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Contains fulltext : 235370.pdf (Publisher’s version ) (Closed access) BACKGROUND AND PURPOSE: In the chronic phase after traumatic brain injury, DTI findings reflect WM integrity. DTI interpretation in the subacute phase is less straightforward. Microbleed evaluation with SWI is straightforward in both phases. We evaluated whether the microbleed concentration in the subacute phase is associated with the integrity of normal-appearing WM in the chronic phase. MATERIALS AND METHODS: Sixty of 211 consecutive patients 18 years of age or older admitted to our emergency department ≤24 hours after moderate to severe traumatic brain injury matched the selection criteria. Standardized 3T SWI, DTI, and T1WI were obtained 3 and 26 weeks after traumatic brain injury in 31 patients and 24 healthy volunteers. At baseline, microbleed concentrations were calculated. At follow-up, mean diffusivity (MD) was calculated in the normal-appearing WM in reference to the healthy volunteers (MD(z)). Through linear regression, we evaluated the relation between microbleed concentration and MD(z) in predefined structures. RESULTS: In the cerebral hemispheres, MD(z) at follow-up was independently associated with the microbleed concentration at baseline (left: B = 38.4 [95% CI 7.5-69.3], P = .017; right: B = 26.3 [95% CI 5.7-47.0], P = .014). No such relation was demonstrated in the central brain. MD(z) in the corpus callosum was independently associated with the microbleed concentration in the structures connected by WM tracts running through the corpus callosum (B = 20.0 [95% CI 24.8-75.2], P < .000). MD(z) in the central brain was independently associated with the microbleed concentration in the cerebral hemispheres (B = 25.7 [95% CI 3.9-47.5], P = .023). CONCLUSIONS: SWI-assessed microbleeds in the subacute phase are associated with DTI-based WM integrity in the chronic phase. These associations are found both within regions and between functionally connected regions.

The Sørensen-Dice index (SDI) is a widely used measure for evaluating medical image segmentation algorithms. It offers a standardized measure of segmentation accuracy which has proven useful. However, it offers diminishing insight when the number of objects is unknown, such as in white matter lesion segmentation of multiple sclerosis (MS) patients. We present a refinement for finer grained parsing of SDI results in situations where the number of objects is unknown. We explore these ideas with two case studies showing what can be learned from our two presented studies. Our first study explores an inter-rater comparison, showing that smaller lesions cannot be reliably identified. In our second case study, we demonstrate fusing multiple MS lesion segmentation algorithms based on the insights into the algorithms provided by our analysis to generate a segmentation that exhibits improved performance. This work demonstrates the wealth of information that can be learned from refined analysis of medical image segmentations.

Introduction In order to augment the certainty of the radiological interpretation of “possible microbleeds” after traumatic brain injury (TBI), we assessed their longitudinal evolution on 3-T SWI in patients with moderate/severe TBI. Methods Standardized 3-T SWI and T1-weighted imaging were obtained 3 and 26 weeks after TBI in 31 patients. Their microbleeds were computer-aided detected and classified by a neuroradiologist as no, possible, or definite at baseline and follow-up, separately (single-scan evaluation). Thereafter, the classifications were re-evaluated after comparison between the time-points (post-comparison evaluation). We selected the possible microbleeds at baseline at single-scan evaluation and recorded their post-comparison classification at follow-up. Results Of the 1038 microbleeds at baseline, 173 were possible microbleeds. Of these, 53.8% corresponded to no microbleed at follow-up. At follow-up, 30.6% were possible and 15.6% were definite. Of the 120 differences between baseline and follow-up, 10% showed evidence of a pathophysiological change over time. Proximity to extra-axial injury and proximity to definite microbleeds were independently predictive of becoming a definite microbleed at follow-up. The reclassification level differed between anatomical locations. Conclusions Our findings support disregarding possible microbleeds in the absence of clinical consequences. In selected cases, however, a follow-up SWI-scan could be considered to exclude evolution into a definite microbleed.

Introduction Recent studies have shown that neuroimaging markers of cerebral small vessel disease can also regress over time. We investigated the cognitive consequences of regression of small vessel disease markers. Patients and methods Two hundred and seventy-six participants of the RUNDMC study underwent neuroimaging and cognitive assessments at three time-points over 8.7 years. We semi-automatically assessed white matter hyperintensities volumes and manually rated lacunes and microbleeds. We analysed differences in cognitive decline and accompanying brain atrophy between participants with regression, progression and stable small vessel disease by analysis of variance. Results Fifty-six participants (20.3%) showed regression of small vessel disease markers: 31 (11.2%) white matter hyperintensities regression, 10 (3.6%) vanishing lacunes and 27 (9.8%) vanishing microbleeds. Participants with regression showed a decline in overall cognition, memory, psychomotor speed and executive function similar to stable small vessel disease. Participants with small vessel disease progression showed more cognitive decline compared with stable small vessel disease (p Discussion Regression of small vessel disease markers was associated with similar cognitive decline compared to stable small vessel disease and did not accompany brain atrophy, suggesting that small vessel disease regression follows a relatively benign clinical course. Future studies are required to validate these findings and to assess the role of vascular risk factor control on small vessel disease regression and possible recovery of clinical symptoms. Conclusion Our findings of comparable cognitive decline between participants with regression and stable small vessel disease might suggest that small vessel disease regression has a relative benign cognitive outcome.

Objective:To investigate the temporal dynamics of cerebral small vessel disease (SVD) by 3 consecutive assessments over a period of 9 years, distinguishing progression from regression.Methods:Changes in SVD markers of 276 participants of the Radboud University Nijmegen Diffusion Tensor and Magnetic Resonance Imaging Cohort (RUN DMC) cohort were assessed at 3 time points over 9 years. We assessed white matter hyperintensities (WMH) volume by semiautomatic segmentation and rated lacunes and microbleeds manually. We categorized baseline WMH severity as mild, moderate, or severe according to the modified Fazekas scale. We performed mixed-effects regression analysis including a quadratic term for increasing age.Results:Mean WMH progression over 9 years was 4.7 mL (0.54 mL/y; interquartile range 0.95–5.5 mL), 20.3% of patients had incident lacunes (2.3%/y), and 18.9% had incident microbleeds (2.2%/y). WMH volume declined in 9.4% of the participants during the first follow-up interval, but only for 1 participant (0.4%) throughout the whole follow-up. Lacunes disappeared in 3.6% and microbleeds in 5.7% of the participants. WMH progression accelerated over time: including a quadratic term for increasing age during follow-up significantly improved the model (p < 0.001). SVD progression was predominantly seen in participants with moderate to severe WMH at baseline compared to those with mild WMH (odds ratio [OR] 35.5, 95% confidence interval [CI] 15.8–80.0, p < 0.001 for WMH progression; OR 5.7, 95% CI 2.8–11.2, p < 0.001 for incident lacunes; and OR 2.9, 95% CI 1.4–5.9, p = 0.003 for incident microbleeds).Conclusions:SVD progression is nonlinear, accelerating over time, and a highly dynamic process, with progression interrupted by reduction in some, in a population that on average shows progression.

Lacunes of presumed vascular origin (lacunes) are associated with an increased risk of stroke, gait impairment, and dementia and are a primary imaging feature of the small vessel disease. Quantification of lacunes may be of great importance to elucidate the mechanisms behind neuro-degenerative disorders and is recommended as part of study standards for small vessel disease research. However, due to the different appearance of lacunes in various brain regions and the existence of other similar-looking structures, such as perivascular spaces, manual annotation is a difficult, elaborative and subjective task, which can potentially be greatly improved by reliable and consistent computer-aided detection (CAD) routines. In this paper, we propose an automated two-stage method using deep convolutional neural networks (CNN). We show that this method has good performance and can considerably benefit readers. We first use a fully convolutional neural network to detect initial candidates. In the second step, we employ a 3D CNN as a false positive reduction tool. As the location information is important to the analysis of candidate structures, we further equip the network with contextual information using multi-scale analysis and integration of explicit location features. We trained, validated and tested our networks on a large dataset of 1075 cases obtained from two different studies. Subsequently, we conducted an observer study with four trained observers and compared our method with them using a free-response operating characteristic analysis. Shown on a test set of 111 cases, the resulting CAD system exhibits performance similar to the trained human observers and achieves a sensitivity of 0.974 with 0.13 false positives per slice. A feasibility study also showed that a trained human observer would considerably benefit once aided by the CAD system., Comment: 11 pages, 7 figures

Contains fulltext : 174846.pdf (Publisher’s version ) (Closed access) OBJECTIVES: To investigate time to enhancement (TTE) as novel dynamic parameter for lesion classification in breast magnetic resonance imaging (MRI). METHODS: In this retrospective study, 157 women with 195 enhancing abnormalities (99 malignant and 96 benign) were included. All patients underwent a bi-temporal MRI protocol that included ultrafast time-resolved angiography with stochastic trajectory (TWIST) acquisitions (1.0x0.9x2.5mm, temporal resolution 4.32s), during the inflow of contrast agent. TTE derived from TWIST series and relative enhancement versus time curve type derived from volumetric interpolated breath-hold examination (VIBE) series were assessed and combined with basic morphological information to differentiate benign from malignant lesions. Receiver operating characteristic analysis and kappa statistics were applied. RESULTS: TTE had a significantly better discriminative ability than curve type (p

ObjectiveTo investigate the prevalence of asymptomatic diffusion-weighted imaging–positive (DWI+) lesions in individuals with cerebral small vessel disease (SVD) and identify their role in the origin of SVD markers on MRI.MethodsWe included 503 individuals with SVD from the Radboud University Nijmegen Diffusion Tensor and Magnetic Resonance Imaging Cohort (RUN DMC) study (mean age 65.6 years [SD 8.8], 56.5% male) with 1.5T MRI in 2006 and, if available, follow-up MRI in 2011 and 2015. We screened DWI scans (n = 1,152) for DWI+ lesions, assessed lesion evolution on follow-up fluid-attenuated inversion recovery, T1 and T2* images, and examined the association between DWI+ lesions and annual SVD progression (white matter hyperintensities [WMH], lacunes, microbleeds).ResultsWe found 50 DWI+ lesions in 39 individuals on 1,152 DWI (3.4%). Individuals with DWI+ lesions were older (p = 0.025), more frequently had a history of hypertension (p = 0.021), and had a larger burden of preexisting SVD MRI markers (WMH, lacunes, microbleeds: all p < 0.001) compared to individuals without DWI+ lesions. Of the 23 DWI+ lesions with available follow-up MRI, 14 (61%) evolved into a WMH, 8 (35%) resulted in a cavity, and 1 (4%) was no longer visible. Presence of DWI+ lesions was significantly associated with annual WMH volume increase and yearly incidence of lacunes and microbleeds (all p < 0.001).ConclusionOver 3% of individuals with SVD have DWI+ lesions. Although DWI+ lesions play a role in the progression of SVD, they may not fully explain progression of SVD markers on MRI, suggesting that other factors than acute ischemia are at play.

Incident parkinsonism in patients with comparable cerebral small vessel disease (SVD) burden is not fully explained by presence of SVD alone. We therefore investigated if severity of SVD, SVD location, incidence of SVD and/or brain atrophy plays a role in this distinct development of parkinsonism.Participants were from the RUN DMC study, a prospective cohort of 503 individuals with SVD. Parkinsonism was diagnosed according to the UKPDS brain bank criteria. Fine and Gray method was used to assess the association between SVD and incident parkinsonism. Differences in white matter hyperintensities (WMH) progression and brain atrophy were calculated with a linear mixed effect analysis.After a median follow-up of 8.6 years, 32 of 501 participants developed parkinsonism (6.4%). The highest WMH load was found in the frontal lobe for both groups. Presence of more than one lacune at baseline was higher in the group who developed parkinsonism, especially in the frontal lobe (22% versus 3%, p 0.001) and basal ganglia (12.5% versus 1%, p-value0.001). The annual rate of total brain atrophy was significantly higher for those who developed parkinsonism compared to those who did not (8.7 ml [95%CI 7.1-10.3] and 4.9 ml [95%CI 4.5-5.3], respectively). While WMH progression was not different, incidence of lacunes and microbleeds was higher in the group with parkinsonism.The risk of parkinsonism in patients with SVD is especially increased when WMH and lacunes are present in the frontal lobe. A higher brain atrophy rate might further increase this risk.

Contains fulltext : 203440.pdf (Publisher’s version ) (Open Access) Background: White matter hyperintensities (WMH) constitute the visible spectrum of cerebral small vessel disease (SVD) markers and are associated with cognitive decline, although they do not fully account for memory decline observed in individuals with SVD. We hypothesize that WMH might exert their effect on memory decline indirectly by affecting remote brain structures such as the hippocampus. We investigated the temporal interactions between WMH, hippocampal atrophy and memory decline in older adults with SVD. Methods: 503 participants of the RUNDMC study underwent neuroimaging and cognitive assessments up to 3 times over 8.7 years. We assessed WMH volumes semi-automatically and calculated hippocampal volumes (HV) using FreeSurfer. We used linear mixed effects models and causal mediation analyses to assess both interaction and mediation effects of hippocampal atrophy in the associations between WMH and memory decline, separately for working memory (WM) and episodic memory (EM). Results: Linear mixed effect models revealed that the interaction between WMH and hippocampal volumes explained memory decline (WM: beta=0.067; 95%CI[0.024–0.111]; p

Purpose: Automated 3D breast ultrasound (ABUS) has been proposed as a complementary screening modality to mammography for early detection of breast cancers. To facilitate the interpretation of ABUS images, automated diagnosis and detection techniques are being developed, in which malignant lesion segmentation plays an important role. However, automated segmentation of cancer in ABUS is challenging since lesion edges might not be well defined. In this study, the authors aim at developing an automated segmentation method for malignant lesions in ABUS that is robust to ill-defined cancer edges and posterior shadowing. Methods: A segmentation method using depth-guided dynamic programming based on spiral scanning is proposed. The method automatically adjusts aggressiveness of the segmentation according to the position of the voxels relative to the lesion center. Segmentation is more aggressive in the upper part of the lesion (close to the transducer) than at the bottom (far away from the transducer), where posterior shadowing is usually visible. The authors used Dice similarity coefficient (Dice) for evaluation. The proposed method is compared to existing state of the art approaches such as graph cut, level set, and smart opening and an existing dynamic programming method without depth dependence. Results: In a dataset of 78 cancers, our proposed segmentation method achieved a mean Dice of 0.73 ± 0.14. The method outperforms an existing dynamic programming method (0.70 ± 0.16) on this task (p = 0.03) and it is also significantly (p < 0.001) better than graph cut (0.66 ± 0.18), level set based approach (0.63 ± 0.20) and smart opening (0.65 ± 0.12). Conclusions: The proposed depth-guided dynamic programming method achieves accurate breast malignant lesion segmentation results in automated breast ultrasound.