Subject-specific Four-dimensional Liver Motion Modeling Based on Registration of Dynamic MRI

Overview

Journal J Med Imaging (Bellingham)

Specialty Radiology

Date 2016 Aug 6

PMID 27493981

Citations 7

Authors

Yolanda H Noorda

Lambertus W Bartels

Max A Viergever

Josien P W Pluim

Affiliations

Soon will be listed here.

Abstract

Magnetic resonance-guided high intensity focused ultrasound treatment of the liver is a promising noninvasive technique for ablation of liver lesions. For the technique to be used in clinical practice, however, the issue of liver motion needs to be addressed. A subject-specific four-dimensional liver motion model is presented that is created based on registration of dynamically acquired magnetic resonance data. This model can be used for predicting the tumor motion trajectory for treatment planning and to indicate the tumor position for treatment guidance. The performance of the model was evaluated on a dynamic scan series that was not used to build the model. The method achieved an average Dice coefficient of 0.93 between the predicted and actual liver profiles and an average vessel misalignment of 3.0 mm. The model performed robustly, with a small variation in the results per subject. The results demonstrate the potential of the model to be used for MRI-guided treatment of liver lesions. Furthermore, the model can possibly be applied in other image-guided therapies, for instance radiotherapy of the liver.

Citing Articles

Motion modeling from 4D MR images of liver simulating phantom.

Kavaluus H, Koivula L, Salli E, Seppala T, Saarilahti K, Tenhunen M J Appl Clin Med Phys. 2022; 23(7):e13611.

PMID: 35413145 PMC: 9278689. DOI: 10.1002/acm2.13611.

Simulated accuracy assessment of small footprint body-mounted probe alignment device for MRI-guided cryotherapy of abdominal lesions.

Shono N, Ninni B, King F, Kato T, Tokuda J, Fujimoto T Med Phys. 2020; 47(6):2337-2349.

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Automatic self-gated 4D-MRI construction from free-breathing 2D acquisitions applied on liver images.

Vazquez Romaguera L, Olofsson N, Plantefeve R, Lugez E, De Guise J, Kadoury S Int J Comput Assist Radiol Surg. 2019; 14(6):933-944.

PMID: 30887421 DOI: 10.1007/s11548-019-01941-1.

Automatic error correction using adaptive weighting for vessel-based deformable image registration.

Ren J, Green M, Huang X, Abdalbari A Biomed Eng Lett. 2019; 7(2):173-181.

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Image registration assessment in radiotherapy image guidance based on control chart monitoring.

Xia W, Breen S J Med Imaging (Bellingham). 2018; 5(2):021221.

PMID: 29564368 PMC: 5849944. DOI: 10.1117/1.JMI.5.2.021221.

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