dr. Mariann Bienz for providing comments and feedback. We would like to thank members of the Major laboratory for their feedback and expertise regarding experimental design and project directio

International Liver Transplantation Society Global Census:First Look at Pediatric Liver Transplantation Activity Around the World

Background. Over 16 000 children under the age of 15 died worldwide in 2017 because of liver disease. Pediatric liver transplantation (PLT) is currently the standard of care for these patients. The aim of this study is to describe global PLT activity and identify variations between regions. Methods. A survey was conducted from May 2018 to August 2019 to determine the current state of PLT. Transplant centers were categorized into quintile categories according to the year they performed their first PLT. Countries were classified according to gross national income per capita. Results. One hundred eight programs from 38 countries were included (68% response rate). 10 619 PLTs were performed within the last 5 y. High-income countries performed 4992 (46.4%) PLT, followed by upper-middle- (4704 [44·3%]) and lower-middle (993 [9·4%])-income countries. The most frequently used type of grafts worldwide are living donor grafts. A higher proportion of lower-middle-income countries (68·7%) performed ≥25 living donor liver transplants over the last 5 y compared to high-income countries (36%; P = 0.019). A greater proportion of programs from high-income countries have performed ≥25 whole liver transplants (52.4% versus 6.2%; P = 0.001) and ≥25 split/reduced liver transplants (53.2% versus 6.2%; P &lt; 0.001) compared to lower-middle-income countries. Conclusions. This study represents, to our knowledge, the most geographically comprehensive report on PLT activity and a first step toward global collaboration and data sharing for the greater good of children with liver disease; it is imperative that these centers share the lead in PLT.</p