A particle filter based autocontouring algorithm for lung tumor tracking using dynamic magnetic resonance imaging

Bourque, Alexandra E; Bedwani, Stéphane; Filion, Édith; Carrier, Jean‐François

doi:10.1118/1.4961403

Cited by 14 publications

(21 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is unfeasible for a human expert to continually contour images at a rate of lower than 0.5 s per image for the duration of treatment. To address this, our group and others have previously proposed real time autocontouring algorithms. A key feature to imaging and contouring in real time is that it does not rely on regular, predictable motion of lung tumor.…”

Section: Introductionmentioning

confidence: 99%

Evaluating performance of a user‐trained MR lung tumor autocontouring algorithm in the context of intra‐ and interobserver variations

et al. 2017

View full text Add to dashboard Cite

Purpose: Real-time tracking of lung tumors using magnetic resonance imaging (MRI) has been proposed as a potential strategy to mitigate the ill-effects of breathing motion in radiation therapy. Several autocontouring methods have been evaluated against a "gold standard" of a single human expert user. However, contours drawn by experts have inherent intra-and interobserver variations. In this study, we aim to evaluate our user-trained autocontouring algorithm with manually drawn contours from multiple expert users, and to contextualize the accuracy of these autocontours within intra-and interobserver variations. Methods: Six nonsmall cell lung cancer patients were recruited, with institutional ethics approval. Patients were imaged with a clinical 3 T Philips MR scanner using a dynamic 2D balanced SSFP sequence under free breathing. Three radiation oncology experts, each in two separate sessions, contoured 130 dynamic images for each patient. For autocontouring, the first 30 images were used for algorithm training, and the remaining 100 images were autocontoured and evaluated. Autocontours were compared against manual contours in terms of Dice's coefficient (DC) and Hausdorff distances (d H ). Intra-and interobserver variations of the manual contours were also evaluated. Results: When compared with the manual contours of the expert user who trained it, the algorithm generates autocontours whose evaluation metrics (same session: DC = 0.90(0.03), d H = 3.8(1.6) mm; different session DC = 0.88(0.04), d H = 4.3(1.5) mm) are similar to or better than intraobserver variations (DC = 0.88(0.04), and d H = 4.3(1.7) mm) between two sessions. The algorithm's autocontours are also compared to the manual contours from different expert users with evaluation metrics (DC = 0.87(0.04), d H = 4.8(1.7) mm) similar to interobserver variations (DC = 0.87(0.04), d H = 4.7(1.6) mm). Conclusions: Our autocontouring algorithm delineates tumor contours (<20 ms per contour), in dynamic MRI of lung, that are comparable to multiple human experts (several seconds per contour), but at a much faster speed. At the same time, the agreement between autocontours and manual contours is comparable to the intra-and interobserver variations. This algorithm may be a key component of the real time tumor tracking workflow for our hybrid Linac-MR device in the future.

show abstract

Section: Introductionmentioning

confidence: 99%

Evaluating performance of a user‐trained MR lung tumor autocontouring algorithm in the context of intra‐ and interobserver variations

et al. 2017

View full text Add to dashboard Cite

show abstract

“…The most straightforward approach is to derive the position information directly from the MR image. Often used in this case is autocontouring, in which the tumor or organ is automatically delineated [202][203][204][205][206][207][208][209][210]. Alternative methods include template matching, in which a template shape, for example of an organ or tumor, is located on the dynamic MR images [175,177,211,212], artificial neural networks [175,178] and nonrigid image registration [157].…”

Section: Tracking and Motion Modelingmentioning

confidence: 99%

Beyond T2 and 3T: New MRI techniques for clinicians

Knowles

Friedrich

Fischer

et al. 2019

Clinical and Translational Radiation Oncology

View full text Add to dashboard Cite

show abstract

“…Several groups have shown that it is feasible to localise lung tumours in these images using template matching [144,97,145]. More advanced algorithms deploy artificial neural networks [144,146], scale-invariant feature transforms [147,148], or particle filtering [149] to quickly delineate the tumour in each image. The orientation of these 2D imaging planes with respect to the tumour motion can be freely set and even altered during image acquisition.…”

Section: Intrafractional Motion Managementmentioning

confidence: 99%

MRI-guided lung SBRT: Present and future developments

2017

View full text Add to dashboard Cite

Stereotactic body radiotherapy (SBRT) is rapidly becoming an alternative to surgery for the treatment of early-stage non-small cell lung cancer patients. Lung SBRT is administered in a hypo-fractionated, conformal manner, delivering high doses to the target. To avoid normal-tissue toxicity, it is crucial to limit the exposure of nearby healthy organs-at-risk (OAR). Current image-guided radiotherapy strategies for lung SBRT are mostly based on X-ray imaging modalities. Although still in its infancy, magnetic resonance imaging (MRI) guidance for lung SBRT is not exposure-limited and MRI promises to improve crucial soft-tissue contrast. Looking beyond anatomical imaging, functional MRI is expected to inform treatment decisions and adaptations in the future. This review summarises and discusses how MRI could be advantageous to the different links of the radiotherapy treatment chain for lung SBRT: diagnosis and staging, tumour and OAR delineation, treatment planning, and inter-or intrafractional motion management. Special emphasis is placed on a new generation of hybrid MRI treatment devices and their potential for real-time adaptive radiotherapy.

show abstract

A particle filter based autocontouring algorithm for lung tumor tracking using dynamic magnetic resonance imaging

Abstract: This work presents a proof of concept of a new autocontouring algorithm for NSCLC patients on dynamic MR images. The contours were generated in good agreement with the expert's contours.

Cited by 14 publications

References 49 publications

Evaluating performance of a user‐trained MR lung tumor autocontouring algorithm in the context of intra‐ and interobserver variations

Evaluating performance of a user‐trained MR lung tumor autocontouring algorithm in the context of intra‐ and interobserver variations

Beyond T2 and 3T: New MRI techniques for clinicians

MRI-guided lung SBRT: Present and future developments

Contact Info

Product

Resources

About