Quantitative brain stem assessment in discriminating neurodegenerative disorders from normal pressure hydrocephalus.

Background And Purpose: Differentiating idiopathic normal pressure hydrocephalus (iNPH) from neurodegenerative disorders such as progressive supranuclear palsy (PSP), Multiple System Atrophy-parkinsonian type (MSA-P), and vascular dementia (VaD) is challenging due to overlapping clinical and neuroimaging findings. This study assesses if quantitative brain stem and cerebellum metrics can aid in this differentiation.

Methods: We retrospectively compared the sagittal midbrain area, midbrain to pons ratio, MR parkinsonism index (MRPI), and cerebellar atrophy in 30 PSP patients, 31 iNPH patients, 27 MSA-P patients, 32 VaD patients, and 25 healthy controls.

Composite attenuation correction method using a Ge-transmission multi-atlas for quantitative brain PET/MR.

In positron emission tomography (PET), Ge-transmission scanning is considered the gold standard in attenuation correction (AC) though not available in current dual imaging systems. In this experimental study we evaluated a novel AC method for PET/magnetic resonance (MR) imaging which is essentially based on a composite database of multiple Ge-transmission maps and T1-weighted (T1w) MR image-pairs (composite transmission, CTR-AC). This proof-of-concept study used retrospectively a database with 125 pairs of co-registered Ge-AC maps and T1w MR images from anatomical normal subjects and a validation dataset comprising dynamic [C]PE2I PET data from nine patients with Parkinsonism.

Risk Factors and Outcomes of Infection Before and After Allogeneic Hematopoietic Cell Transplantation.

carbapenemase (KPC)-producing (KPC-Kp) emerge as a major healthcare concern worldwide. Despite the significance of infections before and after allogeneic hematopoietic cell transplantation (alloHCT), the burden of KP infections has not been extensively evaluated. We studied the incidence, risk factors, and outcomes of consecutive alloHCT recipients with Kp isolates before and after alloHCT.

Accuracy and precision of zero-echo-time, single- and multi-atlas attenuation correction for dynamic [C]PE2I PET-MR brain imaging.

Background: A valid photon attenuation correction (AC) method is instrumental for obtaining quantitatively correct PET images. Integrated PET/MR systems provide no direct information on attenuation, and novel methods for MR-based AC (MRAC) are still under investigation. Evaluations of various AC methods have mainly focused on static brain PET acquisitions.

Evaluation of zero-echo-time attenuation correction for integrated PET/MR brain imaging-comparison to head atlas and Ge-transmission-based attenuation correction.

Background: MRI does not offer a direct method to obtain attenuation correction maps as its predecessors (stand-alone PET and PET/CT), and bone visualisation is particularly challenging. Recently, zero-echo-time (ZTE) was suggested for MR-based attenuation correction (AC). The aim of this work was to evaluate ZTE- and atlas-AC by comparison to Ge-transmission scan-based AC.

Kernel-based self-organized maps trained with supervised bias for gene expression data analysis.

Self-Organized Maps (SOMs) are a popular approach for analyzing genome-wide expression data. However, most SOM based approaches ignore prior knowledge about functional gene categories. Also, Self Organized Map (SOM) based approaches usually develop topographic maps with disjoint and uniform activation regions that correspond to a hard clustering of the patterns at their nodes.

Gene expression data analysis with a dynamically extended self-organized map that exploits class information.

Motivation: Currently the most popular approach to analyze genome-wide expression data is clustering. One of the major drawbacks of most of the existing clustering methods is that the number of clusters has to be specified a priori. Furthermore, by using pure unsupervised algorithms prior biological knowledge is totally ignored Moreover, most current tools lack an effective framework for tight integration of unsupervised and supervised learning for the analysis of high-dimensional expression data and only very few multi-class supervised approaches are designed with the provision for effectively utilizing multiple functional class labeling.