Novel methods of reconstructing the tracer distribution in myocardial perfusion images are being considered for lowcount
and sparse sampling scenarios. Few examples of low count scenarios are when the amount of radioisotope
administered or the acquisition time is lowered, in gated studies where individual gates are reconstructed. Examples of
sparse angular sampling scenarios are patient motion correction in traditional SPECT where few angles are acquired at
any given pose and in multi-pinhole SPECT where the geometry is sparse and truncated by design. The reconstruction
method is based on the assumption that the tracer distribution is sparse in the transform domain, which is enforced by a
sparsity-promoting penalty on the transform coefficients. In this work we investigated the curvelet transform as the
sparse basis for myocardial perfusion SPECT. The objective is to determine if myocardial perfusion images can be
efficiently represented in this transform domain, which can then be exploited in a penalized maximum likelihood (PML)
reconstruction scheme for improving defect detection in low-count/ sparse sampling scenarios. The performance of this
algorithm is compared to standard OSEM with 3D Gaussian post-filtering using bias-variance plots and numerical
observer studies. The Channelized Non-prewhitening Observer (CNPW) was used for defect detection task in a “signalknown-
statistically” LROC study. Preliminary investigations indicate better bias-variance characteristics and superior
CNPW performance with the proposed curvelet basis. However, further assessment using more defect locations and
human observer evaluation is needed for clinical significance.
Polar maps have been used to assist clinicians diagnose coronary artery diseases (CAD) in single photon emission
computed tomography (SPECT) myocardial perfusion imaging. Herein, we investigate the optimization of collimator
design for perfusion defect detection in SPECT imaging when reconstruction includes modeling of the collimator. The
optimization employs an LROC clinical model observer (CMO), which emulates the clinical task of polar map detection
of CAD. By utilizing a CMO, which better mimics the clinical perfusion-defect detection task than previous SKE based
observers, our objective is to optimize collimator design for SPECT myocardial perfusion imaging when reconstruction
includes compensation for collimator spatial resolution. Comparison of lesion detection accuracy will then be employed
to determine if a lower spatial resolution hence higher sensitivity collimator design than currently recommended could be
utilized to reduce the radiation dose to the patient, imaging time, or a combination of both. As the first step in this
investigation, we report herein on the optimization of the three-dimensional (3D) post-reconstruction Gaussian filtering
of and the number of iterations used to reconstruct the SPECT slices of projections acquired by a low-energy generalpurpose
(LEGP) collimator. The optimization was in terms of detection accuracy as determined by our CMO and four
human observers. Both the human and all four CMO variants agreed that the optimal post-filtering was with sigma of
the Gaussian in the range of 0.75 to 1.0 pixels. In terms of number of iterations, the human observers showed a
preference for 5 iterations; however, only one of the variants of the CMO agreed with this selection. The others showed a
preference for 15 iterations. We shall thus proceed to optimize the reconstruction parameters for even higher sensitivity
collimators using this CMO, and then do the final comparison between collimators using their individually optimized
parameters with human observers and three times the test images to reduce the statistical variation seen in our present
results.
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