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Personalized scan path planning for robotic ultrasound in head and neck lesions
Ryosuke Tsumura1, Toshifumi Tomioka2, Yoshihiko Koseki3
1Health and Medical Research Institute, National Institute of Advanced Industrial Science and Technology, Tsukuba, Ibaraki, Japan. ryosuke.tsumura@aist.go.jp.
Purpose:
Scan path planning is an essential technology for fully automated robotic ultrasound (US). During US screening in head and neck lesions, it is necessary to observe the entire neck, including the thyroid, parotid, submandibular glands, and cervical lymph nodes efficiently. This study developed a personalized scan path planning for robotic US in head and neck lesions.
Methods:
We proposed the personalized scan path planning that can estimate the scan position by applying the non-rigid registration between the patient neck image and the similar neck image among the previous patients. To select the most similar neck image in the database, the unique similarity index with the combination of multiple similarity metrics (neck area, position of the centroid, SSIM, Dice coefficient, and contours of the neck area) was developed. Fifty neck images were used to determine the optimal combination experimentally. The non-rigid registration from each of the 50 images to the other 49 images, resulting in a total of 2,450 registration pairs, was conducted, and the correlations between each similarity metric and registration error were analyzed.
Results:
The best-performing similarity index achieved a correlation coefficient of 0.730 between the similarity score and registration error, which shows that the proposed index can select the similar image with a high accuracy of scan path registration. With the proposed scan path planning, the feasibility study is performed on six healthy subjects. The results showed a success rate of 94.4% for obtaining the specific target images required for the neck and head US examinations.
Conclusion:
The proposed scan path planning allows the scan to adapt to individual differences in the neck, which can contribute to the automated robotic US in head and neck lesions.
