Name
Title | ||
|---|---|---|
A bayesian framework for estimating respiratory liver motion from sparse measurements |
Abstract | ||
|---|---|---|
In this paper, we present an approach for modelling and predicting organ motion from partial information. We used 4D-MRI sequences of 12 subjects to build a statistical population model for respiratory motion of the liver. Using a Bayesian reconstruction approach, a pre-operative CT scan and a few known surrogate markers, we are able to accurately predict the position of the entire liver at all times. The surrogates may, for example, come from ultrasound, portal images captured during radiotherapy or from implanted electromagnetic beacons. In leave-one-out experiments, we achieve an average prediction error of 1.2 mm over sequences of 20 min with only three surrogates. Our model is accurate enough for clinically relevant treatment intervals and has the potential to be used for adapting the gating window in tumour therapy or even for tracking a tumour continuously during irradiation. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-28557-8_26 | Abdominal imaging |
Keywords | Field | DocType |
sparse measurement,respiratory motion,statistical population model,average prediction error,accurate enough,gating window,organ motion,tumour therapy,bayesian framework,electromagnetic beacon,entire liver,bayesian reconstruction approach,respiratory liver motion | Respiratory motion,Organ Motion,Artificial intelligence,Computed tomography,Statistical population,Surgery,Medicine,Mean squared prediction error,Pattern recognition,Statistical model,Radiology,Ultrasound,Bayesian probability | Conference |
Citations | PageRank | References |
9 | 0.76 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Frank Preiswerk | 1 | 77 | 7.16 |
| Patrik Arnold | 2 | 48 | 3.83 |
| Beat Fasel | 3 | 86 | 6.97 |
| Philippe C. Cattin | 4 | 367 | 46.80 |