Radiation exposure of patients during medical imaging has become a major concern, with computed tomography (CT) and cardiac single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) being the biggest contributors. In this project our aim will be to dramatically reduce radiation dose in cardiac SPECT MPI based on inexpensive software techniques that can be translated readily to clinical practice. Our initial results suggest that at least an eightfold reduction in radiation doe might be possible, which could lead to an estimated reduction of 6500 cancer deaths per year in the U.S. alone. In lay terms, cardiac SPECT MPI is used routinely to evaluate heart disease, allowing the physician to assess blood flow reaching the heart wall, the motion of the heart, and whether the heart wall's tissue is viable. This form of imaging involves administration of a radioactive pharmaceutical (tracer) to the patient, which exposes the patient to radiation; thus, i would be desirable to minimize tracer dose used during imaging. Image quality is determined by the amount of tracer used, and these levels were chosen prior to recent technological improvements that can produce high-quality images at lower dose; therefore, there is clear evidence that doses can be lowered, and indeed there is considerable current interest in doing so. We are proposing a translational research project to thoroughly and quantitatively study the extent to which administered dose might be reduced without sacrificing image quality. This work will build, in particular, on new four-dimensional (4D) image reconstruction techniques and respiratory motion compensation approaches we have developed. Furthermore, we propose a new dosing approach we call personalized imaging, in which a computer algorithm will be trained to predict, for a given patient, the minimum dose required to obtain the current level of image quality, so that the administered dose is no more than necessary. In 4D reconstruction, a computer algorithm tracks the heart's beating motion, and uses this information to improve image quality by smoothing image noise in a way that preserves image details. In respiratory motion compensation, the patient's breathing and body motions are tracked by external sensors, and this information is used to reduce the appearance of motion blur in the images, and thereby improve diagnostic accuracy. The proposed personalized imaging approach builds on our group's extensive experience in machine learning. We expect that the combination of these various strategies will greatly reduce radiation dose, and because the improvements are based on software, the cost of these enhancements is very low. The project will result in recommendations for image reconstruction algorithms and parameters to be used in the clinic, along with corresponding tracer dose recommendations. The personalized imaging method will be implemented as a user-friendly computer program that customizes the dose for each given patient.

Public Health Relevance

Radiation exposure of patients via medical imaging has become a significant concern. This project will investigate software methods that may allow the radiation dose in cardiac SPECT imaging to be reduced by a factor of eight or more. This will be accomplished by: optimizing the image processing that is used, further developing new and better image processing methods, and developing a method of 'personalized imaging', in which, for each individual patient, the minimum amount of imaging agent needed to obtain a good image is computed.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
5R01HL122484-02
Application #
8842707
Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Danthi, Narasimhan
Project Start
2014-05-01
Project End
2019-04-30
Budget Start
2015-05-01
Budget End
2016-04-30
Support Year
2
Fiscal Year
2015
Total Cost
$758,935
Indirect Cost
$107,870
Name
Illinois Institute of Technology
Department
Biomedical Engineering
Type
Schools of Engineering
DUNS #
042084434
City
Chicago
State
IL
Country
United States
Zip Code
60616
Haro Alonso, David; Wernick, Miles N; Yang, Yongyi et al. (2018) Prediction of cardiac death after adenosine myocardial perfusion SPECT based on machine learning. J Nucl Cardiol :
Song, Chao; Yang, Yongyi; Pretorius, P Hendrik et al. (2018) 4D non-local means post-filtering for cardiac gated SPECT. Phys Med Biol 63:035026
Ljungberg, Michael; Pretorius, P Hendrik (2018) SPECT/CT: an update on technological developments and clinical applications. Br J Radiol 91:20160402
Ba, Alexandre; Abbey, Craig K; Baek, Jongduk et al. (2018) Inter-laboratory comparison of channelized hotelling observer computation. Med Phys 45:3019-3030
Qi, Wenyuan; Yang, Yongyi; Song, Chao et al. (2017) 4-D Reconstruction With Respiratory Correction for Gated Myocardial Perfusion SPECT. IEEE Trans Med Imaging 36:1626-1635
Dasari, Paul K R; Könik, Arda; Pretorius, P Hendrik et al. (2017) Correction of hysteretic respiratory motion in SPECT myocardial perfusion imaging: Simulation and patient studies. Med Phys 44:437-450
Massanes, Francesc; Brankov, Jovan G (2016) Full receiver operating characteristic curve estimation using two alternative forced choice studies. J Med Imaging (Bellingham) 3:011010
Qi, Wenyuan; Yang, Yongyi; Wernick, Miles N et al. (2016) Limited-angle effect compensation for respiratory binned cardiac SPECT. Med Phys 43:443
Pretorius, P Hendrik; Johnson, Karen L; King, Michael A (2016) Evaluation of Rigid-Body Motion Compensation in Cardiac Perfusion SPECT Employing Polar-Map Quantification. IEEE Trans Nucl Sci 63:1419-1425