Neural-network based autocontouring algorithm for intrafractional XXXX tracking using Linac-MR.

@entity5 @entity1 @entity2 @entity5529
PURPOSE: To develop a neural-network based autocontouring algorithm for intrafractional @entity2 tracking using Linac-MR and evaluate its performance with phantom and in-vivo MR images. METHODS: An autocontouring algorithm was developed to determine both the shape and position of a @entity2 from each intrafractional MR image. A pulse-coupled neural network was implemented in the algorithm for contrast improvement of the @entity5 region. Prior to treatment, to initiate the algorithm, an expert user needs to contour the @entity5 and its maximum anticipated range of motion in pretreatment MR images. During treatment, however, the algorithm processes each intrafractional MR image and automatically generates a @entity5 contour without further user input. The algorithm is designed to produce a @entity5 contour that is the most similar to the expert's manual one. To evaluate the autocontouring algorithm in the author's Linac-MR environment which utilizes a 0.5 T MRI, a motion phantom and four @entity2 @entity1 were imaged with 3 T MRI during normal breathing, and the image noise was degraded to reflect the image noise at 0.5 T. Each of the pseudo-0.5 T images was autocontoured using the author's algorithm. In each test image, the Dice similarity index (DSI) and @entity5529 ( @entity5529 ) between the expert's manual contour and the algorithm generated contour were calculated, and their centroid positions were compared (  dcentroid). RESULTS: The algorithm successfully contoured the shape of a moving @entity5 from dynamic MR images acquired every 275 ms. From the phantom study, mean DSI of 0.95-0.96, mean @entity5529 of 2.61-2.82 mm, and mean   dcentroid of 0.68-0.93 mm were achieved. From the in-vivo study, the author's algorithm achieved mean DSI of 0.87-0.92, mean @entity5529 of 3.12-4.35 mm, as well as   dcentroid of 1.03-1.35 mm. Autocontouring speed was less than 20 ms for each image. CONCLUSIONS: The authors have developed and evaluated a @entity2 autocontouring algorithm for intrafractional @entity5 tracking using Linac-MR. The autocontouring performance in the Linac-MR environment was evaluated using phantom and in-vivo MR images. From the in-vivo study, the author's algorithm achieved 87%-92% of contouring agreement and centroid tracking accuracy of 1.03-1.35 mm. These results demonstrate the feasibility of @entity2 autocontouring in the author's laboratory's Linac-MR environment.
