Lutz Tellmann | Searcharxiv

arXiv2026

PET Attenuation Map Generation for Brain Imaging from Virtual Non-Contrast and Virtual Non-Iodine Images Obtained with Photon Counting CT Scanners

This study presents a transformation scheme for converting measured Hounsfield units (HUs) into linear attenuation coefficients (LACs) for 511 keV gamma photons in iodine contrast-enhanced angiographic CT images. The method is based on spectral CT data acquired from tissue-equivalent inserts of the Sun Nuclear electron density phantom. The resulting transformation will be used to develop an attenuation map template for our in-house developed BrainPET 7T system. Three approaches were used to obtain the attenuation maps. The first approach is based on angiographic spectral CT scans with iodinated contrast agent administration, and the computation of virtual non contrast (VNC) and virtual non-iodine (VNI) images. In this case, two images are combined: bone regions are segmented from the VNI images, and the corresponding voxel values in the VNC images are replaced, yielding a single quantitative monochromatic CT image. The second approach relies exclusively on VNC images, whereas the third approach uses only VNI images. The transformation schemes from HUs to LACs were established for different tube voltages (120 kV and 140 kV) and virtual monochromatic energies (64 keV, 67 keV, 70 keV, 76 keV, 80 keV, and 100 keV), showing the HUs dependency on the energy setting of the reconstructed virtual monochromatic energies. The accuracy and precision of the proposed transformation scheme were further evaluated using previously acquired angiographic CT datasets. Spectral CT can be expected to minimize the systematic error when converting HUs to LACs for 511 keV gamma rays using virtual monochromatic images (VMIs).

physics.med-ph↗

arXiv2025

Fast Maximum Likelihood Positioning for a Staggered Layer Scintillation PET Detector

In this study, we propose a fast implementation of a Maximum Likelihood Positioning (MLP) algorithm to estimate the energy and identify the active scintillator pixel in staggered layer scintillation detectors for PET. The staggered layer design with pixelated scintillators enables the determination of the gamma's depth of interaction and facilitates an iteration-free formulation of the MLP algorithm. The efficacy of the algorithm optimization was tested on a scintillation detector block designed for an ultra-high field BrainPET 7T, comprising three scintillator pixel layers. The three layers contain 24 x 24, 24 x 23 and 23 x 22 scintillator pixels, respectively, with a pixel pitch of 2 mm in both directions and layer thicknesses of 9, 8 and 7 mm. Calibration measurements, in combination with an automated calibration script, were used to obtain the expected counts of scintillation photons required in the MLP algorithm. Using Single-Instruction-Multiple-Data parallelization, multi-threading and optimized cache lines, a maximum processing speed of approximately 22.5 million singles per second was achieved on a platform with four Intel Xeon Platinum 8168 CPUs and 60 threads, encompassing all required processing steps. The automatic calibration failed for 1 to 15 individual scintillator pixels in approximately 10 per cent of the 120 scintillation detector blocks, necessitating manual correction. After applying the energy correction to the positioned single events, an energy resolution of of 12 +/- 2 per cent FWHM was obtained for the entire scintillation block. This value is very close to the energy resolutions measured for the individual scintillator pixels, proving that the MLP accurately identifies the scintillating pixel and that the energy correction method effectively compensates for the light collection variations of the SiPM array.

physics.ins-det↗