Autism spectrum disorder characterization in children by capturing local-regional brain changes in MRI.

PURPOSE: To design a multiscale descriptor capable of capturing complex local-regional unfolding patterns to support quantitation and diagnosis of autism spectrum disorders (ASD) using T1-weighted structural magnetic resonance images (MRI) with voxel size of 1 × 1 × 1 mm. METHODS: The proposed image descriptor uses an adapted multiscale representation, the Curvelet transform, interpretable in terms of texture (local) and shape (regional) to characterize brain regions, and a Generalized Gaussian Distribution (GGD) to reduce feature dimensionality. In this approach, each MRI is first parcelled into 3D anatomical regions. Each resultant region is represented by a single 2D image where slices are placed next to each other. Each 2D image is characterized by mapping it to the Curvelet space and each of the different Curvelet sub-bands is described by the set of GGD parameters. To assess the discriminant power of the proposed descriptor, a classification model per brain region was built to differentiate ASD patients from control subjects. Models were constructed with support vector machines and evaluated using two samples from heterogeneous databases, namely Autism Brain Imaging Data Exchange - ABIDE I (34 ASD and 34 controls, mean age 11.46 ± 2.03 and 11.53 ± 1.79 yr, respectively, male population) and ABIDE II (42 ASD and 41 controls, mean age 10.09 ± 1.37 and 10.52 ± 1.27 yr, respectively, male population), for a total of 151 individuals. RESULTS: When the model was trained with ABIDE II sample and tested with ABIDE I on a hold-out validation, an area under receiver operator curve (AUC) of 0.69 was computed. When each sample was independently used under a cross-validation scheme, the estimated AUC was 0.75 ± 0.02 for ABIDE I and 0.77 ± 0.01 for ABIDE II. This analysis determined a set of discriminant regions widely reported in the literature as characteristic of ASD. CONCLUSIONS: The presented image descriptor demonstrated differences at local and regional level when high differences were observed in the Curvelet sub-bands. The method is simple in conceptual terms, robust to several sources of noise, and has a very low computational cost.

Incidental Finding of Cryptococcus on Prostate Biopsy for Prostate Adenocarcinoma Following Cardiac Transplant: Case Report and Review of the Literature.

BACKGROUND Cryptococcus is the third most common invasive fungal organism in immunocompromised patients, including transplant patients, and usually involves the central nervous system and lungs, with a median time to infection of 25 months. We report a case of Cryptococcus of the prostate gland, found as an incidental finding on prostate biopsy for prostate adenocarcinoma, four months following cardiac transplantation. CASE REPORT A 62-year-old male African-American who had a cardiac transplant four months previously, underwent a six-core prostate biopsy for a two-year history of increasing prostate-specific antigen (PSA) levels, and a recent history of non-specific urinary tract symptoms. A prostatic adenocarcinoma, Gleason grade 4+4=8, was diagnosed on histopathology, and 'foamy' cells were seen in the biopsies. Histochemical stains, including Grocott methenamine silver (GMS), and periodic acid-Schiff (PAS) showed abundant round and oval 5-7 µm diameter fungal elements; mucicarmine highlighted the fungal polysaccharide capsule, diagnostic for Cryptococcus. Cryptococcal antigen detection was made by the latex agglutination test and cultures. We reviewed the literature and found 70 published cases (from 1946-2008) of Cryptococcus of the prostate gland, with only one previous case presenting five years following cardiac transplantation. CONCLUSIONS Fungal infections of the prostate are rare, and occur mainly in immunocompromised patients. We present a unique case of prostatic Cryptococcus found incidentally at four months following cardiac transplantation. This case report highlights the need to consider atypical fungal infection as a differential diagnosis for prostatitis in immunosuppressed patients, including transplant patients.

A sparse Bayesian representation for super-resolution of cardiac MR images.

High-quality cardiac magnetic resonance (CMR) images can be hardly obtained when intrinsic noise sources are present, namely heart and breathing movements. Yet heart images may be acquired in real time, the image quality is really limited and most sequences use ECG gating to capture images at each stage of the cardiac cycle during several heart beats. This paper presents a novel super-resolution algorithm that improves the cardiac image quality using a sparse Bayesian approach. The high-resolution version of the cardiac image is constructed by combining the information of the low-resolution series -observations from different non-orthogonal series composed of anisotropic voxels - with a prior distribution of the high-resolution local coefficients that enforces sparsity. In addition, a global prior, extracted from the observed data, regularizes the solution. Quantitative and qualitative validations were performed in synthetic and real images w.r.t to a baseline, showing an average increment between 2.8 and 3.2 dB in the Peak Signal-to-Noise Ratio (PSNR), between 1.8% and 2.6% in the Structural Similarity Index (SSIM) and 2.% to 4% in quality assessment (IL-NIQE). The obtained results demonstrated that the proposed method is able to accurately reconstruct a cardiac image, recovering the original shape with less artifacts and low noise.