No abstract

Quantification of dynamic contrast-enhanced (DCE) MRI based on pharmacokinetic modeling requires specification of the arterial input function (AIF).

A decrease in PK parameter values obtained from DCE-MRI in children with JIA likely reflects diminution of disease activity. This technique may be used as an objective follow-up measure of therapeutic efficacy in patients with JIA. MR imaging can detect persistent synovitis in patients considered to be in clinical remission.

Purpose: To transition from an in‐house incident reporting system to a ROILS standards system with the intent to develop a safety focused culture in the Department and enroll in ROILS. Methods: Since the AAPM Safety Summit (2010) several safety and reporting systems have been implemented within the Department. Specific checklists and SBAR reporting systems were introduced. However, the active learning component was lost due to reporting being viewed with distrust and possible retribution.To Facilitate introducing ROILS each leader in the Department received a copy of the ROILS participation guide. Four specific tasks were assigned to each leader: develop a reporting tree, begin the ROILS based system, facilitate adopting ROILS Terminology, and educate the staff on expectations of safety culture. Next, the ROILS questions were broken down into area specific questions (10–15) per departmental area. Excel spreadsheets were developed for each area and setup for error reporting entries. The Role of the Process Improvement Committee (PI) has been modified to review and make recommendations based on the ROILS entries. Results: The ROILS based Reporting has been in place for 4 months. To date 64 reports have been entered. Since the adoption of ROILS the reporting of incidents has increased from 2/month to 18/month on average. Three reports had a dosimetric effect on the patient (<5%) dose variance. The large majority of entries have been Characterized as Processes not followed or not sure how to Characterize, and Human Behavior. Conclusion: The majority of errors are typo's that create confusion. The introduction of the ROILS standards has provided a platform for making changes to policies that increase patient safety. The goal is to develop a culture that sees reporting at a national level as a safe and effective way to improve our safety, and to dynamically learn from other institutions reporting.

Purpose To implement and evaluate an image‐based Winston‐Lutz (WL) test to measure the displacement between ExacTrac imaging origin and radiation isocenter on a Novalis Tx system using RIT V6.2 software analysis tools. Displacement between imaging and radiation isocenters was tracked over time. The method was applied for cone‐based and MLC‐based WL tests. Methods The Brainlab Winston‐Lutz phantom was aligned to room lasers. The ExacTrac imaging system was then used to detect the Winston‐ Lutz phantom and obtain the displacement between the center of the phantom and the imaging origin. EPID images of the phantom were obtained at various gantry and couch angles and analyzed with RIT calculating the phantom center to radiation isocenter displacement. The RIT and Exactrac displacements were combined to calculate the displacement between imaging origin and radiation isocenter. Results were tracked over time. Results Mean displacements between ExacTrac origin and radiation isocenter were: VRT: −0.1mm ± 0.3mm, LNG: 0.5mm ± 0.2mm, LAT: 0.2mm ± 0.2mm (vector magnitude of 0.7 ± 0.2mm). Radiation isocenter was characterized by the mean of the standard deviations of the WL phantom displacements: σVRT: 0.2mm, σLNG: 0.4mm, σLAT: 0.6mm. The linac couch base was serviced to reduce couch walkout. This reduced σLAT to 0.2mm. These measurements established a new baseline of radiation isocenter‐imaging origin coincidence. Conclusion The image‐based WL test has ensured submillimeter localization accuracy using the ExacTrac imaging system. Standard deviations of ExacTrac‐radiation isocenter displacements indicate that average agreement within 0.3mm is possible in each axis. This WL test is a departure from the tradiational WL in that imaging origin/radiation isocenter agreement is the end goal not lasers/radiation isocenter.

Purpose: Often in the clinical arena, physicians rely on medical physicists to provide dose estimates. This project focused on evaluating the BED to the spinal cord for patients undergoing retreatment after conventionally fractionated RT. The BED calculation method is based on published research on human and animal spinal cord reirradiations. Methods: BED is calculated separately for the first and upcoming treatments using the planning system to estimate the dose to the spinal cord in the overlap region from each course. The cumulative BED is calculated as the sum of the BEDs of the first treatment and the upcoming treatment. The tolerance BED is calculated from the QUANTEC (2010) value for 0.2% rate of myelopathy of 50 Gy in 2 Gy fractions. If the reirradiation follows the initial irradiation by at least six months, the cumulative BED is compared to the tolerance BED multiplied by 1.25 to account for repair of RT‐induced subclinical damage. If an RT course was delivered BID, then an additional factor (Hm = 0.35) was used to account for incomplete repair of sublethal damage during the BID course. Results: BED calculations were performed using this method to assist clinical decisions for spinal cord reirradiations. Patients were treated to doses as high as 59 Gy to the spinal cord while BED calculations showed the dose to the spinal cord was effectively below the QUANTEC tolerance for 0.2% rate of myelopathy. Conclusion: Application of this BED calculation methodology for spinal reirradiations places retreatment dose in the context of current clinical research. The department is analyzing spinal cord BED calculations factoring in recovery observed in a primate model predicting 50%, 60%, and 70% recovery of the initial RT course dose at 1 year, 2 years, and 3 years post‐RT respectively given an initial treatment of up to 44 Gy.

Purpose: The study intent is to provide a practical method of extracting Conformity Index for multiple Stereotactic Radiosurgery (SRS) target plans generated from BrainLab iPlan RT Dose 4.1 planning system (CIiPlan). Methods: CIiPlan is the ratio between normal tissue volume (VN, IDL) and target volume (VPTV, IDL) encompassed by the prescription isodose line (IDL). BrainLab iPlan RT Dose 4.1 planning and physics documents lack details on how to retrieve and interpret CIiPlan parameters of multiple SRS targets. The CIiPlan values for multiple lesions are misleading and in most cases gave bigger values than what is reflected from the plan. To understand and demonstrate the underlying issues CIiPlan values of patient plans with multiple SRS targets were extracted using two different methods. Method 1: CIiPlan values were directly obtained with all target plans present (CITN,iPlan). Method 2: CIiPlan values were extracted by assigning 100% weighting for one of the targets and 0 weighting for the rest of the targets (CIIN,iPlan). This process was t repeated for each target. In both cases CIiPlan were read from DVH graphs at the prescription IDL. Results: CITN,iPlan values from Method 1 were larger than CIIN,iPlan values obtained using Method 2. The difference was pronounced for smaller lesions. Even though targets were far apart and were not covered with the same prescription IDL, CIiPlan values from Method 1 were based on normal tissue volume encompassed by 5 treatment IDL from all targets. Carefully analyzing CIiPlan values from the two methods we were able to provide appropriate CIiPlan formulas that gave good estimate of both methods values. Conclusion: CIiPlan for multiple SRS targets need be analyzed with great caution; especially if small lesions and big lesions are part of same plan. CIIN,iPlan values based on each target normal tissue volume encompassed by the prescription IDL should be used.

Purpose: To evaluate the quality of two CT/MRI image fusion algorithms used for 3D‐CRT and IMRT treatment planning Method and Materials: Computed tomography‐magnetic resonance imaging (CT‐MRI) fusion was performed for nine patients with brain/head & neck lesions. Some patients have undergone both three dimensional conformal therapy (3D‐CRT) and intensity modulated radiation therapy (IMRT) using two commercially available treatment planning systems which we will refer to as system (1) and system (2), respectively. In order to quantify the fusion results bony landmarks, such as the ramus of the mandible or part of the skull bone, were outlined on the reformatted MRI and the position of the outline in reference to the same bony landmark on the CT image were measured. Results: Based on what was acceptable fusion for our clinicians, this preliminary study showed registration accuracy between CT and MRI within 3 mm and 2 mm for system (1) and (2), respectively. On system 2 the quality of the fusion, the bar display, was in the range 62.5% – 100%. We also found that getting full bar (100% — ‘perfect match’) is possible with reasonable effort if one uses three fiducial marks. Though the bar showed 100% fusion quality the fusion results were not satisfactory. We found better fusion using five or more fiducial points with 62.5 % to 75 % fusion quality. Conclusion: Fusion software in both systems provides sufficient CT‐MRI fusion accuracy. However, one has to be careful in interpreting the semi‐quantitative bar display in system (2) fusion results. We found that instead of going for a perfect matching bar using few fiducial marks one has to use more points, at least five, even if one gets lower percentage match as this is averaged over more landmarks.