Evaluation of Wall Thickness on Myocardial SPECT Using Reconstruction Algorithms in a Solid-State Dedicated Cardiac Camera

Objectives: A spectrum dynamics (SD) algorithm was novel reconstruction algorithm of a solid-state dedicated cardiac camera on myocardial SPECT. The SD algorithm is improved a spatial resolution. However, we observe in the case that myocardial wall thickness was almost normal in spite of a patient with hypertrophic cardiomyopathy (HCM) when the SD algorithm used. The purpose of this study is to evaluate the effects of myocardial wall thickness using the ordered subset expectation maximization (OSEM) and the SD algorithms. Methods: Myocardial SPECT was performed using a D-SPECT (Spectrum Dynamics) and reconstructed using the OSEM and SD algorithms in both cardiac phantom and clinical studies. In cardiac phantom study, the myocardial wall was scanned with varying wall thickness (10, 15, 20, 25, 30, 35, and 40 mm). The count profile curve and the left ventricular (LV) volume were compared for myocardial wall thickness in the cardiac phantom. In clinical study, seven patients with HCM (6 males and a female; mean age, 67±13 years) underwent cardiac SPECT and echocardiography. We compared the end diastolic volume (EDV), end systolic volume (ESV), and left ventricular ejection fraction (LVEF) values calculated by quantitative gated SPECT software of the myocardial SPECT images. Results: In cardiac phantom study when the myocardial wall thickness was from 25 to 40, the count profile curve of the SD algorithm was smaller width than the OSEM algorithm. The LV volume of the phantom for myocardial thickness 40 mm was lower with the SD algorithm (77 ml) than the OSEM algorithm (83 ml). In clinical study, the EDVs, ESVs, and LVEFs obtained from the OSEM and SD algorithms correlated with echocardiography (OSEM: r=0.72, r=0.73, and r=0.68; SD: r=0.71, r=0.71, and r=0.69, respectively). Conclusion: The SD algorithm for HCM may underestimate myocardial wall thickness. It was suggested that OSEM was more suitable for the case of HCM than SD algorithm.