ROC analysis for assessment of lesion detection performance in 3D PET: influence of reconstruction algorithms

Image quality in positron emission tomography (PET) can be assessed with physical parameters, as spatial resolution and signal-to-noise ratio, or using psychophysical approaches, which include the observer performance and the considered task (ROC analysis). For PET in oncology, such a task is the detection of hot lesions. The aim of the present study was to assess the lesion detection performance due to adequate modeling of the scanner and the measurement process in the image reconstruction process. We compared the standard OSEM software of the manufacturer with a sophisticated fully 3D iterative reconstruction technique (USC MAP). A rectangular phantom with 6 oblique line sources in a homogeneous background (2.6 kBq/ml 18F) was imaged dynamically with an ECAT EXACT HR+ scanner in 3D mode. Reconstructed activity contrasts varied between 15 and 0, as the line sources were filled with 11C (3.2 MBq/ml). Measured attenuation and standard randoms, dead time, and scatter corrections of the manufacturer were employed. For the ROC analysis, a software tool presented a cut-out of the phantom (15 x 15 pixels) to two observers. These cut-outs were rated (5 classes) and the area Az under the ROC curve was determined as a measure of detection performance. The improvement for Az with USC MAP compared to the OSEM reconstructions ranged between 0.02 and 0.23 for signal-to-noise ratios of the background between 2.8 and 3.1 and lesion contrast between 2.1 and 4.2. This study demonstrates that adequate modeling of the measurement process in the reconstruction algorithm improves the detection of small hot lesions markedly.