Diagnostic and therapeutic update on primary duodenogastric reflux

The authors, after some remarks on the pathophysiology of primary alkaline gastritis, analyze the main methods, available at present, for diagnosis; they particularly consider the importance of endoscopy and histology, gastroesophageal 24-hours pH-metry and 99mTc HIDA scintigraphy. Successively, they present the criteria for the identification of patients who will predictably benefit from surgical treatment, and examine advantages and limits of the two main corrective surgical techniques for the duodenogastric reflux in patients not previously operated on the gastrointestinal tract: duodenal extramucosal myotomy according to Mattioli, relatively easy to perform, and the "duodenal switch" according to De Meester, technically more complex, but pathophysiologically more acceptable, completely eliminating the duodenogastric reflux.     

Minimal-scan filtered backpropagation algorithms for diffraction tomography

The filtered backpropagation (FBPP) algorithm, originally developed by Devaney [Ultrason. Imaging 4, 336 (1982)], has been widely used for reconstructing images in diffraction tomography. It is generally known that the FBPP algorithm requires scattered data from a full angular range of 2 pi for exact reconstruction of a generally complex-valued object function. However, we reveal that one needs scattered data only over the angular range 0 < or = phi < or = 3 pi/2 for exact reconstruction of a generally complex-valued object function. Using this insight, we develop and analyze a family of minimal-scan filtered backpropagation (MS-FBPP) algorithms, which, unlike the FBPP algorithm, use scattered data acquired from view angles over the range 0 < or = phi < or = 3 pi/2. We show analytically that these MS-FBPP algorithms are mathematically identical to the FBPP algorithm. We also perform computer simulation studies for validation, demonstration, and comparison of these MS-FBPP algorithms. The numerical results in these simulation studies corroborate our theoretical assertions.     

An improved reconstruction algorithm for 3-D diffraction tomography using spherical-wave sources

Diffraction tomography (DT) is an inversion technique that reconstructs the refractive index distribution of a weakly scattering object. In this paper, a novel reconstruction algorithm for three-dimensional diffraction tomography employing spherical-wave sources is mathematically developed and numerically implemented. Our algorithm is numerically robust and is much more computationally efficient than the conventional filtered backpropagation algorithm. Our previously developed algorithm for DT using plane-wave sources is contained as a special case.     

Numerically robust minimal‐scan reconstruction algorithms for diffraction tomography via radon transform inversion

It is widely believed that measurements from a full angular range of 2π are generally required to exactly reconstruct a complex‐valued refractive index distribution in diffraction tomography (DT). In this work, we developed a new class of minimal‐scan reconstruction algorithms for DT that utilizes measurements only over the angular range 0 ≤ ? ≤ 3π/2 to perform an exact reconstruction. These algorithms, referred to as minimal‐scan estimate‐combination (MS‐E‐C) reconstruction algorithms, effectively operate by transforming the DT reconstruction problem into a conventional x‐ray CT reconstruction problem that requires inversion of the Radon transform. We performed computer simulations to compare the noise and numerical properties of the MS‐E‐C algorithms against existing filtered backpropagation‐based algorithms. © 2002 Wiley Periodicals, Inc. Int J Imaging Syst Technol 12, 84–91, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.10014     

Analysis and neural network modeling of the nonlinear correlates of habituation in the vestibulo-ocular reflex

Through the process of habituation, continued exposure to low-frequency (0.01 Hz) rotation in the dark produced suppression of the low-frequency response of the vestibulo-ocular reflex (VOR) in goldfish. The response did not decay gradually, as might be expected from an error-driven learning process, but displayed several nonlinear and nonstationary features. They included asymmetrical response suppression, magnitude-dependent suppression for lower- but not higher-magnitude head rotations, and abrupt-onset suppressions suggestive of a switching mechanism. Microinjection of lidocaine into the vestibulocerebellum of habituated goldfish resulted in a temporary dishabituation. This suggests that the vestibulocerebellum mediates habituation, presumably through Purkinje cell inhibition of vestibular nuclei neurons. The habituated VOR data were simulated with a feed-forward, nonlinear neural network model of the VOR in which only Purkinje cell inhibition of vestibular nuclei neurons was varied. The model suggests that Purkinje cell inhibition may switch in to introduce nonstationarities, and cause asymmetry and magnitude-dependency in the VOR to emerge from the essential nonlinearity of vestibular nuclei neurons.     

Half-time image reconstruction in thermoacoustic tomography

Thermoacoustic tomography (TAT) is an emerging imaging technique with great potential for a wide range of biomedical imaging applications. In this paper, we propose and investigate reconstruction approaches for TAT that are based on the half-time reflectivity tomography paradigm. We reveal that half-time reconstruction approaches permit for the explicit control of statistically complementary information that can result in the optimal reduction of image variances. We also show that half-time reconstruction approaches can mitigate image artifacts due to heterogeneous acoustic properties of an object. Reconstructed images and numerical results produced from simulated and experimental TAT measurement data are employed to demonstrate these effects.     

Optimization and FROC analysis of rule-based detection schemes using a multiobjective approach

Computerized detection schemes have the potential of increasing diagnostic accuracy in medical imaging by alerting radiologists to lesions that they initially overlooked. These schemes typically employ multiple parameters such as threshold values or filter weights to arrive at a detection decision. In order for the system to have high performance, the values of these parameters need to be set optimally. Conventional optimization techniques are designed to optimize a scalar objective function. The task of optimizing the performance of a computerized detection scheme, however, is clearly a multiobjective problem: we wish to simultaneously improve the sensitivity and false-positive rate of the system. In this work we investigate a multiobjective approach to optimizing computerized rule-based detection schemes. In a multiobjective optimization, multiple objectives are simultaneously optimized, with the objective now being a vector-valued function. The multiobjective optimization problem admits a set of solutions, known as the Pareto-optimal set, which are equivalent in the absence of any information regarding the preferences of the objectives. The performances of the Pareto-optimal solutions can be interpreted as operating points on an optimal free-response receiver operating characteristic (FROC) curve, greater than or equal to the points on any possible FROC curve for a given dataset and detection scheme. It is demonstrated that generating FROC curves in this manner eliminates several known problems with conventional FROC curve generation techniques for rule-based detection schemes. We employ the multiobjective approach to optimize a rule-based scheme for clustered microcalcification detection that has been developed in our laboratory.     

Local cone-beam tomography image reconstruction on chords

We develop reconstruction algorithms for local cone-beam tomography for use with generalized scanning trajectories. The algorithms are grounded theoretically in a recently developed chord-based theory for exact image reconstruction and principles of lambda tomography. Being chord based, they are distinct mathematically and conceptually from conventional local tomography reconstruction algorithms. The salient feature of our algorithms is that they permit reconstruction of discontinuities in the profiles of the object function along chords. By consideration of all possible chords, a 3D image that describes the locations of object discontinuities can be reconstructed. Results from microlocal analysis are applied for understanding the object features that can be reconstructed stably by use of the algorithms. A computer-simulation study is conducted to demonstrate the algorithms and compare their performance with an existing algorithm.     

Multiobjective genetic optimization of diagnostic classifiers withimplications for generating receiver operating characteristiccurves

It is well understood that binary classifiers have two implicit objective functions (sensitivity and specificity) describing their performance. Traditional methods of classifier training attempt to combine these two objective functions (or two analogous class performance measures) into one so that conventional scalar optimization techniques can be utilized. This involves incorporating a priori information into the aggregation method so that the resulting performance of the classifier is satisfactory for the task at hand. We have investigated the use of a niched Pareto multiobjective genetic algorithm (GA) for classifier optimization. With niched Pareto GA's, an objective vector is optimized instead of a scalar function, eliminating the need to aggregate classification objective functions. The niched Pareto GA returns a set of optimal solutions that are equivalent in the absence of any information regarding the preferences of the objectives. The a priori knowledge that was used for aggregating the objective functions in conventional classifier training can instead be applied post-optimization to select from one of the series of solutions returned from the multiobjective genetic optimization. We have applied this technique to train a linear classifier and an artificial neural network (ANN), using simulated datasets. The performances of the solutions returned from the multiobjective genetic optimization represent a series of optimal (sensitivity, specificity) pairs, which can be thought of as operating points on a receiver operating characteristic (ROC) curve. All possible ROC curves for a given dataset and classifier are less than or equal to the ROC curve generated by the niched Pareto genetic optimization.