The long-term objective of this grant is to use the recent rapid advances in parallel computational hardware and associated software to enable new approaches to image science and image-quality assessment in the context of single-photon emission computed tomography (SPECT). To achieve this broad objective, research under this grant strives to develop new concepts, mathematical theories and models as well as the new computational hardware and algorithms needed to implement them. One major area of emphasis is on system models in which the object is described as a function of space and time rather than as a discrete array of voxels. In this approach, the system is described by an operator rather than a matrix, and we focus on exact mathematical descriptions of this operator either through analytical singular-value decomposition or by means of the Fourier crosstalk matrix. This analysis will allow determination of the inevitable null functions (invisible objects)for any particular system and will show how the null space can be controlled by the use of a positivity constraint in the reconstruction. The object function can be regarded as one sample function of a spatiotemporal random process, and another area of emphasis will be the full infinite-dimensional characterization of this random process through a concept called a characteristic functional. Knowledge of this functional will lead to an understanding of the statistics of features derived from a reconstructed image and to new methods for computing task-based metrics of image quality. The raw image data in SPECT and other photon-counting modalities is not a pixelized projection image but rather a list of attributes, such as position, energy and time of arrival, of each detected photon. The measured attributes can be regarded as samples from a complicated random point process, and we have derived the operator that relates the mean of this process to the spatiotemporal object function. We will study the properties of this operator in detail and relate it to image quality. This analysis will permit a rigorous study of the relation between radiation dose and image quality. Other studies will investigate new figures of merit for image quality and apply them to the optimization of SPECT systems and to the new field of adaptive SPECT. Computational power for these efforts will be obtained either through next-generation graphics processing units such as the Nvidia Maxwell or with arrays of CPUs and coprocessors. Parallel code to implement all of these advances will be made available through the web.

Public Health Relevance

Image science provides the basic experimental, theoretical and computational framework needed for the objective analysis and optimization of medical imaging systems. In this grant we strive to expand the tools of image science within the context of a particular modality, Single- Photon Emission Computed Tomography or SPECT, and to develop new computational methods for assessing and improving image quality as defined in terms of specific medical tasks. By harnessing the massive computational power now becoming available at relatively low cost, we will be able to solve a number of long-standing problems in image science and advance the state-of-the-art in medical and biomedical imaging.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
5R01EB000803-23
Application #
8636473
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Sastre, Antonio
Project Start
1990-07-01
Project End
2016-03-31
Budget Start
2014-04-01
Budget End
2015-03-31
Support Year
23
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of Arizona
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
City
Tucson
State
AZ
Country
United States
Zip Code
85721
Barrett, Harrison H; Alberts, David S; Woolfenden, James M et al. (2016) Therapy operating characteristic curves: tools for precision chemotherapy. J Med Imaging (Bellingham) 3:023502
Clarkson, Eric; Cushing, Johnathan B (2016) Shannon information for joint estimation/detection tasks and complex imaging systems. J Opt Soc Am A Opt Image Sci Vis 33:286-92
Kupinski, M K; Clarkson, E (2015) Method for optimizing channelized quadratic observers for binary classification of large-dimensional image datasets. J Opt Soc Am A Opt Image Sci Vis 32:549-65
Könik, Arda; Kupinski, Meredith; Pretorius, P Hendrik et al. (2015) Comparison of the scanning linear estimator (SLE) and ROI methods for quantitative SPECT imaging. Phys Med Biol 60:6479-94
Barrett, Harrison H; Myers, Kyle J; Hoeschen, Christoph et al. (2015) Task-based measures of image quality and their relation to radiation dose and patient risk. Phys Med Biol 60:R1-75
Jha, Abhinav K; Barrett, Harrison H; Frey, Eric C et al. (2015) Singular value decomposition for photon-processing nuclear imaging systems and applications for reconstruction and computing null functions. Phys Med Biol 60:7359-85
Park, Ryeojin; Kim, Dae Wook; Barrett, Harrison H (2013) Synthetic phase-shifting for optical testing: point-diffraction interferometry without null optics or phase shifters. Opt Express 21:26398-417
Jha, Abhinav K; Clarkson, Eric; Kupinski, Matthew A (2013) An ideal-observer framework to investigate signal detectability in diffuse optical imaging. Biomed Opt Express 4:2107-23
Kupinski, Meredith K; Clarkson, Eric W; Barrett, Harrison H (2013) Scanning linear estimation: improvements over region of interest (ROI) methods. Phys Med Biol 58:1283-301
Hunter, William C J; Barrett, Harrison H; Lewellen, Tom K et al. (2013) Multiple-hit parameter estimation in monolithic detectors. IEEE Trans Med Imaging 32:329-37

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