Relationship of EEG sources of neonatal seizures to acute perinatal brain lesions seen on MRI: a pilot study.

Even though it is known that neonatal seizures are associated with acute brain lesions, the relationship of electroencephalographic (EEG) seizures to acute perinatal brain lesions visible on magnetic resonance imaging (MRI) has not been objectively studied. EEG source localization is successfully used for this purpose in adults, but it has not been sufficiently explored in neonates. Therefore, we developed an integrated method for ictal EEG dipole source localization based on a realistic head model to investigate the utility of EEG source imaging in neonates with postasphyxial seizures. We describe here our method and compare the dipole seizure localization results with acute perinatal lesions seen on brain MRI in 10 full-term infants with neonatal encephalopathy. Through experimental studies, we also explore the sensitivity of our method to the electrode positioning errors and the variations in neonatal skull geometry and conductivity. The localization results of 45 focal seizures from 10 neonates are compared with the visual analysis of EEG and MRI data, scored by expert physicians. In 9 of 10 neonates, dipole locations showed good relationship with MRI lesions and clinical data. Our experimental results also suggest that the variations in the used values for skull conductivity or thickness have little effect on the dipole localization, whereas inaccurate electrode positioning can reduce the accuracy of source estimates. The performance of our fused method indicates that ictal EEG source imaging is feasible in neonates and with further validation studies, this technique can become a useful diagnostic tool.

Systems thinking and designerly tools for medical device design in engineering curricula.

Background: In this paper we focus on medical device development (MDD) in Industrial Design Engineering (IDE) academia. We want to find which methods our MDD-students currently use, where our guidance has shortcomings and where it brings added value. Methods: We have analysed 19 master and 3 doctoral MDD-theses in our IDE curriculum. The evaluation focusses around four main themes: 1) regulatory 2) testing 3) patient-centricity and 4) systemic design. Results: Regulatory aspects and medical testing procedures seem to be disregarded frequently. We assume this is because of a lack of MDD experience and the small thesis timeframe. Furthermore, many students applied medical-oriented systemic tools, which enhances multiperspectivism. However, we found an important lack in the translation to the List of Specifications and to business models of these medical devices. Finally, students introduced various participatory techniques, but seem to struggle with implementing this in the setting of evidence-based medicine.

Channelized Hotelling observers for the assessment of volumetric imaging data sets.

Current clinical practice is rapidly moving in the direction of volumetric imaging. For two-dimensional (2D) images, task-based medical image quality is often assessed using numerical model observers. For three-dimensional (3D) images, however, these models have been little explored so far. In this work, first, two novel designs of a multislice channelized Hotelling observer (CHO) are proposed for the task of detecting 3D signals in 3D images. The novel designs are then compared and evaluated in a simulation study with five different CHO designs: a single-slice model, three multislice models, and a volumetric model. Four different random background statistics are considered, both gaussian (noncorrelated and correlated gaussian noise) and non-gaussian (lumpy and clustered lumpy backgrounds). Overall, the results show that the volumetric model outperforms the others, while the disparity between the models decreases for greater complexity of the detection task. Among the multislice models, the second proposed CHO could most closely approach the volumetric model, whereas the first new CHO seems to be least affected by the number of training samples.

Robust segmentation methods with an application to aortic pulse wave velocity calculation.

Aortic stiffness has proven to be an important diagnostic and prognostic factor of many cardiovascular diseases, as well as an estimate of overall cardiovascular health. Pulse wave velocity (PWV) represents a good measure of the aortic stiffness, while the aortic distensibility is used as an aortic elasticity index. Obtaining the PWV and the aortic distensibility from magnetic resonance imaging (MRI) data requires diverse segmentation tasks, namely the extraction of the aortic center line and the segmentation of aortic regions, combined with signal processing methods for the analysis of the pulse wave. In our study non-contrasted MRI images of abdomen were used in healthy volunteers (22 data sets) for the sake of non-invasive analysis and contrasted magnetic resonance (MR) images were used for the aortic examination of Marfan syndrome patients (8 data sets). In this research we present a novel robust segmentation technique for the PWV and aortic distensibility calculation as a complete image processing toolbox. We introduce a novel graph-based method for the centerline extraction of a thoraco-abdominal aorta for the length calculation from 3-D MRI data, robust to artifacts and noise. Moreover, we design a new projection-based segmentation method for transverse aortic region delineation in cardiac magnetic resonance (CMR) images which is robust to high presence of artifacts. Finally, we propose a novel method for analysis of velocity curves in order to obtain pulse wave propagation times. In order to validate the proposed method we compare the obtained results with manually determined aortic centerlines and a region segmentation by an expert, while the results of the PWV measurement were compared to a validated software (LUMC, Leiden, the Netherlands). The obtained results show high correctness and effectiveness of our method for the aortic PWV and distensibility calculation.

A realistic volume conductor model of the neonatal head: methods, challenges and applications.

Developing a realistic volume conductor head model is an important step towards a non-invasive investigation of neuro-electrical activity in the brain. For adults, different volume conductor head models have been designed and successfully used for electroencephalography (EEG) source analysis. However, creating appropriate neonatal volume conductor head model for EEG source analysis is a challenging task mainly due to insufficient knowledge of head tissue conductivities and complex anatomy of the developing newborn brain. In this work, we present a pipeline for modeling a realistic volume conductor model of the neonatal head, where we address the modeling challenges and propose our solutions. We also discuss the use of our realistic volume conductor head model for neonatal EEG source analysis.

Brain blood vessel segmentation using line-shaped profiles.

Segmentation of cerebral blood vessels is of great importance in diagnostic and clinical applications, especially for embolization of cerebral aneurysms and arteriovenous malformations (AVMs). In order to perform embolization of the AVM, the structural and geometric information of blood vessels from 3D images is of utmost importance. For this reason, the in-depth segmentation of cerebral blood vessels is usually done as a fusion of different segmentation techniques, often requiring extensive user interaction. In this paper we introduce the idea of line-shaped profiling with an application to brain blood vessel and AVM segmentation, efficient both in terms of resolving details and in terms of computation time. Our method takes into account both local proximate and wider neighbourhood of the processed pixel, which makes it efficient for segmenting large blood vessel tree structures, as well as fine structures of the AVMs. Another advantage of our method is that it requires selection of only one parameter to perform segmentation, yielding very little user interaction.

Centerline calculation for extracting abdominal aorta in 3-D MRI images.

The examination of abdominal aorta is an effective way to diagnose many cardiovascular diseases. Aortic stiffness measured by pulse wave velocity (PWV) calculation is a good estimate of overall cardiovascular health. Calculation of pulse wave velocity requires the length of abdominal aorta as an input parameter, while the structure of abdominal aorta can be used for diagnostic purposes. For the sake of non-invasive diagnostics, non-contrasted MRI images of aorta were used. Due to the "black-blood" imaging, a lot of artifacts are present and a robust centerline extraction method is needed. In this research we develop a novel graph-based method for extracting centerlines of abdominal aorta for length calculation. Our method is robust to artifacts and noise and applicable to any imaging modality.

Segmentation of brain blood vessels using projections in 3-D CT angiography images.

Segmenting cerebral blood vessels is of great importance in diagnostic and clinical applications, especially in quantitative diagnostics and surgery on aneurysms and arteriovenous malformations (AVM). Segmentation of CT angiography images requires algorithms robust to high intensity noise, while being able to segment low-contrast vessels. Because of this, most of the existing methods require user intervention. In this work we propose an automatic algorithm for efficient segmentation of 3-D CT angiography images of cerebral blood vessels. Our method is robust to high intensity noise and is able to accurately segment blood vessels with high range of luminance values, as well as low-contrast vessels.

Automatic 3D graph cuts for brain cortex segmentation in patients with focal cortical dysplasia.

In patients with intractable epilepsy, focal cortical dysplasia (FCD) is the most frequent malformation of cortical development. Identification of subtle FCD lesions using brain MRI scans is very often based on the cortical thickness measurement, where brain cortex segmentation is required as a preprocessing step. However, the accuracy of the selected segmentation method can highly affect the final FCD lesion detection. In this work, we propose an improved graph cuts algorithm integrating Markov random field-based energy function for more accurate brain cortex MRI segmentation. Our method uses three-label graph cuts and preforms automatic 3D MRI brain cortex segmentation integrating intensity and boundary information. The performance of the method is tested on both simulated MR brain images with different noise levels and real patients with FCD lesions. Experimental quantitative segmentation results showed that the proposed method is effective, robust to noise and achieves higher accuracy than other popular brain MRI segmentation methods. The qualitative validation, visually verified by a medical expert, showed that the FCD lesions were segmented well as a part of the cortex, indicating increased thickness and cortical deformation. The proposed technique can be successfully used in this, as well as in many other clinical applications.

Brain volume segmentation in newborn infants using multi-modal MRI with a low inter-slice resolution.

Brain volume segmentation from neonatal magnetic resonance images (MRI) offers the possibility of exploring the developmental changes, measuring the brain growth, detecting early disorders and three-dimensional (3D) volume reconstruction. However, such segmentation is challenging mainly due to the fast growth process, complex anatomy of the developing brain and often poor MRI quality. Existing techniques are mainly developed for adult brain and are not applicable to neonates or require additional corrections. In this paper we present an algorithm for brain volume segmentation in neonates using T1-weighted (T1-w) and T2-weighted (T2-w) MRI with a low inter-slice resolution. The method incorporates both intensity and edge information and consists of three main steps: image pre-processing, brain segmentation and 3D brain reconstruction. Our algorithm is tested on real neonatal brain MRI with a gestational age between 39-41 weeks and achieves performance comparable to manual segmentation. Also, experimental segmentation results show that our method is effective and more accurate than segmentation methods originally developed for adults.

Segmentation of airways in lungs using projections in 3-D CT angiography images.

In diagnosing lung diseases, the structure and shape of airways in lungs are of great importance. In this paper we propose a novel method for segmenting low-contrast 3-D CTA images of airways in lungs. Our approach is an edge-detecting slice-by-slice segmentation method, capable of segmenting low contrasted airway regions. Our segmentation using projections method shows robustness in images with high presence of noise.

Segmentation and length measurement of the abdominal blood vessels in 3-D MRI images.

In diagnosing diseases and planning surgeries the structure and length of blood vessels is of great importance. In this research we develop a novel method for the segmentation of 2-D and 3-D images with an application to blood vessel length measurements in 3-D abdominal MRI images. Our approach is robust to noise and does not require contrast-enhanced images for segmentation. We use an effective algorithm for skeletonization, graph construction and shortest path estimation to measure the length of blood vessels of interest.

Segmentation of white matter flaring areas in ultrasound images of very-low-birth-weight preterm infants.

In this article, we present an interactive algorithm segmenting white brain matter, visible as hyperechoic flaring areas in ultrasound (US) images of preterm infants with periventricular leukomalacia (PVL). The algorithm combines both the textural properties of pathological brain tissue and mathematical morphology operations. An initial flaring area estimate is derived from a multifeature multiclassifier tissue texture classifier. This area is refined based on the structural properties of the choroid plexus, a brain feature known to have characteristics similar to flaring. Subsequently, a combination of a morphological closing, gradient and opening by reconstruction operation determines the final flaring area boundaries. Experimental results are compared with a gold standard constructed from manual flaring area delineations of 12 medical experts. In addition, we compared our algorithm to an existing active contour method. The results show our technique agrees to the gold standard with statistical significance and outperforms the existing method in accuracy. Finally, using the flaring area as a criterion we improve the sensitivity of PVL detection up to 98% as compared with the state of the art.

Development of a realistic head model for EEG event-detection and source localization in newborn infants.

In this work we present an integrated method for electroencephalography (EEG) source localization in newborn infants, based on a realistic head model. To build a realistic head model we propose an interactive hybrid segmentation method for T1 magnetic resonance images (MRI), consisting of active contours, fuzzy c-means (FCM) clustering and mathematical morphology. Subsequently, we solve the localization problem using a spike train detection algorithm and an algorithm that deals with the forward and inverse problem. The performance of this fused method indicates that our realistic head model is suitable for the accurate localization of the EEG activity. We will present both initial qualitative and quantitative results.

A multi-modal 2D/3D registration scheme for preterm brain images.

Periventricular leukomalacia (PVL) is a neonatal brain pathology occurring on preterms with a very low birth weight (<1500 g). Next to ultrasound (US) imaging, which is the first and most common step, magnetic resonance image (MRI) volumes are used for the inspection of this pathology. Since on both modalities, up to now, we still lack a golden standard for the quantification of the pathology cross-validation through a multi-modal registration is highly beneficial to the clinical diagnosis. In this article we present a semi-automatic 2D US-3D MRI registration scheme combining an interactive initialization step, B-spline image interpolation, a mutual information based metric and an evolutionary algorithm optimization scheme.

Improved segmentation of ultrasound brain tissue incorporating expert evaluation.

The quantitative analysis of medical ultrasound images for the purpose of diagnosis is a difficult task due to the speckle noise present in the images. Nowadays medical doctors depend strongly on the visual interpretation of the images which is subjective to some account. Trying to reduce this noise should assist the experts in a better understanding of some pathologies. We focus on a brain disease called periventricular leukomalacia, also called white matter damage, which occurs frequently on premature neonates. For the moment the affected brain tissue is segmented semi-automatically using two different techniques that take the speckle noise into little account. Here we propose a framework which includes an efficient preprocessing step and relying on expert-based evaluation we develop an integrated segmentation method, which yields a more accurate and better reproducible segmentation.