An image regression motion prediction technique for MRI‐guided radiotherapy evaluated in single‐plane cine imaging

Ginn, John S.; Ruan, Dan; Low, Daniel A.; Lamb, James

doi:10.1002/mp.13948

Cited by 10 publications

(17 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Note that this process is currently 2D, due to a lack of commercially available real-time 3D imaging sequences. Recent studies however showed promising approaches towards realtime 3D MR image acquisition (53)(54)(55)(56) and reconstruction (57).…”

Section: Real-time Mrimentioning

confidence: 99%

Technical Challenges of Real-Time Adaptive MR-Guided Radiotherapy

Thorwarth

Low

2021

Front. Oncol.

Self Cite

View full text Add to dashboard Cite

In the past few years, radiotherapy (RT) has experienced a major technological innovation with the development of hybrid machines combining magnetic resonance (MR) imaging and linear accelerators. This new technology for MR-guided cancer treatment has the potential to revolutionize the field of adaptive RT due to the opportunity to provide high-resolution, real-time MR imaging before and during treatment application. However, from a technical point of view, several challenges remain which need to be tackled to ensure safe and robust real-time adaptive MR-guided RT delivery. In this manuscript, several technical challenges to MR-guided RT are discussed. Starting with magnetic field strength tradeoffs, the potential and limitations for purely MR-based RT workflows are discussed. Furthermore, the current status of real-time 3D MR imaging and its potential for real-time RT are summarized. Finally, the potential of quantitative MR imaging for future biological RT adaptation is highlighted.

show abstract

Section: Real-time Mrimentioning

confidence: 99%

Technical Challenges of Real-Time Adaptive MR-Guided Radiotherapy

Thorwarth

Low

2021

Front. Oncol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The gating decision accuracy was defined as the proportion of predicted gating decisions that were correct compared to those derived directly from the images. The PPV was defined as the proportion of predicted beam‐on gating decisions that were correct 18 . Additionally, the effect of using a confidence estimator threshold on gating duty cycle was determined.…”

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%

See 1 more Smart Citation

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

et al. 2020

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…For the problem of intraoperative target organ movement, many scholars and medical institutions have proposed various technical solutions, such as the passive pressure technique based on constrained organ motion space and the method of deep inspiration breath-hold [7,8], Gating technique based on the perception of organ motion cycle pattern [9] and movement prediction compensation algorithm [10], and methods such as four-dimensional CT and slow CT based on image scanning technology [11]. However, it is difficult to obtain the real movement of the intraoperative target area in real-time for all the above schemes.…”

Section: Introductionmentioning

confidence: 99%

Research on Intraoperative Organ Motion Tracking Method Based on Fusion of Inertial and Electromagnetic Navigation

et al. 2021

View full text Add to dashboard Cite

The intraoperative movement of target organs is currently a key and difficult problem that restricts clinical surgery diagnosis and treatment, especially radiotherapy. Unexpected posture changes can cause the deviation of the target area during the operation, thereby affecting the treatment effect and even causing severe complications. Therefore, it is necessary to track the movement of target organs in real-time and accurately to improve the effect and safety of the surgical treatment. This paper proposes an intraoperative organ motion tracking method based on the fusion of inertial navigation and electromagnetic navigation., which can directly measure the target organ's movement under non-invasive or minimally invasive conditions using the human natural cavity. The proposed method for identifying the magnetic field's interference state uses the extended Kalman filter to fuse the inertial measurement unit and the electromagnetic positioning information to achieve target organ movement tracking under different interference conditions. It effectively improved the real-time and robustness of intraoperative organ tracking. Simultaneously, based on the 9-axis inertial measurement unit and a 6-DOF electromagnetic positioning system, the catheter-type target organ tracker was developed, and then under the simulated electromagnetic interference environment, dynamic and static verification experiments were carried out, respectively. The maximum tracking error of displacement and attitude is 2.75 mm and 0.127 rad, respectively, under severe electromagnetic disturbance through experiments. Under non-electromagnetic interference conditions, its displacement and attitude's maximum tracking errors are 0.94 mm and 0.011 rad, respectively. The results prove that the target organ tracking method based on the fusion of inertial navigation and electromagnetic navigation is feasible. It can realize the tracking and measurement of the target organ's movement within the clinical permission error range.INDEX TERMS Inertial navigation, target organ tracking, intraoperative navigation; fusion algorithm.

show abstract

An image regression motion prediction technique for MRI‐guided radiotherapy evaluated in single‐plane cine imaging

Cited by 10 publications

References 33 publications

Technical Challenges of Real-Time Adaptive MR-Guided Radiotherapy

Technical Challenges of Real-Time Adaptive MR-Guided Radiotherapy

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

Research on Intraoperative Organ Motion Tracking Method Based on Fusion of Inertial and Electromagnetic Navigation

Contact Info

Product

Resources

About