The STRAT-PARK cohort: A personalized initiative to stratify Parkinson's disease.

The STRAT-PARK initiative aims to provide a platform for stratifying Parkinson's disease (PD) into biological subtypes, using a bottom-up, multidisciplinary biomarker-based and data-driven approach. PD is a heterogeneous entity, exhibiting high interindividual clinicopathological variability. This diversity suggests that PD may encompass multiple distinct biological entities, each driven by different molecular mechanisms.

Transient global amnesia: 7 Tesla MRI reveals more hippocampal lesions with diffusion restriction compared to 1.5 and 3 Tesla MRI.

Purpose: To assess the ability of 7 T MRI to detect hippocampal DWI lesions in the acute phase of TGA compared to 1.5 T/3 T MRI.

Methods: Patients with a clinical diagnosis consistent with TGA and a 1.

Pseudo-T2 mapping for normalization of T2-weighted prostate MRI.

Objective: Signal intensity normalization is necessary to reduce heterogeneity in T2-weighted (T2W) magnetic resonance imaging (MRI) for quantitative analysis of multicenter data. AutoRef is an automated dual-reference tissue normalization method that normalizes transversal prostate T2W MRI by creating a pseudo-T2 map. The aim of this study was to evaluate the accuracy of pseudo-T2s and multicenter standardization performance for AutoRef with three pairs of reference tissues: fat/muscle (AutoRef), femoral head/muscle (AutoRef) and pelvic bone/muscle (AutoRef).

Effects of echo time on IVIM quantifications of locally advanced breast cancer in clinical diffusion-weighted MRI at 3 T.

Purpose: The purpose of this study was to investigate the effects of echo time dependence in IVIM quantification of the pseudo-diffusion fraction in breast cancer and whether correcting for the echo time dependence offers added clinical value.

Materials And Methods: Fifteen patients with biopsy-proven breast cancer underwent a 3 T MRI examination with an extended DWI protocol at two different echo times (TE = 53 ms, b = 0, 50 s/mm ; TE = 77 ms, b = 0, 50, 120, 200, 400, 700 s/mm ). Volumes of interest were delineated around the tumors.

Structural connectivity-based segmentation of the human entorhinal cortex.

The medial (MEC) and lateral entorhinal cortex (LEC), widely studied in rodents, are well defined and characterized. In humans, however, the exact locations of their homologues remain uncertain. Previous functional magnetic resonance imaging (fMRI) studies have subdivided the human EC into posteromedial (pmEC) and anterolateral (alEC) parts, but uncertainty remains about the choice of imaging modality and seed regions, in particular in light of a substantial revision of the classical model of EC connectivity based on novel insights from rodent anatomy.

Exploring the diagnostic potential of adding T2 dependence in diffusion-weighted MR imaging of the prostate.

Background: Magnetic resonance imaging (MRI) is essential in the detection and staging of prostate cancer. However, improved tools to distinguish between low-risk and high-risk cancer are needed in order to select the appropriate treatment.

Purpose: To investigate the diagnostic potential of signal fractions estimated from a two-component model using combined T2- and diffusion-weighted imaging (T2-DWI).

National indication-based diagnostic reference level values in computed tomography: Preliminary results from Ghana.

Purpose: This study was conducted to develop national indication-based DRL values for common indications of adult computed tomography (CT) examinations for clinical application in Ghana.

Materials And Methods: The methodological approach recommended by the International Commission on Radiological Protection (ICRP), Publication 135, for the development of DRLs, was employed. Studies on CT infrastructure, common indications and quality control tests were first undertaken.

Understanding diffusion-weighted MRI analysis: Repeatability and performance of diffusion models in a benign breast lesion cohort.

Diffusion-weighted MRI (DWI) is an important tool for oncology research, with great clinical potential for the classification and monitoring of breast lesions. The utility of parameters derived from DWI, however, is influenced by specific analysis choices. The purpose of this study was to critically evaluate repeatability and curve-fitting performance of common DWI signal representations, for a prospective cohort of patients with benign breast lesions.

Discrimination of Breast Cancer from Healthy Breast Tissue Using a Three-component Diffusion-weighted MRI Model.

Purpose: Diffusion-weighted MRI (DW-MRI) is a contrast-free modality that has demonstrated ability to discriminate between predefined benign and malignant breast lesions. However, how well DW-MRI discriminates cancer from all other breast tissue voxels in a clinical setting is unknown. Here we explore the voxelwise ability to distinguish cancer from healthy breast tissue using signal contributions from the newly developed three-component multi-b-value DW-MRI model.

Modeling the diffusion-weighted imaging signal for breast lesions in the b = 200 to 3000 s/mm range: quality of fit and classification accuracy for different representations.

Purpose: To evaluate different non-Gaussian representations for the diffusion-weighted imaging (DWI) signal in the b-value range 200 to 3000 s/mm in benign and malignant breast lesions.

Methods: Forty-three patients diagnosed with benign (n = 18) or malignant (n = 25) tumors of the breast underwent DWI (b-values 200, 600, 1200, 1800, 2400, and 3000 s/mm ). Six different representations were fit to the average signal from regions of interest (ROIs) at different b-value ranges.

Stromal Collagen Content in Breast Tumors Correlates With In Vivo Diffusion-Weighted Imaging: A Comparison of Multi b-Value DWI With Histologic Specimen From Benign and Malignant Breast Lesions.

Background: Increased deposition and reorientation of stromal collagen fibers are associated with breast cancer progression and invasiveness. Diffusion-weighted imaging (DWI) may be sensitive to the collagen fiber organization in the stroma and could provide important biomarkers for breast cancer characterization.

Purpose: To understand how collagen fibers influence water diffusion in vivo and evaluate the relationship between collagen content and the apparent diffusion coefficient (ADC) and the signal fractions of the biexponential model using a high b-value scheme.

Feasibility of contrast-enhanced MRI derived textural features to predict overall survival in locally advanced breast cancer.

Background: The prognosis for women with locally advanced breast cancer (LABC) is poor and there is a need for better treatment stratification. Gray-level co-occurrence matrix (GLCM) texture analysis of magnetic resonance (MR) images has been shown to predict pathological response and could become useful in stratifying patients to more targeted treatments.

Purpose: To evaluate the ability of GLCM textural features obtained before neoadjuvant chemotherapy to predict overall survival (OS) seven years after diagnosis of patients with LABC.

Correction to: Semi-automatic segmentation from intrinsically-registered 18F-FDG-PET/MRI for treatment response assessment in a breast cancer cohort: comparison to manual DCE-MRI.

The original version of this article unfortunately contained a mistake in Fig. 6.

Semi-automatic segmentation from intrinsically-registered 18F-FDG-PET/MRI for treatment response assessment in a breast cancer cohort: comparison to manual DCE-MRI.

Objectives: To investigate the reliability of simultaneous positron emission tomography and magnetic resonance imaging (PET/MRI)-derived biomarkers using semi-automated Gaussian mixture model (GMM) segmentation on PET images, against conventional manual tumor segmentation on dynamic contrast-enhanced (DCE) images.

Materials And Methods: Twenty-four breast cancer patients underwent PET/MRI (following 18F-fluorodeoxyglucose (18F-FDG) injection) at baseline and during neoadjuvant treatment, yielding 53 data sets (24 untreated, 29 treated). Two-dimensional tumor segmentation was performed manually on DCE-MRI images (manual DCE) and using GMM with corresponding PET images (GMM-PET).

Accuracy of breast cancer lesion classification using intravoxel incoherent motion diffusion-weighted imaging is improved by the inclusion of global or local prior knowledge with bayesian methods.

Background: Diffusion-weighted MRI (DWI) has potential to noninvasively characterize breast cancer lesions; models such as intravoxel incoherent motion (IVIM) provide pseudodiffusion parameters that reflect tissue perfusion, but are dependent on the details of acquisition and analysis strategy.

Purpose: To examine the effect of fitting algorithms, including conventional least-squares (LSQ) and segmented (SEG) methods as well as Bayesian methods with global shrinkage (BSP) and local spatial (FBM) priors, on the power of IVIM parameters to differentiate benign and malignant breast lesions.

Study Type: Prospective patient study.

Relationship between kurtosis and bi-exponential characterization of high b-value diffusion-weighted imaging: application to prostate cancer.

Background: High b-value diffusion-weighted imaging has application in the detection of cancerous tissue across multiple body sites. Diffusional kurtosis and bi-exponential modeling are two popular model-based techniques, whose performance in relation to each other has yet to be fully explored.

Purpose: To determine the relationship between excess kurtosis and signal fractions derived from bi-exponential modeling in the detection of suspicious prostate lesions.

Relative enhanced diffusivity: noise sensitivity, protocol optimization, and the relation to intravoxel incoherent motion.

Objective: To explore the relationship between relative enhanced diffusivity (RED) and intravoxel incoherent motion (IVIM), as well as the impact of noise and the choice of intermediate diffusion weighting (b value) on the RED parameter.

Materials And Methods: A mathematical derivation was performed to cast RED in terms of the IVIM parameters. Noise analysis and b value optimization was conducted by using Monte Carlo calculations to generate diffusion-weighted imaging data appropriate to breast and liver tissue at three different signal-to-noise ratios.

Support vector machine for breast cancer classification using diffusion-weighted MRI histogram features: Preliminary study.

Background: Diffusion-weighted MRI (DWI) is currently one of the fastest developing MRI-based techniques in oncology. Histogram properties from model fitting of DWI are useful features for differentiation of lesions, and classification can potentially be improved by machine learning.

Purpose: To evaluate classification of malignant and benign tumors and breast cancer subtypes using support vector machine (SVM).

A Simplified Approach to Measure the Effect of the Microvasculature in Diffusion-weighted MR Imaging Applied to Breast Tumors: Preliminary Results.

Purpose To evaluate the relative change of the apparent diffusion coefficient (ADC) at low- and medium-b-value regimens as a surrogate marker of microcirculation, to study its correlation with dynamic contrast agent-enhanced (DCE) magnetic resonance (MR) imaging-derived parameters, and to assess its potential for differentiation between malignant and benign breast tumors. Materials and Methods Ethics approval and informed consent were obtained. From May 2013 to June 2015, 61 patients diagnosed with either malignant or benign breast tumors were prospectively recruited.

Stimulated echo diffusion tensor imaging (STEAM-DTI) with varying diffusion times as a probe of breast tissue.

Purpose: To explore the application of diffusion tensor imaging (DTI) for breast tissue and breast pathologies using a stimulated-echo acquisition mode (STEAM) with variable diffusion times.

Materials And Methods: In this Health Insurance Portability and Accountability Act-compliant study, approved by the local institutional review board, eight patients and six healthy volunteers underwent an MRI examination at 3 Tesla including STEAM-DTI with several diffusion times ranging from 68.5 to 902.

Effect of Task-Correlated Physiological Fluctuations and Motion in 2D and 3D Echo-Planar Imaging in a Higher Cognitive Level fMRI Paradigm.

Purpose: To compare 2D and 3D echo-planar imaging (EPI) in a higher cognitive level fMRI paradigm. In particular, to study the link between the presence of task-correlated physiological fluctuations and motion and the fMRI contrast estimates from either 2D EPI or 3D EPI datasets, with and without adding nuisance regressors to the model. A signal model in the presence of partly task-correlated fluctuations is derived, and predictions for contrast estimates with and without nuisance regressors are made.

Quantitative comparison of PET performance-Siemens Biograph mCT and mMR.

Background: Integrated clinical whole-body PET/MR systems were introduced in 2010. In order to bring this technology into clinical usage, it is of great importance to compare the performance with the well-established PET/CT. The aim of this study was to evaluate PET performance, with focus on image quality, on Siemens Biograph mMR (PET/MR) and Siemens Biograph mCT (PET/CT).

Diffusion weighted imaging for the differentiation of breast tumors: From apparent diffusion coefficient to high order diffusion tensor imaging.

Background: To compare "standard" diffusion weighted imaging, and diffusion tensor imaging (DTI) of 2(nd) and 4(th) -order for the differentiation of malignant and benign breast lesions.

Methods: Seventy-one patients were imaged at 3 Tesla with a 16-channel breast coil. A diffusion weighted MRI sequence including b = 0 and b = 700 in 30 directions was obtained for all patients.

PET/MR brain imaging: evaluation of clinical UTE-based attenuation correction.

Unlabelled: One of the greatest challenges in PET/MR imaging is that of accurate MR-based attenuation correction (AC) of the acquired PET data, which must be solved if the PET/MR modality is to reach its full potential. The aim of this study was to investigate the performance of Siemens' most recent version (VB20P) of MR-based AC of head PET data, by comparing it to CT-based AC.

Methods: (18)F-FDG PET data from seven lymphoma and twelve lung cancer patients examined with a Biograph mMR PET/MR system were reconstructed with both CT-based and MR-based AC, avoiding sources of error arising when comparing PET data from different systems.