Neural‐network based autocontouring algorithm for intrafractional lung‐tumor tracking using Linac‐MR. Issue 5 (15th April 2015)

• Record Type: Journal Article
• Authors: Yun, Jihyun; Yip, Eugene; Gabos, Zsolt; Wachowicz, Keith; Rathee, Satyapal; Fallone, B. G.
• Is Part Of: Medical physics. Volume 42:Issue 5(2015)
• Page Start: 2296
• Page End: 2310
• Publication Date: 2015-04-15
• Subjects: biomedical MRI -- cancer -- linear accelerators -- lung -- medical image processing -- neural nets -- object tracking -- phantoms -- pneumodynamics -- tumours; Clinical applications -- Neural engineering -- Pneumodyamics, respiration -- Image forming and processing; Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Linear accelerators; intrafraction motion management -- lung‐tumor tracking -- Linac‐MR -- MRI guidance -- organ motion compensation; Cancer -- Medical magnetic resonance imaging -- Lungs -- Medical image noise -- Erosion -- Image detection systems -- Medical image contrast; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4916657
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: To develop a neural‐network based autocontouring algorithm for intrafractional lung‐tumor 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 lung tumor from each intrafractional MR image. A pulse‐coupled neural network was implemented in the algorithm for contrast improvement of the tumor region. Prior to treatment, to initiate the algorithm, an expert user needs to contour the tumor 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 tumor contour without further user input. The algorithm is designed to produce a tumor 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 lung cancer patients 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 Hausdorff distance (HD) between the expert's manual contour and the algorithm generated contour were calculated, and their centroid positions were compared (Δ d centroid ). Results: The algorithm successfully contoured the shape of a moving tumor from dynamic MR images acquired every 275 ms. From the phantom study, mean DSI of 0.95–0.96, mean HD of 2.61–2.82 mm, and mean Δ d centroid 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 HD of 3.12–4.35 mm, as well as Δ d centroid of 1.03–1.35 mm. Autocontouring speed was less than 20 ms for each image. Conclusions: The authors have developed and evaluated a lung tumor autocontouring algorithm for intrafractional tumor 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 lung tumor autocontouring in the author's laboratory's Linac‐MR environment.