John M. Boone

Analysis and correction of imperfections in the image intensifier-TV-digitizer imaging chain.

Image intensifier-television-video digitizer (IITVD) systems are commonly used for digital planar image acquisition in radiology. However, the well-known distortions inherent in these systems limit their utility in research and in some clinical applications where quantitatively correct images are required. Software correction techniques have been implemented which restore both the spatial and grey scale quantitative integrity, allowing IITVD systems to be used as analytical research tools. Previously reported and novel correction techniques were used to reduce veiling glare, pincushion distortion, dc bias, and residual shading effects. The results indicate that excellent quantitative integrity can be achieved when these straightforward artifact reduction techniques are employed.

An analytical model of the scattered radiation distribution in diagnostic radiology

A simple scatter model is used to analytically derive the point spread function (PSF) for scattered radiation in diagnostic radiology. The resulting equation is a function of four physical parameters; object thickness, object-to-detector distance (air gap), and the linear attenuation coefficients for both primary and scatter radiation. Though the model is based upon single scattering, it is shown that by reducing the scatter attenuation coefficient the analytic model compares well to the multiple scattering PSF determined using Monte Carlo analysis.

Dual‐energy mammography: A detector analysis

Dual-energy mammography acquisition scenarios employing single-shot techniques are examined using computer simulation. A figure of merit of the signal-to-noise ratio squared over the glandular dose was chosen for the optimization task due to its exposure independence. Doses were evaluated using Monte Carlo techniques. The effects of kilovoltage, prepatient filtration, front detector thickness, mid-detector filtration thickness and composition were studied. Of the six detector pairs studied (Y2O2S/Gd2O2S, SrFBr/BaFBr, Y2O2S/LaOBr, Y2O2S/CaWO4, Y2O2S/YTaO4, and Y2O2S/LuTaO4), Y2O2S/Gd2O2S and SrFBr/BaFBr were found to be the best combinations. The effects of scatter and signal quantization were also examined. An alternative display technique whereby the tissue-subtracted (i.e., calcium) image is colorized and overlaid onto the conventional mammogram is introduced.

Monte Carlo simulation of the scattered radiation distribution in diagnostic radiology.

Monte Carlo techniques were employed to evaluate the point spread function (PSF) of scattered radiation in diagnostic radiology. The Monte Carlo procedure is described and shown to compare well with Monte Carlo scatter analysis of other authors. The intensity and distribution of the PSF are described independently. The effects of object thickness, air gap, and beam spectra are examined. An analytic derivation of the scatter PSF is presented in a companion article, and the Monte Carlo results discussed herein are used for comparison.

Neural networks in radiologic diagnosis. I. Introduction and illustration.

Artificial neural networks (NNs) process information in a manner similar to the way the human brain is thought to process information. Neural networks have potential application in radiology as an artificial intelligence technique that can provide computer-aided diagnostic assistance for the practicing radiologist. The basic characteristics of NNs and the manner in which information propagates through an NN are discussed in nontechnical language, to assist the diagnostic radiologist in understanding the basic principles of neurocomputing. Computer-aided diagnosis selection in pediatric chest radiography using NNs is discussed in a companion article.

Recognition of chest radiograph orientation for picture archiving and communications systems display using neural networks

A neural network classification scheme was developed that enables a picture archiving and communications system workstation to determine the correct orientation of posteroanterior or anteroposterior chest images. This technique permits thoracic images to be displayed conventionally when called up on the workstation, and therefore reduces the need for reorientation of the image by the observer. Feature data were extracted from 1,000 digitized chest radiographs and used to train a two-layer neural network designed to classify the image into one of the eight possible orientations for a posteroanterior chest image. Once trained, the neural network identified the correct image orientation in 888 of 1,000 images that had not previously been seen by the neural network. Of the 112 images that were incorrectly classified, 106 were mirror images of the correct orientation, whereas only 6 actually had the caudal-cranial axis aligned incorrectly. The causes for misalignment are discussed.

Parametrized x‐ray absorption in diagnostic radiology from Monte Carlo calculations: Implications for x‐ray detector design

The integral dose to the patient and image signal to noise ratio (SNR) are inexorably coupled in x-ray-based diagnostic imaging. Advancements and optimal design of imaging devices need to consider the SNR as well as patient dose. The figure of merit, FOM = (SNR)2/(integral dose), is a useful parameter in optimizing detector designs because it is independent of input exposure, and therefore eliminates exposure as a design consideration. Although numerical calculation of the SNR is relatively straightforward in most cases, the integral dose calculation is made complex due to its scatter components high dependency on both x-ray energy and patient thickness. Monte Carlo calculations over a range of monoenergetic x-ray energies were used to calculate total energy absorption, and the results are parametrized using polynomial expressions. The results are shown to be applicable to any arbitrary polyenergetic spectrum. An example using the above FOM is given to illustrate the utility of the parametrized results. The parametrized results may prove useful in the computer simulations of x-ray detector systems where the above FOM is utilized.

X‐ray spectral reconstruction from attenuation data using neural networks

An artificial neural network using input data derived from attenuation measurements was trained to generate spectral profiles (relative number of photons versus energy). Once the relative spectral distribution is reconstructed, absolute spectra (number of photons per unit exposure spectral distribution is reconstructed, absolute spectra (number of photons per unit exposure versus energy) can be calculated. A neural network was trained on spectra generated mathematically using the Birch-Marshall model, combined with attenuation data, calculated from the spectra by numerical integration. Whereas attenuation data can be calculated in a straightforward manner from the x-ray spectra, the reverse is not true. Several neural networks were successfully taught to reconstruct the spectra, given the attenuation data. The networks were tested using kV/inherent filtration combinations that were not in the training set, and the performance of the reconstruction was excellent. Noise in the attenuation data was simulated to test the effects of noise propagation in the reconstruction. The effects of network architecture and data averaging on noise propagation were investigated. Experimentally determined spectral data complied by Fewell were also used to train a neural network, and the results of the reconstruction were also found to be excellent.

A fluoroscopy-based computed tomography scanner for small specimen research

RATIONALE AND OBJECTIVES A small-laboratory computed tomography (CT) system using a fluoroscopic system and a personal computer was fabricated and tested. The motivation for building this specimen scanner was to provide medical researchers with the capability of using CT as a practical tool in their research, as well as to provide an opportunity for hands-on CT instruction. METHODS AND MATERIALS The CT system was constructed using mostly off-the-shelf items; however, the CT stage itself was custom fabricated and software development was necessary. In addition, a personal computer and a standard fluoroscopy system were used. RESULTS The spatial resolution was found to match the 228-microns sampling limitation, yielding approximately 2 line pairs per mm. Iodine contrast sensitivity studies showed that 1% solution of 370 mg/ml iodine solution was easily detected (P = .05). CONCLUSIONS A small CT scanner for specimen research can be economically constructed, and is capable of good performance. The authors found substantial interest on the part of small animal researchers involved in a wide variety of medical research.

Radiographic equalization using computer-controlled filter wheels

Equalization is advantageous in some radiographic applications in order to reduce the dynamic range of the x-ray signal incident on the detector. A technique is discussed which employs several filter wheels, which are radio-transparent wheels mounted near the x-ray tube which can be rotated under computer control. The wheels are designed to contain complex patterns of attenuator material on the annular region of each wheel which intersects the x-ray beam. Rotation of the wheels changes the attenuator pattern presented to the x-ray beam, and therefore this system is capable of regional exposure compensation. The use of multiple filter wheels provides a large selection of compensation patterns, for example, an 8 wheel system with 30 patterns per wheel would allow 1011 patterns. Two different design strategies are discussed, one aimed at digital subtraction angiography, and another at chest equalization. Clinical data bases of 191 DSA images and 250 chest radiographs were employed with computer simulation to evaluate the potential of the filter wheel equalization technique.