Automatic seed picking for brachytherapy postimplant validation with 3D CT images

Purpose An accurate position of the needle is vitally important in low‐dose‐rate seed implantation brachytherapy. Our paper aims to implement a mixed reality navigation system to assist with the placement of the I125 seed implantation thoracoabdominal tumor brachytherapy needle and to validate the accuracy and quality of this type of method. Methods With the surgical navigation system, based on mixed reality through a novel modified multi‐information fusion method, the fusion of virtual organs and a preoperative plan for a real patient and the tracking of surgical tools in real time were achieved. Personalized image recognition and pose estimation were used to track needle punctures in real time and to perform registration processes. After a one‐time registration with a hexagonal prism tracker that used an iterative closest point algorithm, all information, including medical images and volume renderings of organs, needles, and seeds, was precisely merged with the patient. Doctors were able to observe the tumor target and to visualize the preoperative plan. This system was validated in both phantom and animal experiments. The accuracy of this system was validated by calculating the positional and rotational error of each needle insertion. The accuracy of implantation of each seed was determined in an animal experiment to test the accuracy in low‐dose‐rate brachytherapy. The efficiency of this system was also validated through time consumption assessments. Results In the phantom experiment, the average error of the needle locations was 0.664 mm and the angle error was 4.74°, average time consumption was 16.1 min with six needles inserted. Based on the results of the animal experiment, the accuracy of the needle insertion was 1.617 mm, while the angle error was 5.574° and the average error of the seed positions was 1.925 mm. Conclusions This paper describes the design and experimental validation of a novel surgical navigation system based on mixed reality for I125 seed brachytherapy for thoracoabdominal tumors. This system was validated through a series of experiments, including phantom experiments and animal experiments. Compared with the traditional image‐guided system, the procedure presented here is convenient, displays clinically acceptable accuracy and reduces the number of CT scans, allowing doctors to perform surgery based on a visualized plan. All the experimental results indicated that the procedure is ready to be applied in further clinical studies.

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“…The Euclidean distance is required for the computation. Each point in

O_{italicMedical}

is captured by the automatic picker program in VTK, while for

O_{italicRoom}

, we use pattern recognition method to obtain the real position.…”

Section: Methodsmentioning

confidence: 99%

Design and validation of a surgical navigation system for brachytherapy based on mixed reality

et al. 2019

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“…In this procedure, the coordinate of the template and the needle is obtained automatically. 24 The dose F I G . 1.…”

Section: A | Auto-positioning System Driven By Preoperative Planningmentioning

confidence: 99%

“…23 Based on a previous description of APSDP, TPS was developed with several functions to cover the entire procedure of the seed implantation treatment, as shown in Fig. 24 The dose F I G . Specifically, there is a navigation module acting as the driving system to realize the connection between the preoperative planning and the robot-assisted system.…”

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confidence: 99%

A novel auto‐positioning method in Iodine‐125 seed brachytherapy driven by preoperative planning

Yang

Jiang

et al. 2019

J Applied Clin Med Phys

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Iodine‐125 seed brachytherapy has great potential in the treatment of malignant tumors. However, the success of this treatment is highly dependent on the ability to accurately position the coplanar template. The aim of this study was to develop an auto‐positioning system for the template with a design focus on efficiency and accuracy. In this study, an auto‐positioning system was presented, which was composed of a treatment planning system (TPS) and a robot‐assisted system. The TPS was developed as a control system for the robot‐assisted system. Then, the robot‐assisted system was driven by the output of the TPS to position the template. Contrast experiments for error validation were carried out in a computed tomography environment to compare with the traditional positioning method (TPM). Animal experiments on Sprague–Dawley rats were also carried out to evaluate the auto‐positioning system. The error validation experiments and animal experiments with this auto‐positioning system were successfully carried out with improved efficiency and accuracy. The error validation experiments achieved a positioning error of 1.04 ± 0.19 mm and a positioning time of 23.15 ± 2.52 min, demonstrating a great improvement compared with the TPM (2.55 ± 0.21 mm and 40.35 ± 2.99 min, respectively). The animal experiments demonstrated that the mean deviation of the seed position was 0.75 mm. The dose‐volume histogram of the preoperative planning showed the same as the postoperative dosimetry validation. A novel auto‐positioning system driven by preoperative planning was established, which exhibited higher efficiency and accuracy compared with the TPM.

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“…The radioactive sources are implanted into planned positions sequentially through the hollow shaft of the needle. Finally, verifying the quality of the operation by postoperative CT 6 …”

Section: Introductionmentioning

confidence: 99%

Automatic needle detection using improved random sample consensus in CT image‐guided lung interstitial brachytherapy

Zheng

Jiang

Yang

et al. 2021

J Applied Clin Med Phys

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Purpose To develop a method for automatically detecting needles from CT images, which can be used in image‐guided lung interstitial brachytherapy to assist needle placement assessment and dose distribution optimization. Material and Methods Based on the preview model parameters evaluation, local optimization combining local random sample consensus, and principal component analysis, the needle shaft was detected quickly, accurately, and robustly through the modified random sample consensus algorithm. By tracing intensities along the axis, the needle tip was determined. Furthermore, multineedles in a single slice were segmented at once using successive inliers deletion. Results The simulation data show that the segmentation efficiency is much higher than the original random sample consensus and yet maintains a stable submillimeter accuracy. Experiments with physical phantom demonstrate that the segmentation accuracy of described algorithm depends on the needle insertion depth into the CT image. Application to permanent lung brachytherapy image is also validated, where manual segmentation is the counterparts of the estimated needle shape. Conclusions From the results, the mean errors in determining needle orientation and endpoint are regulated within 2° and 1 mm, respectively. The average segmentation time is 0.238 s per needle.

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Automatic seed picking for brachytherapy postimplant validation with 3D CT images

Cited by 8 publications

References 23 publications

Design and validation of a surgical navigation system for brachytherapy based on mixed reality

Design and validation of a surgical navigation system for brachytherapy based on mixed reality

A novel auto‐positioning method in Iodine‐125 seed brachytherapy driven by preoperative planning

Automatic needle detection using improved random sample consensus in CT image‐guided lung interstitial brachytherapy

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