Brain areas involved in medial temporal lobe seizures: a principal component analysis of ictal SPECT data

The study describes brain areas involved in medial temporal lobe (mTL) seizures of 12 patients. All patients showed so-called oro-alimentary behavior within the first 20 s of clinical seizure manifestation characteristic of mTL seizures. Single photon emission computed tomography (SPECT) images of regional cerebral blood flow (rCBF) were acquired from the patients in ictal and interictal phases and from normal volunteers. Image analysis employed categorical comparisons with statistical parametric mapping and principal component analysis (PCA) to assess functional connectivity. PCA supplemented the findings of the categorical analysis by decomposing the covariance matrix containing images of patients and healthy subjects into distinct component images of independent variance, including areas not identified by the categorical analysis. Two principal components (PCs) discriminated the subject groups: patients with right or left mTL seizures and normal volunteers, indicating distinct neuronal networks implicated by the seizure. Both PCs were correlated with seizure duration, one positively and the other negatively, confirming their physiological significance. The independence of the two PCs yielded a clear clustering of subject groups. The local pattern within the temporal lobe describes critical relay nodes which are the counterpart of oro-alimentary behavior: (1) right mesial temporal zone and ipsilateral anterior insula in right mTL seizures, and (2) temporal poles on both sides that are densely interconnected by the anterior commissure. Regions remote from the temporal lobe may be related to seizure propagation and include positively and negatively loaded areas. These patterns, the covarying areas of the temporal pole and occipito-basal visual association cortices, for example, are related to known anatomic paths.

Quantitative analysis of OCT characteristics in patients with achromatopsia and blue-cone monochromatism

PURPOSE: To quantify optical coherence tomography (OCT) images of the central retina in patients with blue-cone monochromatism (BCM) and achromatopsia (ACH) compared with healthy control individuals. METHODS: The study included 15 patients with ACH, 6 with BCM, and 20 control subjects. Diagnosis of BCM and ACH was established by visual acuity testing, morphologic examination, color vision testing, and Ganzfeld ERG recording. OCT images were acquired with the Stratus OCT 3 (Carl Zeiss Meditec AG, Oberkochen, Germany). Foveal OCT images were analyzed by calculating longitudinal reflectivity profiles (LRPs) from scan lines. Profiles were analyzed quantitatively to determine foveal thickness and distances between reflectivity layers. RESULTS: Patients with ACH and BCM had a mean visual acuity of 20/200 and 20/60, respectively. Color vision testing results were characteristic of the diseases. The LRPs of control subjects yielded four peaks (P1-P4), presumably representing the RPE (P1), the ovoid region of the photoreceptors (P2), the external limiting membrane (ELM) (P3), and the internal limiting membrane (P4). In patients with ACH, P2 was absent, but foveal thickness (P1-P4) did not differ significantly from that in the control subjects (187 +/- 20 vs. 192 +/- 14 microm, respectively). The distance from P1 to P3 did not differ significantly (78 +/- 10 vs. 82 +/- 5 microm) between ACH and controls subjects. In patients with BCM, P3 was lacking, and P2 advanced toward P1 compared with the control subjects (32 +/- 6 vs. 48 +/- 4 microm). Foveal thickness (153 +/- 16 microm) was significantly reduced compared with that in control subjects and patients with ACH. CONCLUSIONS: Quantitative OCT image analysis reveals distinct patterns for controls subjects and patients with ACH and BCM, respectively. Quantitative analysis of OCT imaging can be useful in differentiating retinal diseases affecting photoreceptors. Foveal thickness is similar in both normal subjects and patients with ACH but is decreased in patients with BCM.

Computer-aided diagnosis of carotid atherosclerosis based on ultrasound image statistics, laws' texture and neural networks

Quantitative characterisation of carotid atherosclerosis and classification into symptomatic or asymptomatic is crucial in planning optimal treatment of atheromatous plaque. The computer-aided diagnosis (CAD) system described in this paper can analyse ultrasound (US) images of carotid artery and classify them into symptomatic or asymptomatic based on their echogenicity characteristics. The CAD system consists of three modules: a) the feature extraction module, where first-order statistical (FOS) features and Laws' texture energy can be estimated, b) the dimensionality reduction module, where the number of features can be reduced using analysis of variance (ANOVA), and c) the classifier module consisting of a neural network (NN) trained by a novel hybrid method based on genetic algorithms (GAs) along with the back propagation algorithm. The hybrid method is able to select the most robust features, to adjust automatically the NN architecture and to optimise the classification performance. The performance is measured by the accuracy, sensitivity, specificity and the area under the receiver-operating characteristic (ROC) curve. The CAD design and development is based on images from 54 symptomatic and 54 asymptomatic plaques. This study demonstrates the ability of a CAD system based on US image analysis and a hybrid trained NN to identify atheromatous plaques at high risk of stroke.

A computer-assisted microscopic analysis of bone tissue developed inside a polyactive plymer implanted into an equine articular surface

One of the most promising applications for the restoration of small or moderately sized focal articular lesions is mosaicplasty (MP). Although recurrent hemarthrosis is a rare complication after MP, recently, various strategies have been designed to find an effective filling material to prevent postoperative bleeding from the donor site. The porous biodegradable polymer Polyactive (PA; a polyethylene glycol terephthalate - polybutylene terephthalate copolymer) represents a promising solution in this respect. A histological evaluation of the longterm PA-filled donor sites obtained from 10 experimental horses was performed. In this study, attention was primarily focused on the bone tissue developed in the plug. A computer-assisted image analysis and quantitative polarized light microscopic measurements of decalcified, longitudinally sectioned, dimethylmethylene blue (DMMB)- and picrosirius red (PS) stained sections revealed that the coverage area of the bone trabecules in the PA-filled donor tunnels was substantially (25%) enlarged compared to the neighboring cancellous bone. For this quantification, identical ROIs (regions of interest) were used and compared. The birefringence retardation values were also measured with a polarized light microscope using monochromatic light. Identical retardation values could be recorded from the bone trabeculae developed in the PA and in the neighboring bone, which indicates that the collagen orientation pattern does not differ significantly among these bone trabecules. Based on our new data, we speculate that PA promotes bone formation, and some of the currently identified degradation products of PA may enhance osteo-conduction and osteoinduction inside the donor canal.

Reconstruction of 3D Vertebral Models from a Single 2D Lateral Fluoroscopic Image

Accurate three-dimensional (3D) models of lumbar vertebrae are required for image-based 3D kinematics analysis. MRI or CT datasets are frequently used to derive 3D models but have the disadvantages that they are expensive, time-consuming or involving ionizing radiation (e.g., CT acquisition). In this chapter, we present an alternative technique that can reconstruct a scaled 3D lumbar vertebral model from a single two-dimensional (2D) lateral fluoroscopic image and a statistical shape model. Cadaveric studies are conducted to verify the reconstruction accuracy by comparing the surface models reconstructed from a single lateral fluoroscopic image to the ground truth data from 3D CT segmentation. A mean reconstruction error between 0.7 and 1.4 mm was found.

Graphical Model-Based Vertebra Identification from X-Ray Image(s)

Automated identification of vertebrae from X-ray image(s) is an important step for various medical image computing tasks such as 2D/3D rigid and non-rigid registration. In this chapter we present a graphical model-based solution for automated vertebra identification from X-ray image(s). Our solution does not ask for a training process using training data and has the capability to automatically determine the number of vertebrae visible in the image(s). This is achieved by combining a graphical model-based maximum a posterior probability (MAP) estimate with a mean-shift based clustering. Experiments conducted on simulated X-ray images as well as on a low-dose low quality X-ray spinal image of a scoliotic patient verified its performance.

Automatic X-ray landmark detection and shape segmentation via data-driven joint estimation of image displacements

In this paper, we propose a new method for fully-automatic landmark detection and shape segmentation in X-ray images. To detect landmarks, we estimate the displacements from some randomly sampled image patches to the (unknown) landmark positions, and then we integrate these predictions via a voting scheme. Our key contribution is a new algorithm for estimating these displacements. Different from other methods where each image patch independently predicts its displacement, we jointly estimate the displacements from all patches together in a data driven way, by considering not only the training data but also geometric constraints on the test image. The displacements estimation is formulated as a convex optimization problem that can be solved efficiently. Finally, we use the sparse shape composition model as the a priori information to regularize the landmark positions and thus generate the segmented shape contour. We validate our method on X-ray image datasets of three different anatomical structures: complete femur, proximal femur and pelvis. Experiments show that our method is accurate and robust in landmark detection, and, combined with the shape model, gives a better or comparable performance in shape segmentation compared to state-of-the art methods. Finally, a preliminary study using CT data shows the extensibility of our method to 3D data.

Quantitative microstructure analysis of polymer-modified mortars

Digital light, fluorescence and electron microscopy in combination with wavelength-dispersive spectroscopy were used to visualize individual polymers, air voids, cement phases and filler minerals in a polymer-modified cementitious tile adhesive. In order to investigate the evolution and processes involved in formation of the mortar microstructure, quantifications of the phase distribution in the mortar were performed including phase-specific imaging and digital image analysis. The required sample preparation techniques and imaging related topics are discussed. As a form of case study, the different techniques were applied to obtain a quantitative characterization of a specific mortar mixture. The results indicate that the mortar fractionates during different stages ranging from the early fresh mortar until the final hardened mortar stage. This induces process-dependent enrichments of the phases at specific locations in the mortar. The approach presented provides important information for a comprehensive understanding of the functionality of polymer-modified mortars.

Medical image computing in diagnosis and intervention of spinal diseases

Spinal image analysis and computer assisted intervention have emerged as new and independent research areas, due to the importance of treatment of spinal diseases, increasing availability of spinal imaging, and advances in analytics and navigation tools. Among others, multiple modality spinal image analysis and spinal navigation tools have emerged as two keys in this new area. We believe that further focused research in these two areas will lead to a much more efficient and accelerated research path, avoiding detours that exist in other applications, such as in brain and heart.

Predicting and monitoring cancer treatment response with diffusion-weighted MRI

An imaging biomarker that would provide for an early quantitative metric of clinical treatment response in cancer patients would provide for a paradigm shift in cancer care. Currently, nonimage based clinical outcome metrics include morphology, clinical, and laboratory parameters, however, these are obtained relatively late following treatment. Diffusion-weighted MRI (DW-MRI) holds promise for use as a cancer treatment response biomarker as it is sensitive to macromolecular and microstructural changes which can occur at the cellular level earlier than anatomical changes during therapy. Studies have shown that successful treatment of many tumor types can be detected using DW-MRI as an early increase in the apparent diffusion coefficient (ADC) values. Additionally, low pretreatment ADC values of various tumors are often predictive of better outcome. These capabilities, once validated, could provide for an important opportunity to individualize therapy thereby minimizing unnecessary systemic toxicity associated with ineffective therapies with the additional advantage of improving overall patient health care and associated costs. In this report, we provide a brief technical overview of DW-MRI acquisition protocols, quantitative image analysis approaches and review studies which have implemented DW-MRI for the purpose of early prediction of cancer treatment response.

Effect of systemic tetracycline on the degradation of tetracycline-impregnated bilayered collagen membranes: an animal study

BACKGROUND: Premature collagen membrane degradation may compromise the outcome of osseous regenerative procedures. Tetracyclines (TTCs) inhibit the catalytic activities of human metalloproteinases. Preprocedural immersion of collagen membranes in TTC and systemic administration of TTC may be possible alternatives to reduce the biodegradation of native collagen membranes. AIM: To evaluate the in vivo degradation of collagen membranes treated by combined TTC immersion and systemic administration. MATERIALS AND METHODS: Seventy-eight bilayered porcine collagen membrane disks were divided into three groups and were immersed in 0, 50, or 100 mg/mL TTC solution. Three disks, one of each of the three groups, were implanted on the calvaria of each of 26 Wistar rats. Thirteen (study group) were administered with systemic TTC (10 mg/kg), while the remaining 13 received saline injections (control group). Calvarial tissues were retrieved after 3 weeks, and histological sections were analyzed by image analysis software. RESULTS: Percentage of remaining collagen area within nonimpregnated membranes was 52.26 ± 20.67% in the study group, and 32.74 ± 13.81% in the control group. Immersion of membranes in 100 mg/mL TTC increased the amount of residual collagen to 63.46 ± 18.19% and 42.82 ± 12.99% (study and control groups, respectively). Immersion in 50 mg/mL TTC yielded maximal residual collagen values: 80.75 ± 14.86% and 59.15 ± 8.01% (study and control groups, respectively). Differences between the TTC concentrations, and between the control and the study groups were statistically significant. CONCLUSIONS: Immersion of collagen membranes in TTC solution prior to their implantation and systemic administration of TTC significantly decreased the membranes' degradation.

Delayed osteoblast differentiation and altered inflammatory response around implants placed in incisor sockets of type 2 diabetic rats

Objective: Central to the process of osseointegration is the recruitment of mesenchymal progenitor cells to the healing site, their proliferation and differentiation to bone synthesising osteoblasts. The process is under the control of pro-inflammatory cytokines and growth factors. The aim of this study was to monitor these key stages of osseointegration and the signalling milieu during bone healing around implants placed in healthy and diabetic bone. Methods: Implants were placed into the sockets of incisors extracted from the mandibles of normal Wistar and diabetic Goto-Kakizaki rats. Mandibles 1-12 weeks post-insertion of the implant were examined by histochemistry and immunocytochemistry to localise the presence of Stro-1- positive mesenchymal progenitor cells, proliferating cellular nuclear antigen proliferative cells, osteopontin and osteocalcin, macrophages, pro-inflammatory cytokines interleukin (IL)-1 , IL-6, tumour necrosis factor (TNF)- and tumour growth factor (TGF)- 1. Image analysis provided a semi-quantification of positively expressing cells. Results: Histological staining identified a delay in the formation of mineralised bone around implants placed in diabetic animals. Within the diabetic bone, the migration of Stro-1 mesenchymal cells in the healing tissue appeared to be unaffected. However, in the diabetic healing bone, the onset of cell proliferation and osteoblast differentiation were delayed and subsequently prolonged compared with normal bone. Similar patterns of change were observed in diabetic bone for the presence of IL-1 , TNF- , macrophages and TGF- 1. Conclusion: The observed alterations in the extracellular presence of pro-inflammatory cytokines, macrophages and growth factors within diabetic tissues that correlate to changes in the signalling milieu, may affect the proliferation and differentiation of mesenchymal progenitor cells in the osseointegration process. To cite this article: Colombo JS, Balani D, Sloan AJ, St Crean J, Okazaki J, Waddington RJ. Delayed osteoblast differentiation and altered inflammatory response around implants placed in incisor sockets of type 2 diabetic rats Clin. Oral Impl. Res22, 2011; 578-586 doi: 10.1111/j.1600-0501.2010.01992.x.