• The choice of b values significantly affects the ADC of breast lesions. • Sensitivity and specificity are not affected by the choice of b values. • b values 0 and 1,000 s/mm (2) are recommended for optimal differentiation between benign and malignant lesions. • Contrast medium prior to DWI does not significantly affect the ADC.

We developed a spin transport model for a diffusive channel with coupled localized states that result in an effective increase of spin precession frequencies and a reduction of spin relaxation times in the system. We apply this model to Hanle spin precession measurements obtained on monolayer epitaxial graphene on SiC(0001). Combined with newly performed measurements on quasi-free-standing monolayer epitaxial graphene on SiC(0001) our analysis shows that the different values for the diffusion coefficient measured in charge and spin transport measurements on monolayer epitaxial graphene on SiC(0001) and the high values for the spin relaxation time can be explained by the influence of localized states arising from the buffer layer at the interface between the graphene and the SiC surface.

BackgroundNearly one third of patients undergoing neoadjuvant chemoradiotherapy (nCRT) for locally advanced esophageal cancer have a pathologic complete response (pCR) of the primary tumor upon histopathological evaluation of the resection specimen. The primary aim of this study is to develop a model that predicts the probability of pCR to nCRT in esophageal cancer, based on diffusion-weighted magnetic resonance imaging (DW-MRI), dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and 18F-fluorodeoxyglucose positron emission tomography with computed tomography (18F-FDG PET-CT). Accurate response prediction could lead to a patient-tailored approach with omission of surgery in the future in case of predicted pCR or additional neoadjuvant treatment in case of non-pCR.MethodsThe PRIDE study is a prospective, single arm, observational multicenter study designed to develop a multimodal prediction model for histopathological response to nCRT for esophageal cancer. A total of 200 patients with locally advanced esophageal cancer - of which at least 130 patients with adenocarcinoma and at least 61 patients with squamous cell carcinoma - scheduled to receive nCRT followed by esophagectomy will be included. The primary modalities to be incorporated in the prediction model are quantitative parameters derived from MRI and 18F-FDG PET-CT scans, which will be acquired at fixed intervals before, during and after nCRT. Secondary modalities include blood samples for analysis of the presence of circulating tumor DNA (ctDNA) at 3 time-points (before, during and after nCRT), and an endoscopy with (random) bite-on-bite biopsies of the primary tumor site and other suspected lesions in the esophagus as well as an endoscopic ultrasonography (EUS) with fine needle aspiration of suspected lymph nodes after finishing nCRT. The main study endpoint is the performance of the model for pCR prediction. Secondary endpoints include progression-free and overall survival.DiscussionIf the multimodal PRIDE concept provides high predictive performance for pCR, the results of this study will play an important role in accurate identification of esophageal cancer patients with a pCR to nCRT. These patients might benefit from a patient-tailored approach with omission of surgery in the future. Vice versa, patients with non-pCR might benefit from additional neoadjuvant treatment, or ineffective therapy could be stopped.Trial registrationThe article reports on a health care intervention on human participants and was prospectively registered on March 22, 2018 under ClinicalTrials.gov Identifier: NCT03474341.

Quantitative DWI has a lower NPV compared to DCE-MRI for evaluation of breast lesions and may therefore not be able to replace DCE-MRI; when implemented after DCE-MRI as problem solver for BI-RADS 3 and 4 lesions, the combined specificity improves significantly. J. Magn. Reson. Imaging 2016;44:1642-1649.

ObjectiveClinical hepatic diffusion weighted imaging (DWI) generally relies on mono-exponential diffusion. The aim was to demonstrate that mono-exponential diffusion in the liver is contaminated by microperfusion and that the bi-exponential model is required.MethodsNineteen fasting healthy volunteers were examined with DWI (seven b-values) using fat suppression and respiratory triggering (1.5 T). Five different regions in the liver were analysed regarding the mono-exponentially fitted apparent diffusion coefficient (ADC), and the bi-exponential model: molecular diffusion (Dslow), microperfusion (Dfast) and the respective fractions (fslow/fast). Data were compared using ANOVA and Kruskal–Wallis tests. Simulations were performed by repeating our data analyses, using just the DWI series acquired with b-values approximating those of previous studies.ResultsMedian mono-exponentially fitted ADCs varied significantly (P < 0.001) between 1.107 and 1.423 × 10−3 mm2/s for the five regions. Bi-exponential fitted Dslow varied between 0.923 and 1.062 × 10−3 mm2/s without significant differences (P = 0.140). Dfast varied significantly, between 17.8 and 46.8 × 10−3 mm2/s (P < 0.001). F-tests showed that the diffusion data fitted the bi-exponential model significantly better than the mono-exponential model (F > 21.4, P < 0.010). These results were confirmed by the simulations.ConclusionADCs of normal liver tissue are significantly dependent on the measurement location because of substantial microperfusion contamination; therefore the bi-exponential model should be used.Key PointsDiffusion weighted MR imaging helps clinicians to differentiate tumours by diffusion propertiesFast moving water molecules experience microperfusion, slow molecules diffusionHepatic diffusion should be measured by bi-exponential models to avoid microperfusion contaminationMono-exponential models are contaminated with microperfusion, resulting in apparent regional diffusion differencesBi-exponential models are necessary to measure diffusion and microperfusion in the liver

Lung cancer, chronic obstructive pulmonary disease (COPD), and coronary artery disease (CAD) are expected to cause most deaths by 2050. State-of-the-art computed tomography (CT) allows early detection of lung cancer and simultaneous evaluation of imaging biomarkers for the early stages of COPD, based on pulmonary density and bronchial wall thickness, and of CAD, based on the coronary artery calcium score (CACS), at low radiation dose. To determine cutoff values for positive tests for elevated risk and presence of disease is one of the major tasks before considering implementation of CT screening in a general population. The ImaLife (Imaging in Lifelines) study, embedded in the Lifelines study, is designed to establish the reference values of the imaging biomarkers for the big three diseases in a well-defined general population aged 45 years and older. In total, 12,000 participants will undergo CACS and chest acquisitions with latest CT technology. The estimated percentage of individuals with lung nodules needing further workup is around 1-2%. Given the around 10% prevalence of COPD and CAD in the general population, the expected number of COPD and CAD is around 1000 each. So far, nearly 4000 participants have been included. The ImaLife study will allow differentiation between normal aging of the pulmonary and cardiovascular system and early stages of the big three diseases based on low-dose CT imaging. This information can be finally integrated into personalized precision health strategies in the general population.

BackgroundMulti-center magnetic resonance imaging (MRI) studies present an opportunity to advance research by pooling data. However, brain measurements derived from MR-images are susceptible to differences in MR-sequence parameters. It is therefore necessary to determine whether there is an interaction between the sequence parameters and the effect of interest, and to minimise any such interaction by careful choice of acquisition parameters. As an exemplar of the issues involved in multi-center studies, we present data from a study in which we aimed to optimize a set of volumetric MRI-protocols to define a protocol giving data that are consistent and reproducible across two centers and over time.MethodsOptimization was achieved based on data quality and quantitative measures, in our case using FreeSurfer and Voxel Based Morphometry approaches. Our approach consisted of a series of five comparisons. Firstly, a single-center dataset was collected, using a range of candidate pulse-sequences and parameters chosen on the basis of previous literature. Based on initial results, a number of minor changes were implemented to optimize the pulse-sequences, and a second single-center dataset was collected. FreeSurfer data quality measures were compared between datasets in order to determine the best performing sequence(s), which were taken forward to the next stage of testing. We subsequently acquired short-term and long-term two-center reproducibility data, and quantitative measures were again assessed to determine the protocol with the highest reproducibility across centers. Effects of a scanner software and hardware upgrade on the reproducibility of the protocols at one of the centers were also evaluated.ResultsAssessing the quality measures from the first two datasets allowed us to define artefact-free protocols, all with high image quality as assessed by FreeSurfer. Comparing the quantitative test and retest measures, we found high within-center reproducibility for all protocols, but lower between-center reproducibility for some protocols than others. The upgrade showed no important effects.ConclusionsWe were able to determine (for the scanners used in this study) an optimised protocol, which gave the highest within- and between-center reproducibility of those assessed, and give details of this protocol here. More generally, we discuss some of the issues raised by multi-center studies and describe a methodical approach to take towards optimization and standardization, and recommend performing this kind of procedure to other investigators.

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