Yih Miin Liew

In vivo assessment of human burn scars through automated quantification of vascularity using optical coherence tomography.

Abstract. In scars arising from burns, objective assessment of vascularity is important in the early identification of pathological scarring, and in the assessment of progression and treatment response. We demonstrate the first clinical assessment and automated quantification of vascularity in cutaneous burn scars of human patients in vivo that uses optical coherence tomography (OCT). Scar microvasculature was delineated in three-dimensional OCT images using speckle decorrelation. The diameter and area density of blood vessels were automatically quantified. A substantial increase was observed in the measured density of vasculature in hypertrophic scar tissues (38%) when compared against normal, unscarred skin (22%). A proliferation of larger vessels (diameter≥100  μm) was revealed in hypertrophic scarring, which was absent from normal scars and normal skin over the investigated physical depth range of 600 μm. This study establishes the feasibility of this methodology as a means of clinical monitoring of scar progression.

Assessment of human burn scars with optical coherence tomography by imaging the attenuation coefficient of tissue after vascular masking

Abstract. The formation of burn-scar tissue in human skin profoundly alters, among other things, the structure of the dermis. We present a method to characterize dermal scar tissue by the measurement of the near-infrared attenuation coefficient using optical coherence tomography (OCT). To generate accurate en face parametric images of attenuation, we found it critical to first identify (using speckle decorrelation) and mask the tissue vasculature from the three-dimensional OCT data. The resulting attenuation coefficients in the vasculature-masked regions of the dermis of human burn-scar patients are lower in hypertrophic (3.8±0.4  mm−1) and normotrophic (4.2±0.9  mm−1) scars than in contralateral or adjacent normal skin (6.3±0.5  mm−1). Our results suggest that the attenuation coefficient of vasculature-masked tissue could be used as an objective means to assess human burn scars.

Reduction of image artifacts in three-dimensional optical coherence tomography of skin in vivo

This paper presents results of in vivo studies on the effect of refractive index-matching media on image artifacts in optical coherence tomography (OCT) images of human skin. These artifacts present as streaks of artificially low backscatter and displacement or distortion of features. They are primarily caused by refraction and scattering of the OCT light beam at the skin surface. The impact of the application of glycerol and ultrasound gel is assessed on both novel skin-mimicking phantoms and in vivo human skin, including assessment of the epidermal thickening caused by the media. Based on our findings, recommendations are given for optimal OCT imaging of skin in vivo.

Imaging of skin birefringence for human scar assessment using polarization-sensitive optical coherence tomography aided by vascular masking

Abstract. We demonstrate the in vivo assessment of human scars by parametric imaging of birefringence using polarization-sensitive optical coherence tomography (PS-OCT). Such in vivo assessment is subject to artifacts in the detected birefringence caused by scattering from blood vessels. To reduce these artifacts, we preprocessed the PS-OCT data using a vascular masking technique. The birefringence of the remaining tissue regions was then automatically quantified. Results from the scars and contralateral or adjacent normal skin of 13 patients show a correspondence of birefringence with scar type: the ratio of birefringence of hypertrophic scars to corresponding normal skin is 2.2±0.2 (mean±standard deviation), while the ratio of birefringence of normotrophic scars to normal skin is 1.1±0.4. This method represents a new clinically applicable means for objective, quantitative human scar assessment.

Motion correction of in vivo three-dimensional optical coherence tomography of human skin using a fiducial marker

This paper presents a novel method based on a fiducial marker for correction of motion artifacts in 3D, in vivo, optical coherence tomography (OCT) scans of human skin and skin scars. The efficacy of this method was compared against a standard cross-correlation intensity-based registration method. With a fiducial marker adhered to the skin, OCT scans were acquired using two imaging protocols: direct imaging from air into tissue; and imaging through ultrasound gel into tissue, which minimized the refractive index mismatch at the tissue surface. The registration methods were assessed with data from both imaging protocols and showed reduced distortion of skin features due to motion. The fiducial-based method was found to be more accurate and robust, with an average RMS error below 20 µm and success rate above 90%. In contrast, the intensity-based method had an average RMS error ranging from 36 to 45 µm, and a success rate from 50% to 86%. The intensity-based algorithm was found to be particularly confounded by corrugations in the skin. By contrast, tissue features did not affect the fiducial-based method, as the motion correction was based on delineation of the flat fiducial marker. The average computation time for the fiducial-based algorithm was approximately 21 times less than for the intensity-based algorithm.

Convolutional neural network regression for short-axis left ventricle segmentation in cardiac cine MR sequences

HighlightsComplete short axis + time left ventricle segmentation in cardiac cine MRI.State‐of‐the‐art results against the left ventricle segmentation challenge dataset.Myocardial wall parameterization via convolutional neural network linear regression. ABSTRACT Automated left ventricular (LV) segmentation is crucial for efficient quantification of cardiac function and morphology to aid subsequent management of cardiac pathologies. In this paper, we parameterize the complete (all short axis slices and phases) LV segmentation task in terms of the radial distances between the LV centerpoint and the endo‐ and epicardial contours in polar space. We then utilize convolutional neural network regression to infer these parameters. Utilizing parameter regression, as opposed to conventional pixel classification, allows the network to inherently reflect domain‐specific physical constraints. We have benchmarked our approach primarily against the publicly‐available left ventricle segmentation challenge (LVSC) dataset, which consists of 100 training and 100 validation cardiac MRI cases representing a heterogeneous mix of cardiac pathologies and imaging parameters across multiple centers. Our approach attained a .77 Jaccard index, which is the highest published overall result in comparison to other automated algorithms. To test general applicability, we also evaluated against the Kaggle Second Annual Data Science Bowl, where the evaluation metric was the indirect clinical measures of LV volume rather than direct myocardial contours. Our approach attained a Continuous Ranked Probability Score (CRPS) of .0124, which would have ranked tenth in the original challenge. With this we demonstrate the effectiveness of convolutional neural network regression paired with domain‐specific features in clinical segmentation. Graphical abstract Figure. No Caption available.

Motion corrected LV quantification based on 3D modelling for improved functional assessment in cardiac MRI.

Cine MRI is a clinical reference standard for the quantitative assessment of cardiac function, but reproducibility is confounded by motion artefacts. We explore the feasibility of a motion corrected 3D left ventricle (LV) quantification method, incorporating multislice image registration into the 3D model reconstruction, to improve reproducibility of 3D LV functional quantification. Multi-breath-hold short-axis and radial long-axis images were acquired from 10 patients and 10 healthy subjects. The proposed framework reduced misalignment between slices to subpixel accuracy (2.88 to 1.21 mm), and improved interstudy reproducibility for 5 important clinical functional measures, i.e. end-diastolic volume, end-systolic volume, ejection fraction, myocardial mass and 3D-sphericity index, as reflected in a reduction in the sample size required to detect statistically significant cardiac changes: a reduction of 21-66%. Our investigation on the optimum registration parameters, including both cardiac time frames and number of long-axis (LA) slices, suggested that a single time frame is adequate for motion correction whereas integrating more LA slices can improve registration and model reconstruction accuracy for improved functional quantification especially on datasets with severe motion artefacts.

Regional assessment of LV wall in infarcted heart using tagged MRI and cardiac modelling

A segmental two-parameter empirical deformable model is proposed for evaluating regional motion abnormality of the left ventricle. Short-axis tagged MRI scans were acquired from 10 healthy subjects and 10 postinfarct patients. Two motion parameters, contraction and rotation, were quantified for each cardiac segment by fitting the proposed model using a non-rigid registration algorithm. The accuracy in motion estimation was compared to a global model approach. Motion parameters extracted from patients were correlated to infarct transmurality assessed with delayed-contrast-enhanced MRI. The proposed segmental model allows markedly improved accuracy in regional motion analysis as compared to the global model for both subject groups (1.22-1.40 mm versus 2.31-2.55 mm error). By end-systole, all healthy segments experienced radial displacement by ~25-35% of the epicardial radius, whereas the 3 short-axis planes rotated differently (basal: 3.3°; mid:  -1° and apical:  -4.6°) to create a twisting motion. While systolic contraction showed clear correspondence to infarct transmurality, rotation was nonspecific to either infarct location or transmurality but could indicate the presence of functional abnormality. Regional contraction and rotation derived using this model could potentially aid in the assessment of severity of regional dysfunction of infarcted myocardium.

Enhanced skin permeation and hydration by magnetic field array: preliminary in-vitro and in-vivo assessment

Objectives The aim of the study was determine the effect of magnetic film array technology on the skin permeation of urea.OBJECTIVES The potential utility of liquid crystalline lipid-based formulations in oral drug delivery is expected to depend critically on their structure formation and stability in gastrointestinal fluids. The phase behaviour of lipid-based liquid crystals formed by phytantriol and glyceryl monooleate, known to form a bicontinuous cubic phase in excess water, was therefore assessed in physiologically-relevant simulated gastrointestinal media. METHODS Fixed composition phase studies, crossed polarised light microscopy (CPLM) and small angle X-ray scattering (SAXS) were used to determine the phase structures formed in phosphate-buffered saline, simulated gastric and intestinal fluids in the presence of model poorly water soluble drugs cinnarizine, diazepam and vitamin E acetate. KEY FINDINGS The phase behaviour of phytantriol in phosphate-buffered saline was very similar to that in water. Increasing concentrations of bile components (bile salts and phospholipids) caused an increase in the lattice parameter of the cubic phase structure for both lipids. Incorporation of cinnarizine and diazepam did not influence the phase behaviour of the phytantriol- or glyceryl monooleate-based systems at physiological temperatures; however, an inverse hexagonal phase formed on incorporation of vitamin E acetate. CONCLUSIONS Phytantriol and glyceryl monooleate have the potential to form stable cubic phase liquid crystalline delivery systems in the gastrointestinal tract. In-vivo studies to assess their sustained-release behaviour are warranted.

Cardiac left ventricle segmentation using convolutional neural network regression

Cardiac MRI is important for the diagnosis and assessment of various cardiovascular diseases. Automated segmentation of the left ventricular (LV) endocardium at end-diastole (ED) and end-systole (ES) enables automated quantification of various clinical parameters including ejection fraction. Neural networks have been used for general image segmentation, usually via per-pixel categorization e.g. “foreground” and “background”. In this paper we propose that the generally circular LV endocardium can be parameterized and the endocardial contour determined via neural network regression. We designed two convolutional neural networks (CNN), one for localization of the LV, and the other for determining the endocardial radius. We trained the networks against 100 datasets from the Medical Image Computing and Computer Assisted Intervention (MICCAI) 2011 challenge, and tested the networks against 45 datasets from the MICCAI 2009 challenge. The networks achieved 0.88 average Dice metric, 2.30 mm average perpendicular distance, and 97.9% good contours, the latter being the highest published result to date. These results demonstrate that CNN regression is a viable and highly promising method for automated LV endocardial segmentation at ED and ES phases, and is capable of generalizing learning between highly distinct training and testing data sets.