The application of automated segmentation methods for tumor delineation on 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) images presents an opportunity to reduce the interobserver variability in radiotherapy (RT) treatment planning. In this work, three segmentation methods were evaluated and compared for rectal and anal cancer patients: (i) Percentage of the maximum standardized uptake value (SUV% max), (ii) fixed SUV cutoff of 2.5 (SUV2.5), and (iii) mathematical technique based on a confidence connected region growing (CCRG) method. A phantom study was performed to determine the SUV% max threshold value and found to be 43%, SUV43% max. The CCRG method is an iterative scheme that relies on the use of statistics from a specified region in the tumor. The scheme is initialized by a subregion of pixels surrounding the maximum intensity pixel. The mean and standard deviation of this region are measured and the pixels connected to the region are included or not based on the criterion that they are greater than a value derived from the mean and standard deviation. The mean and standard deviation of this new region are then measured and the process repeats. FDG-PET-CT imaging studies for 18 patients who received RT were used to evaluate the segmentation methods. A PET avid (PETavid) region was manually segmented for each patient and the volume was then used to compare the calculated volumes along with the absolute mean difference and range for all methods. For the SUV43% max method, the volumes were always smaller than the PETavid volume by a mean of 56% and a range of 21%-79%. The volumes from the SUV2.5 method were either smaller or larger than the PETavid volume by a mean of 37% and a range of 2%-130%. The CCRG approach provided the best results with a mean difference of 9% and a range of 1%-27%. Results show that the CCRG technique can be used in the segmentation of tumor volumes on FDG-PET images, thus providing treatment planners with a clinically viable starting point for tumor delineation and minimizing the interobserver variability in radiotherapy planning.

A new inverse treatment planning system (TPS) for external beam radiation therapy with high energy photons is commercially available that utilizes both dose-volume-based cost functions and a selection of cost functions which are based on biological models. The purpose of this work is to evaluate quality of intensity-modulated radiation therapy (IMRT) plans resulting from the use of biological cost functions in comparison to plans designed using a traditional TPS employing dose-volume-based optimization. Treatment planning was performed independently at two institutions. For six cancer patients, including head and neck (one case from each institution), prostate, brain, liver, and rectal cases, segmental multileaf collimator IMRT plans were designed using biological cost functions and compared with clinically used dose-based plans for the same patients. Dose-volume histograms and dosimetric indices, such as minimum, maximum, and mean dose, were extracted and compared between the two types of treatment plans. Comparisons of the generalized equivalent uniform dose (EUD), a previously proposed plan quality index (fEUD), target conformity and heterogeneity indices, and the number of segments and monitor units were also performed. The most prominent feature of the biologically based plans was better sparing of organs at risk (OARs). When all plans from both institutions were combined, the biologically based plans resulted in smaller EUD values for 26 out of 33 OARs by an average of 5.6 Gy (range 0.24 to 15 Gy). Owing to more efficient beam segmentation and leaf sequencing tools implemented in the biologically based TPS compared to the dose-based TPS, an estimated treatment delivery time was shorter in most (five out of six) cases with some plans showing up to 50% reduction. The biologically based plans were generally characterized by a smaller conformity index, but greater heterogeneity index compared to the dose-based plans. Overall, compared to plans based on dose-volume optimization, plans with equivalent target coverage obtained using the biologically based TPS demonstrate improved dose distributions for the majority of normal structures.

Neutron detection signatures in thin films of semiconducting boron carbide were measured as a function of three applied reverse bias voltages, 0, 1.57 V and 3.15 V. Thermal neutrons were detected at zero bias. As the bias was increased, an increase in efficiency and in peak channel positions was observed. These two significant effects have been attributed to an increase in charge collection at higher bias voltages.

As part of its HL-LHC upgrade program, the CMS Collaboration is developing a High Granularity Calorimeter (CE) to replace the existing endcap calorimeters. The CE is a sampling calorimeter with unprecedented transverse and longitudinal readout for both electromagnetic (CE-E) and hadronic (CE-H) compartments. The calorimeter will be built with ∼30,000 hexagonal silicon modules. Prototype modules have been constructed with 6-inch hexagonal silicon sensors with cell areas of 1.1 cm 2 , and the SKIROC2-CMS readout ASIC. Beam tests of different sampling configurations were conducted with the prototype modules at DESY and CERN in 2017 and 2018. This paper describes the construction and commissioning of the CE calorimeter prototype, the silicon modules used in the construction, their basic performance, and the methods used for their calibration.