Normative brain volumetry derived from different reference populations: impact on single-subject diagnostic assessment in dementia.

Brain imaging data are increasingly made publicly accessible, and volumetric imaging measures derived from population-based cohorts may serve as normative data for individual patient diagnostic assessment. Yet, these normative cohorts are usually not a perfect reflection of a patient's base population, nor are imaging parameters such as field strength or scanner type similar. In this proof of principle study, we assessed differences between reference curves of subcortical structure volumes of normal controls derived from two population-based studies and a case-control study. We assessed the impact of any differences on individual assessment of brain structure volumes. Percentile curves were fitted on the three healthy cohorts. Next, percentile values for these subcortical structures for individual patients from these three cohorts, 91 mild cognitive impairment and 95 Alzheimer's disease cases and patients from the Alzheimer Center, were calculated, based on the distributions of each of the three cohorts. Overall, we found that the subcortical volume normative data from these cohorts are highly interchangeable, suggesting more flexibility in clinical implementation.

A radial structure tensor and its use for shape-encoding medical visualization of tubular and nodular structures.

The concept of curvature and shape-based rendering is beneficial for medical visualization of CT and MRI image volumes. Color-coding of local shape properties derived from the analysis of the local Hessian can implicitly highlight tubular structures such as vessels and airways, and guide the attention to potentially malignant nodular structures such as tumors, enlarged lymph nodes, or aneurysms. For some clinical applications, however, the evaluation of the Hessian matrix does not yield satisfactory renderings, in particular for hollow structures such as airways, and densely embedded low contrast structures such as lymph nodes. Therefore, as a complement to Hessian-based shape-encoding rendering, this paper introduces a combination of an efficient sparse radial gradient sampling scheme in conjunction with a novel representation, the radial structure tensor (RST). As an extension of the well-known general structure tensor, which has only positive definite eigenvalues, the radial structure tensor correlates position and direction of the gradient vectors in a local neighborhood, and thus yields positive and negative eigenvalues which can be used to discriminate between different shapes. As Hessian-based rendering, also RST-based rendering is ideally suited for GPU implementation. Feedback from clinicians indicates that shape-encoding rendering can be an effective image navigation tool to aid diagnostic workflow and quality assurance.

2D/3D registration for X-ray guided bronchoscopy using distance map classification.

In X-ray guided bronchoscopy of peripheral pulmonary lesions, airways and nodules are hardly visible in X-ray images. Transbronchial biopsy of peripheral lesions is often carried out blindly, resulting in degraded diagnostic yield. One solution of this problem is to superimpose the lesions and airways segmented from preoperative 3D CT images onto 2D X-ray images. A feature-based 2D/3D registration method is proposed for the image fusion between the datasets of the two imaging modalities. Two stereo X-ray images are used in the algorithm to improve the accuracy and robustness of the registration. The algorithm extracts the edge features of the bony structures from both CT and X-ray images. The edge points from the X-ray images are categorized into eight groups based on the orientation information of their image gradients. An orientation dependent Euclidean distance map is generated for each group of X-ray feature points. The distance map is then applied to the edge points of the projected CT images whose gradient orientations are compatible with the distance map. The CT and X-ray images are registered by matching the boundaries of the projected CT segmentations to the closest edges of the X-ray images after the orientation constraint is satisfied. Phantom and clinical studies were carried out to validate the algorithm's performance, showing a registration accuracy of 4.19(± 0.5) mm with 48.39(± 9.6) seconds registration time. The algorithm was also evaluated on clinical data, showing promising registration accuracy and robustness.

Multi-modality morphological correlation of axillary lymph nodes.

PURPOSE: The goal of this study is to develop a computerized method that identifies a specific axillary lymph node (ALN) seen on ultrasound (US) with its most likely corresponding node on breast MRI (BMRI). This goal is an important step in developing a preoperative non-invasive method for staging breast cancer on the basis of multi-modality imaging. METHODS: Twenty patients with newly diagnosed breast cancer were scanned on US and MRI. Two expert breast imaging radiologists independently correlated ALNs seen on US with BMRI, and this correlation was used as the gold standard. To correlate ALNs on US and BMRI, the cortex and hilum of each ALN was segmented using an ellipse fitting algorithm, then the ALN long and short axes and maximum cortical thickness (MCT) were computed. Three ALNs were chosen as candidates from the BMRI datasets for each lymph node seen on US. Finally, the Euclidean distances across all measurements between the US ALN and each of the three BMRI candidates were computed, and the smallest distance was reported as the correlation result. RESULTS: Using the expert radiologists identified correlated BMRI slice as the ground truth, the shortest Euclidean distance successfully identified the same lymph node as the radiologists in 13 out of 16 ALNs (81.25%). In negative ALNs, the standard deviation for long and short axes was relatively large but that of maximum cortical thickness was small. Average maximum cortical thickness and its standard deviation measured in US were very close to those measured in MRI. There were no significant differences among the long axis, short axis, and MCT measurements between US and MRI-T2 weighted sequence (P > 0.05 paired t-test). CONCLUSION: We performed a feasibility study which showed that computerized measurements of ALNs might be used to identify the same ALN on different modalities such as US and BMRI. This type of correlation would be valuable as it would allow the use of combined imaging parameters to be applied to the evaluation of ALNs in patients with breast cancer. It is hoped that the combined multi-modality information would provide a more robust non-invasive method of staging the axilla than is currently available.

Spine detection and labeling using a parts-based graphical model.

The detection and extraction of complex anatomical structures usually involves a trade-off between the complexity of local feature extraction and classification, and the complexity and performance of the subsequent structural inference from the viewpoint of combinatorial optimization. Concerning the latter, computationally efficient methods are of particular interest that return the globally-optimal structure. We present an efficient method for part-based localization of anatomical structures which embeds contextual shape knowledge in a probabilistic graphical model. It allows for robust detection even when some of the part detections are missing. The application scenario for our statistical evaluation is spine detection and labeling in magnetic resonance images.