No abstract

The authors report the first demonstration of intrafractional tumor tracking using 2D MR images. During 2 min of tracking, the authors delivered highly conformal dose to a moving target that simulates tumor motions. Compared to static target irradiation, the 50% beam width remains essentially the same (within 0.5 mm), with an increase in 80%-20% penumbra width of less than 1.7 mm in moving target irradiation. These results illustrate potential dosimetric advantages of intrafractional MR tumor tracking in treating mobile tumors as shown for the phantom case.

The purpose of this work is to create a rigorous method of segmenting PET images using an automated iterative technique. To this end a phantom study employing spherical targets was used to determine local (slice specific) threshold levels which produce correct cross-sections based on the contrast between target and background. Numerous target to background activity concentration ratios were investigated but found to have minimal effect in comparison to the influence of target size. Functions were fit to this data and used to construct an iterative threshold segmentation algorithm. In all cases this approach yielded convergence within ten iterations. Iterative threshold segmentation was applied using both an axial and tri-axial approach to the spherical targets and also to two irregularly shaped volumes. Of these two approaches, the tri-axial method proved less susceptible to image noise and better at dealing with partial volume effects at the interface between target and background. For comparative purposes, single thresholds of 28% and 40% were also applied to the spherical data sets. The tri-axial iterative method was found capable of delineating cross sections with areas greater than 250 mm2 to within the maximum resolution possible (1 pixel width). Cross sections of less than 250 mm2 in area were resolved by the tri-axial method to within 2 pixel widths of their true physical extent. Local contrast based iterative threshold segmentation shows promise as a method of rigorously delineating PET target volumes with good accuracy subject to the limitations imposed by the image resolution which currently characterizes this modality.

The ability of the analytic anisotropic algorithm (AAA), a superposition– convolution algorithm implemented in the Eclipse (Varian Medical Systems, Palo Alto, CA) treatment planning system (TPS), to accurately account for the presence of inhomogeneities in simple geometries is examined. The goal of 2% accuracy, as set out by the American Association of Physicists in Medicine Task Group 65, serves as a useful benchmark against which to evaluate the inhomogeneity correction capabilities of this treatment planning algorithm. A planar geometry phantom consisting of upper and lower layers of Solid Water (Gammex rmi, Middleton, WI) separated by a heterogeneity region of variable thickness, is modeled within the Eclipse TPS. Results obtained with the AAA are compared with experimental measurements. Seven different materials, spanning the range from air to aluminum, constitute the inhomogeneity layer. In general, the AAA overpredicts dose beyond low‐density regions and underpredicts dose distal to volumes of high density. In many cases, the deviation between the AAA and experimental results exceeds the Task Group 65 target of 2%. The source of these deviations appears to arise from an inability of the AAA to correctly account for altered attenuation along primary ray paths.PACS number: 87.53.Tf

The present study evaluates the performance of a newly released photon‐beam dose calculation algorithm that is incorporated into an established treatment planning system (TPS). We compared the analytical anisotropic algorithm (AAA) factory‐commissioned with “golden beam data” for Varian linear accelerators with measurements performed at two institutions using 6‐MV and 15‐MV beams. The TG‐53 evaluation regions and criteria were used to evaluate profiles measured in a water phantom for a wide variety of clinically relevant beam geometries. The total scatter factor (TSF) for each of these geometries was also measured and compared against the results from the AAA.At one institute, TLD measurements were performed at several points in the neck and thoracic regions of a Rando phantom; at the other institution, ion chamber measurements were performed in a CIRS inhomogeneous phantom. The phantoms were both imaged using computed tomography (CT), and the dose was calculated using the AAA at corresponding detector locations. Evaluation of measured relative dose profiles revealed that 97%, 99%, 97%, and 100% of points at one institute and 96%, 88%, 89%, and 100% of points at the other institution passed TG‐53 evaluation criteria in the outer beam, penumbra, inner beam, and buildup regions respectively. Poorer results in the inner beam regions at one institute are attributed to the mismatch of the measured profiles at shallow depths with the “golden beam data.”For validation of monitor unit (MU) calculations, the mean difference between measured and calculated TSFs was less than 0.5%; test cases involving physical wedges had, in general, differences of more than 1%. The mean difference between point measurements performed in inhomogeneous phantoms and Eclipse was 2.1% (5.3% maximum) and all differences were within TG‐53 guidelines of 7%. By intent, the methods and evaluation techniques were similar to those in a previous investigation involving another convolution–superposition photon‐beam dose calculation algorithm in another TPS, so that the current work permitted an independent comparison between the two algorithms for which results have been provided.PACS number: 87.53.Dq

Current radiation therapy techniques, such as intensity modulated radiation therapy and three-dimensional conformal radiotherapy rely on the precise delivery of high doses of radiation to well-defined volumes. CT, the imaging modality that is most commonly used to determine treatment volumes cannot, however, easily distinguish between cancerous and normal tissue. The ability of positron emission tomography (PET) to more readily differentiate between malignant and healthy tissues has generated great interest in using PET images to delineate target volumes for radiation treatment planning. At present the accurate geometric delineation of tumor volumes is a subject open to considerable interpretation. The possibility of using a local contrast based approach to threshold segmentation to accurately delineate PET target cross sections is investigated using well-defined cylindrical and spherical volumes. Contrast levels which yield correct volumetric quantification are found to be a function of the activity concentration ratio between target and background, target size, and slice location. Possibilities for clinical implementation are explored along with the limits posed by this form of segmentation.

Dice's coefficients of > 0.96 and > 0.93 are achieved between autocontoured and real tumor shapes, and the position of a tumor can be tracked with RMSE values of < 0.55 and < 0.92 mm in 0.5 and 0.2 T equivalent images, respectively. These results demonstrate the feasibility of lung tumor autocontouring in low field MR images, and, by extension, intrafractional lung tumor tracking with our laboratory's linac-MR system.

A new ANN-based lung-tumor motion predictor is developed for MRI-based intrafractional tumor tracking. The prediction accuracy of our predictor is evaluated using a realistic simulated MR imaging rate and system delays. For 120-520 ms system delays, mean RMSE values of 0.5-0.9 mm (ranges 0.0-2.8 mm from 29 patients) are achieved. Further, the advantage of patient specific ANN structure and IW in lung-tumor motion prediction is demonstrated by a 30%-60% decrease in mean RMSE values.