Peter Santago

Quantification of MR brain images by mixture density and partial volume modeling

The problem of automatic quantification of brain tissue by utilizing single-valued (single echo) magnetic resonance imaging (MRI) brain scans is addressed. It is shown that this problem can be solved without classification or segmentation, a method that may be particularly useful in quantifying white matter lesions where the range of values associated with the lesions and the white matter may heavily overlap. The general technique utilizes a statistical model of the noise and partial volume effect together with a finite mixture density description of the tissues. The quantification is then formulated as a minimization problem of high order with up to six separate densities as part of the mixture. This problem is solved by tree annealing with and without partial volume utilized, the results compared, and the sensitivity of the tree annealing algorithm to various parameters is exhibited. The actual quantification is performed by two methods: a classification-based method called Bayes quantification, and parameter estimation. Results from each method are presented for synthetic and actual data.

Statistical models of partial volume effect

Statistical models of partial volume effect for systems with various types of noise or pixel value distributions are developed and probability density functions are derived. The models assume either Gaussian system sampling noise or intrinsic material variances with Gaussian or Poisson statistics. In particular, a material can be viewed as having a distinct value that has been corrupted by additive noise either before or after partial volume mixing, or the material could have nondistinct values with a Poisson distribution as might be the case in nuclear medicine images. General forms of the probability density functions are presented for the N material cases and particular forms for two- and three-material cases are derived. These models are incorporated into finite mixture densities in order to more accurately model the distribution of image pixel values. Examples are presented using simulated histograms to demonstrate the efficacy of the models for quantification. Modeling of partial volume effect is shown to be useful when one of the materials is present in images mainly as a pixel component.

Performance evaluation of filtered backprojection reconstruction and iterative reconstruction methods for PET images

The filtered backprojection (FBP) algorithm and statistical model based iterative algorithms such as the maximum likelihood (ML) reconstruction or the maximum a posteriori (MAP) reconstruction are the two major classes of tomographic reconstruction methods. The FBP method is widely used in clinical setting while iterative methods have attracted research interests in the past decade. In this paper we studied the performance of the FBP, the ML and the MAP methods using simulated projection data. The experiment showed that the MAP algorithm generated superior image quality in terms of the bias, the variance, and the average mean squared error (MSE) measures.

Semi-automated boundary detection for intravascular ultrasound

Automated boundary detection in intracoronary ultrasound images is hampered by significant time and space variant specular artifacts and incident angle dependent variations in signal intensity from anatomic boundaries. To overcome these problems, the authors have employed a variety of preprocessing steps coupled with minimal operator input to guide an automated boundary detection technique using compensated simulated annealing and linear programming methods. The semi-automated methods work well and provide reproducible estimates of luminal and vessel dimensions under realistic conditions. The methods and preliminary results of such a semi-automated boundary estimation technique are described.<<ETX>>

Extracolonic Findings at Virtual Colonoscopy: An Important Consideration in Asymptomatic Colorectal Cancer Screening

BackgroundVirtual colonoscopy has been evaluated for use as a colorectal cancer screening tool, and in prior studies, it has been estimated that the evaluation of extra-colonic findings adds

Compressive sampling based interior reconstruction for dynamic carbon nanotube micro-CT

28-

Differential Evolution Approach for Regularized Bioluminescence Tomography

34 per patient studied.MethodsAs an ancillary study to a prospective cohort study comparing virtual colonoscopy to conventional colonoscopy for colorectal cancer detection, the investigators retrospectively determined the number and estimated costs of all clinic visits, imaging and laboratory studies, and medical procedures that were generated as a direct result of extra-colonic findings at virtual colonoscopy.ResultsWe enrolled 143 subjects who underwent CTC followed by conventional colonoscopy. Data were available for 136 subjects, and 134 (98%) had at least one extra-colonic finding on CT. Evaluation of extra-colonic findings was performed in 32 subjects (24%). These subjects underwent 73 imaging studies, 30 laboratory studies, 44 clinic visits, 6 medical procedures, and 44 new or return outpatient visits over a mean of 38 months following the CTC. The most common findings causing further evaluation were lung nodules and indeterminate kidney lesions. No extra-colonic malignancies were found in this study. A total of

Genetic algorithms and self-organizing maps: a powerful combination for modeling complex QSAR and QSPR problems

33,690 was spent in evaluating extra-colonic findings, which is

Automatic Colon Segmentation with Dual Scan CT Colonography

248 per patient enrolled.ConclusionsThe cost of the evaluation of extra-colonic findings following virtual colonoscopy may be much higher in actual practice than is suggested by prior studies. This will impact the cost-effectiveness of using virtual colonoscopy for asymptomatic colorectal cancer screening and underscores the importance of standardizing the reporting of extra-colonic findings to encourage appropriate follow-up.

An adaptive template-matching method and its application to the boundary detection of brachial artery ultrasound scans

In the computed tomography (CT) field, one recent invention is the so-called carbon nanotube (CNT) based field emission x-ray technology. On the other hand, compressive sampling (CS) based interior tomography is a new innovation. Combining the strengths of these two novel subjects, we apply the interior tomography technique to local mouse cardiac imaging using respiration and cardiac gating with a CNT based micro-CT scanner. The major features of our method are: (1) it does not need exact prior knowledge inside an ROI; and (2) two orthogonal scout projections are employed to regularize the reconstruction. Both numerical simulations and in vivo mouse studies are performed to demonstrate the feasibility of our methodology.