Study design: Neurotrimin (Ntm) is a member of the family of neural cell adhesion molecules. Its expression pattern suggests that Ntm promotes axonal fasciculation, guides nerve fibers to specific targets and stabilizes synapses as it accumulates coincident with synaptogenesis. Strong labeling of Ntm was observed in motor and sensory areas of the postnatal rat cortex. It is not known whether Ntm is present in adult human spinal cord (SC). In the present study, a monoclonal antibody specific for Ntm (1B1), is applied to the first study of the expression of Ntm in normal and injured adult human SC. Objective: (1) To investigate the expression pattern of Ntm in adult normal human SC, and (2) to observe the changes of Ntm expression after SC injury and compare the differences between normal and injured adult human SC. Methods: Human SC tissue was obtained from necropsies of patients with (n ¼ 5) and without (n ¼ 4) SC injury. The 1B1 Ntm monoclonal antibody was used for immunohistochemical staining on paraffin embedded sections with an ABC kit. Results: (1) In total, 12 slides were analyzed for each group from both cervical and thoracic levels. Motor neurons and Clarke's neurons and glial-like cells were mild to moderately positive in all uninjured SC specimens. (2) In injured SC, no staining was observed in the injury epicenter between two and three levels proximally and distally, but was detected five levels away. (3) In patients older than 67 years of age, Ntm-positive inclusions were present in the white matter of the SC with or without injury. (4) Some meningeal cells were strongly Ntm-positive, especially in the uninjured human SC. Conclusion: Ntm is expressed by motor and Clarke's neurons and glial cells in uninjured human SC. The downregulation of Ntm in the injured SC suggests that its expression is regulated by afferent input.

Purpose: Knowing MLC leaf positioning error over the course of treatment would be valuable for treatment planning, QA design, and patient safety. The objective of the current study was to quantify the MLC positioning accuracy for VMAT delivery of head and neck treatment plans. Methods: A total of 837 MLC log files were collected from 14 head and neck cancer patients undergoing full arc VMAT treatment on one Varian Trilogy machine. The actual and planned leaf gaps were extracted from the retrieved MLC log files. For a given patient, the leaf gap error percentage (LGEP), defined as the ratio of the actual leaf gap over the planned, was evaluated for each leaf pair at all the gantry angles recorded over the course of the treatment. Statistics describing the distribution of the largest LGEP (LLGEP) of the 60 leaf pairs including the maximum, minimum, mean, Kurtosis, and skewness were evaluated. Results: For the 14 studied patients, their PTV located at tonsil, base of tongue, larynx, supraglottis, nasal cavity, and thyroid gland with volume ranging from 72.0 cm3 to 602.0 cm3. The identified LLGEP differed between patients. It ranged from 183.9% to 457.7% with a mean of 368.6%. For the majority of the patients, the LLGEP distributions peaked at non‐zero positions and showed no obvious dependence on gantry rotations. Kurtosis and skewness, with minimum/maximum of 66.6/217.9 and 6.5/12.6, respectively, suggested relatively more peaked while right‐skewed leaf error distribution pattern. Conclusion: The results indicate pattern of MLC leaf gap error differs between patients of lesion located at similar anatomic site. Understanding the systemic mechanisms underlying these observed error patterns necessitates examining more patient‐specific plan parameters in a large patient cohort setting.

Purpose: MRI‐guided‐radiotherapy (MRIGRT) systems lack many features of traditional Linac based RT systems and specialized tests need to be developed to evaluate MLC performance. This work describes automatic tools for the analysis of positional accuracy of an MLC equipped MRIGRT system. Methods: This MLC analysis tool was developed for the MRIdian™ RT system which has three Co‐60 equipped treatment heads each with a double focused MLC containing 30 leaf pairs, leaf thickness is 1.05cm defined at isocenter (SAD 105 cm). For MLC positional analysis a picket fence test was performed using a 25.4cm × 25.4cm Gafchromic™ RTQA2 film placed between 5cm solidwater and a 30cm × 30cm × 1cm jigwire phantom with seven embedded parallel metal strips 4cm apart. A plan was generated to deliver 2Gy per field and seven 23.1cm × 2cm fields centered over each wire in the phantom. For each leaf pair the center of the radiation profile was determined by fitting the horizontal profile with a Gaussian model and determining the center of the FWHM. This was compared with the metal strip location to determine any deviation. The following metrics were used to evaluate the deviations per gantry angle including maximum, minimum, mean, Kurtosis, and skewness. Results: The identified maximum/mean leaf deviations are, 1.32/0.55 mm for gantry 0°, 1.59/0.76 mm for gantry 90°, and 1.19/0.39 mm for gantry 270°. The percentage of leaf deviation less than 1mm are 90.0% at 0°, 74.6% at 90°, and 97.0% at 270°. Kurtosis/skewness of the leaf deviation are 2.41/0.14 at 0°, 2.53/0.23 at 90°, 3.33/0.83 at 270°, respectively. Conclusion: This work presents an automatic tool for evaluation of the MLC position accuracy of the MRIdian™ radiotherapy system which can be used to benchmark the performance of the MLC system for each treatment head and track the results longitudinally.

Purpose: To present a simple method for quantification of dual‐energy CT metal artifact reduction capabilities Methods: A phantom was constructed from solid water and a steel cylinder. Solid water is commonly used for radiotherapy QA, while steel cylinders are readily available in hardware stores. The phantom was scanned on Siemens Somatom 64‐slice dual‐energy CT system. Three CTs were acquired at energies of 80kV (low), 120kV (nominal), and 140kV (high). The low and high energy acquisitions were used to generate dual‐energy (DE) monoenergetic image sets, which also utilized metal artifact reduction algorithm (Maris). Several monoenergetic DE image sets, ranging from 70keV to 190keV were generated. The size of the metal artifact was measured by two different approaches. The first approach measured the distance from the center of the steel cylinder to a location with nominal (undisturbed by metal) HU value for the 120kV, DE 70keV, and DE 190keV image sets. In the second approach, the distance from the center of the cylinder to the edge of the air pocket for the above mentioned three image sets was measured. Results: The DE 190keV synthetic image set demonstrated the largest reduction of the metal artifacts. The size of the artifact was more than three times the actual size of the milled hole in the solid water in the DE 190keV, as compared to more than 7.5 times larger as estimated from the 120kV uncorrected image Conclusion: A simple phantom for quantification of dual‐energy CT metal artifact reduction capabilities was presented. This inexpensive phantom can be easily built from components available in every radiation oncology department. It allows quick assessment and quantification of the properties of different metal artifact reduction algorithms, available on modern dual‐energy CT scanners.