Norbert Pelc is Professor of Radiology and Bioengineering, and Electrical Engineering (by courtesy) at Stanford. An expert in biomedical imaging, he received a doctorate in Medical Radiological Physics from Harvard in 1979. Dr. Pelc worked at GE Medical Systems (1978-1990) where he was involved in research in all the major imaging modalities and was instrumental in the development of CT, MRI, and digital radiography. He joined Stanford in 1990. In 2002, Dr. Pelc was named the Associate Chair for Research of the Radiology department. He served on a number of review panels and was a member of the NIBIB National Advisory Council. He is a Fellow of the International Society of Magnetic Resonance in Medicine, the American Association of Physicist in Medicine, the American Institute for Medical and Biological Engineering, and the American Heart Association. Dr. Pelc is an author of more than 165 peer-reviewed papers and more than 275 abstracts presented at scientific conferences, and he is the inventor of 81 issued US patents.

**Publications**(65)

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^{2}square CdTe macro-pixel were compared: a 4×4 grid, 2×2 grid, or 1×1 composed of pixels with side length 250 μm, 500 μm, or 1 mm, respectively. The same flux was applied to each pixel, and pulse pile-up was ignored. The mean and covariance matrix of measured photon counts is derived analytically using pre-computed spatio-energy response functions (SERF) estimated from Monte Carlo simulations. Based on the Cramer-Rao Lower Bound, a macro-pixel with 250×250 μm

^{2}sub-pixels shows ~2.2 times worse variance than a single 1 mm

^{2}pixel for spectral imaging, while its penalty for effective monoenergetic imaging is <10% compared to a single 1 mm

^{2}pixel.

**Purpose:**Photon counting x-ray detectors (PCXD) may improve dose-efficiency but are hampered by limited count rate. They generally have imperfect energy response. Multi-layer ("in-depth") detectors have been proposed to enable higher count rates but the potential benefit of the depth information has not been explored. We conducted a simulation study to compare in-depth detectors against single layer detectors composed of common materials. Both photon counting and energy integrating modes were studied.

**Methods:**Polyenergetic transmissions were simulated through 25cm of water and 1cm of calcium. For PCXD composed of Si, GaAs or CdTe a 120kVp spectrum was used. For energy integrating x-ray detectors (EIXD) made from GaAs, CdTe or CsI, spectral imaging was done using 80 and 140kVp and matched dose. Semi-ideal and phenomenological energy response models were used. To compare these detectors, we computed the Cramér-Rao lower bound (CRLB) of the variance of basis material estimates.

**Results:**For PCXDs with perfect energy response, depth data provides no additional information. For PCXDs with imperfect energy response and for EIXDs the improvement can be significant. E.g., for a CdTe PCXD with realistic energy response, depth information can reduce the variance by ~50%. The improvement depends on the x-ray spectrum. For a semi-ideal Si detector and a narrow x-ray spectrum the depth information has minimal advantage. For EIXD, the in-depth detector has consistent variance reduction (15% and 17%~19% for water and calcium, respectively).

**Conclusions:**Depth information is beneficial to spectral imaging for both PCXD and EIXD. The improvement depends critically on the detector energy response.

^{-6}cm. Using the same protocol, MLE showed variance-to- CRLB ratio and average bias of 1.0186 ± 0.0002 and (3.10 ± 0.06) x 10

^{-6}cm, respectively, but was 50 times slower in our simulation. Compared to the A-Table method, TLSE gives a more homogenous variance-to-CRLB profile in the operating region. We show that variance-to-CRLB for TLSE is lower by as much as ~36% than A-Table method in the peripheral region of operation (thin or thick objects). The TLSE is a computationally efficient and fast method for implementing material separation technique in PCXDs, with performance parameters comparable to the MLE.

_{2}O

_{2}S screen as our filter and used an acrylic-copper step wedge phantom. Data were collected at 70 and 125 kVp, with and without the filter. The tube current was adjusted to make the exposure rate with and without the filter to be roughly the same. The data were decomposed into basis material images and the variance of the decomposition was measured for each acrylic-copper thickness pair. Simulations were done with the same experimental settings for comparison and validation. The experiments verified that a Gd filter can reduce the variance at fixed dose. The variance reduction is monotonically stronger as the object becomes more attenuating. This study demonstrates the potential of fixed Gd filtration to improve the dose efficiency and material decomposition precision for rapid kVp-switching dual energy systems.

A fast 3D reconstruction algorithm for inverse-geometry CT based on an exact PET rebinning algorithm

*a priori*knowledge. This work focuses on estimating the 0

^{th}and 1

^{st}moments of an image, which can be used to extrapolate a set of truncated projections. If some projections are not truncated, then accurate estimation of the moments can be achieved using only those projections. The more difficult case arises when all projections are truncated by some amount. For this case we make simplifying assumptions and fit the truncated projections with elliptical profiles. From this fit, we estimate the 0

^{th}and 1

^{st}moments of the original image. These estimated moments are then used to perform an extrapolation of the truncated projections, where each projection meets a constraint based on the 0

^{th}and 1

^{st}moments (moment extrapolation). This work focuses on how accurate moment estimates must be for moment extrapolation to be effective. The algorithm was tested on simulated and real data for the head, thorax, and abdomen, and those results were compared to symmetric mirroring by Ohnesorge et al., another extrapolation technique that requires no

*a priori*knowledge. Overall, moment estimation and mass extrapolation alleviates a large amount of image artifact, and can improve on other extrapolation techniques. For the real CT head and abdominal data, the average reconstruction error for mass extrapolation was 48% less than the reconstruction error for symmetric mirroring.

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