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. Statistical analyses determined group differences, sensitivity, specificity, and the area under the receiver operating characteristic curves. RESULTS: There was an overlap in midbrain morphology between PSP and iNPH, as assessed with MRPI, midbrain to pons ratio, and midbrain area. A cutoff value of MRPI > 13 exhibited 84% specificity in distinguishing PSP from iNPH and 100% in discriminating PSP from all other conditions. A cutoff value of midbrain to pons ratio at <0.15 yielded 95% specificity for differentiating PSP from iNPH and 100% from all other conditions. A cutoff value of midbrain area at <87 mm2 exhibited 97% specificity for differentiating PSP from iNPH and 100% from all other conditions. All measures showed low sensitivity. Cerebellar atrophy did not differ significantly among groups. CONCLUSION: Our study questions MRPI's diagnostic performance in distinguishing PSP from iNPH. Simpler indices such as midbrain to pons ratio and midbrain area showed similar or better accuracy. However, all these indices displayed low sensitivity despite significant differences among PSP, MSA-P, and VaD.

Composite attenuation correction method using a 68Ge-transmission multi-atlas for quantitative brain PET/MR.

In positron emission tomography (PET), 68Ge-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 68Ge-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 68Ge-AC maps and T1w MR images from anatomical normal subjects and a validation dataset comprising dynamic [11C]PE2I PET data from nine patients with Parkinsonism. CTR-AC maps were generated by non-rigid image registration of all database T1w MRI to each subject's T1w, applying the same transformation to every 68Ge-AC map, and averaging the resulting 68Ge-AC maps. [11C]PE2I PET images were reconstructed using CTR-AC and a patient-specific 68Ge-AC map as the reference standard. Standardized uptake values (SUV) and quantitative parameters of kinetic analysis were compared, i.e., relative delivery (R1) and non-displaceable binding potential (BPND). CTR-AC showed high accuracy for whole-brain SUV (mean %bias ± SD: 0.5 ± 3.5%), whole-brain R1 (-0.1 ± 3.2%), and putamen BPND (3.7 ± 8.1%). SUV and R1 precision (SD of %bias) were modest and lowest in the anterior cortex, with an R1 %bias of -1.1 ± 6.4%). The prototype CTR-AC is capable of providing accurate MRAC-maps with continuous linear attenuation coefficients though still experimental. The method's accuracy is comparable to the best MRAC methods published so far, both in SUV and as found for ZTE-AC in quantitative parameters of kinetic modelling.

Accuracy and precision of zero-echo-time, single- and multi-atlas attenuation correction for dynamic [11C]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. In this study, we determined the validity of three MRAC methods in a dynamic PET/MR study of the brain. METHODS: Nine participants underwent dynamic brain PET/MR scanning using the dopamine transporter radioligand [11C]PE2I. Three MRAC methods were evaluated: single-atlas (Atlas), multi-atlas (MaxProb) and zero-echo-time (ZTE). The 68Ge-transmission data from a previous stand-alone PET scan was used as reference method. Parametric relative delivery (R1) images and binding potential (BPND) maps were generated using cerebellar grey matter as reference region. Evaluation was based on bias in MRAC maps, accuracy and precision of [11C]PE2I BPND and R1 estimates, and [11C]PE2I time-activity curves. BPND was examined for striatal regions and R1 in clusters of regions across the brain. RESULTS: For BPND, ZTE-MRAC showed the highest accuracy (bias < 2%) in striatal regions. Atlas-MRAC exhibited a significant bias in caudate nucleus (- 12%) while MaxProb-MRAC revealed a substantial, non-significant bias in the putamen (9%). R1 estimates had a marginal bias for all MRAC methods (- 1.0-3.2%). MaxProb-MRAC showed the largest intersubject variability for both R1 and BPND. Standardized uptake values (SUV) of striatal regions displayed the strongest average bias for ZTE-MRAC (~ 10%), although constant over time and with the smallest intersubject variability. Atlas-MRAC had highest variation in bias over time (+10 to - 10%), followed by MaxProb-MRAC (+5 to - 5%), but MaxProb showed the lowest mean bias. For the cerebellum, MaxProb-MRAC showed the highest variability while bias was constant over time for Atlas- and ZTE-MRAC. CONCLUSIONS: Both Maxprob- and ZTE-MRAC performed better than Atlas-MRAC when using a 68Ge transmission scan as reference method. Overall, ZTE-MRAC showed the highest precision and accuracy in outcome parameters of dynamic [11C]PE2I PET analysis with use of kinetic modelling.

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

Objectives: Klebsiella pneumoniae carbapenemase (KPC)-producing K. pneumoniae (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. Methods: We studied the incidence, risk factors, and outcomes of consecutive alloHCT recipients with Kp isolates before and after alloHCT. Results: Among 424 patients who underwent alloHCT in 2008-2018, we studied two groups: those with Kp isolates before (group 1, 52 patients) and those with Kp isolates after alloHCT (group 2, 66 patients). prE-transplant infections were associated with post-transplant infections (p = 0.010), despite secondary prophylaxis. KPC-Kp was isolated in 29% of group 1, and 80% of group 2. Both groups were characterized by a significant burden of moderate-severe acute graft- vs.-host disease (GVHD) [cumulative incidence (CI) of 44.5 and 61.9%, respectively] and severe chronic (CI of 56.7 and 61.9%). Kp infections and GVHD were independent predictive factors of treatment-related mortality (TRM) in both groups. Conclusions: Our study highlights the significant impact of Kp infections on TRM, with GVHD consisting an important underlying factor. As prophylactic measures did not improve rates of post-transplant infections, innovative interventions need to be further investigated to address this major healthcare concern.

Evaluation of zero-echo-time attenuation correction for integrated PET/MR brain imaging-comparison to head atlas and 68Ge-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 68Ge-transmission scan-based AC. Nine patients underwent brain PET/MR and stand-alone PET scanning using the dopamine transporter ligand 11C-PE2I. For each of them, two AC maps were obtained from the MR images: an atlas-based, obtained from T1-weighted LAVA-FLEX imaging with cortical bone inserted using a CT-based atlas, and an AC map generated from proton-density-weighted ZTE images. Stand-alone PET 68Ge-transmission AC map was used as gold standard. PET images were reconstructed using the three AC methods and standardised uptake value (SUV) values for the striatal, limbic and cortical regions, as well as the cerebellum (VOIs) were compared. SUV ratio (SUVR) values normalised for the cerebellum were also assessed. Bias, precision and agreement were calculated; statistical significance was evaluated using Wilcoxon matched-pairs signed-rank test. RESULTS: Both ZTE- and atlas-AC showed a similar bias of 6-8% in SUV values across the regions. Correlation coefficients with 68Ge-AC were consistently high for ZTE-AC (r 0.99 for all regions), whereas they were lower for atlas-AC, varying from 0.99 in the striatum to 0.88 in the posterior cortical regions. SUVR showed an overall bias of 2.9 and 0.5% for atlas-AC and ZTE-AC, respectively. Correlations with 68Ge-AC were higher for ZTE-AC, varying from 0.99 in the striatum to 0.96 in the limbic regions, compared to atlas-AC (0.99 striatum to 0.77 posterior cortex). CONCLUSIONS: Absolute SUV values showed less variability for ZTE-AC than for atlas-AC when compared to 68Ge-AC, but bias was similar for both methods. This bias is largely caused by higher linear attenuation coefficients in atlas- and ZTE-AC image compared to 68Ge-images. For SUVR, bias was lower when using ZTE-AC than for atlas-AC. ZTE-AC shows to be a more robust technique than atlas-AC in terms of both intra- and inter-patient variability.

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. We present a novel Self-Organizing map, the Kernel Supervised Dynamic Grid Self-Organized Map (KSDG-SOM). This model adapts its parameters in a kernel space. Gaussian kernels are used and their mean and variance components are adapted in order to optimize the fitness to the input density. The KSDG-SOM also grows dynamically up to a size defined with statistical criteria. It is capable of incorporating a priori information for the known functional characteristics of genes. This information forms a supervised bias at the cluster formation and the model owns the potentiality of revising incorrect functional labels. The new method overcomes the main drawbacks of most of the existing clustering methods that lack a mechanism for dynamical extension on the basis of a balance between unsupervised and supervised drives.

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. RESULTS: The paper adapts a novel Self-Organizing map called supervised Network Self-Organized Map (sNet-SOM) to the peculiarities of multi-labeled gene expression data. The sNet-SOM determines adaptively the number of clusters with a dynamic extension process. This process is driven by an inhomogeneous measure that tries to balance unsupervised, supervised and model complexity criteria. Nodes within a rectangular grid are grown at the boundary nodes, weights rippled from the internal nodes towards the outer nodes of the grid, and whole columns inserted within the map The appropriate level of expansion is determined automatically. Multiple sNet-SOM models are constructed dynamically each for a different unsupervised/supervised balance and model selection criteria are used to select the one optimum one. The results indicate that sNet-SOM yields competitive performance to other recently proposed approaches for supervised classification at a significantly reduced computational cost and it provides extensive exploratory analysis potentiality within the analysis framework. Furthermore, it explores simple design decisions that are easier to comprehend and computationally efficient.