ABSTRACT
BACKGROUND: Magnetic resonance imaging (MRI) is the gold standard for delineating cancerous lesions in soft tissue. Catheter-based interventions require the accurate placement of multiple long, flexible catheters at the target site. The manual segmentation of catheters in MR images is a challenging and time-consuming task. There is a need for automated catheter segmentation to improve the efficiency of MR-guided procedures. PURPOSE: To develop and assess a machine learning algorithm for the detection of multiple catheters in magnetic resonance images used during catheter-based interventions. METHODS: In this work, a 3D U-Net was trained to retrospectively segment catheters in scans acquired during clinical MR-guided high dose rate (HDR) prostate brachytherapy cases. To assess confidence in segmentation, multiple AI models were trained. On clinical test cases, average segmentation results were used to plan the brachytherapy delivery. Dosimetric parameters were compared to the original clinical plan. Data was obtained from 35 patients who underwent HDR prostate brachytherapy for focal disease with a total of 214 image volumes. 185 image volumes from 30 patients were used for training using a five-fold cross validation split to divide the data for training and validation. To generate confidence measures of segmentation accuracy, five trained models were generated. The remaining five patients (29 volumes) were used to test the performance of the trained model by comparison to manual segmentations of three independent observers and assessment of dosimetric impact on the final clinical brachytherapy plans. RESULTS: The network successfully identified 95% of catheters in the test set at a rate of 0.89 s per volume. The multi-model method identified the small number of cases where AI segmentation of individual catheters was poor, flagging the need for user input. AI-based segmentation performed as well as segmentations by independent observers. Plan dosimetry using AI-segmented catheters was comparable to the original plan. CONCLUSION: The vast majority of catheters were accurately identified by AI segmentation, with minimal impact on plan outcomes. The use of multiple AI models provided confidence in the segmentation accuracy and identified catheter segmentations that required further manual assessment. Real-time AI catheter segmentation can be used during MR-guided insertions to assess deflections and for rapid planning of prostate brachytherapy.
ABSTRACT
Percutaneous needle-based interventions such as transperineal prostate brachytherapy require the accurate placement of multiple needles to treat cancerous lesions within the target organ. To guide needle placement, magnetic resonance imaging (MRI) offers excellent visualization of the target lesion without the need for ionizing radiation. To date, multi-needle insertion relies on a grid template, which limits the ability to steer individual needles. This work describes an MR-compatible robot designed for the sequential insertion of multiple non-parallel needles under MR guidance. The 6-DOF system is designed with an articulated arm to extend the reach of the robot. This strategy presents a novel approach enabling the robot to maneuver around existing needles while minimizing the footprint of the robot. Forward kinematics as well as optimization-based inverse kinematics are presented. The impact of the robot on image quality was tested for four sequences (T1w-TSE, T2w-TSE, THRIVE and EPI) on a 3T Philips Achieva system. Quantification of the signal-to-noise ratio showed a 46% signal loss in a gelatin phantom when the system was powered on but no further adverse effects when the robot was moving. Joint level testing showed a maximum error of 2.10 ± 0.72°s for revolute joints and 0.31 ± 0.60 mm for prismatic joints. The theoretical workspace spans the proposed clinical target surface of 10 x 10 cm. Lastly, the feasibility of multi-needle insertion was demonstrated with four needles inserted under real-time MR-guidance with no visible loss in image quality.
