James W. Gault

Multiple Fault Detection in Combinational Networks

Combinational networks with no internal fan-out are considered from the point of view of testing for multiple faults. Several different approaches utilizing added inputs and observable outputs are considered and the tradeoffs are discussed.

Magnetic resonance image restoration

We introduce a novel technique for Magnetic Resonance Image (MRI) restoration, using a physical model (spin equation) and corresponding basis images. We determine the basis images (proton density and nuclear relaxation times) from the MRI data and use them to obtain excellent restorations.Magnetic Resonance Images depend nonlinearly on proton density,ρ, two nuclear relaxation times,T1 andT2, and two control parameters, TE and TR. We model images a Markov random fields and introduce two maximuma posteriori (MAP) restorations; quadratic smoothing and a nonlinear technique. We also introduce a novel method of global optimization necessary for the nonlinear technique.

The effects of various basis image priors on MR image MAP restoration

We extend a previously reported technique for Magnetic Resonance Image (MRI) restoration, using a physical model (spin equation) and corresponding basis images. We determine the basis images (proton density and nuclear relaxation times) from the MRI data and use them to obtain excellent restorations.Magnetic Resonance Images depend nonlinearly on proton density,ρ, two nuclear relaxation times,T1 andT2, and two control parameters, TE and TR. We model images as Markov random fields and introduce four maximuma posteriori (MAP) restorations, nonlinear techniques using several different prior terms which reduce the correlated noise in the basis images, thereby reducing the noise in the restored MR images. The “product” and “sum” forms for basis (signal) and spatial correlations are discussed, compared and evaluated for various situations and features.

Noise Removal from Multiple MRI Images

We introduce a novel technique for magnetic resonance image (MRI) restoration, using a physical model (spin equation). We determine a set of three basis images (proton density and nuclear relaxation times) from the MRI data using a nonlinear optimization method, and use those images to obtain restorations of the original image. MRIs depend nonlinearly on proton density, two nuclear relaxation times, T1 and T2, and two control parameters, echo time (TE) and relaxation time (TR). We model images as Markov random fields and introduce a maximum a posteriori restoration method, based on nonlinear optimization, which reduces noise while preserving resolution.

Magnetic resonance image analysis

Our intent is to obtain images which most clearly differentiate soft tissue types in Magnetic Resonance Image data. We model the three unknown intrinsic parameter images and the data images as Markov random fields and compare maximum likelihood restorations with two maximum a posteriori (MAP) restorations. The mathematical model of the imaging process is strongly nonlinear in the region of interest, but does not appear to introduce local minima in the resulting constrained multidimensional optimization procedure. The application of non- quadratic prior probabilities however does require global optimization. We have developed a unique approach towards image restoration that produces images with significant improvements when compared to the original data. We have extended previous results that attempt to determine the intrinsic parameters from the MRI data, and have used these intrinsic parameter images to synthesize MR images. MR images with different TE and TR parameters do not require additional use of an MR scanner, since excellent synthetic MR images are obtained using the restored proton density and nuclear relaxation time images.

The design of a user-programmable digital interface (Recent Results)

An interface for digital computers and peripherals is described in this paper. The design process is traced, beginning with the definition of the problem environment, and the derivation of primitive interfacing functions. The functions are associated with four functional classes; data input/output, data storage, data manipulation, and control. Interface capabilities range from control over the synchronization of input and output pulse data to control over the data word widths acceptable. System limitations include technical, timing, and synchronization problems. The interface is modular, generalized, and user programmable. The control is contained in two levels: a user microprogram, and a read only nanoprogram.