Image Generation and Clinical Assessment

It is expected that both noise and activity distribution can have impact on the detectability of a myocardial defect in a cardiac PET study. We performed phantom studies to investigate the detectability of a defect in the myocardium for different noise levels and activity distributions. We evaluated the performance of three reconstruction schemes: Filtered Back-Projection (FBP), Ordinary Poisson Ordered Subset Expectation Maximization (OP–OSEM), and Point Spread Function corrected OSEM (PSF–OSEM). We used the Channelized Hotelling Observer (CHO) for the task of myocardial defect detection. We found that the detectability of a myocardial defect is almost entirely dependent on the noise level and the contrast between the defect and its surroundings.

Selected Figures:

Eugene S. Mananga, Georges El Fakhri, Joshua Schaefferkoetter, Ali A. Bonab, Jinsong Ouyang, Myocardial Defect Detection Using PET-CT: Phantom Studies, PLoS ONE, 2014.

Patient body motion during a cardiac positron emission tomography (PET) scan can severely degrade image quality. We have proposed and evaluated a novel method to detect, estimate, and correct body motion in cardiac PET.

Our method consists of three key components: motion detection, motion estimation, and motion-compensated image reconstruction.

• Detection:
  • Divide PET list-mode data into 1-s bins and compute the center of mass (COM) of the coincidences’ distribution in each bin.
  • Compute the covariance matrix within a 25-s sliding window over the COM signals inside the window
  • Sum of the eigenvalues of the covariance matrix is used to separate the list-mode data into “static” (i.e., body motion free) and “moving” (i.e. contaminated by body motion) frames
• Estimation
  • Select the longest static frame as the reference frame and estimate elastic motion transformations to the reference frame from all other static and sub-moving frames using nonrigid registration
• Reconstruction
  • Reconstruct all the list-mode data into a single image volume in the reference frame by incorporating the estimated motion transformations in the PET system matrix

We evaluated the performance of our approach in both phantom and human studies.

Sun T, Petibon Y, Han PK, Ma C, Kim SJW, Alpert NM, El Fakhri G, Ouyang J. Body motion detection and correction in cardiac PET: phantom and human studies. Med Phys 2019 Nov;46(11):4898-4906

Objective Assessment of Image Quality for Estimation and Detection Tasks

We are developing a rigorous evaluation methodology for objective assessment of image quality for lesion detection and activity quantitation tasks. We have applied our methods to assess the performance of different acquisition (2D vs 3D) and processing methods for variable patient sizes in the context of lesion detection in whole body FDG-PET. Our results show that for lesion detection and activity quantitation tasks, 3D imaging yielded better lesion detectability than 2D (p<0.025, two-tailed paired t-test) in patients of normal size (Body Mass Index [BMI] ¾ 31). However 2D imaging yielded better lesion detectability than 3D in large patients (BMI > 31) as 3D performance deteriorated in large patients (p<0.05). 2D and 3D yielded similar results for different lesion sizes. We have extended our work to the assessment of performance of Time of Flight PET and determined the gains that can be achieved in lung and liver cancer for lesion detection tasks in a cohort of 100 patients in collaboration with Dr. Karp’s lab at UPENN.

Related Papers:

• El Fakhri G., Surti S., Trott C.M., Scheuermann J., Karp J.S. Improvement in Lesion Detection with Whole-Body Oncologic TOF – PET. J. Nucl. Med. 2011; 52: 347-353.
• Surti S., Scheuermann J., El Fakhri G., Daube-Witherspoon M.E., Abi-Hatem N., Moussallem E., Lim R., Benard F., Mankoff D., and Karp J.S. Impact of TOF PET on whole-body oncologic studies: a human observer lesion detection and localization study. J. Nucl. Med. 2011; 52: 712-719.
• El Fakhri G., Santos P., Badawi R.D., Holdsworth C.H., Van den Abbeele A.D., Kijewski M.F. Impact of acquisition geometry, image processing, and patient habitus on tumor detection in whole – body FDG-PET. J. Nucl. Med. 2007; 48: 1951-1960.
• Moore S.C., Kijewski M.F., and El Fakhri G. Collimator optimization for detection and quantitation tasks: application to gallium-67 imaging. IEEE Trans. Med. Imag; 2005; 24: 1347-1356.
• El Fakhri G., Kijewski M.F., Albert M.S., Johnson K.A., and Moore S.C. Quantitative SPECT leads to improved performance in discrimination tasks related to prodromal Alzheimer’s disease. J. Nucl. Med. 2004; 45: 2026-2031.
• El Fakhri G., Kijewski M.F., Johnson K.A., Syrkin G, Killiany R.J., Becker JA, Zimmerman R.E., Albert M.S. MRI-Guided SPECT perfusion measures and volumetric MRI in prodromal Alzheimer’s disease. Arch Neurol 2003; 60: 1066-1072.
• El Fakhri G., Moore S.C., and Kijewski M.F. Optimization of Ga-67 imaging for detection and estimation tasks: dependence of imaging performance on spectral acquisition parameters. Med Phys 2002; 29: 1859-1866.
• Jadvar H, Moore SC, Kijewski MF, Bonab A, Zimmerman RE, Fischman AJ. Evaluation of SPECT imaging systems based on activity estimation in small brain structures. J. Nucl. Med. 2000;41:180P.

Monte Carlo simulation is a numerical tool that uses random numbers in order to approximately solve integral problems. Monte Carlo simulation can be used to model particle propagation in complex geometries and is therefore capable of simulating PET and SPECT acquisitions. We have developed and fully validated a detailed Monte Carlo simulation of block-based scintillation detectors used in PET. This code is currently being used by other researchers to generate realistic PET simulations that are useful to assess the accuracy of various corrections and reconstruction strategies.

Representative Figures:

Figure 1. Energy spectra of a cylindrical water filled phantom obtained with GATE (solid lines) and our simulator (symbols). Finite energy resolution was not modeled so as not to confound potential discrepancies between spectra. These were decomposed in their zeroth, first, second and third scatter orders, corresponding to 0, 1, 2 and 3 interactions in the object before detection. Arrow 1 on the zeroth order shows a Compton edge at 341 keV due to primary photons that scattered once at 180º in the detector. Arrow 2 on the first order shows a Compton edge at 170 keV due to photons that backscattered once in the object and then deposited all their energy in the detector. This figure essentially shows our simulator is as accurate as GATE for estimating energy spectra but is ~10 times faster even without using variance reduction techniques.

Figure 2. Central slice of the NEMA NU-2 2001 image quality phantom imaged on a real GE Discovery ST scanner and simulated with SimSET and our simulator. Modeling crystals and blocks in our simulator allows to model more accurately the spatial resolution of the system and therefore partial volume effect that affect small spheres contrasts.

Related Papers:

• Guérin B. and El Fakhri G. Realistic PET Monte Carlo simulation with pixellated block detectors, light sharing, random coincidences and dead-time modeling. IEEE Trans Nucl Sci. 2008; 55: 942-952.
• Ouyang J., El Fakhri G., Moore S.C. Improved activity estimation with MC-JOSEM versus TEW-JOSEM in ¹¹¹In SPECT. Med. Phys. 2008; 35: 2029-2040.
• Ouyang J., El Fakhri G., Moore S.C. Fast Monte Carlo Simulation Based Joint Iterative Reconstruction for Simultaneous 99mTc/123I Brain SPECT Imaging. Med. Phys. 2007; 34: 3263-3272.
• Moore SC and El Fakhri G. Realistic Monte Carlo simulation of Ga-67 SPECT imaging. IEEE Trans. Nucl. Sci. 2001.

Below is a listing of our published clinical studies. Please follow the links to learn more about each project.

Related Papers

• El Fakhri G., Kardan A., Sitek A., Dorbala S., Abi-Hatem N., Lahoud Y., Fischman A.J., Coughlan M., Yasuda T., Di Carli M.F. Reproducibility and Accuracy of Quantitative Myocardial Blood Flow Assessment Using 82Rb-PET: Comparison with ¹³N-Ammonia. J. Nucl. Med. 2009; 50:1062-1071.
• Anagnostopoulos C., Almonacid A., El Fakhri G., Currilova Z., Sitek A., Roughton M., Dorbala S., Popma J., Di Carli M. Quantitative Relationship Between Coronary Vasodilator Reserve Assessed by Rubidium-82 PET Imaging and Coronary Artery Stenosis Severity. Eur. J. Nucl. Med. Mol. Imag. 2008; 35: 1593-1601.
• Habert M.O., Lacomblez L., Makusd P., El Fakhri G., Pradat P.F., Meininger V. Brain perfusion imaging in amyotrophic lateral sclerosis : Extent of cortical changes according to the severity and topography of motor impairment. Amyotrophic Lateral Sclerosis, 2007; 8: 9-15.
• Kas A., Payoux P., Habert M.O., Malek M., Cointepas Y., El Fakhri G., Itti E., Remy P. Validation of standardized normalization template for statistical parametric mapping analysis of 123I-FPCIT images. J. Nucl. Med; 2007; 48: 1459-1467.
• El Fakhri G., Santos P., Badawi R.D., Holdsworth C.H., Van den Abbeele A.D., Kijewski M.F. Impact of acquisition geometry, image processing, and patient habitus on tumor detection in whole – body FDG-PET. J. Nucl. Med. 2007; 48: 1951-1960.
• Mamede M., El Fakhri G., Abreu-e-Lima P., Gandler W., Nose V., Gerbaudo V. Pre-operative estimation of esophageal tumor metabolic length in FDG PET images with surgical pathology confirmation. Ann Nucl Med. 2007; 21: 553-562.
• Di Carli M.F., Dorbala S., Meserve J., El Fakhri G., Sitek A., Moore S.C. Clinical myocardial perfusion PET-CT. J. Nucl. Med; 2007; 48: 783-793.
• El Fakhri G., Habert M.O., Maksud P., Kas A., Malek Z., Kijewski M.F., and Lacomblez L. Quantitative simultaneous 99mTc-ECD/123I-FP-CIT SPECT in Parkinson disease and multiple system atrophy. Eur. J. Nucl. Med. Mol. Imag. 2006; 33: 87-92.
• El Fakhri G., Kijewski M.F., Albert M.S., Johnson K.A., and Moore S.C. Quantitative SPECT leads to improved performance in discrimination tasks related to prodromal Alzheimer’s disease. J. Nucl. Med. 2004; 45: 2026-2031.
• El Fakhri G., Kijewski M.F., Johnson K.A., Syrkin G, Killiany R.J., Becker JA, Zimmerman R.E., Albert M.S. MRI-Guided SPECT perfusion measures and volumetric MRI in prodromal Alzheimer’s disease. Arch Neurol 2003; 60: 1066-1072.