ArticleThe international journal of medical robotics + computer assisted surgery : MRCAS2018
Respiratory motion estimation of the liver with abdominal motion as a surrogate.
Article in The international journal of medical robotics + computer assisted surgery : MRCAS, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 20 citations in OpenAlex.
- An investigation into the impact of volumetric rescanning and fractionation treatment on dose homogeneity in liver cancer proton therapy.Journal of radiation research · 2024Article
- Fiberbots: Robotic fibers for high-precision minimally invasive surgery.Science advances · 2024Article
- Risk Assessment-Oriented Design of a Needle Insertion Robotic System for Non-Resectable Liver Tumors.Healthcare (Basel, Switzerland) · 2022Article
- Respiratory motion estimation of the liver with abdominal motion as a surrogate.The international journal of medical robotics + computer assisted surgery : MRCAS · 2018Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRespiratory-induced motion (RIM) causes uncertainties in localizing hepatic lesions, which could lead to inaccurate targeting during interventions. One approach to mitigate the problem is respiratory motion estimation (RME), in which the liver motion is estimated by measuring external signals called surrogates.
methodsA learning-based approach has been developed and validated to estimate the RIM of hepatic lesions. External markers placed on the human's abdomen were chosen as surrogates. Accordingly, appropriate motion models (multivariate, Ridge and Lasso regression models) were designed to correlate the liver motion with the abdominal motion, and trained to estimate the superior-inferior (SI) motion of the liver. Three subjects volunteered for 6 sessions of such that liver images acquired by magnetic resonance imaging (MRI) were recorded alongside camera-tracked external markers. RESULTS AND
conclusionsThe proposed machine learning approach was validated in MRI on human subjects and the results show that the approach could estimate the respiratory-induced SI motion of the liver with a mean absolute error (MAE) accuracy below 2 mm.
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Registered trials
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