Proton radiography and fluoroscopy of lung tumors: a Monte Carlo study using patient-specific 4DCT phantoms

A denoising method based on deep learning for proton radiograph using energy resolved dose function

Objective.Proton radiograph has been broadly applied in proton radiotherapy which is affected by scattered protons which result in the lower spatial resolution of proton radiographs than that of x-ray images. Traditional image denoising method may lead to the change of water equivalent path length (WEPL) resulting in the lower WEPL measurement accuracy. In this study, we proposed a new denoising method of proton radiographs based on energy resolved dose function curves.Approach.Firstly, the corresponding relationship between the distortion of WEPL characteristic curve, and energy and proportion of scattered protons was established. Then, to improve the accuracy of proton radiographs, deep learning technique was used to remove scattered protons and correct deviated WEPL values. Experiments on a calibration phantom to prove the effectiveness and feasibility of this method were performed. In addition, an anthropomorphic head phantom was selected to demonstrate the clinical relevance of this technology and the denoising effect was analyzed.Main results.The curves of WEPL profiles of proton radiographs became smoother and deviated WEPL values were corrected. For the calibration phantom proton radiograph, the average absolute error of WEPL values decreased from 2.23 to 1.72, the mean percentage difference of all materials of relative stopping power decreased from 1.24 to 0.39, and the average relative WEPL corrected due to the denoising process was 1.06%. In addition, WEPL values correcting were also observed on the proton radiograph for anthropomorphic head phantom due to this denoising process.Significance.The experiments showed that this new method was effective for proton radiograph denoising and had greater advantages than end-to-end image denoising methods, laying the foundation for the implementation of precise proton radiotherapy.

Focus stacking single-event particle radiography for high spatial resolution images and 3D feature localization

Objective.We demonstrate a novel focus stacking technique to improve spatial resolution of single-event particle radiography (pRad), and exploit its potential for 3D feature detection.Approach.Focus stacking, used typically in optical photography and microscopy, is a technique to combine multiple images with different focal depths into a single super-resolution image. Each pixel in the final image is chosen from the image with the largest gradient at that pixel's position. pRad data can be reconstructed at different depths in the patient based on an estimate of each particle's trajectory (called distance-driven binning; DDB). For a given feature, there is a depth of reconstruction for which the spatial resolution of DDB is maximal. Focus stacking can hence be applied to a series of DDB images reconstructed from a single pRad acquisition for different depths, yielding both a high-resolution projection and information on the features' radiological depth at the same time. We demonstrate this technique with Geant4 simulated pRads of a water phantom (20 cm thick) with five bone cube inserts at different depths (1 × 1 × 1 cm3) and a lung cancer patient.Main results.For proton radiography of the cube phantom, focus stacking achieved a median resolution improvement of 136% compared to a state-of-the-art maximum likelihood pRad reconstruction algorithm and a median of 28% compared to DDB where the reconstruction depth was the center of each cube. For the lung patient, resolution was visually improved, without loss in accuracy. The focus stacking method also enabled to estimate the depth of the cubes within few millimeters accuracy, except for one shallow cube, where the depth was underestimated by 2.5 cm.Significance.Focus stacking utilizes the inherent 3D information encoded in pRad by the particle's scattering, overcoming current spatial resolution limits. It further opens possibilities for 3D feature localization. Therefore, focus stacking holds great potential for future pRad applications.

A likelihood-based particle imaging filter using prior information

BACKGROUND

Particle imaging can increase precision in proton and ion therapy. Interactions with nuclei in the imaged object increase image noise and reduce image quality, especially for multinucleon ions that can fragment, such as helium.

PURPOSE

This work proposes a particle imaging filter, referred to as the Prior Filter, based on using prior information in the form of an estimated relative stopping power (RSP) map and the principles of electromagnetic interaction, to identify particles that have undergone nuclear interaction. The particles identified as having undergone nuclear interactions are then excluded from the image reconstruction, reducing the image noise.

METHODS

The Prior Filter uses Fermi-Eyges scattering and Tschalär straggling theories to determine the likelihood that a particle only interacts electromagnetically. A threshold is then set to reject those particles with a low likelihood. The filter was evaluated and compared with a filter that estimates this likelihood based on the measured distribution of energy and scattering angle within pixels, commonly implemented as the 3σ filter. Reconstructed radiographs from simulated data of a 20-cm water cylinder and an anthropomorphic chest phantom were generated with both protons and helium ions to assess the effect of the filters on noise reduction. The simulation also allowed assessment of secondary particle removal through the particle histories. Experimental data were acquired of the Catphan CTP 404 Sensitometry phantom using the U.S. proton CT (pCT) collaboration prototype scanner. The proton and helium images were filtered with both the prior filtering method and a state-of-the-art method including an implementation of the 3σ filter. For both cases, a dE-E telescope filter, designed for this type of detector, was also applied.

RESULTS

The proton radiographs showed a small reduction in noise (1 mm of water-equivalent thickness [WET]) but a larger reduction in helium radiographs (up to 5-6 mm of WET) due to better secondary filtering. The proton and helium CT images reflected this, with similar noise at the center of the phantom (0.02 RSP) for the proton images and an RSP noise of 0.03 for the proposed filter and 0.06 for the 3σ filter in the helium images. Images reconstructed from data with a dose reduction, up to a factor of 9, maintained a lower noise level using the Prior Filter over the state-of-the-art filtering method.

CONCLUSIONS

The proposed filter results in images with equal or reduced noise compared to those that have undergone a filtering method typical of current particle imaging studies. This work also demonstrates that the proposed filter maintains better performance against the state of the art with up to a nine-fold dose reduction.

Management of Motion and Anatomical Variations in Charged Particle Therapy: Past, Present, and Into the Future

The major aim of radiation therapy is to provide curative or palliative treatment to cancerous malignancies while minimizing damage to healthy tissues. Charged particle radiotherapy utilizing carbon ions or protons is uniquely suited for this task due to its ability to achieve highly conformal dose distributions around the tumor volume. For these treatment modalities, uncertainties in the localization of patient anatomy due to inter- and intra-fractional motion present a heightened risk of undesired dose delivery. A diverse range of mitigation strategies have been developed and clinically implemented in various disease sites to monitor and correct for patient motion, but much work remains. This review provides an overview of current clinical practices for inter and intra-fractional motion management in charged particle therapy, including motion control, current imaging and motion tracking modalities, as well as treatment planning and delivery techniques. We also cover progress to date on emerging technologies including particle-based radiography imaging, novel treatment delivery methods such as tumor tracking and FLASH, and artificial intelligence and discuss their potential impact towards improving or increasing the challenge of motion mitigation in charged particle therapy.

Statistical limitations in proton imaging

Proton imaging is a promising technology for proton radiotherapy as it can be used for: (1) direct sampling of the tissue stopping power, (2) input information for multi-modality RSP reconstruction, (3) gold-standard calibration against concurrent techniques, (4) tracking motion and (5) pre-treatment positioning. However, no end-to-end characterization of the image quality (signal-to-noise ratio and spatial resolution, blurring uncertainty) against the dose has been done. This work aims to establish a model relating these characteristics and to describe their relationship with proton energy and object size. The imaging noise originates from two processes: the Coulomb scattering with the nucleus, producing a path deviation, and the energy loss straggling with electrons. The noise is found to increases with thickness crossed and, independently, decreases with decreasing energy. The scattering noise is dominant around high-gradient edge whereas the straggling noise is maximal in homogeneous regions. Image quality metrics are found to behave oppositely against energy: lower energy minimizes both the noise and the spatial resolution, with the optimal energy choice depending on the application and location in the imaged object. In conclusion, the model presented will help define an optimal usage of proton imaging to reach the promised application of this technology and establish a fair comparison with other imaging techniques.

In vivo range verification in particle therapy

Exploitation of the full potential offered by ion beams in clinical practice is still hampered by several sources of treatment uncertainties, particularly related to the limitations of our ability to locate the position of the Bragg peak in the tumor. To this end, several efforts are ongoing to improve the characterization of patient position, anatomy, and tissue stopping power properties prior to treatment as well as to enable in vivo verification of the actual dose delivery, or at least beam range, during or shortly after treatment. This contribution critically reviews methods under development or clinical testing for verification of ion therapy, based on pretreatment range and tissue probing as well as the detection of secondary emissions or physiological changes during and after treatment, trying to disentangle approaches of general applicability from those more specific to certain anatomical locations. Moreover, it discusses future directions, which could benefit from an integration of multiple modalities or address novel exploitation of the measurable signals for biologically adapted therapy.

The effect of beam purity and scanner complexity on proton CT accuracy

PURPOSE

To determine the dependence of the accuracy in reconstruction of relative stopping power (RSP) with proton computerized tomography (pCT) scans on the purity of the proton beam and the technological complexity of the pCT scanner using standard phantoms and a digital representation of a pediatric patient.

METHODS

The Monte Carlo method was applied to simulate the pCT scanner, using both a pure proton beam (uniform 200 MeV mono-energetic, parallel beam) and the Northwestern Medicine Chicago Proton Center (NMCPC) clinical beam in uniform scanning mode. The accuracy of the simulation was validated with measurements performed at NMCPC including reconstructed RSP images obtained with a preclinical prototype pCT scanner. The pCT scanner energy detector was then simulated in three configurations of increasing complexity: an ideal totally absorbing detector, a single stage detector and a multi-stage detector. A set of 15 cm diameter water cylinders containing either water alone or inserts of different material, size, and position were simulated at 90 projection angles (4° steps) for the pure and clinical proton beams and the three pCT configurations. A pCT image of the head of a detailed digital pediatric phantom was also reconstructed from the simulated pCT scan with the prototype detector.

RESULTS

The RSP error increased for all configurations for insert sizes under 7.5 mm in radius, with a sharp increase below 5 mm in radius, attributed to a limit in spatial resolution. The highest accuracy achievable using the current pCT calibration step phantom and reconstruction algorithm, calculated for the ideal case of a pure beam with totally absorbing energy detector, was 1.3% error in RSP for inserts of 5 mm radius or more, 0.7 mm in range for the 2.5 mm radius inserts, or better. When the highest complexity of the scanner geometry was introduced, some artifacts arose in the reconstructed images, particularly in the center of the phantom. Replacing the step phantom used for calibration with a wedge phantom led to RSP accuracy close to the ideal case, with no significant dependence of RSP error on insert location or material. The accuracy with the multi-stage detector and NMCPC beam for the cylindrical phantoms was 2.2% in RSP error for inserts of 5 mm radius or more, 0.7 mm in range for the 2.5 mm radius inserts, or better. The pCT scan of the pediatric phantom resulted in mean RSP values within 1.3% of the reference RSP, with a range error under 1 mm, except in exceptional situations of parallel incidence on a boundary between low and high density.

CONCLUSIONS

The pCT imaging technique proved to be a precise and accurate imaging tool, rivaling the current x-rays based techniques, with the advantage of being directly sensitive to proton stopping power rather than photon interaction coefficients. Measured and simulated pCT images were obtained from a wobbled proton beam for the first time. Since the in-silico results are expected to accurately represent the prototype pCT, upcoming measurements using the wedge phantom for calibration are expected to show similar accuracy in the reconstructed RSP.

Development of fast patient position verification software using 2D-3D image registration and its clinical experience

To improve treatment workflow, we developed a graphic processing unit (GPU)-based patient positional verification software application and integrated it into carbon-ion scanning beam treatment. Here, we evaluated the basic performance of the software. The algorithm provides 2D/3D registration matching using CT and orthogonal X-ray flat panel detector (FPD) images. The participants were 53 patients with tumors of the head and neck, prostate or lung receiving carbon-ion beam treatment. 2D/3D-ITchi-Gime (ITG) calculation accuracy was evaluated in terms of computation time and registration accuracy. Registration calculation was determined using the similarity measurement metrics gradient difference (GD), normalized mutual information (NMI), zero-mean normalized cross-correlation (ZNCC), and their combination. Registration accuracy was dependent on the particular metric used. Representative examples were determined to have target registration error (TRE) = 0.45 ± 0.23 mm and angular error (AE) = 0.35 ± 0.18° with ZNCC + GD for a head and neck tumor; TRE = 0.12 ± 0.07 mm and AE = 0.16 ± 0.07° with ZNCC for a pelvic tumor; and TRE = 1.19 ± 0.78 mm and AE = 0.83 ± 0.61° with ZNCC for lung tumor. Calculation time was less than 7.26 s.The new registration software has been successfully installed and implemented in our treatment process. We expect that it will improve both treatment workflow and treatment accuracy.

An exponential growth of computational phantom research in radiation protection, imaging, and radiotherapy: a review of the fifty-year history

Radiation dose calculation using models of the human anatomy has been a subject of great interest to radiation protection, medical imaging, and radiotherapy. However, early pioneers of this field did not foresee the exponential growth of research activity as observed today. This review article walks the reader through the history of the research and development in this field of study which started some 50 years ago. This review identifies a clear progression of computational phantom complexity which can be denoted by three distinct generations. The first generation of stylized phantoms, representing a grouping of less than dozen models, was initially developed in the 1960s at Oak Ridge National Laboratory to calculate internal doses from nuclear medicine procedures. Despite their anatomical simplicity, these computational phantoms were the best tools available at the time for internal/external dosimetry, image evaluation, and treatment dose evaluations. A second generation of a large number of voxelized phantoms arose rapidly in the late 1980s as a result of the increased availability of tomographic medical imaging and computers. Surprisingly, the last decade saw the emergence of the third generation of phantoms which are based on advanced geometries called boundary representation (BREP) in the form of Non-Uniform Rational B-Splines (NURBS) or polygonal meshes. This new class of phantoms now consists of over 287 models including those used for non-ionizing radiation applications. This review article aims to provide the reader with a general understanding of how the field of computational phantoms came about and the technical challenges it faced at different times. This goal is achieved by defining basic geometry modeling techniques and by analyzing selected phantoms in terms of geometrical features and dosimetric problems to be solved. The rich historical information is summarized in four tables that are aided by highlights in the text on how some of the most well-known phantoms were developed and used in practice. Some of the information covered in this review has not been previously reported, for example, the CAM and CAF phantoms developed in 1970s for space radiation applications. The author also clarifies confusion about 'population-average' prospective dosimetry needed for radiological protection under the current ICRP radiation protection system and 'individualized' retrospective dosimetry often performed for medical physics studies. To illustrate the impact of computational phantoms, a section of this article is devoted to examples from the author's own research group. Finally the author explains an unexpected finding during the course of preparing for this article that the phantoms from the past 50 years followed a pattern of exponential growth. The review ends on a brief discussion of future research needs (a supplementary file '3DPhantoms.pdf' to figure 15 is available for download that will allow a reader to interactively visualize the phantoms in 3D).

Proton radiography in three dimensions: a proof of principle of a new technique

PURPOSE

Monte Carlo simulations were used to investigate a range of phantom configurations to establish enabling three-dimensional proton radiographic techniques.

METHODS

A large parameter space of stacked phantom geometries composed of tissue inhomogeneity materials such as lung, bone, and cartilage inserted within water background were simulated using a purposefully modified version of TOPAS, an application running on top of the GEANT4 Monte Carlo code. The phantoms were grouped in two classes, one with the inhomogeneity inserted only half-way in the lateral direction and another with complete inhomogeneity insertion. The former class was used to calculate the track count and the energy fluence of the protons as they exit the phantoms either having traversed the inhomogeneity or not. The latter class was used to calculate one yield value accounting for loss of protons due to physical processes only and another yield value accounting for deliberately discarded protons due to large scattering angles. A graphical fingerprinting method was developed to determine the inhomogeneity thickness and location within the phantom based on track count and energy fluence information. Two additional yield values extended this method to the general case which also determines the inhomogeneity material and the phantom thickness.

RESULTS

The graphical fingerprinting method was manually validated for two, and automatically tested for all, tissue materials using an exhaustive set of inhomogeneity geometries for 16 cm thick phantoms. Unique recognition of test phantom configurations was achieved in the large majority of cases. The method in the general case was further tested using an exhaustive set of inhomogeneity and phantom tissues and geometries where the phantom thicknesses ranged between 8 and 24 cm. Unique recognition of the test phantom configurations was achieved only for part of the phantom parameter space. The correlations between the remaining false positive recognitions were analyzed.

CONCLUSIONS

The concept of 3D proton radiography for tissue inhomogeneities of simple geometries was established with the current work. In contrast to conventional 2D proton radiography, the main objective of the demonstrated 3D technique is not proton range. Rather, it is to measure the depth and thickness of an inhomogeneity located in an imaged geometry. Further work is needed to extend and apply the method to more complex geometries.

Particle therapy for noncancer diseases

Radiation therapy using high-energy charged particles is generally acknowledged as a powerful new technique in cancer treatment. However, particle therapy in oncology is still controversial, specifically because it is unclear whether the putative clinical advantages justify the high additional costs. However, particle therapy can find important applications in the management of noncancer diseases, especially in radiosurgery. Extension to other diseases and targets (both cranial and extracranial) may widen the applications of the technique and decrease the cost/benefit ratio of the accelerator facilities. Future challenges in this field include the use of different particles and energies, motion management in particle body radiotherapy and extension to new targets currently treated by catheter ablation (atrial fibrillation and renal denervation) or stereotactic radiation therapy (trigeminal neuralgia, epilepsy, and macular degeneration). Particle body radiosurgery could be a future key application of accelerator-based particle therapy facilities in 10 years from today.

Water-equivalent path length calibration of a prototype proton CT scanner

PURPOSE

The authors present a calibration method for a prototype proton computed tomography (pCT) scanner. The accuracy of these measurements depends upon careful calibration of the energy detector used to measure the residual energy of the protons that passed through the object.

METHODS

A prototype pCT scanner with a cesium iodide (CsI(Tl)) crystal calorimeter was calibrated by measuring the calorimeter response for protons of 200 and 100 MeV initial energies undergoing degradation in polystyrene plates of known thickness and relative stopping power (RSP) with respect to water. Calibration curves for the two proton energies were obtained by fitting a second-degree polynomial to the water-equivalent path length versus calorimeter response data. Using the 100 MeV calibration curve, the RSP values for a variety of tissue-equivalent materials were measured and compared to values obtained from a standard depth-dose range shift measurement using a water-tank. A cylindrical water phantom was scanned with 200 MeV protons and its RSP distribution was reconstructed using the 200 MeV calibration.

RESULTS

It is shown that this calibration method produces measured RSP values of various tissue-equivalent materials that agree to within 0.5% of values obtained using an established water-tank method. The mean RSP value of the water phantom reconstruction was found to be 0.995 ± 0.006.

CONCLUSIONS

The method presented provides a simple and reliable procedure for calibration of a pCT scanner.