Stewart Young

Differences in Regional Brain Volumes Two Months and One Year after Mild Traumatic Brain Injury

Conventional structural imaging is often normal after mild traumatic brain injury (mTBI). There is a need for structural neuroimaging biomarkers that facilitate detection of milder injuries, allow recovery trajectory monitoring, and identify those at risk for poor functional outcome and disability. We present a novel approach to quantifying volumes of candidate brain regions at risk for injury. Compared to controls, patients with mTBI had significantly smaller volumes in several regions including the caudate, putamen, and thalamus when assessed 2 months after injury. These differences persisted but were reduced in magnitude 1 year after injury, suggesting the possibility of normalization over time in the affected regions. More pronounced differences, however, were found in the amygdala and hippocampus, suggesting the possibility of regionally specific responses to injury.

3D vessel axis extraction using 2D calibrated x-ray projections for coronary modeling

A new approach for 3D vessel centreline extraction using multiple, ECG-gated, calibrated X-ray angiographic projections of the coronary arteries is described. The proposed method performs direct extraction of 3D vessel centrelines, without the requirement to either first compute prior 2D centreline estimates, or perform a complete volume reconstruction. A front propagation-based algorithm, initialised with one or more 3D seed points, is used to explore a volume of interest centred on the projection geometrys isocentre. The expansion of a 3D region is controlled by forward projecting boundary points into all projection images to compute vessel response measurements, which are combined into a 3D propagation speed so that the front expands rapidly when all projection images yield high vessel responses. Vessel centrelines are obtained by reconstructing the paths of fastest propagation. Based on these axes, a volume model of the coronaries can be constructed by forward projecting axis points into the 2D images where the borders are detected. The accuracy of the method was demonstrated via a comparison of automatically extracted centrelines with 3D centrelines derived from manually segmented projection data.

A novel bone suppression method that improves lung nodule detection : Suppressing dedicated bone shadows in radiographs while preserving the remaining signal.

PurposeSuppressing thoracic bone shadows in chest radiographs has been previously reported to improve the detection rates for solid lung nodules, however at the cost of increased false detection rates. These bone suppression methods are based on an artificial neural network that was trained using dual-energy subtraction images in order to mimic their appearance.MethodHere, a novel approach is followed where all bone shadows crossing the lung field are suppressed sequentially leaving the intercostal space unaffected. Given a contour delineating a bone, its image region is spatially transferred to separate normal image gradient components from tangential component. Smoothing the normal partial gradient along the contour results in a reconstruction of the image representing the bone shadow only, because all other overlaid signals tend to cancel out each other in this representation.ResultsThe method works even with highly contrasted overlaid objects such as a pacemaker. The approach was validated in a reader study with two experienced chest radiologists, and these images helped improving both the sensitivity and the specificity of the readers for the detection and localization of solid lung nodules. The AUC improved significantly from 0.596 to 0.655 on a basis of 146 images from patients and normals with a total of 123 confirmed lung nodules.ConclusionSubtracting all reconstructed bone shadows from the original image results in a soft image where lung nodules are no longer obscured by bone shadows. Both the sensitivity and the specificity of experienced radiologists increased.

Evaluation of traumatic brain injury patients using a shape-constrained deformable model

Traumatic brain injury (TBI) is often associated with life long neurobehavioral effects in survivors. Imaging has historically supported the detection and acute management of life-threatening complications. However, in order to predict these long term consequences in the increasing number of individuals surviving TBI, there is an emerging need for structural neuroimaging biomarkers that would facilitate detection of milder injuries, allow recovery trajectory monitoring, and identify those at risk for poor functional outcome and disability. This paper presents a methodology capable of identifying such structural biomarkers in MR images of the brain. Results are presented demonstrating the quantitative accuracy of the approach with respect to (i) highly accurate annotations from expert tracers, (ii) an alternative segmentation method in FSL, and (iii) the ability to reproduce statistically significant differences in the volumes of specific structures between well-defined clinical cohorts (TBI vs age-matched healthy controls) in a retrospective analysis study.

Decomposing the bony thorax in X-ray images

The identification and segmentation of target objects from medical images is often confused by other more salient objects in the image. This is a specific problem for X-ray projection images where the shadows of semi-transparent objects are overlaid. A bone shadow may confuse the automated detection of other crossing bones and of important soft tissue findings in the lung like lung nodules. We present a method to identify and remove such bone shadows from a chest radiograph for the purpose of suppressing all bone shadows overlapping with the lung field in a standard posterior-anterior view. In this context an elegant novel approach to the problem of identifying and segmenting overlaid objects is followed: Disturbing objects are identified first and literally removed from the image, therefore no longer confusing the detection of other more subtle objects. This method allowed the identification, segmentation, and suppression of the clavicles, the posterior and the anterior parts of the ribs - one after another. In a clinical study the detection of lung nodules by experienced radiologists was improved after bone suppression.

Construction and comparative evaluation of different activity detection methods in brain FDG‑PET

AimWe constructed and evaluated reference brain FDG-PET databases for usage by three software programs (Computer-aided diagnosis for dementia (CAD4D), Statistical Parametric Mapping (SPM) and NEUROSTAT), which allow a user-independent detection of dementia-related hypometabolism in patients’ brain FDG-PET.MethodsThirty-seven healthy volunteers were scanned in order to construct brain FDG reference databases, which reflect the normal, age-dependent glucose consumption in human brain, using either software. Databases were compared to each other to assess the impact of different stereotactic normalization algorithms used by either software package. In addition, performance of the new reference databases in the detection of altered glucose consumption in the brains of patients was evaluated by calculating statistical maps of regional hypometabolism in FDG-PET of 20 patients with confirmed Alzheimer’s dementia (AD) and of 10 non-AD patients. Extent (hypometabolic volume referred to as cluster size) and magnitude (peak z-score) of detected hypometabolism was statistically analyzed.ResultsDifferences between the reference databases built by CAD4D, SPM or NEUROSTAT were observed. Due to the different normalization methods, altered spatial FDG patterns were found. When analyzing patient data with the reference databases created using CAD4D, SPM or NEUROSTAT, similar characteristic clusters of hypometabolism in the same brain regions were found in the AD group with either software. However, larger z-scores were observed with CAD4D and NEUROSTAT than those reported by SPM. Better concordance with CAD4D and NEUROSTAT was achieved using the spatially normalized images of SPM and an independent z-score calculation. The three software packages identified the peak z-scores in the same brain region in 11 of 20 AD cases, and there was concordance between CAD4D and SPM in 16 AD subjects.ConclusionThe clinical evaluation of brain FDG-PET of 20 AD patients with either CAD4D-, SPM- or NEUROSTAT-generated databases from an identical reference dataset showed similar patterns of hypometabolism in the brain regions known to be involved in AD. The extent of hypometabolism and peak z-score appeared to be influenced by the calculation method used in each software package rather than by different spatial normalization parameters.

Manual annotation, 3-D shape reconstruction, and traumatic brain injury analysis

Bitmask drawing is still the established standard for manual annotation of brain structures by experts. To alleviate problems such as bitmask inconsistencies between slices that lead to jagged contours in corresponding orthogonal cross-sections, we propose a 2-D spline-based contour editing tool in combination with a new algorithm for surface reconstruction from 3-D point clouds. This approach uses a new implicit surface formulation that adapts to the local density of points. We show that manual segmentation of the brainstem, cerebellum, corpus callosum, caudate, putamen, hippocampus and thalamus can be performed with high reproducibility in Magnetic Resonance (MR) data and sufficient accuracy to analyze volume changes for mild Traumatic Brain Injury (TBI) patients. In addition, we show that the new surface reconstruction method allows to reconstruct the shape of brain structures such as the brainstem better than other established surface reconstruction approaches. Our tool can, therefore, not only be used for volume measurements, but may also be used to assess local shape changes of brain structures going along with the progression of neurodegenerative diseases such as TBI.

Optimal feature selection for automated classification of FDG-PET in patients with suspected dementia

FDG-PET is increasingly used for the evaluation of dementia patients, as major neurodegenerative disorders, such as Alzheimers disease (AD), Lewy body dementia (LBD), and Frontotemporal dementia (FTD), have been shown to induce specific patterns of regional hypo-metabolism. However, the interpretation of FDG-PET images of patients with suspected dementia is not straightforward, since patients are imaged at different stages of progression of neurodegenerative disease, and the indications of reduced metabolism due to neurodegenerative disease appear slowly over time. Furthermore, different diseases can cause rather similar patterns of hypo-metabolism. Therefore, classification of FDG-PET images of patients with suspected dementia may lead to misdiagnosis. This work aims to find an optimal subset of features for automated classification, in order to improve classification accuracy of FDG-PET images in patients with suspected dementia. A novel feature selection method is proposed, and performance is compared to existing methods. The proposed approach adopts a combination of balanced class distributions and feature selection methods. This is demonstrated to provide high classification accuracy for classification of FDG-PET brain images of normal controls and dementia patients, comparable with alternative approaches, and provides a compact set of features selected.

Breast Conserving Surgery Outcome Prediction: A Patient-Specific, Integrated Multi-modal Imaging and Mechano-Biological Modelling Framework

Patient-specific surgical predictions of Breast Conserving Therapy, through mechano-biological simulations, could inform the shared decision making process between clinicians and patients by enabling the impact of different surgical options to be visualised. We present an overview of our processing workflow that integrates MR images and three dimensional optical surface scans into a personalised model. Utilising an interactively generated surgical plan, a multi-scale open source finite element solver is employed to simulate breast deformity based on interrelated physiological and biomechanical processes that occur post surgery. Our outcome predictions, based on the pre-surgical imaging, were validated by comparing the simulated outcome with follow-up surface scans of four patients acquired 6 to 12 months post-surgery. A mean absolute surface distance of 3.3i¾?mm between the follow-up scan and the simulation was obtained.

Design of a synthetic database for the validation of non-linear registration and segmentation of magnetic resonance brain images

Image registration and segmentation are two important tasks in medical image analysis. However, the validation of algorithms for non-linear registration in particular often poses significant challenges:1, 2 Anatomical labeling based on scans for the validation of segmentation algorithms is often not available, and is tedious to obtain. One possibility to obtain suitable ground truth is to use anatomically labelled atlas images. Such atlas images are, however, generally limited to single subjects, and the displacement field of the registration between the template and an arbitrary data set is unknown. Therefore, the precise registration error cannot be determined, and approximations of a performance measure like the consistency error must be adapted. Thus, validation requires that some form of ground truth is available. In this work, an approach to generate a synthetic ground truth database for the validation of image registration and segmentation is proposed. Its application is illustrated using the example of the validation of a registration procedure, using 50 magnetic resonance images from different patients and two atlases. Three different non-linear image registration methods were tested to obtain a synthetic validation database consisting of 50 anatomically labelled brain scans.