Purpose: In order to accurately accumulate delivered dose for head and neck cancer patients treated with the Adapt to Position workflow on the 1.5T magnetic resonance imaging (MRI)-linear accelerator (MR-linac), the low-resolution T2-weighted MRIs used for daily setup must be segmented to enable reconstruction of the delivered dose at each fraction. In this study, our goal is to evaluate various autosegmentation methods for head and neck organs at risk (OARs) on on-board setup MRIs from the MR-linac for off-line reconstruction of delivered dose. Methods: Seven OARs (parotid glands, submandibular glands, mandible, spinal cord, and brainstem) were contoured on 43 images by seven observers each. Ground truth contours were generated using a simultaneous truth and performance level estimation (STAPLE) algorithm. 20 autosegmentation methods were evaluated in ADMIRE: 1-9) atlas-based autosegmentation using a population atlas library (PAL) of 5/10/15 patients with STAPLE, patch fusion (PF), random forest (RF) for label fusion; 10-19) autosegmentation using images from a patient's 1-4 prior fractions (individualized patient prior (IPP)) using STAPLE/PF/RF; 20) deep learning (DL) (3D ResUNet trained on 43 ground truth structure sets plus 45 contoured by one observer). Execution time was measured for each method. Autosegmented structures were compared to ground truth structures using the Dice similarity coefficient, mean surface distance, Hausdorff distance, and Jaccard index. For each metric and OAR, performance was compared to the inter-observer variability using Dunn's test with control. Methods were compared pairwise using the Steel-Dwass test for each metric pooled across all OARs. Further dosimetric analysis was performed on three high-performing autosegmentation methods (DL, IPP with RF and 4 fractions (IPP_RF_4), IPP with 1 fraction (IPP_1)), and one low-performing (PAL with STAPLE and 5 atlases (PAL_ST_5)). For five patients, delivered doses from clinical plans were recalculated on setup images with ground truth and autosegmented structure sets. Differences in maximum and mean dose to each structure between the ground truth and autosegmented structures were calculated and correlated with geometric metrics. Results: DL and IPP methods performed best overall, all significantly outperforming inter-observer variability and with no significant difference between methods in pairwise comparison. PAL methods performed worst overall; most were not significantly different from the inter-observer variability or from each other. DL was the fastest method (33 seconds per case) and PAL methods the slowest (3.7 - 13.8 minutes per case). Execution time increased with number of prior fractions/atlases for IPP and PAL. For DL, IPP_1, and IPP_RF_4, the majority (95%) of dose differences were within 250 cGy from ground truth, but outlier differences up to 785 cGy occurred. Dose differences were much higher for PAL_ST_5, with outlier differences up to 1920 cGy. Dose differences showed weak but significant correlations with all geometric metrics (R2 between 0.030 and 0.314). Conclusions: The autosegmentation methods offering the best combination of performance and execution time are DL and IPP_1. Dose reconstruction on on-board T2-weighted MRIs is feasible with autosegmented structures with minimal dosimetric variation from ground truth, but contours should be visually inspected prior to dose reconstruction in an end-to-end dose accumulation workflow.

The taxonomy of Oculimacula, Rhynchosporium and Spermospora is re-evaluated, along with that of phylogenetically related genera. Isolates are identified using comparisons of DNA sequences of the internal transcribed spacer ribosomal RNA locus (ITS), partial translation elongation factor 1-alpha (tef1), actin (act), DNA-directed RNA polymerase II largest (rpb1) and second largest subunit (rpb2) genes, and the nuclear ribosomal large subunit (LSU), combined with their morphological characteristics. Oculimacula is restricted to two species, O. acuformis and O. yallundae, with O. aestiva placed in Cyphellophora, and O. anguioides accommodated in a new genus, Helgardiomyces. Rhynchosporium s. str. is restricted to species with 1-septate conidia and hooked apical beaks, while Rhynchobrunnera is introduced for species with 1–3-septate, straight conidia, lacking any apical beak. Rhynchosporium graminicola is proposed to replace the name R. commune applied to the barley scald pathogen based on nomenclatural priority. Spermospora is shown to be paraphyletic, representing Spermospora (type: S. subulata), with three new species, S. arrhenatheri, S. loliiphila and S. zeae, and Neospermospora gen. nov. (type: N. avenae). Ypsilina (type: Y. graminea), is shown to be monophyletic, but appears to be of minor importance on cereals. Finally, Vanderaaea gen. nov. (type: V. ammophilae), is introduced as a new coelomycetous fungus occurring on dead leaves of Ammophila arenaria.

Background: Conventional MRI poses unique challenges in quantitative analysis due to a lack of specific physical meaning for voxel intensity values. In recent years, intensity standardization methods to optimize MRI signal consistency have been developed to address this problem. However, the effects of standardization methods on the head and neck region have not been previously investigated. Purpose: This study proposes a workflow based on healthy tissue region of interest (ROI) analysis to determine intensity consistency within a patient cohort. Through this workflow, we systematically evaluate different intensity standardization methods for T2-weighted MRI of the head and neck region. Methods: Two image cohorts of five head and neck cancer patients, one with heterogeneous acquisition parameters (median age 59 years [range, 53-61]), and another with homogeneous acquisition parameters from a clinical trial (NCT04265430) (median age 61 years [range, 54-77]) were retrospectively analyzed. The standard deviation of cohort-level normalized mean intensity (SD NMIc), a metric of intensity consistency, was calculated across ROIs to determine the effect of five intensity standardization methods on T2-weighted images. For each cohort, the Friedman test with a subsequent post-hoc Bonferroni-corrected Wilcoxon signed-rank test was conducted to compare SD NMIc among methods. Results: Consistency (SD NMIc across ROIs) between T2-weighted images is substantially more impaired in the cohort with heterogeneous acquisition parameters (0.28 +- 0.04) than in the cohort with homogeneous acquisition parameters (0.15 +- 0.05). Consequently, intensity standardization methods more significantly improve consistency in the cohort with heterogeneous acquisition parameters (corrected p < 0.005 for all methods compared to no standardization) than in the cohort with homogeneous acquisition parameters (corrected p > 0.05 for all methods compared to no standardization). Conclusions: Our findings stress the importance of image acquisition parameter standardization, together with the need for testing intensity consistency before performing quantitative analysis of MRI.

With recent advances in magnetic resonance image-guided radiation therapy (MR-IGRT), Fricke gel dosimetry has demonstrated value for its ability to measure three-dimensional dose distributions in the presence of a strong magnetic field. This strong magnetic field causes hot and cold spots in dose distributions at the interfaces of lung and normal tissue due to a phenomenon known as the electron return effect (ERE). In this paper, we report the development of lung-equivalent gel dosimeters to better measure dose to lung tissue caused by the ERE. Small polystyrene beads of variable sizes were mixed into Fricke xylenol orange gelatin (FXG) and ferrous oxide xylenol orange (FOX) gels. Lung-equivalence was confirmed by measuring the average CT number of each gel. The effects of gel type, bead size, and voxel size on uniformity and signal intensity were investigated. The smallest beads ( < 1 mm) exhibited the best uniformity, with values comparable to conventional gel with 2 mm voxels. Signal intensity followed an inverse relationship with uniformity, but FXG low-density dosimeters generated enough signal to produce acceptable quality images. The spin-lattice relaxation rate (R1 = 1/T1) increased with dose, which enabled us to measure dose to both soft tissue and lung due to the ERE using a phantom simulating the soft tissue-lung interface.