No abstract

A new automated method for quantification of left ventricular function from gated-single photon emission computed tomography (SPECT) images has been developed. The method for quantification of cardiac function (CAFU) is based on a heart shaped model and the active shape algorithm. The model contains statistical information of the variability of left ventricular shape. CAFU was adjusted based on the results from the analysis of five simulated gated-SPECT studies with well defined volumes of the left ventricle. The digital phantom NURBS-based Cardiac-Torso (NCAT) and the Monte-Carlo method SIMIND were used to simulate the studies. Finally CAFU was validated on ten rest studies from patients referred for routine stress/rest myocardial perfusion scintigraphy and compared with Cedar-Sinai quantitative gated-SPECT (QGS), a commercially available program for quantification of gated-SPECT images. The maximal differences between the CAFU estimations and the true left ventricular volumes of the digital phantoms were 11 ml for the end-diastolic volume (EDV), 3 ml for the end-systolic volume (ESV) and 3% for the ejection fraction (EF). The largest differences were seen in the smallest heart. In the patient group the EDV calculated using QGS and CAFU showed good agreement for large hearts and higher CAFU values compared with QGS for the smaller hearts. In the larger hearts, ESV was much larger for QGS than for CAFU both in the phantom and patient studies. In the smallest hearts there was good agreement between QGS and CAFU. The findings of this study indicate that our new automated method for quantification of gated-SPECT images can accurately measure left ventricular volumes and EF.

A decision support system based on neural networks presents interpretations more similar to experienced clinicians compared to a conventional automated quantification software package. This study shows the feasibility of disseminating the expertise of experienced clinicians to less experienced physicians by the use of neural networks.

EXINI demonstrated greater diagnostic accuracy for detection of ischemia and abnormal studies than did PERFEX. EXINI CAD also outperformed its SSS analysis.