Abstract Background Breast cancer causes the largest number of cancer-related deaths among women worldwide. With the aim of improving Positron Emission Tomography (PET) technology for accurate breast cancer diagnosis and staging, we propose a system design based on monolithic crystals with inherent Depth of Interaction (DOI) capabilities and an innovative edgeless detector ring. This approach eliminates the physical gaps between PET detectors, improving the system detection efficiency while potentially enhancing the image quality since edge effects are reduced. We have developed a dedicated breast PET system prototype ( DeepBreast ) to show the feasibility of this design. The system is composed of 14 curved LYSO monolithic scintillators of 12.5 mm thickness glued side-by-side with a high-refractive index compound. The useful transaxial and axial Field of View (FOV) of the system are 160 mm and 50 mm, respectively. A Neural Network technique was used for the x - and y - photon impact position estimation. The impact DOI and energy values were determined using the Voronoi calibration methodology. An initial experimental evaluation of the DeepBreast system has been performed inspired by the NEMA protocols for whole-body and small-animals PET scanners. Results A nearly flat spatial resolution as a function of radial position was obtained, which indicates the DOI capability of the system to mitigate parallax errors. An average spatial resolution of 1.9 ± 0.1 mm, 1.9 ± 0.1 mm and 1.7 ± 0.1 mm FWHM was achieved at the center of the axial FOV for the radial, tangential, and axial directions, respectively. A maximum sensitivity value of 2% was measured at the center of the FOV. The noise equivalent count rate peak reached 15 kcps at 13.4 MBq. Moreover, percent contrast values of 27.9%, 28.8%, 56.8%, 72.5%, 87.2% and 84.2% were achieved for 4.5 mm, 6 mm, 9 mm, 12 mm, 15 mm and 20 mm cylinders of a larger dedicated IQ phantom, respectively. Conclusions The initial experimental results demonstrate the feasibility of the DeepBreast as an innovative PET scanner for breast cancer imaging.
Freire et al. (Thu,) studied this question.