Background: Brain sensing devices are approved today for Parkinson's, essential tremor, and epilepsy therapies. Clinical decisions for implants are often influenced by the premise that patients... Show moreBackground: Brain sensing devices are approved today for Parkinson's, essential tremor, and epilepsy therapies. Clinical decisions for implants are often influenced by the premise that patients will benefit from using sensing technology. However, artifacts, such as ECG contamination, can render such treat-ments unreliable. Therefore, clinicians need to understand how surgical decisions may affect artifact probability. Objectives: Investigate neural signal contamination with ECG activity in sensing enabled neuro -stimulation systems, and in particular clinical choices such as implant location that impact signal fidelity. Methods: Electric field modeling and empirical signals from 85 patients were used to investigate the relationship between implant location and ECG contamination. Results: The impact on neural recordings depends on the difference between ECG signal and noise floor of the electrophysiological recording. Empirically, we demonstrate that severe ECG contamination was more than 3.2x higher in left-sided subclavicular implants (48.3%), when compared to right-sided im -plants (15.3%). Cranial implants did not show ECG contamination. Conclusions: Given the relative frequency of corrupted neural signals, we conclude that implant location will impact the ability of brain sensing devices to be used for "closed-loop" algorithms. Clinical ad-justments such as implant location can significantly affect signal integrity and need consideration. (c) 2021 Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/). Show less
Speelman, E.S.; Brocx, B.; Wilbers, J.E.; Bie, M.J. de; Ivashchenko, O.; Tank, Y.; Molen, A.J. van der 2019
Purpose Whole-body computed tomography (WBCT) is the standard diagnostic method for evaluating polytrauma patients. When patients are unable to elevate their arms, the arms are placed along the... Show morePurpose Whole-body computed tomography (WBCT) is the standard diagnostic method for evaluating polytrauma patients. When patients are unable to elevate their arms, the arms are placed along the body, which affects the image quality negatively. Aim of this systematic review is to evaluate the influence of below the shoulder arm positions on image quality of WBCT. Methods Literature in PubMed and Scopus databases was systematically searched. Results of the papers were stratified into 4 categories: arms elevated, 1 arm up 1 arm down, arms ventrally supported, arms along the body. A qualitative analysis was performed on subjective image quality and a quantitative analysis on objective quality (image noise). Results Eight studies were included with 1421 participants. Various studies reported significantly higher quality scores with arms elevated, compared to arms along the body. Significant differences in objective image quality were found between the arms elevated and the arms ventrally on support group. The arms ventrally supported group had a significantly higher image quality than the arms along the body group. A statistically significant difference was found in objective image quality between the 1 arm up 1 arm down and arms along the body group. No preferential below the shoulders position could be identified. Conclusion Positioning the arms alongside the body results in a poor image quality. Placing the arms on a pillow ventrally to the chest improves image quality. Interestingly, asymmetrical arm positioning has potential to improve the image quality for patients that are unable to elevate the arms. Show less
Klapwijk, E.T.; Van de Kamp, F.; Meulen, M. van der; Peters, S.; Wierenga, L.M. 2019
Performing quality control to detect image artifacts and data-processing errors is crucial in structural magnetic resonance imaging, especially in developmental studies. Currently, many studies... Show morePerforming quality control to detect image artifacts and data-processing errors is crucial in structural magnetic resonance imaging, especially in developmental studies. Currently, many studies rely on visual inspection by trained raters for quality control. The subjectivity of these manual procedures lessens comparability between studies, and with growing study sizes quality control is increasingly time consuming. In addition, both inter-rater as well as intra-rater variability of manual quality control is high and may lead to inclusion of poor quality scans and exclusion of scans of usable quality. In the current study we present the Qoala-T tool, which is an easy and free to use supervised-learning model to reduce rater bias and misclassification in manual quality control procedures using FreeSurfer-processed scans. First, we manually rated quality of N = 784 FreeSurfer-processed T1-weighted scans acquired in three different waves in a longitudinal study. Different supervised-learning models were then compared to predict manual quality ratings using FreeSurfer segmented output data. Results show that the Qoala-T tool using random forests is able to predict scan quality with both high sensitivity and specificity (mean area under the curve (AUC) = 0.98). In addition, the Qoala-T tool was also able to adequately predict the quality of two novel unseen datasets (total N = 872). Finally, analyses of age effects showed that younger participants were more likely to have lower scan quality, underlining that scan quality might confound findings attributed to age effects. These outcomes indicate that this procedure could further help to reduce variability related to manual quality control, thereby benefiting the comparability of data quality between studies. Show less
To improve the effectiveness and efficiency of optical projection tomography (OPT) 3-D reconstruction, we present a fast post-processing pipeline, including cropping, background subtraction, center... Show moreTo improve the effectiveness and efficiency of optical projection tomography (OPT) 3-D reconstruction, we present a fast post-processing pipeline, including cropping, background subtraction, center of rotation (COR) correction, and 3-D reconstruction. Regarding to the COR correction, a novel algorithm based on interest point detection of sinogram is proposed by considering the principle of OPT imaging. Instead of locating the COR on single sinogram, we select equally spaced sinograms in the detected full range of specimen to make the located COR more convincing. The presented post-processing pipeline is implemented in a parallel manner and the experiments show that the average runtime for each image of size 1036 × 1360 × 400 pixels is less than 1 min. To quantify and compare the reconstructed results of different COR correction approaches, the coefficient of variation instead of variance is employed. The results indicate that the proposed COR correction outperforms the three traditional COR alignment approaches in terms of effectiveness and computational complexity. Show less
