Paul Licato
Adjunct Associate Professor | Milwaukee School of Engineering
Milwaukee, WI, UNITED STATES
Paul Licato is an expert in the physics and clinical applications of medical imaging.
Education, Licensure and Certification (2)
M.S.: Physics, University of Wisconsin-Milwaukee 1984
B.S.: Mathematics and Physics, University of Wisconsin-Milwaukee 1982
Biography
Paul Licato is an adjunct assistant professor in the Electrical, Computer and Biomedical Engineering Department at MSOE. He has been a technical innovator in the medical imaging field for more than 30 years. He spent 26 years at GE Healthcare where he contributed to the development of ultra-fast MRI imaging, dual energy computed tomography (CT), CT brain perfusion and image reconstruction improvements for positron emission tomography (PET). He managed several clinical research patient studies in support of the development and regulatory approval of PET/MRI hybrid scanning, interventional x-ray and digital breast tomosynthesis mammography. He has 22 U.S. Patents. Mr. Licato has a BS and MS in physics from the University of Wisconsin - Milwaukee.
Areas of Expertise (7)
Medical Device Clinical Research
X-Ray Imaging
Computed Tomography
Medical Imaging
Magnetic Resonance Imaging
Positron Emission Tomography
Medical Device Regulation
Patents (22)
Clinical review and analysis work flow for lung nodule assessment
U.S. Patent # 8,732,601
2014
Smoothing of dynamic data sets
U.S. Patent # 8,682,051
2014
Methods and apparatus for relative perfusion and/or viability
U.S. Patent # 8,626,263
2014
Systems and methods for displaying multi-energy data
U.S. Patent # 8,115,784
2012
Method of CT perfusion imaging and apparatus for implementing same
U.S. Patent # 7,933,377
2011
Systems and methods for automated diagnosis
U.S. Patent # 8,233,684
2012
Systems and methods for improving a resolution of an image
U.S. Patent # 7,466,790
2008
Methods and apparatus for reducing noise in images
U.S. Patent # 8,155,466
2012
Magnetic resonance imaging with nested gradient pulses
U.S. Patent # 7,047,062
2006
Method and system for performing high temporal resolution bolus detection using CT image projection data
U.S. Patent # 7,983,460
2011
Modular time masking sequence programming for imaging system
U.S. Patent # 6,788,055
2004
Prediction methods and apparatus
U.S. Patent # 7,606,614
2009
Modular time masking sequence programming for imaging system
U.S. Patent # 6,249,120
2001
Real-time MR section cross-reference on replaceable MR localizer images
U.S. Patent # 6,108,573
2000
Graphic application development system for a medical imaging system
U.S. Patent # 7,020,868
2006
Method for producing physical gradient waveforms in magnetic resonance imaging
U.S. Patent # 6,008,648
1999
Method and apparatus for managing workflow in prescribing and processing medical images
U.S. Patent # 7,020,844
2006
Method for producing an off-center image using an EPI pulse sequence
U.S. Patent # 5,689,186
1997
Nutation angle measurement during MRI prescan
U.S. Patent # 5,416,412
1995
Method and apparatus for defining a three-dimensional imaging
U.S. Patent # 6,757,417
2004
Method and apparatus for managing peripheral devices in a medical imaging system
U.S. Patent # 6,356,780
2002
System architecture for medical imaging systems
U.S. Patent # 6,348,793
2002
Selected Publications (5)
Abdominal CT: Comparison of Adaptive Statistical Iterative and Filtered Back Projection Reconstruction Techniques
RadiologySingh, S., Kalra, M.K., Hsieh, J., Licato, P.E., Do, S., Pien, H.H., Blake, M.A
2010 To compare image quality and lesion conspicuity on abdominal computed tomographic (CT) images acquired with different x-ray tube current–time products (50–200 mAs) and reconstructed with adaptive statistical iterative reconstruction (ASIR) and filtered back projection (FBP) techniques.
Multi-material decomposition of spectral CT images
Physics of Medical ImagingMendonça, P.R., Bhotika, R., Maddah, M., Thomsen, B., Dutta, S., Licato, P.E., Joshi, M.C.
2010 Spectral Computed Tomography (Spectral CT), and in particular fast kVp switching dual-energy computed tomography, is an imaging modality that extends the capabilities of conventional computed tomography (CT). Spectral CT enables the estimation of the full linear attenuation curve of the imaged subject at each voxel in the CT volume, instead of a scalar image in Hounsfield units. Because the space of linear attenuation curves in the energy ranges of medical applications can be accurately described through a two-dimensional manifold, this decomposition procedure would be, in principle, limited to two materials. This paper describes an algorithm that overcomes this limitation, allowing for the estimation of N-tuples of material-decomposed images. The algorithm works by assuming that the mixing of substances and tissue types in the human body has the physicochemical properties of an ideal solution, which yields a model for the density of the imaged material mix. Under this model the mass attenuation curve of each voxel in the image can be estimated, immediately resulting in a material-decomposed image triplet. Decomposition into an arbitrary number of pre-selected materials can be achieved by automatically selecting adequate triplets from an application-specific material library. The decomposition is expressed in terms of the volume fractions of each constituent material in the mix; this provides for a straightforward, physically meaningful interpretation of the data. One important application of this technique is in the digital removal of contrast agent from a dual-energy exam, producing a virtual nonenhanced image, as well as in the quantification of the concentration of contrast observed in a targeted region, thus providing an accurate measure of tissue perfusion.
Comparison of Iterative and Filtered Nack Projection Techniques for Abdominal CT: A Prospective Double-Blinded Clinical Study
American Journal of RoentgenologySingh, S., Kalra, M., Hsieh, J., Doyle, M., Licato, P., Blake, M.
2010
Initial use of fast switched dual energy CT for coronary artery disease
Medical Imaging 2010: Physics of Medical ImagingPavlicek, W., Panse, P., Hara, A., Boltz, T., Paden, R., Yamak, D., Licato, P., Chandra, N., Okerlund, D., Dutta, S., Bhotika, R.
2010 Coronary CT Angiography (CTA) is limited in patients with calcified plaque and stents. CTA is unable to confidently differentiate fibrous from lipid plaque. Fast switched dual energy CTA offers certain advantages. Dual energy CTA removes calcium thereby improving visualization of the lumen and potentially providing a more accurate measure of stenosis. Dual energy CTA directly measures calcium burden (calcium hydroxyapatite) thereby eliminating a separate non-contrast series for Agatston Scoring. Using material basis pairs, the differentiation of fibrous and lipid plaques is also possible. Patency of a previously stented coronary artery is difficult to visualize with CTA due to resolution constraints and localized beam hardening artifacts. Monochromatic 70 keV or Iodine images coupled with Virtual Non-stent images lessen beam hardening artifact and blooming. Virtual removal of stainless steel stents improves assessment of in-stent re-stenosis. A beating heart phantom with 'cholesterol' and 'fibrous' phantom coronary plaques were imaged with dual energy CTA. Statistical classification methods (SVM, kNN, and LDA) distinguished 'cholesterol' from 'fibrous' phantom plaque tissue. Applying this classification method to 16 human soft plaques, a lipid 'burden' may be useful for characterizing risk of coronary disease. We also found that dual energy CTA is more sensitive to iodine contrast than conventional CTA which could improve the differentiation of myocardial infarct and ischemia on delayed acquisitions. These phantom and patient acquisitions show advantages with using fast switched dual energy CTA for coronary imaging and potentially extends the use of CT for addressing problem areas of non-invasive evaluation of coronary artery disease.
Multi-material decomposition of spectral CT images
Medical Imaging 2010: Physics of Medical ImagingMendonça, P.R., Bhotika, R., Maddah, M., Thomsen, B., Dutta, S., Licato, P.E., Joshi, M.C.
2010 Spectral Computed Tomography (Spectral CT), and in particular fast kVp switching dual-energy computed tomography, is an imaging modality that extends the capabilities of conventional computed tomography (CT). Spectral CT enables the estimation of the full linear attenuation curve of the imaged subject at each voxel in the CT volume, instead of a scalar image in Hounsfield units. Because the space of linear attenuation curves in the energy ranges of medical applications can be accurately described through a two-dimensional manifold, this decomposition procedure would be, in principle, limited to two materials. This paper describes an algorithm that overcomes this limitation, allowing for the estimation of N-tuples of material-decomposed images. The algorithm works by assuming that the mixing of substances and tissue types in the human body has the physicochemical properties of an ideal solution, which yields a model for the density of the imaged material mix. Under this model the mass attenuation curve of each voxel in the image can be estimated, immediately resulting in a material-decomposed image triplet. Decomposition into an arbitrary number of pre-selected materials can be achieved by automatically selecting adequate triplets from an application-specific material library. The decomposition is expressed in terms of the volume fractions of each constituent material in the mix; this provides for a straightforward, physically meaningful interpretation of the data. One important application of this technique is in the digital removal of contrast agent from a dual-energy exam, producing a virtual nonenhanced image, as well as in the quantification of the concentration of contrast observed in a targeted region, thus providing an accurate measure of tissue perfusion.
