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The main goal of this study is to determine which parameters [e.g. clinical biomarkers, demographics and image-markers using 4D (3D + b-value) diffusion-weighted MRI (DW-MRI)] are more correlated with transplanted kidney status in patients who have undergone kidney transplantation, and can be used for early assessment of acute renal rejection.The study included 16 patients with stable graft function and 37 patients with acute rejection (AR), determined by renal biopsy post-transplantation. 3D DW-MRI of each allograft had been acquired using a series of b-values 50 and 100-1000 in steps of 100 smmANOVA of the saturated model found that odds of AR depended significantly on SPCr, CrCl and ADC at b = 500, 600, 700 and 900 smmPreliminary findings suggest that ADC provides improved detection of AR than lab values alone. At least two non-zero gradient strengths should be used for optimal results. Advances in knowledge: This paper investigated possible correlations between image-based and clinical biomarkers, and the fusion of both with respect to biopsy diagnosis of AR.

Although renal biopsy remains the gold standard for diagnosing the type of renal rejection, it is not preferred due to its invasiveness, recovery time (1–2 weeks), and potential for complications, e.g., bleeding and/or infection. Therefore, there is an urgent need to explore a non-invasive technique that can early classify renal rejection types. In this paper, we develop a computer-aided diagnostic (CAD) system that can classify acute renal transplant rejection (ARTR) types early via the analysis of apparent diffusion coefficients (ADCs) extracted from diffusion-weighted (DW) MRI data acquired at low-(accounting for perfusion) and high-(accounting for diffusion) b-values. The developed framework mainly consists of three steps: (i) data co-alignment using a 3D B-spline-based approach (to handle local deviations due to breathing and heart beat motions) and segmentation of kidney tissue with an evolving geometric (level-set based) deformable model guided by a voxel-wise stochastic speed function, which follows a joint kidney-background Markov-Gibbs random field model accounting for an adaptive kidney shape prior and visual kidney-background appearances of DW-MRI data (image intensities and spatial interactions); (ii) construction of a cumulative empirical distribution of ADC at low and high b-values of the segmented kidney accounting for blood perfusion and water diffusion, respectively, to be our discriminatory ARTR types feature; and (iii) classification of ARTR types (acute tubular necrosis (ATN) anti-body- and T-cell-mediated rejection) based on deep learning of a non-negative constrained stacked autoencoder. Results show that 98% of the subjects were correctly classified in our “leave-one-subject-out” experiments on 39 subjects (namely, 8 out of 8 of the ATN group and 30 out of 31 of the T-cell group). Thus, the proposed approach holds promise as a reliable non-invasive diagnostic tool.

Elizabeth Hollis is the news and social media editor at AAMI. E-mail: ehollis@ aami.org This fall, several U.S. hospitals found themselves grappling with a handful of cases of the Ebola virus. Although facilities expected to be prepared—particularly since they treat other easily transmittable diseases—Ebola has proven trickier. Ebola “has really driven home having security of public health,” said Barbara Knust, an epidemiologist and veterinarian in CDC’s Viral Special Pathogens Branch. She spoke at the Interscience Conference on Antimicrobial Agents and Chemotherapy, held in Washington, DC, in September. “Weak public health infrastructure in one place—in a little corner of the world—can have ripple effects throughout the world,” she said in a presentation during the conference. She was confident in the preparedness of U.S. hospitals, but there are still a lot of unknowns about the virus, causing anxiety among healthcare workers—especially after two Dallas nurses were infected after treating the first Ebola patient in the United Sates. The patient, Thomas Duncan died, but the two nurses have successfully been treated for the virus. “It is obvious that after the incident in Texas, everyone is worried about Ebola transmission in the healthcare setting,” said Genti Koci, a sterile processing professional formerly with Mount Carmel Health System in Columbus, OH. “We don’t know much about the disease, and there is a fear factor that has spread among us through the media.” With the situation changing almost daily, the CDC has issued frequent updates, and healthcare facilities are doing their best to keep their employees safe. “We are following CDC Guidelines and making sure all patient care equipment is cleaned and disinfected per the [instructions for use] (IFUs). However, we are reinforcing the correct application and removal of personal protective equipment (PPE), which seems to be a weak spot in this,” said Nancy Chobin, a consultant and educator with the Saint Barnabas Health Care System. As Jesse Goodman, MD, attending physician at the Washington DC Veterans Affairs Medical Center and MedStar Georgetown University Hospital, noted during an Ebola town hall meeting on Washington D.C.-area radio station WTOP, facilities should be “overclear, overtrained, and overcautious.” That recommendation seems to have resonated with the healthcare community. Donna Swenson, president of Sterile Processing Quality Services, Inc, in Berwyn, IL, has advice to keep facilities prepared. “Both when performing surgery and after the procedure when cleaning the instrumentation, it is necessary for healthcare workers to ensure that they take adequate precautions to protect themselves from blood and body fluid exposure,” she said. That protection includes gowns and drapes. Swenson is co-chair of the committee that developed ANSI/AAMI PB70:2012, Liquid barrier performance and classification of Key facts from WHO

The kidney is a very important complicated filtering organ of the body. When the kidney reaches stage 5 chronic kidney disease, end stage renal failure, the preeminent therapy is renal transplantation. Although it is the best form of treatment, lack of kidney donors is still challenging. Therefore, all efforts should be employed to prolong the survival rate of the transplanted kidney. However, graft dysfunction (e.g., acute rejection) is one of the serious barriers to long term kidney transplant survival. Currently, graft dysfunction’s gold standard of diagnosis is renal biopsy. Although renal biopsy is helpful, it is not preferred due to its invasive nature, high morbidity rates, and expensiveness. Therefore, noninvasive imaging techniques have become the subject of extensive research and interest, giving a strong promise to replace, or at least to decrease, biopsy usage in diagnosing graft dysfunction. This survey will discuss not only the current diagnosis and treatment of graft dysfunction but also the state-of-the-art imaging techniques in detecting acute renal transplant rejection.