Prevention of Radiation Therapy Treatment Deviations by a Novel Combined Biometric, Radiofrequency Identification, and Surface Imaging System

Initial clinical evaluation of a novel combined biometric, radio-frequency identification, and surface imaging system

PURPOSE

To evaluate and characterize the overall clinical functionality and workflow of the newly released Varian Identify system (version 2.3).

METHODS

Three technologies included in the Varian Identify system were evaluated: patient biometric authentication, treatment accessory device identification, and surface-guided radiation therapy (SGRT) function. Biometric authentication employs a palm vein reader. Treatment accessory device verification utilizes two technologies: device presence via Radio Frequency Identification (RFID) and position via optical markers. Surface-guidance was evaluated on both patient orthopedic setup at loading position and surface matching and tracking at treatment isocenter. A phantom evaluation of the consistency and accuracy for Identify SGRT function was performed, including a system consistency test, a translational shift and rotational accuracy test, a pitch and roll accuracy test, a continuous recording test, and an SGRT vs Cone-Beam CT (CBCT) agreement test.

RESULTS

201 patient authentications were verified successfully with palm reader. All patient treatment devices were successfully verified for their presences and positions (indexable devices). The patient real-time orthopedic pose was successfully adjusted to match the reference surface captured at simulation. SGRT-reported shift consistency against couch readout was within (0.1 mm, 0.030). The shift accuracy was within (0.3 mm, 0.10). In continuous recording mode, the maximum variation was 0.2 ± 0.12 mm, 0.030 ± 0.020. The difference between Identify SGRT offset and CBCT was within (1 mm, 10).

CONCLUSIONS

This clinical evaluation confirms that Identify accurately functions for patient palm identification and patient treatment device presence and position verification. Overall SGRT consistency and accuracy was within (1 mm, 10), within the 2 mm criteria of AAPM TG302.

Innovative integration of augmented reality and optical surface imaging: A coarse-to-precise system for radiotherapy positioning

BACKGROUND

Traditional methods of radiotherapy positioning have shortcomings such as fragile skin-markers, additional doses, and lack of information integration. Emerging technologies may provide alternatives for the relevant clinical practice.

PURPOSE

To propose a noninvasive radiotherapy positioning system integrating augmented reality (AR) and optical surface, and to evaluate its feasibility in clinical workflow.

METHODS

AR and structured light-based surface were integrated to implement the coarse-to-precise positioning through two coherent steps, the AR-based coarse guidance and the optical surface-based precise verification. To implement quality assurance, recognition of face and pattern was used for patient authentication, case association, and accessory validation in AR scenes. The holographic images reconstructed from simulation computed tomography (CT) images, guided the initial posture correction by virtual-real alignment. The point clouds of body surface were fused, with the calibration and pose estimation of structured light cameras, and segmented according to the preset regions of interest (ROIs). The global-to-local registration for cross-source point clouds was achieved to calculate couch shifts in six degrees-of-freedom (DoF), which were ultimately transmitted to AR scenes. The evaluation based on phantom and human-body (4 volunteers) included, (i) quality assurance workflow, (ii) errors of both steps and correlation analysis, (iii) receiver operating characteristic (ROC), (iv) distance characteristics of accuracy, and (v) clinical positioning efficiency.

RESULTS

The maximum errors in phantom evaluation were 3.4 ± 2.5 mm in Vrt and 1.4 ± 1.0° in Pitch for the coarse guidance step, while 1.6 ± 0.9 mm in Vrt and 0.6 ± 0.4° in Pitch for the precise verification step. The Pearson correlation coefficients between precise verification and cone beam CT (CBCT) results were distributed in the interval [0.81, 0.85]. In ROC analysis, the areas under the curve (AUC) were 0.87 and 0.89 for translation and rotation, respectively. In human body-based evaluation, the errors of thorax and abdomen (T&A) were significantly greater than those of head and neck (H&N) in Vrt (2.6 ± 1.1 vs. 1.7 ± 0.8, p < 0.01), Lng (2.3 ± 1.1 vs. 1.4 ± 0.9, p < 0.01), and Rtn (0.8 ± 0.4 vs. 0.6 ± 0.3, p = 0.01) while relatively similar in Lat (1.8 ± 0.9 vs. 1.7 ± 0.8, p = 0.07). The translation displacement range, after coarse guidance step, required for high accuracy of the optical surface component of the integrated system was 0-42 mm, and the average positioning duration of the integrated system was significantly less than that of conventional workflow (355.7 ± 21.7 vs. 387.7 ± 26.6 s, p < 0.01).

CONCLUSIONS

The combination of AR and optical surface has utility and feasibility for patient positioning, in terms of both safety and accuracy.

Face Recognition Method under Adaptive Image Matching and Dictionary Learning Algorithm

In this research, a robust face recognition method based on adaptive image matching and a dictionary learning algorithm was proposed. A Fisher discriminant constraint was introduced into the dictionary learning algorithm program so that the dictionary had certain category discrimination ability. The purpose was to use this technology to reduce the influence of pollution, absence, and other factors on face recognition and improve the recognition rate. The optimization method was used to solve the loop iteration to obtain the expected specific dictionary, and the selected specific dictionary was used as the representation dictionary in adaptive sparse representation. In addition, if a specific dictionary was placed in a seed space of the original training data, the mapping matrix can be used to represent the mapping relationship between the specific dictionary and the original training sample, and the test sample could be corrected according to the mapping matrix to remove the contamination in the test sample. Moreover, the feature face method and dimension reduction method were used to process the specific dictionary and the corrected test sample, and the dimensions were reduced to 25, 50, 75, 100, 125, and 150, respectively. In this research, the recognition rate of the algorithm in 50 dimensions was lower than that of the discriminatory low-rank representation method (DLRR), and the recognition rate in other dimensions was the highest. The adaptive image matching classifier was used for classification and recognition. The experimental results showed that the proposed algorithm had a good recognition rate and good robustness against noise, pollution, and occlusion. Health condition prediction based on face recognition technology has the advantages of being noninvasive and convenient operation.

Surface-Guided Patient Setup Versus Traditional Tattoo Markers for Radiation Therapy: Is Tattoo-Less Setup Feasible for Thorax, Abdomen and Pelvis Treatment?

Purpose: In this study, patient setup accuracy was compared between surface guidance and tattoo markers for radiation therapy treatment sites of the thorax, abdomen and pelvis.

Methods and materials: A total of 608 setups performed on 59 patients using both surface-guided and tattoo-based patient setups were analyzed. During treatment setup, patients were aligned to room lasers using their tattoos, and then the six-degree-of-freedom (6DOF) surface-guided offsets were calculated and recorded using AlignRT system. While the patient remained in the same post-tattoo setup position, target localization imaging (radiographic or ultrasound) was performed and these image-guided shifts were recorded. Finally, surface-guided vs tattoo-based offsets were compared to the final treatment position (based on radiographic or ultrasound imaging) to evaluate the accuracy of the two setup methods.

Results: The overall average offsets of tattoo-based and surface-guidance-based patient setups were comparable within 3.2 mm in three principal directions, with offsets from tattoo-based setups being slightly less. The maximum offset for tattoo setups was 2.2 cm vs. 4.3 cm for surface-guidance setups. Larger offsets (ranging from 2.0 to 4.3 cm) were observed for surface-guided setups in 14/608 setups (2.3%). For these same cases, the maximum observed tattoo-based offset was 0.7 cm. Of the cases with larger surface-guided offsets, 13/14 were for abdominal/pelvic treatment sites. Additionally, larger rotations (>3°) were recorded in 18.6% of surface-guided setups. The majority of these larger rotations were observed for abdominal and pelvic sites (~84%).

Conclusions: The small average differences observed between tattoo-based and surface-guidance-based patient setups confirm the general equivalence of the two potential methods, and the feasibility of tattoo-less patient setup. However, a significant number of larger surface-guided offsets (translational and rotational) were observed, especially in the abdominal and pelvic regions. These cases should be anticipated and contingency setup methods planned for.

An Investigation of Radiation Treatment Learning Opportunities in Relation to the Radiation Oncology Electronic Medical Record: A Single Institution Experience

Purpose

A modern radiation oncology electronic medical record (RO-EMR) system represents a sophisticated human-computer interface with the potential to reduce human driven errors and improve patient safety. As the RO-EMR becomes an integral part of clinical processes, it may be advantageous to analyze learning opportunities (LO) based on their relationship with the RO-EMR. This work reviews one institution's documented LO to: (1) study their relationship with the RO-EMR workflow, (2) identify best opportunities to improve RO-EMR workflow design, and (3) identify current RO-EMR workflow challenges.

Methods and Materials

Internal LO reports for an 11-year contiguous period were categorized by their relationship to the RO-EMR. We also identify the specific components of the RO-EMR used or involved in each LO. Additionally, contributing factor categories from the ASTRO/AAPM sponsored Radiation Oncology Incident Learning System's (RO-ILS) nomenclature was used to characterize LO directly linked to the RO-EMR.

Results

A total of 163 LO from the 11-year period were reviewed and analyzed. Most (77.2%) LO involved the RO-EMR in some way. The majority of the LO were the results of human/manual operations. The most common RO-EMR components involved in the studied LO were documentation related to patient setup, treatment session schedule functionality, RO-EMR used as a communication/note-delivery tool, and issues with treatment accessories. Most of the LO had staff lack of attention and policy not followed as 2 of the highest occurring contributing factors.

Conclusions

We found that the majority of LO were related to RO-EMR workflow processes. The high-risk areas were related to manual data entry or manual treatment execution. An evaluation of LO as a function of their relationship with the RO-EMR allowed for opportunities for improvement. In addition to regular radiation oncology quality improvement review and policy update, automated functions in RO-EMR remain highly desirable.

The role of surface-guided radiation therapy for improving patient safety

Emerging data indicates SGRT could improve safety and quality by preventing errors in its capacity as an independent system in the treatment room. The aim of this work is to investigate the utility of SGRT in the context of safety and quality. Three incident learning systems (ILS) were reviewed to categorize and quantify errors that could have been prevented with SGRT: SAFRON (International Atomic Energy Agency), UW-ILS (University of Washington) and AvIC (Skåne University Hospital). A total of 849/9737 events occurred during the pre-treatment review/verification and treatment stages. Of these, 179 (21%) events were predicted to have been preventable with SGRT. The most common preventable events were wrong isocentre (43%) and incorrect accessories (34%), which appeared at comparable rates among SAFRON and UW-ILS. The proportion of events due to wrong accessories was much smaller in the AvIC ILS, which may be attributable to the mandatory use of SGRT in Sweden. Several case scenarios are presented to demonstrate that SGRT operates as a valuable complement to other quality-improvement tools routinely used in radiotherapy. Cases are noted in which SGRT itself caused incidents. These were mostly related to workflow issues and were of low severity. Severity data indicated that events with the potential to be mitigated by SGRT were of higher severity for all categories except wrong accessories. Improved vendor integration of SGRT systems within the overall workflow could further enhance its clinical utility. SGRT is a valuable tool with the potential to increase patient safety and treatment quality in radiotherapy.