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Trapp P, Maier J, Susenburger M, Sawall S, Kachelrieß M. Empirical scatter correction (ESC): CBCT scatter artifact reduction without prior information. Med Phys 2022; 49:4566-4584. [PMID: 35390181 DOI: 10.1002/mp.15656] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 03/24/2022] [Accepted: 03/27/2022] [Indexed: 11/07/2022] Open
Abstract
BACKGROUND The image quality of cone-beam CT (CBCT) scans severely suffers from scattered radiation if no countermeasures are taken. Scatter artifacts may induce cupping and streak artifacts and lead to a reduced image contrast and wrong CT values of the reconstructed volumes. Established software-based approaches for a correction of scattered radiation typically rely on prior knowledge of the CT system, scan parameters, the scanned object, or all of the aforementioned. PURPOSE This study proposes a simple and effective post-processing software-based correction method of scatter artifacts in CBCT scans without specific prior knowledge. METHODS We propose the empirical scatter correction (ESC) which generates scatter-like basis images from each projection image by convolution operations. A linear combination of these basis images is subtracted from the original projection image. The logarithm is taken and an FDK reconstruction is performed. The coefficients needed for the linear combination are determined automatically by a downhill simplex algorithm such that the resulting reconstructed images show no scatter artifacts. We demonstrate the potential of ESC by correcting simulated volumes with Monte Carlo scatter artifacts, a head phantom scan performed on our table-top CBCT, and a pelvis scan from a Varian Edge CBCT scanner. RESULTS ESC is able to improve the image quality of CBCT scans which is shown on the basis of our simulations and on measured data. For a simulated head CT, the CT value difference to the scatter-free reference image was as low as -6 HU after using ESC whereas the uncorrected data deviated by more than -200 HU from the reference data. Simulations of thorax and abdomen CT scans show that although scatter artifacts are not fully removed, anatomical features which were hard to discover prior to the correction become clearly visible and better segmentable with ESC. Similar results are obtained in the phantom measurement where a comparison to a slit scan of our head phantom shows only small differences. The CT values in soft tissue are improved in this measurement, as well. In soft tissue areas with severe scatter artifacts the CT values agree well with those of the slit scan (difference to slit scan: 35 HU corrected, -289 HU uncorrected). Scatter artifacts in measured patient data can also be reduced using the proposed empirical scatter correction. The results are comparable to those achieved with designated correction algorithms installed on the Varian Edge CBCT system. CONCLUSIONS ESC allows to reduce artifacts caused by patient scatter solely based on the projection data. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Philip Trapp
- Division of X-Ray Imaging and Computed Tomography, German Cancer Research Center (DKFZ), Heidelberg, 69120, Germany.,Department of Physics and Astronomy, Ruprecht-Karls-University, Heidelberg, 69120, Germany
| | - Joscha Maier
- Division of X-Ray Imaging and Computed Tomography, German Cancer Research Center (DKFZ), Heidelberg, 69120, Germany
| | - Markus Susenburger
- Division of X-Ray Imaging and Computed Tomography, German Cancer Research Center (DKFZ), Heidelberg, 69120, Germany.,Department of Physics and Astronomy, Ruprecht-Karls-University, Heidelberg, 69120, Germany
| | - Stefan Sawall
- Division of X-Ray Imaging and Computed Tomography, German Cancer Research Center (DKFZ), Heidelberg, 69120, Germany.,Medical Faculty, Ruprecht-Karls-University, Heidelberg, 69120, Germany
| | - Marc Kachelrieß
- Division of X-Ray Imaging and Computed Tomography, German Cancer Research Center (DKFZ), Heidelberg, 69120, Germany.,Medical Faculty, Ruprecht-Karls-University, Heidelberg, 69120, Germany
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Sheng K. Artificial intelligence in radiotherapy: a technological review. Front Med 2020; 14:431-449. [PMID: 32728877 DOI: 10.1007/s11684-020-0761-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2019] [Accepted: 02/14/2020] [Indexed: 12/19/2022]
Abstract
Radiation therapy (RT) is widely used to treat cancer. Technological advances in RT have occurred in the past 30 years. These advances, such as three-dimensional image guidance, intensity modulation, and robotics, created challenges and opportunities for the next breakthrough, in which artificial intelligence (AI) will possibly play important roles. AI will replace certain repetitive and labor-intensive tasks and improve the accuracy and consistency of others, particularly those with increased complexity because of technological advances. The improvement in efficiency and consistency is important to manage the increasing cancer patient burden to the society. Furthermore, AI may provide new functionalities that facilitate satisfactory RT. The functionalities include superior images for real-time intervention and adaptive and personalized RT. AI may effectively synthesize and analyze big data for such purposes. This review describes the RT workflow and identifies areas, including imaging, treatment planning, quality assurance, and outcome prediction, that benefit from AI. This review primarily focuses on deep-learning techniques, although conventional machine-learning techniques are also mentioned.
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Affiliation(s)
- Ke Sheng
- Department of Radiation Oncology, University of California, Los Angeles, CA, 90095, USA.
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Abstract
X-ray scattering significantly limits image quality. Conventional strategies for scatter reduction based on physical equipment or measurements inevitably increase the dose to improve the image quality. In addition, scatter reduction based on a computational algorithm could take a large amount of time. We propose a deep learning-based scatter correction method, which adopts a convolutional neural network (CNN) for restoration of degraded images. Because it is hard to obtain real data from an X-ray imaging system for training the network, Monte Carlo (MC) simulation was performed to generate the training data. For simulating X-ray images of a human chest, a cone beam CT (CBCT) was designed and modeled as an example. Then, pairs of simulated images, which correspond to scattered and scatter-free images, respectively, were obtained from the model with different doses. The scatter components, calculated by taking the differences of the pairs, were used as targets to train the weight parameters of the CNN. Compared with the MC-based iterative method, the proposed one shows better results in projected images, with as much as 58.5% reduction in root-mean-square error (RMSE), and 18.1% and 3.4% increases in peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM), on average, respectively.
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Zheng Y, Jin C, Cui H, Dai H, Yan J, Han P, Hsu B. Improved image resolution on thoracic carcinomas by quantitative 18F-FDG coincidence SPECT/CT in comparison to 18F-FDG PET/CT. J Biomed Res 2019; 34:309-317. [PMID: 32701069 PMCID: PMC7386415 DOI: 10.7555/jbr.33.20190004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Currently, 18F-FDG coincidence SPECT (Co-SPECT)/CT scan still serves as an important tool for diagnosis, staging, and evaluation of cancer treatment in developing countries. We implemented full physical corrections (FPC) to Co-SPECT (quantitative Co-SPECT) to improve the image resolution and contrast along with the capability for image quantitation. FPC included attenuation, scatter, resolution recovery, and noise reduction. A standard NEMA phantom filled with 10:1 F-18 activity concentration ratio in spheres and background was utilized to evaluate image performance. Subsequently, 15 patients with histologically confirmed thoracic carcinomas were included to undergo a 18F-FDG Co-SPECT/CT scan followed by a 18F-FDG PET/CT scan. Functional parameters as SUVmax, SUVmean, SULpeak, and MTV from both quantitative Co-SPECT and PET were analyzed. Image resolution of Co-SPECT for NEMA phantom was improved to reveal the smallest sphere from a diameter of 28 mm to 22 mm (17 mm for PET). The image contrast was enhanced from 1.7 to 6.32 (6.69 for PET) with slightly degraded uniformity in background (3.1% vs. 6.7%) (5.6% for PET). Patients' SUVmax, SUVmean, SULpeak, and MTV measured from quantitative Co-SPECT were overall highly correlated with those from PET (r=0.82–0.88). Adjustment of the threshold of SUVmax and SUV to determine SUVmean and MTV did not further change the correlations with PET (r=0.81–0.88). Adding full physical corrections to Co-SPECT images can significantly improve image resolution and contrast to reveal smaller tumor lesions along with the capability to quantify functional parameters like PET/CT.
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Affiliation(s)
| | | | | | - Haojie Dai
- Nuclear Medicine Department, Danli Hospital, Beijing 100073, China
| | | | | | - Bailing Hsu
- Nuclear Science and Engineering Institute, University of Missouri-Columbia, Columbia, MO 65201, USA
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Mao W, Liu C, Gardner SJ, Siddiqui F, Snyder KC, Kumarasiri A, Zhao B, Kim J, Wen NW, Movsas B, Chetty IJ. Evaluation and Clinical Application of a Commercially Available Iterative Reconstruction Algorithm for CBCT-Based IGRT. Technol Cancer Res Treat 2019; 18:1533033818823054. [PMID: 30803367 PMCID: PMC6373994 DOI: 10.1177/1533033818823054] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 09/26/2018] [Accepted: 11/29/2018] [Indexed: 11/27/2022] Open
Abstract
PURPOSE We have quantitatively evaluated the image quality of a new commercially available iterative cone-beam computed tomography reconstruction algorithm over standard cone-beam computed tomography image reconstruction results. METHODS This iterative cone-beam computed tomography reconstruction pipeline uses a finite element solver (AcurosCTS)-based scatter correction and a statistical (iterative) reconstruction in addition to a standard kernel-based correction followed by filtered back-projection-based Feldkamp-Davis-Kress cone-beam computed tomography reconstruction. Standard full-fan half-rotation Head, half-fan full-rotation Head, and standard Pelvis cone-beam computed tomography protocols have been investigated to scan a quality assurance phantom via the following image quality metrics: uniformity, HU constancy, spatial resolution, low contrast detection, noise level, and contrast-to-noise ratio. An anthropomorphic head phantom was scanned for verification of noise reduction. Clinical patient image data sets for 5 head/neck patients and 5 prostate patients were qualitatively evaluated. RESULTS Quality assurance phantom study results showed that relative to filtered back-projection-based cone-beam computed tomography, noise was reduced from 28.8 ± 0.3 HU to a range between 18.3 ± 0.2 and 5.9 ± 0.2 HU for Full-Fan Head scans, from 14.4 ± 0.2 HU to a range between 12.8 ± 0.3 and 5.2 ± 0.3 HU for Half-Fan Head scans, and from 6.2 ± 0.1 HU to a range between 3.8 ± 0.1 and 2.0 ± 0.2 HU for Pelvis scans, with the iterative cone-beam computed tomography algorithm. Spatial resolution was marginally improved while results for uniformity and HU constancy were similar. For the head phantom study, noise was reduced from 43.6 HU to a range between 24.8 and 13.0 HU for a Full-Fan Head and from 35.1 HU to a range between 22.9 and 14.0 HU for a Half-Fan Head scan. The patient data study showed that artifacts due to photon starvation and streak artifacts were all reduced, and image noise in specified target regions were reduced to 62% ± 15% for 10 patients. CONCLUSION Noise and contrast-to-noise ratio image quality characteristics were significantly improved using the iterative cone-beam computed tomography reconstruction algorithm relative to the filtered back-projection-based cone-beam computed tomography method. These improvements will enhance the accuracy of cone-beam computed tomography-based image-guided applications.
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Affiliation(s)
- Weihua Mao
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
| | - Chang Liu
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
| | - Stephen J. Gardner
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
| | - Farzan Siddiqui
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
| | - Karen C. Snyder
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
| | - Akila Kumarasiri
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
| | - Bo Zhao
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
| | - Joshua Kim
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
| | - Ning Winston Wen
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
| | - Benjamin Movsas
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
| | - Indrin J. Chetty
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
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Siciarz P, Mccurdy B, Alshafa F, Greer P, Hatton J, Wright P. Evaluation of CT to CBCT non-linear dense anatomical block matching registration for prostate patients. Biomed Phys Eng Express 2018. [DOI: 10.1088/2057-1976/aacada] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Maslowski A, Wang A, Sun M, Wareing T, Davis I, Star-Lack J. Acuros CTS: A fast, linear Boltzmann transport equation solver for computed tomography scatter - Part I: Core algorithms and validation. Med Phys 2018; 45:1899-1913. [PMID: 29509970 DOI: 10.1002/mp.12850] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2017] [Revised: 01/23/2018] [Accepted: 02/23/2018] [Indexed: 01/31/2023] Open
Abstract
PURPOSE To describe Acuros® CTS, a new software tool for rapidly and accurately estimating scatter in x-ray projection images by deterministically solving the linear Boltzmann transport equation (LBTE). METHODS The LBTE describes the behavior of particles as they interact with an object across spatial, energy, and directional (propagation) domains. Acuros CTS deterministically solves the LBTE by modeling photon transport associated with an x-ray projection in three main steps: (a) Ray tracing photons from the x-ray source into the object where they experience their first scattering event and form scattering sources. (b) Propagating photons from their first scattering sources across the object in all directions to form second scattering sources, then repeating this process until all high-order scattering sources are computed using the source iteration method. (c) Ray-tracing photons from scattering sources within the object to the detector, accounting for the detector's energy and anti-scatter grid responses. To make this process computationally tractable, a combination of analytical and discrete methods is applied. The three domains are discretized using the Linear Discontinuous Finite Elements, Multigroup, and Discrete Ordinates methods, respectively, which confer the ability to maintain the accuracy of a continuous solution. Furthermore, through the implementation in CUDA, we sought to exploit the parallel computing capabilities of graphics processing units (GPUs) to achieve the speeds required for clinical utilization. Acuros CTS was validated against Geant4 Monte Carlo simulations using two digital phantoms: (a) a water phantom containing lung, air, and bone inserts (WLAB phantom) and (b) a pelvis phantom derived from a clinical CT dataset. For these studies, we modeled the TrueBeam® (Varian Medical Systems, Palo Alto, CA) kV imaging system with a source energy of 125 kVp. The imager comprised a 600 μm-thick Cesium Iodide (CsI) scintillator and a 10:1 one-dimensional anti-scatter grid. For the WLAB studies, the full-fan geometry without a bowtie filter was used (with and without the anti-scatter grid). For the pelvis phantom studies, a half-fan geometry with bowtie was used (with the anti-scatter grid). Scattered and primary photon fluences and energies deposited in the detector were recorded. RESULTS The Acuros CTS and Monte Carlo results demonstrated excellent agreement. For the WLAB studies, the average percent difference between the Monte Carlo- and Acuros-generated scattered photon fluences at the face of the detector was -0.7%. After including the detector response, the average percent differences between the Monte Carlo- and Acuros-generated scatter fractions (SF) were -0.1% without the grid and 0.6% with the grid. For the digital pelvis simulation, the Monte Carlo- and Acuros-generated SFs agreed to within 0.1% on average, despite the scatter-to-primary ratios (SPRs) being as high as 5.5. The Acuros CTS computation time for each scatter image was ~1 s using a single GPU. CONCLUSIONS Acuros CTS enables a fast and accurate calculation of scatter images by deterministically solving the LBTE thus offering a computationally attractive alternative to Monte Carlo methods. Part II describes the application of Acuros CTS to scatter correction of CBCT scans on the TrueBeam system.
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Affiliation(s)
| | - Adam Wang
- Varian Medical Systems, Palo Alto, CA, 94304, USA
| | - Mingshan Sun
- Varian Medical Systems, Palo Alto, CA, 94304, USA
| | - Todd Wareing
- Varian Medical Systems, Palo Alto, CA, 94304, USA
| | - Ian Davis
- Varian Medical Systems, Palo Alto, CA, 94304, USA
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