A blind deconvolution method incorporated with anatomical‐based filtering for partial volume correction: Validations with 123I‐mIBG cardiac SPECT/CT. Issue 12 (6th November 2017) - Wu, Jing; Liu, Hui; Hashemi Zonouz, Taraneh; Sandoval, Veronica M.; Mohy‐ud‐Din, Hassan; Lampert, Rachel J.; Sinusas, Albert J.; Liu, Chi; Liu, Yi‐Hwa
Abstract : Purpose: Segmentation of contrast‐enhanced CT and measurement of SPECT point spread function (PSF) are usually required for conventional partial volume correction (PVC). This study was to develop a segmentation‐free method with blind deconvolution (BD) and anatomical‐based filtering for SPECT PVC. Methods: The proposed method was implemented using an iterative BD algorithm to estimate the restored image and the PSF simultaneously. An anatomical‐based filtering was implemented at each iteration to reduce Gibbs artifact and suppress noise amplification in the deconvolution process. The proposed method was validated with 123 I‐metaiodobenzylguanidine ( 123 I‐mIBG) SPECT/CT imaging of NCAT phantoms with and without myocardial perfusion defect and a physical cardiac phantom. Fifteen heart‐to‐mediastinum ratios (HMRs) were configured in the NCAT and physical phantoms. Correlations between SPECT‐quantified and true HMRs were calculated from images without PVC as well as from BD restored images. The proposed method was also performed on a human 123 I‐mIBG study. Results: Relative bias and standard deviation images of NCAT phantoms showed that the proposed method reduced both bias and noise. Mean relative bias in the simulated normal myocardium was markedly improved (−16.8% ± 0.4% versus −0.8% ± 0.6% for low noise level; −16.7% ± 0.7% versus −2.3% ± 0.9% for high noise level). Mean relative bias in the simulated myocardial defect was also noticeably improved (−12.7% ± 1.2% versus 1.2% ± 1.6% for low noise level; −13.5% ± 2.4% versus −0.9% ± 2.8% for high noise level). The signal to noise ratio (SNR) of the defect was improved from 2.95 ± 0.09 to 4.07 ± 0.16 for low noise level (38% increase of mean), and from 2.56 ± 0.15 to 3.62 ± 0.22 for high noise level (41% increase of mean). For both NCAT and physical phantoms, HMRs calculated from images without PVC were underestimated (correlations between SPECT‐quantified and true HMRs: y = 0.81x + 0.1 for NCAT phantom; y = 0.82x + 0.14 for physical phantom). HMRs from BD restored images were markedly improved (correlations between SPECT‐quantified and true HMRs: y = x + 0.05 for NCAT phantom; y = 0.97x − 0.12 for physical phantom). After applying the proposed PVC method, the estimation error between the SPECT‐quantified and true HMRs was significantly reduced from −0.75 ± 0.57 to 0.04 ± 0.17 for NCAT phantom ( P  = 8e‐05), and from −0.68 ± 0.67 to −0.26 ± 0.42 for physical phantom ( P  = 0.005). The human study demonstrated that the HMR increased by 8% with PVC. Conclusions: The proposed segmentation‐free PVC method has the potential of improving SPECT quantification accuracy and reducing noise without the need for premeasuring the image PSF.