Online Target Volume Estimation and Prediction from an Interlaced Slice Acquisition - A Manifold Embedding and Learning Approach

Ginn, John S.; Lamb, James; Ruan, Dan

doi:10.1007/978-3-030-32486-5_10

Cited by 2 publications

(3 citation statements)

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“…Motion predictions are obtained using an image regression motion prediction method previously developed by our group in the context of MRI‐guided radiotherapy 18,19 . In this setting, each observation is a set of images.…”

Section: Methodsmentioning

confidence: 99%

“…Motion predictions are obtained using an image regression motion prediction method previously developed by our group in the context of MRI-guided radiotherapy. 18,19 In this setting, each observation is a set of images. The prediction method identifies the K observations in a training set that are most similar to the current observation, derives weights that best estimate the current observation as linear combination of these training elements, and predicts motion by combining the future locations associated with the training elements with the same weights.…”

Section: A1 Proposed Confidence Estimatormentioning

confidence: 99%

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A motion prediction confidence estimation framework for prediction‐based radiotherapy gating

et al. 2020

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Purpose: Motion prediction can compensate for latency in image-guided radiotherapy and has been an active area of research. However, motion predictions are subject to error and variations. We have developed and evaluated a novel motion prediction confidence estimation framework to improve the efficacy and robustness of prediction-based radiotherapy gating decision-making. The specific scenario of adaptive gating in magnetic resonance imaging (MRI)-guided radiotherapy is studied as an example, but the method generalizes to other modalities and motion management setups. Methods: The proposed prediction confidence estimator is based on a generic training/testing paradigm and consists of a weighted combination of three components: the prediction model's goodness of fit, variation in the prediction using a leave-one-out process and the velocity of the tracked target. Roughly, these terms quantify respectively the consistency between prediction and the training data, the robustness of model inference, and the stability due to target speed. The weight parameters and the action level in triggering beam-off decision are optimized. The method is assessed and validated in 8 healthy volunteer and 13 patient studies using a 0.35T MRI-guided radiotherapy system predicting 0.25-0.33 s ahead. The effect of the action level on the predicted gating decision accuracy, beam-on positive predictive value (PPV) and median distance between the predicted and ground-truth target centroids were evaluated. Statistical significance was evaluated using a paired t-test. The tradeoff between these performance metrics and gating duty cycle was assessed. Results: Use of the confidence estimator threshold increased gating accuracy by up to 2.42%, increased PPV by up to 3.00%, and reduced the median centroid distance up to 0.28 mm. The confidence estimator threshold on average increased gating accuracy to 96.5% (P = 2.08 9 10 À4 ), increased PPV to 96.7% (P = 1.46 9 10 À5 ), reduced the median centroid distance to 0.54 mm (P = 1.71 9 10 À5 ) at the cost of reducing the gating duty cycle by 14.3% to 48.5%. Hyperparameter tuning revealed that contrary to intuition, the velocity term offered only minimal performance improvement in some cases but also introduced potential stability issues. The combination of goodness of fit and leave-one-out prediction variation provided the most effective confidence estimator, yielding universally better performance in gating decisions. Conclusion: Confidence estimation utilizing prediction model fitness criterion and validation principles can complement prediction methods to guide MRI-guided radiotherapy gating. Results from both volunteer and patient studies showed improved gating quality.

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Section: Methodsmentioning

confidence: 99%

Section: A1 Proposed Confidence Estimatormentioning

confidence: 99%

A motion prediction confidence estimation framework for prediction‐based radiotherapy gating

et al. 2020

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“…Alternative approaches were proposed to overcome the lack of data to train 4D motion models by using surrogate signals derived from 2D MR images [156,157]. Another possibility is represented by the combination of single and multi-slice MR image information, as reported by Ginn et al who demonstrated the feasibility of obtaining out of slice tumour motion information from the analysis of 2D MR images by using ML models trained on the DVFs calculated among the 10 most recent images and a reference image [158,159]. While the model agreed well with gating directly on high frame-rate images, out-of-plane motion remains an issue and the use of an internal image-based surrogate will likely be more precise.…”

Section: Advanced Imaging and Motion Managementmentioning

confidence: 99%

Artificial Intelligence in magnetic Resonance guided Radiotherapy: Medical and physical considerations on state of art and future perspectives

et al. 2021

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Online Target Volume Estimation and Prediction from an Interlaced Slice Acquisition - A Manifold Embedding and Learning Approach

Cited by 2 publications

References 8 publications

A motion prediction confidence estimation framework for prediction‐based radiotherapy gating

A motion prediction confidence estimation framework for prediction‐based radiotherapy gating

Artificial Intelligence in magnetic Resonance guided Radiotherapy: Medical and physical considerations on state of art and future perspectives

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