Ali Zifan

A new method of three‐dimensional coronary artery reconstruction from X‐ray angiography: Validation against a virtual phantom and multislice computed tomography

Objective: To develop and implement a method for three‐dimensional (3D) reconstruction of coronary arteries from conventional monoplane angiograms. Background: 3D reconstruction of conventional coronary angiograms is a promising imaging modality for both diagnostic and interventional purposes. Methods: Our method combines image enhancement, automatic edge detection, an iterative method to reconstruct the centerline of the artery and reconstruction of the diameter of the vessel by taking into consideration foreshortening effects. The X‐Ray‐based 3D coronary trees were compared against phantom data from a virtual arterial tree projected into two planes as well as computed tomography (CT)‐based coronary artery reconstructions in patients subjected to coronary angiography. Results: Comparison against the phantom arterial tree demonstrated perfect agreement with the developed algorithm. Visual comparison against the CT‐based reconstruction was performed in the 3D space, in terms of the direction angle along the centerline length of the left anterior descending and circumflex arteries relative to the main stem, and location and take‐off angle of sample bifurcation branches from the main coronary arteries. Only minimal differences were detected between the two methods. Inter‐ and intraobserver variability of our method was low (intra‐class correlation coefficients > 0.8). Conclusion: The developed method for coronary artery reconstruction from conventional angiography images provides the geometry of coronary arteries in the 3D space.

Simulation of cardiac motion on non-Newtonian, pulsating flow development in the human left anterior descending coronary artery.

This study aimed at investigating the effect of myocardial motion on pulsating blood flow distribution of the left anterior descending coronary artery in the presence of atheromatous stenosis. The moving 3D arterial tree geometry has been obtained from conventional x-ray angiograms obtained during the heart cycle and includes a number of major branches. The geometry reconstruction model has been validated against projection data from a virtual phantom arterial tree as well as with CT-based reconstruction data for the same patient investigated. Reconstructions have been obtained for a number of temporal points while linear interpolation has been used for all intermediate instances. Blood has been considered as a non-Newtonian fluid. Results have been obtained using the same pulse for the inlet blood flow rate but with fixed arterial tree geometry as well as under steady-state conditions corresponding to the mean flow rate. Predictions indicate that myocardial motion has only a minor effect on flow distribution within the arterial tree relative to the effect of the blood pressure pulse.

Microarray image enhancement by denoising using decimated and undecimated multiwavelet transforms

In this paper, we present a new approach to deal with the noise inherent in the microarray image processing procedure. We use the denoising capabilities of decimated and undecimated multiwavelet transforms, DMWT and UMWT respectively, for the removal of noise from microarray data. Multiwavelet transforms, with appropriate initialization, provide sparser representation of signals than wavelet transforms so that their difference from noise can be clearly identified. Also, the redundancy of the UMWT transform is particularly useful in image denoising in order to capture the salient features such as noise or transients. We compare this method with the discrete and stationary wavelet transforms, denoted by DWT and SWT, respectively, and the Wiener filter for denoising microarray images. Results show enhanced image quality using the proposed approach, especially in the undecimated case in which the results are comparable and often outperform that of the stationary wavelet transform. Both multiwavelet transforms outperform the DWT and the Wiener filter.

A Predictive Model to Identify Patients With Fecal Incontinence Based on High-Definition Anorectal Manometry

BACKGROUND & AIMS Three-dimensional high-definition anorectal manometry (3D-HDAM) is used to assess anal sphincter function; it determines profiles of regional pressure distribution along the length and circumference of the anal canal. There is no consensus, however, on the best way to analyze data from 3D-HDAM to distinguish healthy individuals from persons with sphincter dysfunction. We developed a computer analysis system to analyze 3D-HDAM data and to aid in the diagnosis and assessment of patients with fecal incontinence (FI). METHODS In a prospective study, we performed 3D-HDAM analysis of 24 asymptomatic healthy subjects (control subjects; all women; mean age, 39 ± 10 years) and 24 patients with symptoms of FI (all women; mean age, 58 ± 13 years). Patients completed a standardized questionnaire (FI severity index) to score the severity of FI symptoms. We developed and evaluated a robust prediction model to distinguish patients with FI from control subjects using linear discriminant, quadratic discriminant, and logistic regression analyses. In addition to collecting pressure information from the HDAM data, we assessed regional features based on shape characteristics and the anal sphincter pressure symmetry index. RESULTS The combination of pressure values, anal sphincter area, and reflective symmetry values was identified in patients with FI versus control subjects with an area under the curve value of 1.0. In logistic regression analyses using different predictors, the model identified patients with FI with an area under the curve value of 0.96 (interquartile range, 0.22). In discriminant analysis, results were classified with a minimum error of 0.02, calculated using 10-fold cross-validation; different combinations of predictors produced median classification errors of 0.16 in linear discriminant analysis (interquartile range, 0.25) and 0.08 in quadratic discriminant analysis (interquartile range, 0.25). CONCLUSIONS We developed and validated a novel prediction model to analyze 3D-HDAM data. This system can accurately distinguish patients with FI from control subjects.

Three-Dimensional Myoarchitecture of the Lower Esophageal Sphincter and Esophageal Hiatus Using Optical Sectioning Microscopy

Studies to date have failed to reveal the anatomical counterpart of the lower esophageal sphincter (LES). We assessed the LES and esophageal hiatus morphology using a block containing the human LES and crural diaphragm, serially sectioned at 50 μm intervals and imaged at 8.2 μm/pixel resolution. A 3D reconstruction of the tissue block was reconstructed in which each of the 652 cross sectional images were also segmented to identify the boundaries of longitudinal (LM) and circular muscle (CM) layers. The CM fascicles on the ventral surface of LES are arranged in a helical/spiral fashion. On the other hand, the CM fascicles from the two sides cross midline on dorsal surface and continue as sling/oblique muscle on the stomach. Some of the LM fascicles of the esophagus leave the esophagus to enter into the crural diaphragm and the remainder terminate into the sling fibers of the stomach. The muscle fascicles of the right crus of diaphragm which form the esophageal hiatus are arranged like a “noose” around the esophagus. We propose that circumferential squeeze of the LES and crural diaphragm is generated by a unique myo-architectural design, each of which forms a “noose” around the esophagus.

The use of the Kalman filter in the automated segmentation of EIT lung images.

In this paper, we present a new pipeline for the fast and accurate segmentation of impedance images of the lungs using electrical impedance tomography (EIT). EIT is an emerging, promising, non-invasive imaging modality that produces real-time, low spatial but high temporal resolution images of impedance inside a body. Recovering impedance itself constitutes a nonlinear ill-posed inverse problem, therefore the problem is usually linearized, which produces impedance-change images, rather than static impedance ones. Such images are highly blurry and fuzzy along object boundaries. We provide a mathematical reasoning behind the high suitability of the Kalman filter when it comes to segmenting and tracking conductivity changes in EIT lung images. Next, we use a two-fold approach to tackle the segmentation problem. First, we construct a global lung shape to restrict the search region of the Kalman filter. Next, we proceed with augmenting the Kalman filter by incorporating an adaptive foreground detection system to provide the boundary contours for the Kalman filter to carry out the tracking of the conductivity changes as the lungs undergo deformation in a respiratory cycle. The proposed method has been validated by using performance statistics such as misclassified area, and false positive rate, and compared to previous approaches. The results show that the proposed automated method can be a fast and reliable segmentation tool for EIT imaging.

Automatic Detection of Coronary Vessels Using Mutli-scale Texture Dictionaries

In this paper we present a new automatic method for coronary artery vessel detection. We employ a texture modelling approach based on image textons as texture features, in the context of a classification experiment, where we attempt to discriminate between vessel and non-vessel like shapes in X-ray angiogram images. Experiments were conducted on a real patient database. The results show that the proposed model can perform well and distinguish vessel areas from others in an efficient manner, and outperforms other existing methods.

Measurement of peak esophageal luminal cross-sectional area utilizing nadir intraluminal impedance.

Multichannel intraluminal impedance (MII) is currently used to monitor gastroesophageal reflux and esophageal bolus clearance. We describe a novel methodology to measure maximal luminal cross‐sectional area (CSA) during bolus transport from MII measurements.

Medical Image Deblurring via Lagrangian Pursuit in Frame Dictionaries

Medical image deblurring attempts to recover the original human organ boundaries prior to degradation by an optical imaging system, e.g. MRI, CT or Ultrasound. In this paper, we aim to achieve deblurring by the non-linear approximation of medical images in a well chosen basis. The proposed method decomposes medical images over elementary waveforms chosen in a redundant dictionary composed of Morlet and Curvelet frames, which are highly suitable for curved edges. It is well known that finding an ideal sparse transform adapted to all medical images is hopeless. As the dictionary is redundant, we proceed by using a Lagrangian pursuit in order to find the optimal set of the dictionary vectors which represent the few coefficients that contain the information we are looking for and give a robust geometric image description. The proposed method in most instances outperforms, common deblurring methods using translation invariant Wavelet, Tikhonov and TV regularization algorithms.

Temporal and spectral properties of esophageal mucosal blood perfusion: a comparison between normal subjects and nutcracker esophagus patients.

The mechanism of esophageal pain in patients with nutcracker esophagus (NE) and other esophageal motor disorders is not known. Our recent study shows that baseline esophageal mucosal perfusion, measured by laser Doppler perfusion monitoring, is lower in NE patients compared to controls. The goal of our current study was to perform a more detailed analysis of esophageal mucosal blood perfusion (EMBP) waveform of NE patients and controls to determine the optimal EMBP biomarkers that combined with suitable statistical learning models produce robust discrimination between the two groups.