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Distinguishing malignant from benign liver lesions based on magnetic resonance imaging (MRI) is an important but often challenging task, especially in noncirrhotic livers. We developed and externally validated a radiomics model to quantitatively assess T2-weighted MRI to distinguish the most common malignant and benign primary solid liver lesions in noncirrhotic livers. Data sets were retrospectively collected from three tertiary referral centers (A, B, and C) between 2002 and 2018. Patients with malignant (hepatocellular carcinoma and intrahepatic cholangiocarcinoma) and benign (hepatocellular adenoma and focal nodular hyperplasia) lesions were included. A radiomics model based on T2-weighted MRI was developed in data set A using a combination of machine learning approaches. The model was internally evaluated on data set A through cross-validation, externally validated on data sets B and C, and compared to visual scoring of two experienced abdominal radiologists on data set C. The overall data set included 486 patients (A: 187, B: 98, and C: 201). The radiomics model had a mean area under the curve (AUC) of 0.78 upon internal validation on data set A and a similar AUC in external validation (B: 0.74 and C: 0.76). In data set C, the two radiologists showed moderate agreement (Cohen's κ: 0.61) and achieved AUCs of 0.86 and 0.82. Our T2-weighted MRI radiomics model shows potential for distinguishing malignant from benign primary solid liver lesions. External validation indicated that the model is generalizable despite substantial MRI acquisition protocol differences. Pending further optimization and generalization, this model may aid radiologists in improving the diagnostic workup of patients with liver lesions. [ABSTRACT FROM AUTHOR]

This study sought to investigate the extent of hypertensive exposure as assessed by cardiovascular magnetic resonance imaging (MRI) in relation to cerebral small vessel disease (CSVD) and cognitive impairment, with the aim of understanding the role of hypertension in the early stages of deteriorating brain health. Preserving brain health into advanced age is one of the great challenges of modern medicine. Hypertension is thought to induce vascular brain injury through exposure of the cerebral microcirculation to increased pressure/pulsatility. Cardiovascular MRI provides markers of (subclinical) hypertensive exposure, such as aortic stiffness by pulse wave velocity (PWV), left ventricular (LV) mass index (LVMi), and concentricity by mass-to-volume ratio. A total of 559 participants from the Heart-Brain Connection Study (431 patients with manifest cardiovascular disease and 128 control participants), age 67.8 ± 8.8 years, underwent 3.0-T heart-brain MRI and extensive neuropsychological testing. Aortic PWV, LVMi, and LV mass-to-volume ratio were evaluated in relation to presence of CSVD and cognitive impairment. Effect modification by patient group was investigated by interaction terms; results are reported pooled or stratified accordingly. Aortic PWV (odds ratio [OR]: 1.17; 95% confidence interval [CI]: 1.05 to 1.30 in patient groups only), LVMi (in carotid occlusive disease, OR: 5.69; 95% CI: 1.63 to 19.87; in other groups, OR: 1.30; 95% CI: 1.05 to 1.62]) and LV mass-to-volume ratio (OR: 1.81; 95% CI: 1.46 to 2.24) were associated with CSVD. Aortic PWV (OR: 1.07; 95% CI: 1.02 to 1.13) and LV mass-to-volume ratio (OR: 1.27; 95% CI: 1.07 to 1.51) were also associated with cognitive impairment. Relations were independent of sociodemographic and cardiac index and mostly persisted after correction for systolic blood pressure or medical history of hypertension. Causal mediation analysis showed significant mediation by presence of CSVD in the relation between hypertensive exposure markers and cognitive impairment. The extent of hypertensive exposure is associated with CSVD and cognitive impairment beyond clinical blood pressure or medical history. The mediating role of CSVD suggests that hypertension may lead to cognitive impairment through the occurrence of CSVD. [ABSTRACT FROM AUTHOR]

Summary Background & aims Low skeletal muscle mass and density have recently been discovered as prognostic and predictive parameters to guide interventions in various populations, including cancer patients. The gold standard for body composition analysis in cancer patients is computed tomography (CT). To date, the effect of contrast-enhancement on muscle composition measurements has not been established. The aim of this study was to determine the effect of contrast-enhancement on skeletal muscle mass and density measurements on four-phase CT studies. Design In this observational study, two observers measured cross-sectional skeletal muscle area corrected for patients' height (skeletal muscle index [SMI]) and density (SMD) at the level of the third lumbar vertebra on 50 randomly selected CT examinations with unenhanced, arterial, and portal-venous phases. The levels of agreement between enhancement phases for SMI and SMD were calculated using intra-class correlation coefficients (ICCs). Results Mean SMI was 42.5 (±9.9) cm2/m2 on the unenhanced phase, compared with 42.8 (±9.9) and 43.6 (±9.9) cm2/m2 for the arterial and portal-venous phase, respectively (both p < 0.01). Mean SMD was lower for the unenhanced phase (30.9 ± 8.0 Hounsfield units [HU]) compared with the arterial (38.0 ± 9.9 HU) and portal-venous (38.7 ± 9.2 HU) phase (both p < 0.001). No significant difference was found between SMD in the portal-venous and arterial phase (p = 0.161). The ICCs were excellent (≥0.992) for all SMIs and for SMD between the contrast-enhanced phases (0.949). The ICCs for the unenhanced phase compared with the arterial (0.676) and portal-venous (0.665) phase were considered fair to good. Conclusions Statistically significant differences in SMI were observed between different enhancement phases. However, further work is needed to assess the clinical relevance of these small differences. Contrast-enhancement strongly influenced SMD values. Studies using this measure should therefore use the portal-venous phase of contrast-enhanced CT examinations. [ABSTRACT FROM AUTHOR]

Rationale and Objectives Magnetic resonance angiography (MRA) is a well-established modality for the assessment of renal artery stenosis. Using dedicated quantitative analyses, MRA can become a useful tool for assessing renal artery dimensions in patients referred for renal sympathetic denervation (RDN) and for providing accurate measurements of vascular response after RDN. The purpose of this study was to test the reproducibility of a novel MRA quantitative imaging tool and to validate these measurements against intravascular ultrasound (IVUS). Materials and Methods In nine patients referred for renal denervation, renal artery dimensions were measured. Bland–Altman analysis was used to assess the intraobserver and interobserver reproducibility. Results Mean lumen diameter was 5.8 ± 0.7 mm, with a very good intraobserver and interobserver variability of 0.7% (reproducibility: bias, 0 mm; standard deviation [SD], 0.1 mm) and 1.2% (bias, 0 mm; SD, 0.1 mm), respectively. Mean total lumen volume was 1035.3 ± 403.6 mm 3 with good intraobserver and interobserver variability of 2.9% (bias, −9.7 mm 3 ; SD, 34.0 mm 3 ) and 2.8% (bias, −11.4 mm 3 ; SD, 42.4 mm 3 ). The correlation (Pearson R ) between mean lumen diameter measured with MRA and IVUS was 0.750 ( P = .002). Conclusions Using a novel MRA quantitative imaging tool, renal artery dimensions can be measured with good reproducibility and accuracy. MRA-derived diameters and volumes correlated well with IVUS measurements. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: Aneurysm morphodynamics is potentially relevant for assessing aneurysm rupture risk. A method is proposed for automated quantification and visualization of intracranial aneurysm morphodynamics from electrocardiogram (ECG)-gated computed tomography angiography (CTA) data. Materials and Methods: A prospective study was performed in 19 aneurysms from 14 patients with diagnostic workup for recently discovered aneurysms (n = 15) or follow-up of untreated known aneurysms (n = 4). The study was approved by the Institutional Review Board of the hospital and written informed consent was obtained from each patient. An image postprocessing method was developed for quantifying aneurysm volume changes and visualizing local displacement of the aneurysmal wall over a heart cycle using multiphase ECG-gated (four-dimensional) CTA. Percentage volume changes over the heart cycle were determined for aneurysms, surrounding arteries, and the skull. Results: Pulsation of the aneurysm and its surrounding vasculature during the heart cycle could be assessed from ECG-gated CTA data. The percentage aneurysmal volume change ranged from 3% to 18%. Conclusion: ECG-gated CTA can be used to study morphodynamics of intracranial aneurysms. The proposed image analysis method is capable of quantifying the volume changes and visualizing local displacement of the vascular structures over the cardiac cycle. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: The aim of this study was to automatically detect and quantify calcium lesions for the whole heart as well as per coronary artery on non-contrast-enhanced cardiac computed tomographic images. Materials and Methods: Imaging data from 366 patients were randomly selected from patients who underwent computed tomographic calcium scoring assessments between July 2004 and May 2009 at Erasmum MC, Rotterdam. These data included data sets with 1.5-mm and 3.0-mm slice spacing reconstructions and were acquired using four different scanners. The scores of manual observers, who annotated the data using commercially available software, served as ground truth. An automatic method for detecting and quantifying calcifications for each of the four main coronary arteries and the whole heart was trained on 209 data sets and tested on 157 data sets. Statistical testing included determining Pearson''s correlation coefficients and Bland-Altman analysis to compare performance between the system and ground truth. Wilcoxon''s signed-rank test was used to compare the interobserver variability to the system''s performance. Results: Automatic detection of calcified objects was achieved with sensitivity of 81.2% per calcified object in the 1.5-mm data set and sensitivity of 86.6% per calcified object in the 3.0-mm data set. The system made an average of 2.5 errors per patient in the 1.5-mm data set and 2.2 errors in the 3.0-mm data set. Pearson''s correlation coefficients of 0.97 (P < .001) for both 1.5-mm and 3.0-mm scans with respect to the calcium volume score of the whole heart were found. The average R values over Agatston, mass, and volume scores for each of the arteries (left circumflex coronary artery, right coronary artery, and left main and left anterior descending coronary arteries) were 0.93, 0.96, and 0.99, respectively, for the 1.5-mm scans. Similarly, for 3.0-mm scans, R values were 0.94, 0.94, and 0.99, respectively. Risk category assignment was correct in 95% and 89% of the data sets in the 1.5-mm and 3-mm scans. Conclusions: An automatic vessel-specific coronary artery calcium scoring system was developed, and its feasibility for calcium scoring in individual vessels and risk category classification has been demonstrated. [Copyright &y& Elsevier]

PURPOSE: To investigate the feasibility of navigation with three-dimensional (3D) rotational radiographic data for transpedicular percutaneous needle introduction and to compare navigation with 3D rotational radiographic data with conventional fluoroscopic guidance. MATERIALS AND METHODS: A navigation system was coupled to a 3D rotational radiographic imaging system. In a cadaver study, 60 biopsy needles were introduced into vertebral bodies with fluoroscopic guidance or navigation with 3D rotational radiographic data by two interventionalists with different levels of experience in percutaneous procedures. Radiation exposure, fluoroscopy and introduction times, and needle position were evaluated and compared. RESULTS: For 3D rotational radiographic navigation, the needle position was equivalent for both interventionalists (7 mm). For fluoroscopic guidance, the interventionalist with less experience in percutaneous procedures had significantly more pedicle cortex violations than the other interventionalist (eight vs one). Radiation exposure measured at the operators'' hand was lower for 3D rotational radiography–guided needle introductions. Radiation exposure measured at the body wall of the cadaver was equivalent between modalities (9 mGy). CONCLUSIONS: Navigation with 3D rotational radiographic image data is feasible for transpedicular percutaneous needle introduction and has two advantages compared with fluoroscopic guidance: it poses less strict requirements on the expertise of the interventionalist, and it reduces radiation exposure to the interventionalist. [Copyright &y& Elsevier]

Rationale and Objectives: The aim of this work is to study how the limited spatial resolution of a computed tomographic (CT) system affects the imaging of small high-density structures. This knowledge is relevant not only to understand and interpret clinical data, but also to apply and develop quantification methods for calcifications and stented vessels. Materials and Methods: A dedicated phantom containing small differently sized aluminum cylinders was imaged on a 64-slice multidetector row CT (MDCT) while varying acquisition and reconstruction parameters from a high-resolution protocol. In addition, a bead phantom was imaged to estimate the point spread function (PSF) for the different parameter settings. The accuracy in determining object density and size was established for various imaging protocols and compared with simulations based on the estimated PSF. Results: Attenuation values and size measurements were accurate for objects larger than two times the size of the system PSF at the full-width-at-half-maximum. For smaller objects, attenuation values were increasingly underestimated and size was increasingly overestimated. The convolution kernel had the most influence on object signal and size. Use of edge-enhancing kernels yielded more accurate size measurements and higher signal for small objects. However, their application was constrained by noise amplification and edge-ringing artifacts, which led to lower signal-to-noise ratio, degrading the visualization of low densities and small high-density objects. Conclusion: Results presented in this report provide insight into limitations in the quantification of small high-density structures and their effect on the visualization of surrounding tissues with recently developed MDCT systems. [ABSTRACT FROM AUTHOR]

This article presents the evaluation of an interactive multiscale watershed segmentation algorithm for segmenting tumors in magnetic resonance brain images of patients scheduled for neuronavigational procedures.The watershed method is compared with manual delineation with respect to accuracy, repeatability, and efficiency.In the 20 patients included in this study, the measured volume of the tumors ranged from 2.7 to 81.9 cm3. A comparison of the tumor volumes measured with watershed segmentation to the volumes measured with manual delineation shows that the two methods are interchangeable according to the Bland and Altman criterion, and thus equally accurate. The repeatability of the watershed method and the manual method are compared by looking at the similarity of the segmented volumes. The similarity for intraobserver and interobserver variability for watershed segmentation is 96.4% and 95.3%, respectively, compared with 93.5% and 90.0% for manual outlining, from which it may be concluded that the watershed method is more repeatable. Moreover, the watershed algorithm is on average three times faster than manual outlining.The watershed method has an accuracy comparable to that of manual delineation and outperforms manual outlining on the criteria of repeatability and efficiency. [Copyright &y& Elsevier]

Rationale and Objectives. A method is presented to track guide wires during endovascular interventions under X-ray fluoroscopy. Accurate guide wire tracking can be used to improve guide wire visualization in the low quality fluoroscopic images, and to estimate the position of the guide wire in world coordinates for navigation purposes.Materials and Methods. A two-step procedure is used to track the guide wire in subsequent frames. First, the position of the guide wire is obtained by fitting a spline to the image. Subsequently, the spline is iteratively moved toward the tip of the guide wire for accurate tip localization. For both steps, a feature image is used in which line-like structures are enhanced. The method is validated using a reference standard, obtained by manual tracings of three observers.Results. The method is evaluated on 20 image sequences, 10 sequences with a J-tipped guide wire and 10 with a straight guide wire. The tracking success was 96% for J-tipped and 100% for straight guide wires, whereas accurate endpoint localization could be performed in 91.3% and 94.4% of the frames respectively, with a tip localization error of less than 1.5 mm.Conclusions. Accurate endpoint localization can be performed for both J-tipped and straight guide wires and therefore the presented tracking method can be used for navigation purposes. [Copyright &y& Elsevier]

B Background: b Gastrointestinal stromal tumors (GISTs) are rare mesenchymal tumors of the gastrointestinal (GI) tract. Predicting the c-KIT mutational status of GISTs led to an AUC of 0.52 (95% CI 0.32-0.72) for predicting all c-KIT mutations, an AUC of 0.51 (95% CI 0.29-0.72) for predicting a c-KIT exon 9 mutation, and an AUC of 0.61 (95% CI 0.47-0.74) for predicting a c-KIT exon 11 mutation. B Conclusions b : The results of this pilot study showed the potential of radiomics to distinguish GIST from other GI tumors, but no potential in predicting c-KIT mutational status of GISTs. [Extracted from the article]

An abstract of the article "Robust motion correction in the frequency domain of cardiac MR stress perfusion sequences," by Vikas Gupta, Martijn Van de Giessen, Hortense Kirisli, Sharon Kirschbaum, Rob J. van der Geest, Wiro J. Niessen and Boudewijn Lelieveldt is presented.

An automated method for registering B-mode ultrasound (US) and magnetic resonance imaging (MRI) of the carotid arteries is proposed. The registration uses geometric features, namely, lumen centerlines and lumen segmentations, which are extracted fully automatically from the images after manual annotation of three seed points in US and MRI. The registration procedure starts with alignment of the lumen centerlines using a point-based registration algorithm. The resulting rigid transformation is used to initialize a rigid and subsequent non-rigid registration procedure that jointly aligns centerlines and segmentations by minimizing a weighted sum of the Euclidean distance between centerlines and the dissimilarity between segmentations. The method was evaluated in 28 carotid arteries from eight patients and six healthy volunteers. First, the automated US lumen segmentation method was validated and optimized in a cross-validation experiment. Next, the effect of the weighting parameter of the proposed registration dissimilarity metric and the control point spacing in the non-rigid registration was evaluated. Finally, the proposed registration method was evaluated in comparison to an existing intensity-and-point-based method, a registration using only the centerlines and a registration using manual US lumen segmentations. Registration accuracy was measured in terms of the mean surface distance between manual US segmentations and the registered MRI segmentations. The average mean surface distance was 0.78 ± 0.34 mm for all subjects, 0.65 ± 0.09 mm for healthy volunteers and 0.87 ± 0.42 mm for patients. The results for the complete set were significantly better (Wilcoxon test, p < 0.01) than the results for the intensity-and-point-based method and the centerline-based registration method. We conclude that the proposed method can robustly and accurately register US and MR images of the carotid artery, allowing multimodal analysis of the carotid plaque to improve plaque assessment. [ABSTRACT FROM AUTHOR]

Physical activity has been suggested as modifiable factor that might contribute to improving cognitive and brain function during aging. However, previous studies were mainly of cross-sectional design and did not consider effects of time or potential reverse causality. We aimed to investigate the bidirectional associations of physical activity with brain structure in middle-aged and older adults. Overall, 4365 participants (64.01 ± 10.82 years; 56% women) from the Rotterdam Study had physical activity and brain structure assessed on at least one of 2 timepoints ('baseline': 2006–2012 or 'follow-up': 2012-2017, median duration between visits: 5 years). Physical activity was assessed through the LASA Physical Activity Questionnaire. T1-weighted MRI and diffusion tensor imaging were used to quantify brain volumes and white matter microstructure, respectively. Cross-lagged panel models were performed to estimate bidirectional associations, and linear mixed-effects models to investigate the consistency of findings. Larger total brain volume (β = 0.067, 95%-confidence interval 0.035;0.099, p FDR = 0.001), gray matter volume (β = 0.063, 0.031;0.096, p FDR = 0.002), and white matter volume (β = 0.051, 0.020;0.083, p FDR = 0.013) at baseline were associated with higher levels of sports at follow-up. Lower global mean diffusivity at baseline was associated with higher levels of walking at follow-up (β = -0.074, -0.111;-0.037, p FDR = 0.001). No associations were found between physical activity levels at baseline and brain metrics at follow-up. In conclusion, larger brain volumes and white matter microstructure at baseline were associated with individuals remaining more physically active at follow-up. Overall, this study identified older adults with potentially advanced brain aging status as being at higher risk of physical inactivity over time, and therefore as potential target group for prevention and novel intervention strategies. [ABSTRACT FROM AUTHOR]

Cerebral X-ray digital subtraction angiography (DSA) is a widely used imaging technique in patients with neurovascular disease, allowing for vessel and flow visualization with high spatio-temporal resolution. Automatic artery–vein segmentation in DSA plays a fundamental role in vascular analysis with quantitative biomarker extraction, facilitating a wide range of clinical applications. The widely adopted U-Net applied on static DSA frames often struggles with disentangling vessels from subtraction artifacts. Further, it falls short in effectively separating arteries and veins as it disregards the temporal perspectives inherent in DSA. To address these limitations, we propose to simultaneously leverage spatial vasculature and temporal cerebral flow characteristics to segment arteries and veins in DSA. The proposed network, coined CAVE, encodes a 2D+time DSA series using spatial modules, aggregates all the features using temporal modules, and decodes it into 2D segmentation maps. On a large multi-center clinical dataset, CAVE achieves a vessel segmentation Dice of 0.84 (± 0.04) and an artery–vein segmentation Dice of 0.79 (± 0.06). CAVE surpasses traditional Frangi-based k -means clustering (P < 0.001) and U-Net (P < 0.001) by a significant margin, demonstrating the advantages of harvesting spatio-temporal features. This study represents the first investigation into automatic artery–vein segmentation in DSA using deep learning. The code is publicly available at https://github.com/RuishengSu/CAVE_DSA. [Display omitted] • The first automatic deep learning-based method for artery–vein segmentation in DSA is proposed. • CAVE generates artery–vein segmentations from 2D+time DSA series with variable frame lengths. • CAVE simultaneously harnesses spatial vasculature and temporal contrast flow characteristics. • CAVE promises to facilitate fast, accurate, and objective vasculature interpretation in DSA. [ABSTRACT FROM AUTHOR]

Intraoperative brain deformation is one of the most contributing factors to the inaccuracy of image-guided surgery systems. One option for correcting this deformation is to acquire 3D ultrasound images during surgery and using these to update the information provided by the preoperatively acquired MR data. To compare ultrasound volumes acquired at different stages of surgery, non-rigid registration techniques are necessary. As a first step towards this approach, the accuracy and robustness of entropy-based rigid registration methods for ultrasound volumes have been examined.From the robustness measurements on patient data, it could be concluded that the entropy-based methods mutual information and normalized mutual information (NMI) with a number of histogram bins of 64 or 128 using partial volume interpolation perform robustly for rigid registration of ultrasound volumes of brain tumours. When no brain deformations occur between the two ultrasound acquisitions, accuracies of around 0.5 mm can be achieved. [Copyright &y& Elsevier]

Brain pathology develops at different rates between individuals with similar burden of risk factors, possibly explained by brain resistance. We examined if education contributes to brain resistance by studying its influence on the association between vascular risk factors and brain pathology. In 4111 stroke-free and dementia-free community-dwelling participants (62.9 ± 10.7 years), we explored the association between vascular risk factors (hypertension and the Framingham Stroke Risk Profile [FRSP]) and imaging markers of brain pathology (markers of cerebral small vessel disease and brain volumetry), stratified by educational attainment level. Associations of hypertension and FSRP with markers of brain pathology were not significantly different between levels of educational attainment. Certain associations appeared weaker in those with higher compared to lower educational attainment, particularly for white matter hyperintensities (WMH). Supplementary residual analyses showed significant associations between higher educational attainment and stronger resistance to WMH among others. Our results suggest a role for educational attainment in resistance to vascular brain pathology. Yet, further research is needed to better characterize determinants of brain resistance. [ABSTRACT FROM AUTHOR]

• In this position paper, we provide a collection of views on the role of AI in the COVID-19 pandemic, from the clinical needs to the design of AI-based systems, to the translation of the developed tools to the clinic. • We highlight key factors in designing system solutions - per specific task; as well as design issues in managing the disease on the national level. • We focus on three specific use-cases for which AI systems can be built: from the early disease detection, the management of the disease in a hospital setting, and building patient-specific predictive models that require the combination of imaging with additional clinical features. • Infrastructure considerations and population modeling in two European countries will be described. • This pandemic has made the practical and scientific challenges of making AI solutions very explicit. A discussion concludes this paper, with a list of challenges facing the community in the AI road ahead. In this position paper, we provide a collection of views on the role of AI in the COVID-19 pandemic, from clinical requirements to the design of AI-based systems, to the translation of the developed tools to the clinic. We highlight key factors in designing system solutions - per specific task; as well as design issues in managing the disease at the national level. We focus on three specific use-cases for which AI systems can be built: early disease detection, management in a hospital setting, and building patient-specific predictive models that require the combination of imaging with additional clinical data. Infrastructure considerations and population modeling in two European countries will be described. This pandemic has made the practical and scientific challenges of making AI solutions very explicit. A discussion concludes this paper, with a list of challenges facing the community in the AI road ahead. [ABSTRACT FROM AUTHOR]

Brain imaging data are increasingly made publicly accessible, and volumetric imaging measures derived from population-based cohorts may serve as normative data for individual patient diagnostic assessment. Yet, these normative cohorts are usually not a perfect reflection of a patient's base population, nor are imaging parameters such as field strength or scanner type similar. In this proof of principle study, we assessed differences between reference curves of subcortical structure volumes of normal controls derived from two population-based studies and a case-control study. We assessed the impact of any differences on individual assessment of brain structure volumes. Percentile curves were fitted on the three healthy cohorts. Next, percentile values for these subcortical structures for individual patients from these three cohorts, 91 mild cognitive impairment and 95 Alzheimer's disease cases and patients from the Alzheimer Center, were calculated, based on the distributions of each of the three cohorts. Overall, we found that the subcortical volume normative data from these cohorts are highly interchangeable, suggesting more flexibility in clinical implementation. • Subcortical volume normative data from 3 populations are highly interchangeable. • The hippocampus volume percentiles were very robust across populations. • Results suggest more flexibility in clinical implementation of these percentiles. [ABSTRACT FROM AUTHOR]

Hippocampal volume and shape are known magnetic resonance imaging biomarkers of neurodegeneration. Recently, hippocampal texture has been shown to improve prediction of dementia in patients with mild cognitive impairment, but it is unknown whether texture adds prognostic information beyond volume and shape and whether the predictive value extends to cognitively healthy individuals. Using 510 subjects from the Rotterdam Study, a prospective, population-based cohort study, we investigated if hippocampal volume, shape, texture, and their combination were predictive of dementia and determined how predictive performance varied with time to diagnosis and presence of early clinical symptoms of dementia. All features showed significant predictive performance with the area under the receiver operating characteristic curve ranging from 0.700 for texture alone to 0.788 for the combination of volume and texture. Although predictive performance extended to those without objective cognitive complaints or mild cognitive impairment, performance decreased with increasing follow-up time. We conclude that a combination of multiple hippocampal features on magnetic resonance imaging performs better in predicting dementia in the general population than any feature by itself. • Investigated magnetic resonance imaging–based dementia prediction in a population setting. • Hippocampal volume, shape, and texture are predictive for dementia in this setting. • The combination of different magnetic resonance imaging–based features improves predictive value. • All features are predictive in subjects without objective cognitive complaints. • Volume and texture combined are predictive up to 6 years before diagnosis. [ABSTRACT FROM AUTHOR]

Abstract With aging, the brain undergoes several structural changes. These changes reflect the normal aging process and are therefore not necessarily pathologic. In fact, better understanding of these normal changes is an important cornerstone to also disentangle pathologic changes. Several studies have investigated normal brain aging, both cross-sectional and longitudinal, and focused on a broad range of magnetic resonance imaging (MRI) markers. This study aims to comprise the different aspects in brain aging, by performing a comprehensive longitudinal assessment of brain aging, providing trajectories of volumetric (global and lobar; subcortical and cortical), microstructural, and focal (presence of microbleeds, lacunar or cortical infarcts) brain imaging markers in aging and the sequence in which these markers change in aging. Trajectories were calculated on 10,755 MRI scans that were acquired between 2005 and 2016 among 5286 persons aged 45 years and older from the population-based Rotterdam Study. The average number of MRI scans per participant was 2 scans (ranging from 1 to 4 scans), with a mean interval between MRI scans of 3.3 years (ranging from 0.2 to 9.5 years) and an average follow-up time of 5.2 years (ranging from 0.3 to 9.8 years). We found that trajectories of the different volumetric, microstructural, and focal markers show nonlinear curves, with accelerating change with advancing age. We found earlier acceleration of change in global and lobar volumetric and microstructural markers in men compared with women. For subcortical and cortical volumes, results show a mix of more linear and nonlinear trajectories, either increasing, decreasing, or stable over age for the subcortical and cortical volume and thickness. Differences between men and women are visible in several parcellations; however, the direction of these differences is mixed. The presence of focal markers show a nonlinear increase with age, with men having a higher probability for cortical or lacunar infarcts. The data presented in this study provide insight into the normal aging process in the brain, and its variability. [ABSTRACT FROM AUTHOR]

To study the relation between the microstructure of white matter in the brain and hearing function in older adults we carried out a population-based, cross-sectional study. In 2562 participants of the Rotterdam Study, we conducted diffusion tensor imaging to determine the microstructure of the white-matter tracts. We performed pure-tone audiogram and digit-in-noise tests to quantify hearing acuity. Poorer white-matter microstructure, especially in the association tracts, was related to poorer hearing acuity. After differentiating the separate white-matter tracts in the left and right hemisphere, poorer white-matter microstructure in the right superior longitudinal fasciculus and the right uncinate fasciculus remained significantly associated with worse hearing. These associations did not significantly differ between middle-aged (51–69 years old) and older (70–100 years old) participants. Progressing age was thus not found to be an effect modifier. In a voxel-based analysis no voxels in the white matter were significantly associated with hearing impairment. [ABSTRACT FROM AUTHOR]

Thyroid hormone (TH) is crucial during neurodevelopment, but high levels of TH have been linked to neurodegenerative disorders. No data on the association of thyroid function with brain imaging in the general population are available. We therefore investigated the association of thyroid-stimulating hormone and free thyroxine (FT4) with magnetic resonance imaging (MRI)-derived total intracranial volume, brain tissue volumes, and diffusion tensor imaging measures of white matter microstructure in 4683 dementia- and stroke-free participants (mean age 60.2, range 45.6–89.9 years). Higher FT4 levels were associated with larger total intracranial volumes (β = 6.73 mL, 95% confidence interval = 2.94–9.80). Higher FT4 levels were also associated with larger total brain and white matter volumes in younger individuals, but with smaller total brain and white matter volume in older individuals ( p -interaction 0.02). There was a similar interaction by age for the association of FT4 with mean diffusivity on diffusion tensor imaging ( p -interaction 0.026). These results are in line with differential effects of TH during neurodevelopmental and neurodegenerative processes and can improve the understanding of the role of thyroid function in neurodegenerative disorders. [ABSTRACT FROM AUTHOR]

We investigated the association of specific retinal sublayer thicknesses on optical coherence tomography (OCT) with brain magnetic resonance imaging (MRI) markers. We included 2124 persons (mean age 67.0 years; 56% women) from the Rotterdam Study who had gradable retinal OCT images and brain MRI scans. Thickness of retinal nerve fiber layer (RNFL), ganglion cell layer (GCL), and inner plexiform layer were measured on OCT images. Volumetric, microstructural, and focal markers of brain tissue were assessed on MRI. We found that thinner RNFL, GCL, and inner plexiform layer were associated with smaller gray-matter and white-matter volume. Furthermore, we found that thinner RNFL and GCL were associated with worse white-matter microstructure. No association was found between retinal sublayer thickness and white-matter lesion volumes, cerebral microbleeds, or lacunar infarcts. Markers of retinal neurodegeneration are associated with markers of cerebral atrophy, suggesting that retinal OCT may provide information on neurodegeneration in the brain. [ABSTRACT FROM AUTHOR]