High-precision quality assurance of robotic couches with six degrees of freedom

The potential use of deep learning in performing autocorrection of setup errors in patients receiving radiotherapy

INTRODUCTION

Modern radiotherapy practice relies on multiple approaches for verification of patient positioning. All of these techniques require experienced radiotherapists who understand the anatomical landmarks and the limitations of the used verification techniques. We explore the feasibility of using Artificial intelligence in assisted patient positions using acquired port images (PFIs) and digital reconstructed radiographs (DRRs).

METHODS

A retrospective study was conducted on patients with brain and aerodigestive tract malignancy who were treated with radiotherapy between 2018 and 2023. A neural network was built to examine and perform auto-correction of the misaligned PFIs and DRRs images. The performance of the neural network was assessed quantitatively by mean-absolute errors (MAE) and mean-squared errors (MSE), and qualitatively by a survey which was sent to 30 experienced medical professionals in the field of radiation therapy.

RESULTS

The total number of patients included in this study was 156 patients. 96 of the patients were treated for aerodigestive tract malignancy while the remaining were treated for brain tumours. The neural network achieved MAE of 27.430 and 27.437 for training and validation sets, respectively, and MSE of 0.5505, and 0.5565 for training and validation sets, respectively. Nineteen medical professionals responded to the survey. They reported a median accuracy score of 8 out of 10.

CONCLUSION

Our neural network is just one step further in the automation of modern radiotherapy services by using AI-assisted correction of setup errors.

IMPLICATIONS FOR PRACTICE

This study demonstrated the potential role of AI in assisting radiotherapists with patient positioning corrections during radiotherapy treatment. Further research is needed to validate the effectiveness of this approach in clinical practice.

Contribution and advances of robotics in percutaneous oncological interventional radiology

The advent of robotic systems in interventional radiology marks a significant evolution in minimally invasive medical procedures, offering enhanced precision, safety, and efficiency. This review comprehensively analyzes the current state and applications of robotic system usage in interventional radiology, which can be particularly helpful for complex procedures and in challenging anatomical regions. Robotic systems can improve the accuracy of interventions like microwave ablation, radiofrequency ablation, and irreversible electroporation. Indeed, studies have shown a notable decrease of an average 30% in the mean deviation of probes, and a 40% lesser need for adjustments during interventions carried out with robotic assistance. Moreover, this review highlights a 35% reduction in radiation dose and a stable-to-30% reduction in operating time associated with robot-assisted procedures compared to manual methods. Additionally, the potential of robotic systems to standardize procedures and minimize complications is discussed, along with the challenges they pose, such as setup duration, organ movement, and a lack of tactile feedback. Despite these advancements, the field still grapples with a dearth of randomized controlled trials, which underscores the need for more robust evidence to validate the efficacy and safety of robotic system usage in interventional radiology.

Evaluation of a novel phantom for the quality assurance of a six-degree-of-freedom couch 3D-printed at multiple centres

This study aimed to validate a bespoke 3D-printed phantom for use in quality assurance (QA) of a 6 degrees-of-freedom (6DoF) treatment couch. A novel phantom design comprising a main body with internal cube structures, was fabricated at five centres using Polylactic Acid (PLA) material, with an additional phantom produced incorporating a PLA-stone hybrid material. Correctional setup shifts were determined using image registration by 3D-3D matching of high HU cube structures between obtained cone-beam computer tomography (CBCT) images to reference CTs, containing cubes with fabricated rotational offsets of 3.5°, 1.5° and -2.5° in rotation, pitch, and roll, respectively. Average rotational setup shifts were obtained for each phantom. The reproducibility of 3D-printing was probed by comparing the internal cube size as well as Hounsfield Units between each of the uniquely produced phantoms. For the five PLA phantoms, the average rot, pitch and roll correctional differences from the fabricated offsets were -0.3 ± 0.2°, -0.2 ± 0.5° and 0.2 ± 0.3° respectively, and for the PLA hybrid these differences were -0.09 ± 0.14°, 0.30 ± 0.00° and 0.03 ± 0.10°. There was found to be no statistically significant difference in average cube size between the five PLA printed phantoms, with the significant difference (P < 0.05) in HU of one phantom compared to the others attributed to setup choice and material density. This work demonstrated the capability producing a novel 3D-printed 6DoF couch QA phantom design, at multiple centres, with each unique model capable of sub-degree couch correction.

Automating QA analysis for a six-degree-of-freedom (6DOF) couch using image displacement and an accelerometer sensor

The purpose of this study is to develop an approach for automating quality assurance (QA) analysis for a six-degree-of-freedom (6DOF) couch using image displacement and an accelerometer sensor. A cubic phantom was fabricated using 3D printing and the accelerometer sensor was embedded in the phantom to measure the couch in the pitch and roll directions. The accuracy and reliability of image displacement and the accelerometer sensor were investigated prior to their practical use for 6DOF couch QA. Image displacement performance had an accuracy and reliability of 0.026 ± 0.026 mm for the translation direction and 0.021 ± 0.016° for the rotation direction. Accelerometer sensor performance had an accuracy and reliability of 0.023 ± 0.018° for pitch rotation and 0.051 ± 0.024° for roll rotation. Automating QA analysis was used to perform 6DOF couch QA, and the couch position errors measured using image displacement were less than 0.99 mm, 0.91 mm, 0.82 mm for the vertical, longitudinal, lateral translation in range between ±20 mm, and 0.07°, 0.23°, and 0.2° for pitch, roll, and yaw rotation in range between ±3° whereas the couch position errors measured using the accelerometer sensor were less than 0.1° and 0.19° for the pitch and roll rotation in range between ±3°.

Quality assurance of isocentres for passive proton beam nozzles using motion capture cameras

PURPOSE

The objective of this work is to determine mechanical, radiation, and imaging isocentres in three-dimensional (3D) coordinates and verifying coincidence of isocentres of passively scattered proton beam using a visual tracking system (VTS) and an in-house developed phantom named the Eagle.

METHODS

The Eagle phantom consists of two modules: The first, named Eagle-head, is used for determining 3D mechanical isocentre of gantry rotation. The second, named Eagle-body, is used for determining 3D radiation and imaging isocentres. The Eagle-body has four slots wherein radiochromic films were inserted for measuring the 3D radiation isocentre and a metal bead was embedded in the centre of one cube to determine the imaging isocentre; this was determined by analysing cone-beam computed tomography images of the cube. Infrared reflective markers that can be tracked by VTS were attached to the Eagle at predetermined locations. The tracked data were converted into 3D treatment room coordinates. The developed method was compared with other methods to assess accuracy.

RESULTS

The isocentres were determined in mm with respect to the laser isocentre. The mechanical, radiation, and imaging isocentres were (-0.289, 0.189, 0.096), (-0.436, -0.217, 0.009), and (0.134, 0.142, 0.103), respectively. When compared with other methods, the difference in coordinates was (-0.033, -0.107, 0.014) and (0.003, 0.067, 0.039) for radiation and imaging isocentres, respectively.

CONCLUSION

The developed system was found to be useful in providing fast and accurate measurements of the three isocentres in the 3D treatment room coordinate system.