TY - JOUR
T1 - Local Contractive Registration with Biomechanical Model
T2 - Assessing Microwave Ablation after Compensation for Tissue Shrinkage
AU - Liu, Dingkun
AU - Ai, Danni
AU - Fu, Tianyu
AU - Gao, Yuanjin
AU - Fan, Jingfan
AU - Song, Hong
AU - Xiao, Deqiang
AU - Liang, Ping
AU - Yang, Jian
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - Microwave ablation (MWA) is a minimally invasive procedure for the treatment of liver tumor. Accumulating clinical evidence has considered the minimal ablative margin (MAM) as a significant predictor of local tumor progression (LTP). In clinical practice, MAM assessment is typically carried out through image registration of pre- and post-MWA images. However, this process faces two main challenges: non-homologous match between tumor and coagulation with inconsistent image appearance, and tissue shrinkage caused by thermal dehydration. These challenges result in low precision when using traditional registration methods for MAM assessment. In this paper, we present a local contractive nonrigid registration method using a biomechanical model (LC-BM) to address these challenges and precisely assess the MAM. The LC-BM contains two consecutive parts: 1) local contractive decomposition (LC-part), which reduces the incorrect match between the tumor and coagulation and quantifies the shrinkage in the external coagulation region, and 2) biomechanical model constraint (BM-part), which compensates for the shrinkage in the internal coagulation region. After quantifying and compensating for tissue shrinkage, the warped tumor is overlaid on the coagulation, and then the MAM is assessed. We evaluated the method using prospectively collected data from 36 patients with 47 liver tumors, comparing LC-BM with 11 state-of-the-art methods. LTP was diagnosed through contrast-enhanced MR follow-up images, serving as the ground truth for tumor recurrence. LC-BM achieved the highest accuracy (97.9%) in predicting LTP, outperforming other methods. Therefore, our proposed method holds significant potential to improve MAM assessment in MWA surgeries.
AB - Microwave ablation (MWA) is a minimally invasive procedure for the treatment of liver tumor. Accumulating clinical evidence has considered the minimal ablative margin (MAM) as a significant predictor of local tumor progression (LTP). In clinical practice, MAM assessment is typically carried out through image registration of pre- and post-MWA images. However, this process faces two main challenges: non-homologous match between tumor and coagulation with inconsistent image appearance, and tissue shrinkage caused by thermal dehydration. These challenges result in low precision when using traditional registration methods for MAM assessment. In this paper, we present a local contractive nonrigid registration method using a biomechanical model (LC-BM) to address these challenges and precisely assess the MAM. The LC-BM contains two consecutive parts: 1) local contractive decomposition (LC-part), which reduces the incorrect match between the tumor and coagulation and quantifies the shrinkage in the external coagulation region, and 2) biomechanical model constraint (BM-part), which compensates for the shrinkage in the internal coagulation region. After quantifying and compensating for tissue shrinkage, the warped tumor is overlaid on the coagulation, and then the MAM is assessed. We evaluated the method using prospectively collected data from 36 patients with 47 liver tumors, comparing LC-BM with 11 state-of-the-art methods. LTP was diagnosed through contrast-enhanced MR follow-up images, serving as the ground truth for tumor recurrence. LC-BM achieved the highest accuracy (97.9%) in predicting LTP, outperforming other methods. Therefore, our proposed method holds significant potential to improve MAM assessment in MWA surgeries.
KW - Nonrigid registration
KW - biomechanical model
KW - microwave ablation assessment
KW - tissue shrinkage
UR - http://www.scopus.com/inward/record.url?scp=85173327388&partnerID=8YFLogxK
U2 - 10.1109/JBHI.2023.3318893
DO - 10.1109/JBHI.2023.3318893
M3 - Article
C2 - 37747865
AN - SCOPUS:85173327388
SN - 2168-2194
VL - 28
SP - 415
EP - 426
JO - IEEE Journal of Biomedical and Health Informatics
JF - IEEE Journal of Biomedical and Health Informatics
IS - 1
ER -