Respiratory motion estimation of the liver with abdominal motion as a surrogate

Fahmi, Shamel; Simonis, F.F.J.; Abayazid, Momen

doi:10.1002/rcs.1940

Cited by 15 publications

(8 citation statements)

References 44 publications

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“…During exhalation, the process is reversed, so the abdominal cavity organs are moving back to their original position (upwards), while the rib cage diminishes its volume. Authors have analyzed various solutions to solve this issue: motion (tumor) chasing [ 38 ] and motion gating [ 39 ]. Tumor chasing in real time is a very demanding task and is difficult to be achieved correctly.…”

Section: Methodsmentioning

confidence: 99%

Risk Assessment-Oriented Design of a Needle Insertion Robotic System for Non-Resectable Liver Tumors

et al. 2022

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Medical robotics is a highly challenging and rewarding field of research, especially in the development of minimally invasive solutions for the treatment of the worldwide leading cause of death, cancer. The aim of the paper is to provide a design methodology for the development of a safe and efficient medical robotic system for the minimally invasive, percutaneous, targeted treatment of hepatocellular carcinoma, which can be extended with minimal modification for other types of abdominal cancers. Using as input a set of general medical requirements to comply with currently applicable standards, and a set of identified hazards and failure modes, specific methods, such as the Analytical Hierarchy Prioritization, Risk Analysis and fuzzy logic Failure Modes and Effect Analysis have been used within a stepwise approach to help in the development of a medical device targeting the insertion of multiple needles in brachytherapy procedures. The developed medical device, which is visually guided using CT scanning, has been tested for validation in a medical environment using a human-size ballistic gel liver, with promising results. These prove that the robotic system can be used for the proposed medical task, while the modular approach increases the chances of acceptance.

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Section: Methodsmentioning

confidence: 99%

Risk Assessment-Oriented Design of a Needle Insertion Robotic System for Non-Resectable Liver Tumors

et al. 2022

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“…The marker motion has high temporal resolution and does not drift over time. In addition, optical markers can be easily attached to the skin and do not cause discomfort to the patient 28 . Therefore, the motion of the optical marker on the skin is used as the surrogate signal in this work.…”

Section: Methodsmentioning

confidence: 99%

“…In addition, optical markers can be easily attached to the skin and do not cause discomfort to the patient. 28 Therefore, the motion of the optical marker on the skin is used as the surrogate signal in this work. As an implicit corresponding model, the model based on ϵ-SVR has been proven to be able to correlate body surface marker motion and organ tumor motion effectively, 11 which is adopted in this work.…”

Section: B Respiratory Motion Model Of Tumormentioning

confidence: 99%

Diffeomorphic respiratory motion estimation of thoracoabdominal organs for image‐guided interventions

et al. 2021

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Purpose Percutaneous image‐guided interventions are commonly used for the diagnosis and treatment of cancer. In practice, physiological breathing‐induced motion increases the difficulty of accurately inserting needles into tumors without impairing the surrounding vital structures. In this work, we propose a data‐driven patient‐specific hierarchical respiratory motion estimation framework to accurately estimate the position of a tumor and surrounding vital tissues in real time. Methods The motion of optical markers attached to the chest or abdomen skin is used as a surrogate signal to estimate tumor motion based on ɛ‐support vector regression (ɛ‐SVR). With the estimated tumor motion as the input, a novel respiratory motion model is developed to estimate the diffeomorphic deformation field of the whole organ (liver or lung) without intraoperative, iterative optimization computations. The respiratory motion model of the whole organ is established in Lie algebra space based on the kriging algorithm to ensure that the estimated deformation field is diffeomorphic, optimal, and unbiased. Preoperative prior knowledge for modeling the motion of whole organs is obtained by deformation registration between four‐dimensional computed tomography (4D CT) images using a hybrid diffeomorphic registration method. Results and Conclusions Experimental results on an in vivo beagle dog show that the minimum value of the determinant of the Jacobian of the estimated deformation field is greater than zero, so the estimated deformation field of the whole liver with our method is diffeomorphic. The mean position error of the tumor is 1.2 mm corresponding to a mean accuracy improvement of 76.5%, and the mean position error of the whole liver is 2.1 mm, corresponding to a mean accuracy improvement of 37.9%. The experimental results based on public human subject data show that the mean position error of the tumor is 1.1 mm, corresponding to a mean accuracy improvement of 83.1%, and the mean position error of the whole lung is 2.1 mm, corresponding to a mean accuracy improvement of 41.4%. The positioning errors for the tumor and whole organ are hierarchical and consistent with clinical demand.

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“…Linear regression [7][8][9] is the most common mathematical representation between target motion and surrogate data. Before modelling phase, the synchronized target and surrogate data are captured.…”

Section: Introductionmentioning

confidence: 99%

Liver Tumor Motion Estimation Based on Respiration-Induced Skin Change Using Principal Component Analysis and Nearest Neighbor Method

2022

Proceedings of WCSE 2022 Spring Event: 2022 9th International Conference on Industrial Engineering and Applications

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This paper proposes a liver tumor motion tracking approach based on respiration-induced skin change by using principal component analysis (PCA) and nearest neighbor estimation (NNE). The proposed method is abbreviated as PCA-NNE and is developed on the NNE in our previous work. The PCA-NNE and NNE are both estimation models by correlating internal target motion and its external surrogate. Their difference is that the surrogate for NNE is the marker attached on skin, while for PCA-NNE, the surrogate is the entire skin change. In PCA-NNE, PCA derives several principal components (PCs) from skin, and then the PCs were inputted into NNE to estimate target motion. Through this way, PCA-NNE simplify the selection of surrogate. Similar to NNE, PCA-NNE needs data to construct patient-specific model before application. To evaluate PCA-NNE, we use a digital torso phantom to create abdominal computed tomography (CT) images of a virtual patient. By setting different breathing parameters and locations of a lesion, we simulated 21 sets of respiration-correlated CT with a length of nearly 1 minutes, and derived synchronized liver tumor motion and skin change from them. For each set, the data during the first 2 ~ 3 respiration periods were used for modeling. The remaining data were used for evaluation. Two metrics were adopted for assessment. The root-mean-square error (RMSE) was used to validate its accuracy. The estimation time per sample (t) was used to evaluate its real-time performance. The results showed a RMSE of < 3mm and a t of < 15ms for all evaluation datasets. It suggested a good precision and real-time performance for PCA-NNE.

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Respiratory motion estimation of the liver with abdominal motion as a surrogate

Cited by 15 publications

References 44 publications

Risk Assessment-Oriented Design of a Needle Insertion Robotic System for Non-Resectable Liver Tumors

Risk Assessment-Oriented Design of a Needle Insertion Robotic System for Non-Resectable Liver Tumors

Diffeomorphic respiratory motion estimation of thoracoabdominal organs for image‐guided interventions

Liver Tumor Motion Estimation Based on Respiration-Induced Skin Change Using Principal Component Analysis and Nearest Neighbor Method

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