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Objective. We propose a novel four-layer depth-of-interaction (DOI) encoding phoswich detector using lutetium–yttrium oxyothosilicate (LYSO) and bismuth germanate (BGO) scintillator crystal arrays for high sensitivity and high spatial resolution small animal PET imaging. Approach. The detector was comprised of a stack of four alternating LYSO and BGO scintillator crystal arrays coupled to an 8 × 8 multi-pixel photon counter (MPPC) array and read out by a PETsys TOFPET2 application specific integrated circuit. The four layers from the top (gamma ray entrance) to the bottom (facing the MPPC) consisted of a 24 × 24 array of 0.99 × 0.99 × 6 mm3 LYSO crystals, a 24 × 24 array of 0.99 × 0.99 × 6 mm3 BGO crystals, a 16 × 16 array of 1.53 × 1.53 × 6 mm3 LYSO crystals and a 16 × 16 array of 1.53 × 1.53 × 6 mm3 BGO crystals. Main results. Events that occurred in the LYSO and BGO layers were first separated by measuring the pulse energy (integrated charge) and duration (time over threshold (ToT)) from the scintillation pulses. Convolutional neural networks (CNNs) were then used to distinguish between the top and lower LYSO layers and between the upper and bottom BGO layers. Measurements with the prototype detector showed that our proposed method successfully identified events from all four layers. The CNN models achieved a classification accuracy of 91% for distinguishing the two LYSO layers and 81% for distinguishing the two BGO layers. The measured average energy resolution was 13.1% ± 1.7% for the top LYSO layer, 34.0% ± 6.3% for the upper BGO layer, 12.3% ± 1.3% for the lower LYSO layer, and 33.9% ± 6.9% for the bottom BGO layer. The timing resolution between each individual layer (from the top to the bottom) and a single crystal reference detector was 350 ps, 2.8 ns, 328 ps, and 2.1 ns respectively. Significance. In conclusion, the proposed four-layer DOI encoding detector achieved high performance and is an attractive choice for next-generation high sensitivity and high spatial resolution small animal positron emission tomography systems.

HiPET is a recently developed prototype preclinical PET scanner dedicated to high sensitivity and high resolution molecular imaging. The HiPET system employs a phoswich depth of interaction (DOI) detector design, which also allows identification of the large majority of the cross layer crystal scatter (CLCS) events. This work evaluates its performance characteristics following the National Electrical Manufacturers Association (NEMA) NU4-2008 protocol. The HiPET consists of twenty flat panel type detectors arranged in two rings. The inner diameter is 160 mm and the axial field of view (FOV) is 104 mm. Each detector is comprised of two layers of phoswich scintillator crystal arrays, a tapered, pixelated glass lightguide and a multi anode photomultiplier tube (MAPMT). The front (gamma ray entrance) layer is a 48 × 48 pixelated cerium doped lutetium yttrium orthosilicate (LYSO) scintillator array with individual crystals measuring 1.01 × 1.01 × 6.1 mm. The back (towards the PMT) layer is a 32 × 32 pixelated bismuth germanate (BGO) scintillator array with individual crystals measuring 1.55 × 1.55 × 8.9 mm. For energy windows of 250-650 keV and 350-650 keV, the peak absolute sensitivity at the center of the FOV was 13.5% and 10.4% including CLCS events, and 11.8% and 8.9% excluding CLCS events, respectively. The average detector energy resolution derived by averaging the individual crystal spectra was 11.7% ± 1.4% for LYSO and 17.0% ± 1.4% for BGO. The 3D ordered-subsets expectation maximization (OSEM) reconstructed image of a point source in air, ranged from 0.73 mm to 1.19 mm, with an average value of 0.93 ± 0.09 mm at all measured locations. The peak noise equivalent count rate (NECR) and scatter fraction were 179 kcps at 12.4 MBq and 6.9% for the mouse-sized phantom, and 63 kcps at 11.3 MBq and 18.3% for the rat-sized phantom. For the NEMA image quality phantom, the uniformity was 5.8%, and the spillover ratios measured in the water- and air-filled cold region chambers were 0.047 and 0.044, respectively. The recovery coefficients (RC) ranged from 0.31 to 0.92. These results and in vivo evaluation demonstrate that the HiPET can achieve high quality molecular imaging for biomedical applications.

Delineation of major torso organs is a key step of mouse micro-CT image analysis. This task is challenging due to low soft tissue contrast and high image noise, therefore anatomical prior knowledge is needed for accurate prediction of organ regions. In this work, we develop a deeply supervised fully convolutional network which uses the organ anatomy prior learned from independently acquired contrast-enhanced micro-CT images to assist the segmentation of non-enhanced images. The network is designed with a two-stage workflow which firstly predicts the rough regions of multiple organs and then refines the accuracy of each organ in local regions. The network is trained and evaluated with 40 mouse micro-CT images. The volumetric prediction accuracy (Dice score) varies from 0.57 for the spleen to 0.95 for the heart. Compared to a conventional atlas registration method, our method dramatically improves the Dice of the abdominal organs by 18%-26%. Moreover, the incorporation of anatomical prior leads to more accurate results for small-sized low-contrast organs (e.g. the spleen and kidneys). We also find that the localized stage of the network has better accuracy than the global stage, indicating that localized single organ prediction is more accurate than global multiple organ prediction. With this work, the accuracy and efficiency of mouse micro-CT image analysis are greatly improved and the need for using contrast agent and high x-ray dose is potentially reduced.