Dose distributions of proton therapy plans are robust against lowering the resolution of CTs combined with increasing noise.

Background: Treatment planning in radiation therapy (RT) is performed on image sets acquired with commercial x-ray computed tomography (CT) scanners. Considering an increased frequency of verification scans for adaptive RT and the advent of alternatives to x-ray CTs, there is a need to review the requirements for image sets used in RT planning.

Purpose: This study aims to derive the required image quality (IQ) for the computation of the dose distribution in proton therapy (PT) regarding spatial resolution and the combination of spatial resolution and noise.

Quantitative evaluation of a deep learning-based framework to generate whole-body attenuation maps using LSO background radiation in long axial FOV PET scanners.

Purpose: Attenuation correction is a critically important step in data correction in positron emission tomography (PET) image formation. The current standard method involves conversion of Hounsfield units from a computed tomography (CT) image to construct attenuation maps (µ-maps) at 511 keV. In this work, the increased sensitivity of long axial field-of-view (LAFOV) PET scanners was exploited to develop and evaluate a deep learning (DL) and joint reconstruction-based method to generate µ-maps utilizing background radiation from lutetium-based (LSO) scintillators.

A CT-less approach to quantitative PET imaging using the LSO intrinsic radiation for long-axial FOV PET scanners.

Purpose: Long-axial field-of-view (FOV) positron emission tomography (PET) scanners have gained a lot of interest in the recent years. Such scanners provide increased sensitivity and enable unique imaging opportunities that were not previously feasible. Benefiting from the high sensitivity of a long-axial FOV PET scanner, we studied a computed tomography (CT)-less reconstruction algorithm for the Siemens Biograph Vision Quadra with an axial FOV of 106 cm.

Count rate performance of brain-dedicated PET scanners: a Monte Carlo simulation study.

In recent years, there has been a renewed interest in brain-dedicated PET imaging systems, particularly in the context of combined PET/MR imaging. We are currently designing a brain-dedicated PET insert suitable for an ultra-high field brain-dedicated MR scanner, the Siemens Magnetom 7T MR scanner. In this paper, an investigation on the count rate performance of several possible detectors through a series of Monte Carlo simulations is reported.

A study of inter-crystal scatter in dual-layer offset scintillator arrays for brain-dedicated PET scanners.

A dual-layer offset (DLO) detector enables depth-of-interaction (DOI) through light sharing between two layers of scintillation arrays with a single-ended readout (SER) scheme. However, the SER scheme in DLO detectors may lead to a layer misassignment when inter-crystal scattering occurs. The aim of this work is to study inter-crystal scattering and evaluate the effects of layer misidentifications in DLO detectors on the performance of scanners suitable for a brain-dedicated PET insert.