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Contains fulltext : 251923.pdf (Publisher’s version ) (Open Access), 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.

Item does not contain fulltext, 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.

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<0.01; EM: beta=0.061; 95%CI[0.025–0.098]; p<0.01), with better model fit when the WMH*HV interaction term was added to the model, for both WM (likelihood ratio test, X2(1)=9.3, p<0.01) and for EM (likelihood ratio test, X2(1)=10.7, p<0.01). Mediation models showed that both baseline WMH volume (beta=-0.170; p=0.001) and hippocampal atrophy (beta=0.126; p=0.009) were independently related to EM decline, but the effect of baseline WMH on EM decline was not mediated by hippocampal atrophy (p-value indirect effect: 0.572). Conclusions: Memory decline in elderly with SVD was best explained by the interaction of WMH and hippocampal volumes. The relationship between WMH and memory was not causally mediated by hippocampal atrophy, suggesting that memory decline during aging is a heterogeneous condition in which different pathologies co

Contains fulltext : 207749.pdf (publisher's version ) (Open Access), Objective To 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.Methods We 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).Results We 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).Conclusion Over 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.

Contains fulltext : 201918.pdf (publisher's version ) (Closed access), 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 < 0.001 for cognitive index and memory; p < 0.01 for executive function), although significance disappeared after adjusting for age and sex. Loss of total brain, gray matter and white matter volume did not differ between participants with small vessel disease regression and stable small vessel disease, while participants with small vessel disease progression showed more volume loss of total brain and gray matter compared to those with stable small vessel disease (p < 0.05), although significance disappeared after adjustments. 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 requ

Radboud University, 23 augustus 2019, Promotor : Karssemeijer, N. Co-promotores : Mann, R.M., Platel, B., Contains fulltext : 205657.pdf (publisher's version ) (Open Access)

Contains fulltext : 191933.pdf (publisher's version ) (Closed access)

Radboud University, 8 maart 2018, Promotores : Karssemeijer, N., Heskes, T., Marchiori, E. Co-promotor : Platel, B., Contains fulltext : 183226.pdf (publisher's version ) (Open Access), This thesis is devoted to developing fully automated methods for quantification of small vessel disease imaging bio-markers, namely WMHs and lacunes, using vari- ous machine learning/deep learning and computer vision techniques. The rest of the thesis is organized as follows: Chapter 2 describes a conventional machine learning method for automated detection of WMHs. It should be noted that this method is optimized to detect WMHs of all size, including small lesions which are much more difficult to spot, rather than accurately delineating the WMH boundaries. Chap- ter 3 describes a customized deep learning method for automated segmentation of WMHs. In Chapter 4, we develop and experiment with a biologically inspired sam- pling method combined with deep neural networks. Chapter 5 is devoted for delv- ing deep into transfer learning of the trained deep networks on different domains for the WMH segmentation task. Finally, in Chapter 6, we describe a two-stage deep learning method for detection of lacunes.

Contains fulltext : 173113.pdf (publisher's version ) (Closed access), OBJECTIVE: To investigate the effect of dedicated Computer Aided Detection (CAD) software for automated breast ultrasound (ABUS) on the performance of radiologists screening for breast cancer. METHODS: 90 ABUS views of 90 patients were randomly selected from a multi-institutional archive of cases collected between 2010 and 2013. This dataset included normal cases (n=40) with >1year of follow up, benign (n=30) lesions that were either biopsied or remained stable, and malignant lesions (n=20). Six readers evaluated all cases with and without CAD in two sessions. CAD-software included conventional CAD-marks and an intelligent minimum intensity projection of the breast tissue. Readers reported using a likelihood-of-malignancy scale from 0 to 100. Alternative free-response ROC analysis was used to measure the performance. RESULTS: Without CAD, the average area-under-the-curve (AUC) of the readers was 0.77 and significantly improved with CAD to 0.84 (p=0.001). Sensitivity of all readers improved (range 5.2-10.6%) by using CAD but specificity decreased in four out of six readers (range 1.4-5.7%). No significant difference was observed in the AUC between experienced radiologists and residents both with and without CAD. CONCLUSIONS: Dedicated CAD-software for ABUS has the potential to improve the cancer detection rates of radiologists screening for breast cancer.

Contains fulltext : 173122.pdf (Publisher’s version ) (Closed access), In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion segmentation challenge providing training and test data to registered participants. The training data consisted of five subjects with a mean of 4.4 time-points, and test data of fourteen subjects with a mean of 4.4 time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. Eleven teams submitted results using state-of-the-art lesion segmentation algorithms to the challenge, with ten teams presenting their results at the conference. We present a quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms. The challenge presented three unique opportunities: (1) the sharing of a rich data set; (2) collaboration and comparison of the various avenues of research being pursued in the community; and (3) a review and refinement of the evaluation metrics currently in use. We report on the performance of the challenge participants, as well as the construction and evaluation of a consensus delineation. The image data and manual delineations will continue to be available for download, through an evaluation website2 as a resource for future researchers in the area. This data resource provides a platform to compare existing methods in a fair and consistent manner to each other and multiple manual raters.

Contains fulltext : 178616.pdf (publisher's version ) (Open Access)

Item does not contain fulltext, PURPOSE: New ultrafast view-sharing sequences have enabled breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to be performed at high spatial and temporal resolution. The aim of this study is to evaluate the diagnostic potential of textural features that quantify the spatiotemporal changes of the contrast-agent uptake in computer-aided diagnosis of malignant and benign breast lesions imaged with high spatial and temporal resolution DCE-MRI. METHOD: The proposed approach is based on the textural analysis quantifying the spatial variation of six dynamic features of the early-phase contrast-agent uptake of a lesion's largest cross-sectional area. The textural analysis is performed by means of the second-order gray-level co-occurrence matrix, gray-level run-length matrix and gray-level difference matrix. This yields 35 textural features to quantify the spatial variation of each of the six dynamic features, providing a feature set of 210 features in total. The proposed feature set is evaluated based on receiver operating characteristic (ROC) curve analysis in a cross-validation scheme for random forests (RF) and two support vector machine classifiers, with linear and radial basis function (RBF) kernel. Evaluation is done on a dataset with 154 breast lesions (83 malignant and 71 benign) and compared to a previous approach based on 3D morphological features and the average and standard deviation of the same dynamic features over the entire lesion volume as well as their average for the smaller region of the strongest uptake rate. RESULT: The area under the ROC curve (AUC) obtained by the proposed approach with the RF classifier was 0.8997, which was significantly higher (P = 0.0198) than the performance achieved by the previous approach (AUC = 0.8704) on the same dataset. Similarly, the proposed approach obtained a significantly higher result for both SVM classifiers with RBF (P = 0.0096) and linear kernel (P = 0.0417) obtaining AUC of 0.8876 and 0.8548

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<0.001 and p=0.026 for reader 1 and 2, respectively). Including morphology, sensitivity of TWIST and VIBE assessment was equivalent (p=0.549 and p=0.344, respectively). Specificity and diagnostic accuracy were significantly higher for TWIST than for VIBE assessment (p<0.001). Inter-reader agreement in differentiating malignant from benign lesions was almost perfect for TWIST evaluation (kappa=0.86) and substantial for conventional assessment (kappa=0.75). CONCLUSIONS: TTE derived from ultrafast TWIST acquisitions is a valuable parameter that allows robust differentiation between malignant and benign breast lesions with high accuracy.

Contains fulltext : 173096.pdf (publisher's version ) (Open Access), 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.

Contains fulltext : 173146.pdf (publisher's version ) (Open Access), Modern Computed Tomography (CT) scanners are capable of acquiring contrast dynamics of the whole brain, adding functional to anatomical information. Soft tissue segmentation is important for subsequent applications such as tissue dependent perfusion analysis and automated detection and quantification of cerebral pathology. In this work a method is presented to automatically segment white matter (WM) and gray matter (GM) in contrast- enhanced 4D CT images of the brain. The method starts with intracranial segmentation via atlas registration, followed by a refinement using a geodesic active contour with dominating advection term steered by image gradient information, from a 3D temporal average image optimally weighted according to the exposures of the individual time points of the 4D CT acquisition. Next, three groups of voxel features are extracted: intensity, contextual, and temporal. These are used to segment WM and GM with a support vector machine. Performance was assessed using cross validation in a leave-one-patient-out manner on 22 patients. Dice coefficients were 0.81 +/- 0.04 and 0.79 +/- 0.05, 95% Hausdorff distances were 3.86 +/- 1.43 and 3.07 +/- 1.72 mm, for WM and GM, respectively. Thus, WM and GM segmentation is feasible in 4D CT with good accuracy.

Contains fulltext : 178607.pdf (preprint version ) (Open Access) Contains fulltext : 178607.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 162679.pdf (publisher's version ) (Open Access)

Item does not contain fulltext, PURPOSE: To evaluate the performance of an automated computer-aided detection (CAD) system to detect breast cancers that were overlooked or misinterpreted in a breast MRI screening program for women at increased risk. METHODS: We identified 40 patients that were diagnosed with breast cancer in MRI and had a prior MRI examination reported as negative available. In these prior examinations, 24 lesions could retrospectively be identified by two breast radiologists in consensus: 11 were scored as visible and 13 as minimally visible. Additionally, 120 normal scans were collected from 120 women without history of breast cancer or breast surgery participating in the same MRI screening program. A fully automated CAD system was applied to this dataset to detect malignant lesions. RESULTS: At 4 false-positives per normal case, the sensitivity for the detection of cancer lesions that were visible or minimally visible in retrospect in prior-negative examinations was 0.71 (95% CI=0.38-1.00) and 0.31 (0.07-0.59), respectively. CONCLUSIONS: A substantial proportion of cancers that were misinterpreted or overlooked in an MRI screening program was detected by a CAD system in prior-negative examinations. It has to be clarified with further studies if such a CAD system has an influence on the number of misinterpreted and overlooked cancers in clinical practice when results are given to a radiologist.

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