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Radiation therapy treatments are typically planned based on a single image set, assuming that the patient’s anatomy and its position relative to the delivery system remains constant during the course of treatment. Similarly, the prescription dose assumes constant biological dose-response over the treatment course. However, variations can and do occur on multiple time scales. For treatment sites with significant intra-fractional motion, geometric changes happen over seconds or minutes, while biological considerations change over days or weeks. At an intermediate timescale, geometric changes occur between daily treatment fractions. Adaptive radiation therapy is applied to consider changes in patient anatomy during the course of fractionated treatment delivery. While traditionally adaptation has been done off-line with replanning based on new CT images, online treatment adaptation based on on-board imaging has gained momentum in recent years due to advanced imaging techniques combined with treatment delivery systems. Adaptation is particularly important in proton therapy where small changes in patient anatomy can lead to significant dose perturbations due to the dose conformality and finite range of proton beams. This review summarizes the current state-of-the-art of on-line adaptive proton therapy and identifies areas requiring further research.

The high conformality of intensity-modulated proton therapy (IMPT) dose distributions causes treatment plans to be sensitive to geometrical changes during the course of a fractionated treatment. This can be addressed using adaptive proton therapy (APT). One important question in APT is the frequency of adaptations performed during a fractionated treatment, which is related to the question whether plan adaptation has to be done online or offline. The purpose of this work is to investigate the impact of weekly and daily online IMPT plan adaptation on the treatment quality for head and neck patients. A cohort of ten head and neck patients with daily acquired cone-beam CT (CBCT) images was evaluated retrospectively. Dose tracking of the IMPT treatment was performed for three scenarios: base plan with no adaptation (BP), weekly online adaptation (OAW), and daily online adaptation (OAD). Both adaptation schemes used an in-house developed online APT workflow, performing Monte Carlo dose calculations on scatter-corrected CBCTs. IMPT plan adaptation was achieved by only tuning the weights of a subset of beamlets, based on deformable image registration from the planning CT to each CBCT. Although OAD mitigated random delivery errors more effectively than OAW on a fraction per fraction basis, both OAW and OAD achieved the clinical goals for all ten patients, while BP failed for six cases. In the high-risk CTV, accumulated values of D 98% ranged between 97.15% and 99.73% of the prescription dose for OAD, with a median of 98.07%. For OAW, values between 95.02% and 99.26% were obtained, with a median of 97.61% of the prescription dose. Otherwise, the dose to most organs at risk was similar for all three scenarios. Globally, our results suggest that OAW could be used as an alternative approach to OAD for most patients in order to reduce the clinical workload.

Adaptive proton therapy (APT) is a promising approach for the treatment of head and neck cancers. One crucial element of APT is daily volumetric imaging of the patient in the treatment position. Such data can be acquired with cone-beam computed tomography (CBCT), although scatter artifacts make uncorrected CBCT images unsuitable for proton therapy dose calculation. The purpose of this work is to evaluate the performance of a U-shape deep convolutive neural network (U-Net) to perform projection-based scatter correction and enable fast and accurate dose calculation on CBCT images in the context of head and neck APT. CBCT projections are simulated for a cohort of 48 head and neck patients using a GPU accelerated Monte Carlo (MC) code . A U-Net is trained to reproduce MC projection-based scatter correction from raw projections. The accuracy of the scatter correction is experimentally evaluated using CT and CBCT images of an anthropomorphic head phantom. The potential of the method for head and neck APT is assessed by comparing proton therapy dose distributions calculated on scatter-free, uncorrected and scatter-corrected CBCT images. Finally, dose calculation accuracy is estimated in experimental patient images using a previously validated empirical scatter correction as reference. The mean and mean absolute HU differences between scatter-free and scatter-corrected images are -0.8 and 13.4 HU, compared to -28.6 and 69.6 HU for the uncorrected images. In the head phantom, the root-mean square difference of proton ranges calculated in the reference CT and corrected CBCT is 0.73 mm. The average 2%/2 mm gamma pass rate for proton therapy plans optimized in the scatter free images and re-calculated in the scatter-corrected ones is 98.89%. In experimental CBCT patient images, a 3%/3 mm passing rate of 98.72% is achieved between the proposed method and the reference one. All CBCT projection volume could be corrected in less than 5 seconds.

To promote accurate image-guided radiotherapy (IGRT) for a proton pencil beam scanning (PBS) system, a new quality assurance (QA) procedure employing a cone-shaped scintillator detector has been developed for multiple QA tasks in a semi-automatic manner. The cone-shaped scintillator detector (XRV-124, Logos Systems, CA) is sensitive to both x-ray and proton beams. It records scintillation on the cone surface as a 2D image, from which the geometry of the radiation field that enters and exits the cone can be extracted. Utilizing this feature, QA parameters that are essential to PBS IGRT treatment were measured and analyzed. The first applications provided coincidence checks of laser, imaging and radiation isocenters, and dependencies on gantry angle and beam energies. The analysis of the Winston-Lutz test was made available by combining the centricity measurements of the x-ray beam and the pencil beam. The accuracy of the gantry angle was validated against console readings provided by the digital encoder and an agreement of less than 0.2° was found. The accuracy of the position measurement was assessed with a robotic patient positioning system (PPS) and an agreement of less than 0.5 mm was obtained. The centricity of the two onboard x-ray imaging systems agreed well with that from the routinely used Digital Imaging Positioning System (DIPS), up to a consistent small shift of (-0.5 mm, 0.0 mm, -0.3 mm). The pencil beam spot size, in terms of σ of Gaussian fitting, agreed within 0.2 mm for most energies when compared to the conventional measurements by a 2D ion-chamber array (MatriXX-PT, IBA Dosimetry, Belgium). The cone-shaped scintillator system showed advantages in making multi-purpose measurements with a single setup. The in-house algorithms were successfully implemented to measure and analyze key QA parameters in a semi-automatic manner. This study presents an alternative and more efficient approach for IGRT QA for PBS and potentially for linear accelerators.

To develop an online plan adaptation algorithm for intensity modulated proton therapy (IMPT) based on fast Monte Carlo dose calculation and cone beam CT (CBCT) imaging. A cohort of ten head and neck cancer patients with an average of six CBCT scans were studied. To adapt the treatment plan to the new patient geometry, contours were propagated to the CBCTs with a vector field (VF) calculated with deformable image registration between the CT and the CBCTs. Within the adaptive planning algorithm, beamlets were shifted following the VF at their distal falloff and raytraced in the CBCT to adjust their energies, creating a geometrically adapted plan. Four geometric adaptation modes were studied: unconstrained geometric shifts (Free), isocenter shift (Iso), a range shifter (RS), or isocenter shift and range shifter (Iso-RS). After evaluation of the geometrical adaptation, the weights of a selected subset of beamlets were automatically tuned using MC-generated influence matrices to fulfill the original plan requirements. All beamlet calculations were done with a fast Monte Carlo running on a GPU (graphics processing unit). Geometrical adaptation alone only worked with small anatomy changes. The weight-tuned adaptation worked for every fraction, with the Free and Iso modes performing similarly and being superior than the two range shifters modes. The mean V95 and V107 were 99.4 ± 0.9 and 6.4% ± 4.7% in the Free mode with weight tuning. The calculation time per fraction was ~5 min, but further task parallelization could reduce it to ~1-2 min for delivery adaptation right after patient setup. An online adaptation algorithm was developed that significantly improved the treatment quality for inter-fractional geometry changes. Clinical implementation of the algorithm would allow delivery adaptation right before treatment and thus allow planning margin reductions for IMPT.