Abstract-Compressive sensing (CS) has proved effective for tomographic reconstruction from sparsely collected data or under-sampled measurements, which are practically important for few-view CT, tomosynthesis, interior tomography, and so on. To perform sparse-data CT, the iterative reconstruction commonly uses regularizers in the CS framework. Currently, how to choose the parameters adaptively for regularization is a major open problem. In this paper, inspired by the idea of machine learning especially deep learning, we unfold a state-of-theart "fields of experts" based iterative reconstruction scheme up to a number of iterations for data-driven training, construct a Learned Experts' Assessment-based Reconstruction Network (LEARN) for sparse-data CT, and demonstrate the feasibility and merits of our LEARN network. The experimental results with our proposed LEARN network produces a superior performance with the well-known Mayo Clinic Low-Dose Challenge Dataset relative to several state-of-the-art methods, in terms of artifact reduction, feature preservation, and computational speed. This is consistent to our insight that because all the regularization terms and parameters used in the iterative reconstruction are now learned from the training data, our LEARN network utilizes application-oriented knowledge more effectively and recovers underlying images more favorably than competing algorithms. Also, the number of layers in the LEARN network is only 50, reducing the computational complexity of typical iterative algorithms by orders of magnitude.Index Terms-Computed tomography (CT), sparse-data CT, iterative reconstruction, compressive sensing, fields of experts, machine learning, deep learning

Innovative cell-based therapies, including hepatic tissue engineering following hepatocyte transplantation, are considered as theoretical alternatives to liver transplant or for partial replacement of liver function in patients. However, recent progress in hepatic tissue engineering has been hampered by low initial hepatocyte engraftment and insufficient blood supply in vivo. We developed an intact 3D scaffold of an extracellular matrix (ECM) derived from a decellularized liver lobe, with layer-by-layer (LbL) heparin deposition to avoid thrombosis, which we repopulated with hepatocytes and successfully implanted as a tissue-engineered liver (TEL) into the portal system. The TEL provided sufficient volume for transplantation of cell numbers representing up to 10% of whole-liver equivalents and was perfused by portal vein blood. Treatment of extended hepatectomized rats with a TEL improved liver function and prolonged survival; mean lifespan was extended from 16 to 72 h. At 72 h postoperation, the TEL sustained functional and viable hepatocytes. In conclusion, we propose the TEL as a state-of-the-art substitute for whole-liver transplantation and as a proof of concept for the technology that will eventually allow for the transplantation of a reconstituted liver.

Purpose To identify cerebral radiomic features related to diagnosis and subtyping of attention deficit hyperactivity disorder (ADHD) and to build and evaluate classification models for ADHD diagnosis and subtyping on the basis of the identified features. Materials and Methods A consecutive cohort of 83 age- and sex-matched children with newly diagnosed and never-treated ADHD (mean age 10.83 years ± 2.30; range, 7-14 years; 71 boys, 40 with ADHD-inattentive [ADHD-I] and 43 with ADHD-combined [ADHD-C, or inattentive and hyperactive]) and 87 healthy control subjects (mean age, 11.21 years ± 2.51; range, 7-15 years; 72 boys) underwent anatomic and diffusion-tensor magnetic resonance (MR) imaging. Features representing the shape properties of gray matter and diffusion properties of white matter were extracted for each participant. The initial feature set was input into an all-relevant feature selection procedure within cross-validation loops to identify features with significant discriminative power for diagnosis and subtyping. Random forest classifiers were constructed and evaluated on the basis of identified features. Results No overall difference was found between children with ADHD and control subjects in total brain volume (1069830.00 mm ± 90743.36 vs 1079 213.00 mm ± 92742.25, respectively; P = .51) or total gray and white matter volume (611978.10 mm ± 51622.81 vs 616960.20 mm ± 51872.93, respectively; P = .53; 413532.00 mm ± 41 114.33 vs 418173.60 mm ± 42395.48, respectively; P = .47). The mean classification accuracy achieved with classifiers to discriminate patients with ADHD from control subjects was 73.7%. Alteration in cortical shape in the left temporal lobe, bilateral cuneus, and regions around the left central sulcus contributed significantly to group discrimination. The mean classification accuracy with classifiers to discriminate ADHD-I from ADHD-C was 80.1%, with significant discriminating features located in the default mode network and insular cortex. Conclusion The results of this study provide preliminary evidence that cerebral morphometric alterations can allow discrimination between patients with ADHD and control subjects and also between the most common ADHD subtypes. By identifying features relevant for diagnosis and subtyping, these findings may advance the understanding of neurodevelopmental alterations related to ADHD. RSNA, 2017 Online supplemental material is available for this article.

The MVD level was not related to tumor size, capsule statuo, Edmondson's grade, alpha-fetoprotein level, associated cirrhosis, gamma-glutamyltransferase and serum HBsAg status. In the entire series, tumor size was the only factor influencing survival after curative resection. However, in patients with small HCC, the MVD level was an independent factor of disease-free survival. The pathological and clinical implications of different types of tumor vessels in HCC remain to be studied.

IMPORTANCE Accumulating evidence supports the hypothesis that cerebral white matter abnormalities are involved in the pathophysiology of schizophrenia; however, findings from in vivo neuroimaging studies have been inconsistent. Besides confounding factors, including age, illness duration, and medication effects, an additional cause for the inconsistent results may be heterogeneity in the nature of white matter alterations associated with the disorder.OBJECTIVE To investigate whether different patterns of white matter abnormalities exist in a large cohort of medication-naive patients with first-episode schizophrenia and the relationship between such patterns and clinical parameters. DESIGN, SETTING, AND PARTICIPANTSA cross-sectional diffusion tensor imaging study of 113 medication-naive patients with first-episode schizophrenia and 110 demographically matched healthy control individuals. The study was conducted in the mental health center of West