Demonstration of real-time positron emission tomography biology-guided radiotherapy delivery to targets.

BACKGROUND: Biology-guided radiotherapy (BgRT) is a novel technology that uses positron emission tomography (PET) data to direct radiotherapy delivery in real-time. BgRT enables the precise delivery of radiation doses based on the PET signals emanating from PET-avid tumors on the fly. In this way, BgRT uniquely utilizes radiotracer uptake as a biological beacon for controlling and adjusting dose delivery in real-time to account for target motion. PURPOSE: To demonstrate using real-time PET for BgRT delivery on the RefleXion X1 radiotherapy machine. The X1 radiotherapy machine is a rotating ring-gantry radiotherapy system that generates a nominal 6MV photon beam, PET, and computed tomography (CT) components. The system utilizes emitted photons from PET-avid targets to deliver effective radiation beamlets or pulses to the tumor in real-time. METHODS: This study demonstrated a real-time PET BgRT delivery experiment under three scenarios. These scenarios included BgRT delivering to (S1 ) a static target in a homogeneous and heterogeneous environment, (S2 ) a static target with a hot avoidance structure and partial PET-avid target, and (S3 ) a moving target. The first step was to create stereotactic body radiotherapy (SBRT) and BgRT plans (offline PET data supported) using RefleXion's custom-built treatment planning system (TPS). Additionally, to create a BgRT plan using PET-guided delivery, the targets were filled with 18F-Fluorodeoxyglucose (FDG), which represents a tumor/target, that is, PET-avid. The background materials were created in the insert with homogeneous water medium (for S1 ) and heterogeneous water with styrofoam mesh medium. A heterogeneous background medium simulated soft tissue surrounding the tumor. The treatment plan was then delivered to the experimental setups using a pre-commercial version of the X1 machine. As a final step, the dosimetric accuracy for S1 and S2 was assessed using the ArcCheck analysis tool-the gamma criteria of 3%/3 mm. For S3 , the delivery dose was quantified using EBT-XD radiochromic film. The accuracy criteria were based on coverage, where 100% of the clinical target volume (CTV) receives at least 97% of the prescription dose, and the maximum dose in the CTV was ≤130% of the maximum planned dose (97 % ≤ CTV ≤ 130%). RESULTS: For the S1, both SBRT and BgRT deliveries had gamma pass rates greater than 95% (SBRT range: 96.9%-100%, BgRT range: 95.2%-98.9%), while in S2 , the gamma pass rate was 98% for SBRT and between 95.2% and 98.9% for BgRT plan delivering. For S3 , both SBRT and BgRT motion deliveries met CTV dose coverage requirements, with BgRT plans delivering a very high dose to the target. The CTV dose ranges were (a) SBRT:100.4%-120.4%, and (b) BgRT: 121.3%-139.9%. CONCLUSIONS: This phantom-based study demonstrated that PET signals from PET-avid tumors can be utilized to direct real-time dose delivery to the tumor accurately, which is comparable to the dosimetric accuracy of SBRT. Furthermore, BgRT delivered a PET-signal controlled dose to the moving target, equivalent to the dose distribution to the static target. A future study will compare the performance of BgRT with conventional image-guided radiotherapy.

The technical design and concept of a PET/CT linac for biology-guided radiotherapy.

This is a summary of the design and concept of the RefleXion X1, a system for biology-guided radiotherapy (BgRT). This system is a multi-modal tomography (PET, fan-beam kVCT, and MVD) treatment machine that utilizes imaging and therapy planes for optimized beam delivery of IMRT, SBRT, SRS, and BgRT radiotherapy regimens. For BgRT delivery specifically, annihilation photons emanating outward from a PET-avid tumor are used to guide the delivery of beamlets of radiation to the tumor at sub-second latency. With the integration of PET detectors, rapid beam-station delivery, real-time tracking, and high-frequency multi-leaf collimation, the BgRT system has the potential to deliver a highly conformal treatment to malignant lesions while minimizing dose to surrounding healthy tissues. Furthermore, the potential use of a single radiotracer injection to guide radiotherapy to multiple targets opens avenues for debulking in advanced and metastatic disease states.

A probabilistic framework based on hidden markov model for fiducial identification in image-guided radiation treatments.

Fiducial tracking is a common target tracking method widely used in image-guided procedures such as radiotherapy and radiosurgery. In this paper, we present a multifiducial identification method that incorporates context information in the process. We first convert the problem into a state sequence problem by establishing a probabilistic framework based on a hidden Markov model (HMM), where prior probability represents an individual candidate's resemblance to a fiducial; transition probability quantifies the similarity of a candidate set to the fiducials' geometrical configuration; and the Viterbi algorithm provides an efficient solution. We then discuss the problem of identifying fiducials using stereo projections, and propose a special, higher order HMM, which consists of two parallel HMMs, connected by an association measure that captures the inherent correlation between the two projections. A novel algorithm, the concurrent viterbi with association (CVA) algorithm, is introduced to efficiently identify fiducials in the two projections simultaneously. This probabilistic framework is highly flexible and provides a buffer to accommodate deformations. A simple implementation of the CVA algorithm is presented to evaluate the efficacy of the framework. Experiments were carried out using clinical images acquired during patient treatments, and several examples are presented to illustrate a variety of clinical situations. In the experiments, the algorithm demonstrated a large tracking range, computational efficiency, ease of use, and robustness that meet the requirements for clinical use.

A fast, accurate, and automatic 2D-3D image registration for image-guided cranial radiosurgery.

The authors developed a fast and accurate two-dimensional (2D)-three-dimensional (3D) image registration method to perform precise initial patient setup and frequent detection and correction for patient movement during image-guided cranial radiosurgery treatment. In this method, an approximate geometric relationship is first established to decompose a 3D rigid transformation in the 3D patient coordinate into in-plane transformations and out-of-plane rotations in two orthogonal 2D projections. Digitally reconstructed radiographs are generated offline from a preoperative computed tomography volume prior to treatment and used as the reference for patient position. A multiphase framework is designed to register the digitally reconstructed radiographs with the x-ray images periodically acquired during patient setup and treatment. The registration in each projection is performed independently; the results in the two projections are then combined and converted to a 3D rigid transformation by 2D-3D geometric backprojection. The in-plane transformation and the out-of-plane rotation are estimated using different search methods, including multiresolution matching, steepest descent minimization, and one-dimensional search. Two similarity measures, optimized pattern intensity and sum of squared difference, are applied at different registration phases to optimize accuracy and computation speed. Various experiments on an anthropomorphic head-and-neck phantom showed that, using fiducial registration as a gold standard, the registration errors were 0.33 +/- 0.16 mm (s.d.) in overall translation and 0.29 degrees +/- 0.11 degrees (s.d.) in overall rotation. The total targeting errors were 0.34 +/- 0.16 mm (s.d.), 0.40 +/- 0.2 mm (s.d.), and 0.51 +/- 0.26 mm (s.d.) for the targets at the distances of 2, 6, and 10 cm from the rotation center, respectively. The computation time was less than 3 s on a computer with an Intel Pentium 3.0 GHz dual processor.

An anthropomorphic phantom study of the accuracy of Cyberknife spinal radiosurgery.

OBJECTIVE: Stereotactic radiosurgery requires the highest degree of accuracy in target identification and localization. When targeting paraspinal lesions, the CyberKnife radiosurgical system (Accuray, Inc., Sunnyvale, CA) uses implanted stainless steel fiducials. The purpose of this study was to evaluate the total system for clinically relevant accuracy of this approach. METHODS: The clinically relevant accuracy of the CyberKnife depends on 1) the accuracy of beam delivery, which in turn represents a compilation of robot and camera image-tracking errors, and 2) the inherent accuracy of target localization that stems from computed tomographic imaging and treatment planning. The clinically relevant accuracy was measured at three different CyberKnife facilities using head and torso phantoms loaded with packs of radiochromic film and expressed as a displacement of the dose contours from the treatment planning. RESULTS: The mean clinically relevant error, as measured at three different CyberKnife facilities, was determined to be 0.7 +/- 0.3 mm, which did not vary with computed tomographic slice thickness in a range of 0.625 to 1.5 mm. The average treatment delivery precision was 0.3 +/- 0.1 mm. Fiducial tracking error was less than 0.3 mm for radial translations up to 14 mm and less than 0.7 mm for rotations up to 4.5 degrees. CONCLUSION: For the treatment of relatively stationary spinal lesions targeted with fiducial tracking, the CyberKnife system is capable of submillimeter accuracy.