Deep learning based automatic contour refinement for inaccurate auto-segmentation in MR-guided adaptive radiotherapy.

Objective.Fast and accurate auto-segmentation is essential for magnetic resonance-guided adaptive radiation therapy (MRgART). Deep learning auto-segmentation (DLAS) is not always clinically acceptable, particularly for complex abdominal organs. We previously reported an automatic contour refinement (ACR) solution of using an active contour model (ACM) to partially correct the DLAS contours. This study aims to develop a DL-based ACR model to work in conjunction with ACM-ACR to further improve the contour accuracy.Approach.The DL-ACR model was trained and tested using bowel contours created by an in-house DLAS system from 160 MR sets (76 from MR-simulation and 84 from MR-Linac). The contours were classified into acceptable, minor-error and major-error groups using two approaches of contour quality classification (CQC), based on the AAPM TG-132 recommendation and an in-house classification model, respectively. For the major-error group, DL-ACR was applied subsequently after ACM-ACR to further refine the contours. For the minor-error group, contours were directly corrected by DL-ACR without applying an initial ACM-ACR. The ACR workflow was performed separately for the two CQC methods and was evaluated using contours from 25 image sets as independent testing data.Main results.The best ACR performance was observed in the MR-simulation testing set using CQC by TG-132: (1) for the major-error group, 44% (177/401) were improved to minor-error group and 5% (22/401) became acceptable by applying ACM-ACR; among these 177 contours that shifted from major-error to minor-error with ACM-ACR, DL-ACR further refined 49% (87/177) to acceptable; and overall, 36% (145/401) were improved to minor-error contours, and 30% (119/401) became acceptable after sequentially applying ACM-ACR and DL-ACR; (2) for the minor-error group, 43% (320/750) were improved to acceptable contours using DL-ACR.Significance.The obtained ACR workflow substantially improves the accuracy of DLAS bowel contours, minimizing the manual editing time and accelerating the segmentation process of MRgART.

Comparative accuracy and resolution assessment of two prototype proton computed tomography scanners.

BACKGROUND: Improving the accuracy of relative stopping power (RSP) in proton therapy may allow reducing range margins. Proton computed tomography (pCT) has been shown to provide state-of-the-art RSP accuracy estimation, and various scanner prototypes have recently been built. The different approaches used in scanner design are expected to impact spatial resolution and RSP accuracy. PURPOSE: The goal of this study was to perform the first direct comparison, in terms of spatial resolution and RSP accuracy, of two pCT prototype scanners installed at the same facility and by using the same image reconstruction algorithm. METHODS: A phantom containing cylindrical inserts of known RSP was scanned at the phase-II pCT prototype of the U.S. pCT collaboration and at the commercially oriented ProtonVDA scanner. Following distance-driven binning filtered backprojection reconstruction, the radial edge spread function of high-density inserts was used to estimate the spatial resolution. RSP accuracy was evaluated by the mean absolute percent error (MAPE) over the inserts. No direct imaging dose estimation was possible, which prevented a comparison of the two scanners in terms of RSP noise. RESULTS: In terms of RSP accuracy, both scanners achieved the same MAPE of 0.72% when excluding the porous sinus insert from the evaluation. The ProtonVDA scanner reached a better overall MAPE when all inserts and the body of the phantom were accounted for (0.81%), compared to the phase-II scanner (1.14%). The spatial resolution with the phase-II scanner was found to be 0.61 lp/mm, while for the ProtonVDA scanner somewhat lower at 0.46 lp/mm. CONCLUSIONS: The comparison between two prototype pCT scanners operated in the same clinical facility showed that they both fulfill the requirement of an RSP accuracy of about 1%. Their spatial resolution performance reflects the different design choices of either a scanner with full tracking capabilities (phase-II) or of a more compact tracker system, which only provides the positions of protons but not their directions (ProtonVDA).

A comparison of proton stopping power measured with proton CT and x-ray CT in fresh postmortem porcine structures.

PURPOSE: Currently, calculations of proton range in proton therapy patients are based on a conversion of CT Hounsfield units of patient tissues into proton relative stopping power. Uncertainties in this conversion necessitate larger proximal and distal planned target volume margins. Proton CT can potentially reduce these uncertainties by directly measuring proton stopping power. We aim to demonstrate proton CT imaging with complex porcine samples, to analyze in detail three-dimensional regions of interest, and to compare proton stopping powers directly measured by proton CT to those determined from x-ray CT scans. METHODS: We have used a prototype proton imaging system with single proton tracking to acquire proton radiography and proton CT images of a sample of porcine pectoral girdle and ribs, and a pig's head. We also acquired close in time x-ray CT scans of the same samples and compared proton stopping power measurements from the two modalities. In the case of the pig's head, we obtained x-ray CT scans from two different scanners and compared results from high-dose and low-dose settings. RESULTS: Comparing our reconstructed proton CT images with images derived from x-ray CT scans, we find agreement within 1% to 2% for soft tissues and discrepancies of up to 6% for compact bone. We also observed large discrepancies, up to 40%, for cavitated regions with mixed content of air, soft tissue, and bone, such as sinus cavities or tympanic bullae. CONCLUSIONS: Our images and findings from a clinically realistic proton CT scanner demonstrate the potential for proton CT to be used for low-dose treatment planning with reduced margins.

An empirical artifact correction for proton computed tomography.

PURPOSE: To reduce image artifacts of proton computed tomography (pCT) from a preclinical scanner, for imaging of the relative stopping power (RSP) needed for particle therapy treatment planning using a simple empirical artifact correction method. METHODS: We adapted and employed a correction method previously used for beam-hardening correction in x-ray CT which makes use of a single scan of a custom-built homogeneous phantom with known RSP. Exploiting the linearity of the filtered backprojection operation, a function was found which corrects water-equivalent path lengths (RSP line integrals) in experimental scans using a prototype pCT scanner. The correction function was applied to projection values of subsequent scans of a homogeneous water phantom, a sensitometric phantom with various inserts and an anthropomorphic head phantom. Data were acquired at two different incident proton energies to test the robustness of the method. RESULTS: Inaccuracies in the detection process caused an offset and known ring artifacts in the water phantom which were considerably reduced using the proposed method. The mean absolute percentage error (MAPE) of mean RSP values of all inserts of the sensitometric phantom and the water phantom was reduced from 0.87% to 0.44% and from 0.86% to 0.48% for the two incident energies respectively. In the head phantom a clear reduction of artifacts was observed. CONCLUSIONS: Image artifacts of experimental pCT scans with a prototype scanner could substantially be reduced both in homogeneous, heterogeneous and anthropomorphic phantoms. RSP accuracy was also improved.

Particle-Tracking Proton Computed Tomography-Data Acquisition, Preprocessing, and Preconditioning.

Proton CT (pCT) is a promising new imaging technique that can reconstruct relative stopping power (RSP) more accurately than x-ray CT in each cubic millimeter voxel of the patient. This, in turn, will result in better proton range accuracy and, therefore, smaller planned tumor volumes (PTV). The hardware description and some reconstructed images have previously been reported. In a series of two contributions, we focus on presenting the software algorithms that convert pCT detector data to the final reconstructed pCT images for application in proton treatment planning. There were several options on how to accomplish this, and we will describe our solutions at each stage of the data processing chain. In the first paper of this series, we present the data acquisition with the pCT tracking and energy-range detectors and how the data are preprocessed, including the conversion to the well-formatted track information from tracking data and water-equivalent path length from the data of a calibrated multi-stage energy-range detector. These preprocessed data are then used for the initial image formation with an FDK cone-beam CT algorithm. The output of data acquisition, preprocessing, and FDK reconstruction is presented along with illustrative imaging results for two phantoms, including a pediatric head phantom. The second paper in this series will demonstrate the use of iterative solvers in conjunction with the superiorization methodology to further improve the images resulting from the upfront FDK image reconstruction and the implementation of these algorithms on a hybrid CPU/GPU computer cluster.

Analysis of characteristics of images acquired with a prototype clinical proton radiography system.

PURPOSE: Verification of patient-specific proton stopping powers obtained in the patient's treatment position can be used to reduce the distal and proximal margins needed in particle beam planning. Proton radiography can be used as a pretreatment instrument to verify integrated stopping power consistency with the treatment planning CT. Although a proton radiograph is a pixel by pixel representation of integrated stopping powers, the image may also be of high enough quality and contrast to be used for patient alignment. This investigation quantifies the accuracy and image quality of a prototype proton radiography system on a clinical proton delivery system. METHODS: We have developed a clinical prototype proton radiography system designed for integration into efficient clinical workflows. We tested the images obtained by this system for water-equivalent thickness (WET) accuracy, image noise, and spatial resolution. We evaluated the WET accuracy by comparing the average WET and rms error in several regions of interest (ROI) on a proton radiograph of a custom peg phantom. We measured the spatial resolution on a CATPHAN Line Pair phantom and a custom edge phantom by measuring the 10% value of the modulation transfer function (MTF). In addition, we tested the ability to detect proton range errors due to anatomical changes in a patient with a customized CIRS pediatric head phantom and inserts of varying WET placed in the posterior fossae of the brain. We took proton radiographs of the phantom with each insert in place and created difference maps between the resulting images. Integrated proton range was measured from an ROI in the difference maps. RESULTS: We measured the WET accuracy of the proton radiographic images to be ±0.2 mm (0.33%) from known values. The spatial resolution of the images was 0.6 lp/mm on the line pair phantom and 1.13 lp/mm on the edge phantom. We were able to detect anatomical changes producing changes in WET as low as 0.6 mm. CONCLUSION: The proton radiography system produces images with image quality sufficient for pretreatment range consistency verification.

Proof of concept image artifact reduction by energy-modulated proton computed tomography (EMpCT).

PURPOSE: To reduce imaging artifacts and improve image quality of a specific proton computed tomography (pCT) prototype scanner by combining pCT data acquired at two different incident proton energies to avoid protons stopping in sub-optimal detector sections. METHODS: Image artifacts of a prototype pCT scanner are linked to protons stopping close to internal structures of the scanner's multi-stage energy detector. We aimed at avoiding such protons by acquiring pCT data at two different incident energies and combining the data in post-processing from which artifact-reduced images of the relative stopping power (RSP) were calculated. Energy-modulated pCT (EMpCT) images were assessed visually and quantitatively and compared to the original mono-energetic images in terms of RSP accuracy and noise. Data were acquired for a homogeneous water phantom. RESULTS: RSP images reconstructed from the mono-energetic datasets displayed local image artifacts which were ring-shaped due to the homogeneity of the phantom. The merged EMpCT dataset achieved a superior visual image quality with reduced artifacts and only minor remaining rings. The inter-quartile range (25/75) of RSP values was reduced from 0.7% with the current standard acquisition to 0.2% with EMpCT due to the reduction of ring artifacts. In this study, dose was doubled compared to a standard scan, but we discuss strategies to reduce excess dose. CONCLUSIONS: EMpCT allows to effectively avoid regions of the energy detector that cause image artifacts. Thereby, image quality is improved.

Technical Note: A fast and monolithic prototype clinical proton radiography system optimized for pencil beam scanning.

PURPOSE: To demonstrate a proton-imaging system based on well-established fast scintillator technology to achieve high performance with low cost and complexity, with the potential of a straightforward translation into clinical use. METHODS: The system tracks individual protons through one (X, Y) scintillating fiber tracker plane upstream and downstream of the object and into a 13-cm -thick scintillating block residual energy detector. The fibers in the tracker planes are multiplexed into silicon photomultipliers (SiPMs) to reduce the number of electronics channels. The light signal from the residual energy detector is collected by 16 photomultiplier tubes (PMTs). Only four signals from the PMTs are output from each event, which allows for fast signal readout. A robust calibration method of the PMT signal to residual energy has been developed to obtain accurate proton images. The development of patient-specific scan patterns using multiple input energies allows for an image to be produced with minimal excess dose delivered to the patient. RESULTS: The calibration of signals in the energy detector produces accurate residual range measurements limited by intrinsic range straggling. We measured the water-equivalent thickness (WET) of a block of solid water (physical thickness of 6.10 mm) with a proton radiograph. The mean WET from all pixels in the block was 6.13 cm (SD 0.02 cm). The use of patient-specific scan patterns using multiple input energies enables imaging with a compact range detector. CONCLUSIONS: We have developed a prototype clinical proton radiography system for pretreatment imaging in proton radiation therapy. We have optimized the system for use with pencil beam scanning systems and have achieved a reduction of size and complexity compared to previous designs.