Jose Manuel Udias

Intraoperative electron radiation therapy (IOERT) is commonly delivered without intraoperative imaging, limiting patient-specific planning and requiring fast, reliable treatment calculations in the operating room. As a result, monitor unit (MU) calculations are often performed using spreadsheets and static look-up tables derived from measurements in water. We present Kali MC, an open source, Python-based software developed to streamline IOERT absorbed dose visualization, MU calculation and report generation for treatments delivered with the Liac HWL mobile accelerator. The software provides a graphical interface to visualize precomputed Monte Carlo absorbed dose distributions in water and performs MU calculations using experimentally determined output factors. Optional atmospheric pressure correction and predefined rescaling factors are supported to improve consistency of dose delivery and target coverage with the prescription isodose. Kali MC also generates customizable PDF treatment reports and exports DICOM RT Plan objects to facilitate integration with record-and-verify systems. By integrating validated dosimetric data, correction methods, and 3D dose visualization into a transparent platform, Kali MC supports efficient intraoperative decision-making; however, its use should be locally commissioned and evaluated according to institution-specific procedures and equipment characteristics.

Accurate timing characterization of detected radiation events in Positron Emission Tomography (PET) provides Time-of-flight (TOF) information for detected coincidences, which improves the signal-to-noise ratio of the reconstructed images. In this work, we propose a method, called MLTiming, to train machine learning (ML) models to assign accurate timestamps to events measured in radiation detectors training using just the signals detected by the individual single-channel detectors, without the need of resorting to other detector signals nor simulations. We performed a proof-of-concept study using three different neural network (NN) architectures, including the recently proposed Kolmogorov-Arnold network (KAN), trained with event pairs constructed with the measured signal and a delayed version of it. The delay is used as a label in the training process. The proposed trained model was evaluated with coincidence data acquired with a ²²Na point source using a pair of LaBr₃(Ce) crystals in the shape of truncated cones coupled to fast photomultiplier tubes (PMTs). The detected γ-ray pulses were sampled at 5 Gsamples/s. A 3% improvement in Coincidence Time Resolution (CTR) over an ultra optimized genetic algorithm–enhanced digital CFD (D-CFD) method (246±3 ps to 238±1 ps) was achieved. The possibility of training ML models for each individual detector using only the information contained in their own detected events may facilitate the use of ML models for large and medium-sized scanners, as well as other research areas. MLTiming is available at https://github.com/wjosefw/MLTiming.git.

A search for a β -delayed fission ( β DF ) decay branch of isomerically pure samples of Au g 178 and Au m 178 was performed at the ISOLDE-CERN facility. Two complementary detection systems capable of registering α decays and fission fragments, the ISOLDE Decay Station and the ASET (Alpha SETup), were used. Despite very high statistics of produced Au 178 nuclei, no fission fragments were detected. Upper limits of β DF probabilities of P β DF ( Au g 178 ) < 1.11 ( 2 ) × 10 − 8 and P β DF ( Au m 178 ) < 9.7 ( 2 ) × 10 − 9 were determined. Corresponding lower limits of β DF partial half-lives were deduced as well, and the results are discussed in the context of experimental systematics of β DF in the neutron-deficient region of the nuclear chart.

High-sensitivity organ-specific PET inserts employing long scintillation crystals in combination with small ring diameters require depth-of-interaction (DOI) encoding to ensure uniform spatial resolution across the field of view. In this study, we have developed high-resolution, DOI-capable detectors based on the Hamamatsu C13500 TOF-PET modules, using 1.6W1.6 mm2 LSO crystals coupled to 4W4 mm2 SiPMs. Detector configurations were designed either for a brain PET/MR insert (26 mm long crystals with a top light guide (TLG) for DOI encoding via light spread) or for a breast PET/MR insert (15 mm long with a TLG; 15 mm long with a stacked dual-layer configuration). Corresponding reference configurations without DOI capability were included to assess the impact of DOI implementation on detector performance. The 26 mm long brain detector configuration with TLG achieved an energy resolution of 12.1% and a coincidence time resolution (CTR) of 684 ps, with a degradation of 148 ps compared to the non-DOI variant. A newly developed DOI parameter calculation method improved DOI resolution from 9.1 1 1.7 mm to 7.1 1 1.8 mm. The 15 mm long single layer breast detector configuration with TLG yielded an energy resolution of 10.8%, a CTR of 460 ps, and a DOI resolution of 6.3 1 1.8 mm. The 15 mm dual layer detector configuration provided comparable performance (11.5% energy resolution, 605 ps CTR) while eliminating the need for DOI calibration. For both the 15 and 26 mm single layer crystal block configurations, introducing a TLG preserved crystal identification and energy resolution. Overall, the DOI-capable designs demonstrated only minor performance trade-offs, supporting the feasibility of both approaches for integration into organ-specific PET/MR inserts.