Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, 06520, USA.
Department of Internal Medicine (Cardiology), Yale University, New Haven, CT, 06520, USA.
Med Phys. 2017 Dec;44(12):6435-6446. doi: 10.1002/mp.12622. Epub 2017 Nov 6.
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.
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 I-metaiodobenzylguanidine ( 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 I-mIBG study.
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.
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.
传统的部分容积校正（PVC）通常需要进行对比增强 CT 分割和 SPECT 点扩散函数（PSF）的测量。本研究旨在开发一种无需分割的方法，结合盲反卷积（BD）和基于解剖的滤波进行 SPECT PVC。
该方法使用迭代 BD 算法同时估计重建图像和 PSF。在每个迭代中实施基于解剖的滤波，以减少Gibbs 伪影并抑制去卷积过程中的噪声放大。使用具有和不具有心肌灌注缺陷的 NCAT 体模和物理心脏体模验证了所提出的方法。在 NCAT 和物理体模中配置了 15 个心脏与纵隔比（HMR）。从没有 PVC 的图像以及从 BD 恢复的图像中计算了 SPECT 定量和真实 HMR 之间的相关性。该方法还应用于人体 I-间碘苄胍（I-mIBG）SPECT/CT 研究。
NCAT 体模的相对偏差和标准偏差图像表明，该方法降低了偏差和噪声。模拟正常心肌的平均相对偏差明显改善（低噪声水平下为-16.8%±0.4%，比-0.8%±0.6%；高噪声水平下为-16.7%±0.7%，比-2.3%±0.9%）。模拟心肌缺陷的平均相对偏差也有明显改善（低噪声水平下为-12.7%±1.2%，比 1.2%±1.6%；高噪声水平下为-13.5%±2.4%，比-0.9%±2.8%）。缺陷的信噪比（SNR）从低噪声水平的 2.95±0.09 提高到 4.07±0.16（平均提高 38%），从低噪声水平的 2.56±0.15 提高到 3.62±0.22（平均提高 41%）。对于 NCAT 和物理体模，没有 PVC 的图像计算的 HMR 被低估（SPECT 定量和真实 HMR 之间的相关性：y=0.81x+0.1 用于 NCAT 体模；y=0.82x+0.14 用于物理体模）。BD 恢复图像的 HMR 明显改善（SPECT 定量和真实 HMR 之间的相关性：y=x+0.05 用于 NCAT 体模；y=0.97x-0.12 用于物理体模）。应用所提出的 PVC 方法后，SPECT 定量和真实 HMR 之间的估计误差从-0.75±0.57 显著降低到-0.04±0.17（用于 NCAT 体模，P=8e-05），从-0.68±0.67 降低到-0.26±0.42（用于物理体模，P=0.005）。人体研究表明，应用 PVC 后 HMR 增加了 8%。
该无分割 PVC 方法无需预先测量图像 PSF，具有提高 SPECT 定量准确性和降低噪声的潜力。
