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In 2016, we published a method for processing dual-energy computed tomography (DECT) data called system-independent rho-e/Z-e ( $\rho _{\mathrm {e}}/Z_{\mathrm {e}}$ ) or SIRZ. Using data from multiple DECT systems and spectra, SIRZ estimated the electron density $\rho _{\mathrm {e}}$ and effective atomic number $Z_{\mathrm {e}}$ (based on published X-ray cross sections), for a set of known specimens. However, the decomposition process required complex spectral modeling of the DECT system, which made SIRZ difficult to implement and automate. This paper describes the subsequent work on “SIRZ-2” to simplify the spectral modeling, automate the process, and improve its range and versatility. The SIRZ-2 basis functions are more accurate for the X-ray energy range ( $Z_{\mathrm {e}}$ of 6–20) were scanned with multiple spectral pairs (up to 200 keV) on seven different DECT systems, including a commercial airport luggage scanner. For these varied tests, the average SIRZ-2 relative errors for $\rho _{\mathrm {e}}$ estimates were four times lower (0.7% compared to 3.0% for SIRZ), while average $Z_{\mathrm {e}}$ relative errors were comparably low for both methods ( $\rho _{\mathrm {e}}$ , $Z_{\mathrm {e}}$ ) that can be directly compared across DECT systems and over time.

We present a new decomposition approach for dual-energy computed tomography (DECT) called SIRZ that provides precise and accurate material description, independent of the scanner, over diagnostic energy ranges (30 to 200 keV). System independence is achieved by explicitly including a scanner-specific spectral description in the decomposition method, and a new X-ray-relevant feature space. The feature space consists of electron density, ${\rho _{\rm e}}$ , and a new effective atomic number, ${{\rm Z}_{\rm e}}$ , which is based on published X-ray cross sections. Reference materials are used in conjunction with the system spectral response so that additional beam-hardening correction is not necessary. The technique is tested against other methods on DECT data of known specimens scanned by diverse spectra and systems. Uncertainties in accuracy and precision are less than 3% and 2% respectively for the ( ${\rho _{\rm e}}$ , ${{\rm Z}_{\rm e}}$ ) results compared to prior methods that are inaccurate and imprecise (over 9%).

An efficient method for accurately calculating the center-of-rotation, or projection center, for parallel computed tomography projection data, or sinograms, is described. This method uses all the data in the sinogram to estimate the center by a least-squares technique and requires no previous calibration scan. The method also finds the object's center-of-mass without reconstructing its image. Since the method uses the measured data, it is sensitive to noise in the measurements, but that sensitivity is relatively small compared to other techniques. Examples of its use on simulated and actual data are included. For fan-beam data over 360 degrees , two related methods are described to find the center in the presence or absence of a midline offset. >

x-Ray computed axial tomography (CT) provides cross-sectional views of materials, components, and assemblies for industrial non-destructive evaluation. We have applied CT imaging to quantitatively measure the 3-D distribution ogf x-ray attenuation at reasonably high resolutions. In our industrial x-ray CT-studies, we have centered on two technical approaches: a first-generation translate/rotate CT system that consist of well-collimated (∼ 0.55 mm) photon source detector, and a third-generation scanner that uses a fluoroscopy detector.

A class of hybrid domain-iterative algorithms for 2-D image reconstruction is described. The consistent iterative reconstruction-reprojection (CIRR) algorithm was designed and implemented along with other classical domain-iterative methods. The algorithms are tested on the hollow projections problem, where simple object models are assumed known. Results show that the CIRR algorithm is robust and accurate, and converges quickly. >

Our Computerized Ultrasound Risk Evaluation (CURE) system has been developed to the engineering prototype stage and generated unique data sets of both transmission and reflection ultrasound (US). This paper will help define the clinical underpinnings of the developmental process and interpret the imaging results from a similar perspective. The CURE project was designed to incorporate numerous diagnostic parameters to improve upon two major areas of early breast cancer detection. CURE may provide improved tissue characterization of breast masses and reliable detection of abnormal microcalcifications found in some breast cancers and ductal carcinoma in situ (DCIS). Current breast US is limited to mass evaluation, whereas mammography also detects and guides biopsy of malignant calcifications. Screening with CURE remains a distant goal, but improved follow-up of mammographic abnormalities may represent a feasible breakthrough.