Image Reconstruction In Cone-beam CT Research Articles

Study on Tooth Cone Beam CT Image Reconstruction Based on Improved U-net Network

Study on Tooth Cone Beam CT Image Reconstruction Based on Improved U-net Network

4D-AirNet: a temporally-resolved CBCT slice reconstruction method synergizing analytical and iterative method with deep learning

Four-dimensional (4D) cone-beam CT (CBCT) reconstructs temporally-resolved phases of 3D volumes often with the same amount of projection data that are meant for reconstructing a single 3D volume. 4D CBCT is a sparse-data problem that is very challenging for high-quality 4D CBCT image reconstruction. Here we develop a new method, namely 4D-AirNet, that synergizes analytical and iterative method with deep learning for high-quality temporally-resolved CBCT slice reconstruction. 4D-AirNet is an unrolling method using the optimization framework of fused analytical and iterative reconstruction (AIR), which is based on proximal forward-backward splitting (PFBS). Three different strategies are developed for 4D-AirNet: random-phase (RP), prior-guided (PG), and all-phase (AP). RP-AirNet and PG-AirNet utilize phase-by-phase training and reconstruction, while PG-AirNet also uses a prior image reconstructed with all-phase projection data. Dense connectivity is built into 4D-AirNet networks for improved reconstruction quality. In contrast, AP-AirNet trains and reconstructs all phases simultaneously. In addition, the joint regularization method of DL and conventional spatiotemporal total variation (TV) is investigated. 4D-AirNet methods were evaluated in comparison with conventional iterative (TV) and deep learning (LEARN) methods, using simulated 2D-t CBCT scans from a lung dataset with various sparse-data levels. The reconstruction results suggest 4D-AirNet methods outperform TV and LEARN, and AP-AirNet provides the best reconstruction quality overall.

Scatter correction based on adaptive photon path-based Monte Carlo simulation method in Multi-GPU platform

Monte Carlo (MC)-based simulation is the most precise method in scatter correction for Cone-beam CT (CBCT). Nonetheless, the existing MC methods cannot be fully applied in clinical due to its low efficiency. The traditional MC simulations perform calculations via a particle-by-particle scheme, which leads to high computation costs because abundant photons do not reach the X-ray detector in transport. The conventional approaches cannot control where the particle ends. Hence, it unavoidably waste lots of time in transporting numerous photons that have no contribution to the signal at the detector, yielding a low computational efficiency. To solve the problem, an innovative GPU-based Metropolis MC (gMMC) method was proposed. Compared with the traditional ones, the Metropolis based algorithm utilizes a path-by-path sampling method. The method can automatically control each particle path and eventually accelerate the convergence. In this paper, we firstly take planning CT image as prior information because of its precise CT value, and utilize gMMC to estimate scatter signal. Then the scatter signals are removed from the raw CBCT projections. Afterwards, FDK reconstruction is performed to obtain the corrected image,some accelerating strategies including reducing photon history number, pixels sampling, projection angles sampling and reconstructed image down-sampling achieve adaptive fast CBCT image reconstruction. For having high computational efficiency, we implemented the whole workflow on a 4-GPU workstation. In order to verify the feasibility of the the method, the experiment of several cases are conducted including simulation, phantom, and real patient cases. Results indicate that the image contrast becomes better, the scatter artifacts are eliminated. The maximum error (emax), the minimum error (emin), the 95th percentile error (e95%), average error (¯e) are reduced from 264, 56, 14 and 21 HU to 28, 10, 3 and 7 HU in full-fan case, and from 387, 5, 19 and 95 HU to 39, 2, 2 and 6 HU in the half-fan case. In terms of computation time, the MC simulation time of all cases is within 2.5 seconds, and the total time is within 15 seconds.

Development of a 4D Digital Phantom for Cone-Beam CT (CBCT) Imaging on the Varian On-Board Imager (OBI)

Mitigating effects of respiratory motion during image guided radiotherapy (IGRT) is important especially during thoracic and abdomen scanning protocols such as cone-beam CT (CBCT) imaging. However, the lack of ‘ground-truth’ in validating new algorithms has always been a challenge. The objective of this study is to outline the development of a novel 4D digital phantom for simulation of respiratory motion effects during CBCT image reconstruction based on Varian On-Board Imager (OBI): Half-Fan (HF) operating mode geometry. A set of actual 4D Magnetic Resonance (MR) data was used to develop the digital phantom. Firstly, the MR data sequencewasextendedto mimic a standard CBCT imaging acquisition protocol. Then, the images were segmented into several organs of interest and assigned with respective CT attenuation values. Subsequently, 2D projections of the developed digital phantom were simulated using the Varian OBI geometry. A Poisson noise model was also incorporated to the projection data to realistically simulate quantum noise that is present in an actual clinical environment. Three types of projections were then reconstructed using the standard 3D Feldkamp-Davis-Kress (FDK) algorithm, projections: without noise, with noise, and with noise and reconstructed with an additional Hann filter. As validation, the reconstructed images were compared against a single-frame of the developed phantom; quantitatively, using normalized root mean squared error (NRMSE) and qualitatively, using difference images. The results indicated that the phantom managed to display a consistent trend in modeling the effects of respiratory motion on the reconstructed images. On average, the NRMSE values for all three reconstructed images within the entire field-of-view (FOV) were evaluated to be approximately 29.07±0.22%. Nonetheless, the difference images indicated a large error in areas largely affected by respiratory motion. The NRMSE of a region-of-interest (ROI) near the affected area was evaluated as 51.26% that constitute to a significant +22.19% difference.

Statistical weights for model-based reconstruction in cone-beam CT with electronic noise and dual-gain detector readout

Cone-beam CT (CBCT) systems commonly incorporate a flat-panel detector (FPD) with multiple-gain readout capability to reduce electronic noise and extend dynamic range. In this work, we report a penalized weighted least-squares (PWLS) method for CBCT image reconstruction with a system model that includes the electronic noise characteristics of FPDs, including systems with dynamic-gain or dual-gain (DG) readout in which the electronic noise is spatially varying. Statistical weights in PWLS were modified to account for the contribution of the electronic noise (algorithm denoted ), and the method was combined with a certainty-based approach that improves the homogeneity of spatial resolution (algorithm denoted ). The methods were tested in phantom studies designed to stress DG readout characteristics and translated to a clinical study for CBCT of patients with head traumas. The method demonstrated superior noise-resolution tradeoffs compared to filtered back-projection (FBP) and conventional PWLS. For example, with spatial resolution (edge-spread function width) matched at 0.65 mm, reduced variance by 28%–39% and 15%–25% compared to FBP and PWLS, respectively. The method achieved more homogeneous spatial resolution than while maintaining similar variance reduction. These findings were confirmed in clinical studies, which showed ~20% variance reduction in peripheral regions of the brain, potentially improving visual image quality in detection of epidural and/or subdural intracranial hemorrhage. The results are consistent with the general notion that incorporating a more accurate system model improves performance in optimization-based statistical CBCT reconstruction—in this case, a more accurate model for (spatially varying) electronic noise to improve detectability of low-contrast lesions.

Motion vector field phase-to-amplitude resampling for 4D motion-compensated cone-beam CT

We propose a phase-to-amplitude resampling (PTAR) method to reduce motion blurring in motion-compensated (MoCo) 4D cone-beam CT (CBCT) image reconstruction, without increasing the computational complexity of the motion vector field (MVF) estimation approach. PTAR is able to improve the image quality in reconstructed 4D volumes, including both regular and irregular respiration patterns. The PTAR approach starts with a robust phase-gating procedure for the initial MVF estimation and then switches to a phase-adapted amplitude gating method. The switch implies an MVF-resampling, which makes them amplitude-specific. PTAR ensures that the MVFs, which have been estimated on phase-gated reconstructions, are still valid for all amplitude-gated reconstructions. To validate the method, we use an artificially deformed clinical CT scan with a realistic breathing pattern and several patient data sets acquired with a TrueBeamTM integrated imaging system (Varian Medical Systems, Palo Alto, CA, USA). Motion blurring, which still occurs around the area of the diaphragm or at small vessels above the diaphragm in artifact-specific cyclic motion compensation (acMoCo) images based on phase-gating, is significantly reduced by PTAR. Also, small lung structures appear sharper in the images. This is demonstrated both for simulated and real patient data. A quantification of the sharpness of the diaphragm confirms these findings. PTAR improves the image quality of 4D MoCo reconstructions compared to conventional phase-gated MoCo images, in particular for irregular breathing patterns. Thus, PTAR increases the robustness of MoCo reconstructions for CBCT. Because PTAR does not require any additional steps for the MVF estimation, it is computationally efficient. Our method is not restricted to CBCT but could rather be applied to other image modalities.

Task-based statistical image reconstruction for high-quality cone-beam CT

Task-based analysis of medical imaging performance underlies many ongoing efforts in the development of new imaging systems. In statistical image reconstruction, regularization is often formulated in terms to encourage smoothness and/or sharpness (e.g. a linear, quadratic, or Huber penalty) but without explicit formulation of the task. We propose an alternative regularization approach in which a spatially varying penalty is determined that maximizes task-based imaging performance at every location in a 3D image. We apply the method to model-based image reconstruction (MBIR—viz., penalized weighted least-squares, PWLS) in cone-beam CT (CBCT) of the head, focusing on the task of detecting a small, low-contrast intracranial hemorrhage (ICH), and we test the performance of the algorithm in the context of a recently developed CBCT prototype for point-of-care imaging of brain injury. Theoretical predictions of local spatial resolution and noise are computed via an optimization by which regularization (specifically, the quadratic penalty strength) is allowed to vary throughout the image to maximize local task-based detectability index (). Simulation studies and test-bench experiments were performed using an anthropomorphic head phantom. Three PWLS implementations were tested: conventional (constant) penalty; a certainty-based penalty derived to enforce constant point-spread function, PSF; and the task-based penalty derived to maximize local detectability at each location. Conventional (constant) regularization exhibited a fairly strong degree of spatial variation in , and the certainty-based method achieved uniform PSF, but each exhibited a reduction in detectability compared to the task-based method, which improved detectability up to ~15%. The improvement was strongest in areas of high attenuation (skull base), where the conventional and certainty-based methods tended to over-smooth the data. The task-driven reconstruction method presents a promising regularization method in MBIR by explicitly incorporating task-based imaging performance as the objective. The results demonstrate improved ICH conspicuity and support the development of high-quality CBCT systems.

Task-driven image acquisition and reconstruction in cone-beam CT

This work introduces a task-driven imaging framework that incorporates a mathematical definition of the imaging task, a model of the imaging system, and a patient-specific anatomical model to prospectively design image acquisition and reconstruction techniques to optimize task performance. The framework is applied to joint optimization of tube current modulation, view-dependent reconstruction kernel, and orbital tilt in cone-beam CT. The system model considers a cone-beam CT system incorporating a flat-panel detector and 3D filtered backprojection and accurately describes the spatially varying noise and resolution over a wide range of imaging parameters in the presence of a realistic anatomical model. Task-based detectability index (d′) is incorporated as the objective function in a task-driven optimization of image acquisition and reconstruction techniques. The orbital tilt was optimized through an exhaustive search across tilt angles ranging ±30°. For each tilt angle, the view-dependent tube current and reconstruction kernel (i.e. the modulation profiles) that maximized detectability were identified via an alternating optimization. The task-driven approach was compared with conventional unmodulated and automatic exposure control (AEC) strategies for a variety of imaging tasks and anthropomorphic phantoms. The task-driven strategy outperformed the unmodulated and AEC cases for all tasks. For example, d′ for a sphere detection task in a head phantom was improved by 30% compared to the unmodulated case by using smoother kernels for noisy views and distributing mAs across less noisy views (at fixed total mAs) in a manner that was beneficial to task performance. Similarly for detection of a line-pair pattern, the task-driven approach increased d′ by 80% compared to no modulation by means of view-dependent mA and kernel selection that yields modulation transfer function and noise-power spectrum optimal to the task. Optimization of orbital tilt identified the tilt angle that reduced quantum noise in the region of the stimulus by avoiding highly attenuating anatomical structures. The task-driven imaging framework offers a potentially valuable paradigm for prospective definition of acquisition and reconstruction protocols that improve task performance without increase in dose.

Patient-Specific Minimum-Dose Imaging Protocols for Statistical Image Reconstruction in C-arm Cone-Beam CT Using Correlated Noise Injection.

A new method for accurately portraying the impact of low-dose imaging techniques in C-arm cone-beam CT (CBCT) is presented and validated, allowing identification of minimum-dose protocols suitable to a given imaging task on a patient-specific basis in scenarios that require repeat intraoperative scans. To accurately simulate lower-dose techniques and account for object-dependent noise levels (x-ray quantum noise and detector electronics noise) and correlations (detector blur), noise of the proper magnitude and correlation was injected into the projections from an initial CBCT acquired at the beginning of a procedure. The resulting noisy projections were then reconstructed to yield low-dose preview (LDP) images that accurately depict the image quality at any level of reduced dose in both filtered backprojection and statistical image reconstruction. Validation studies were conducted on a mobile C-arm, with the noise injection method applied to images of an anthropomorphic head phantom and cadaveric torso across a range of lower-dose techniques. Comparison of preview and real CBCT images across a full range of techniques demonstrated accurate noise magnitude (within ~5%) and correlation (matching noise-power spectrum, NPS). Other image quality characteristics (e.g., spatial resolution, contrast, and artifacts associated with beam hardening and scatter) were also realistically presented at all levels of dose and across reconstruction methods, including statistical reconstruction. Generating low-dose preview images for a broad range of protocols gives a useful method to select minimum-dose techniques that accounts for complex factors of imaging task, patient-specific anatomy, and observer preference. The ability to accurately simulate the influence of low-dose acquisition in statistical reconstruction provides an especially valuable means of identifying low-dose limits in a manner that does not rely on a model for the nonlinear reconstruction process or a model of observer performance.

Development of a Cone-beam CT Image Reconstruction Method for a Moving Target Using a Radiotherapy Imaging System

Development of a Cone-beam CT Image Reconstruction Method for a Moving Target Using a Radiotherapy Imaging System

Non-Coplanar Cone Beam CT Reconstruction With Limited Angle of Projections Using Rigid Image Registration Facilitated Prior Image Constrained Compressed Sensing

Non-coplanar beams are vitally important for achieving the optimal dose distributions in both cranial and many noncranial sites. Treatment verification of such beams with couch rotation/kicks, however, is a challenging problem, particularly for the application of cone beam CT (CBCT). To avoid any collision between the gantry, couch, patient, and on-board imaging system, there is often a very limited angular range available for CBCT acquisition at treatment position. The purpose of this work is to develop a non-coplanar cone beam CT (CBCT) image reconstruction method from limited angle of projections.

Simultaneous motion estimation and image reconstruction (SMEIR) for 4D cone‐beam CT

Image reconstruction and motion model estimation in four-dimensional cone-beam CT (4D-CBCT) are conventionally handled as two sequential steps. Due to the limited number of projections at each phase, the image quality of 4D-CBCT is degraded by view aliasing artifacts, and the accuracy of subsequent motion modeling is decreased by the inferior 4D-CBCT. The objective of this work is to enhance both the image quality of 4D-CBCT and the accuracy of motion model estimation with a novel strategy enabling simultaneous motion estimation and image reconstruction (SMEIR). The proposed SMEIR algorithm consists of two alternating steps: (1) model-based iterative image reconstruction to obtain a motion-compensated primary CBCT (m-pCBCT) and (2) motion model estimation to obtain an optimal set of deformation vector fields (DVFs) between the m-pCBCT and other 4D-CBCT phases. The motion-compensated image reconstruction is based on the simultaneous algebraic reconstruction technique (SART) coupled with total variation minimization. During the forward- and backprojection of SART, measured projections from an entire set of 4D-CBCT are used for reconstruction of the m-pCBCT by utilizing the updated DVF. The DVF is estimated by matching the forward projection of the deformed m-pCBCT and measured projections of other phases of 4D-CBCT. The performance of the SMEIR algorithm is quantitatively evaluated on a 4D NCAT phantom. The quality of reconstructed 4D images and the accuracy of tumor motion trajectory are assessed by comparing with those resulting from conventional sequential 4D-CBCT reconstructions (FDK and total variation minimization) and motion estimation (demons algorithm). The performance of the SMEIR algorithm is further evaluated by reconstructing a lung cancer patient 4D-CBCT. Image quality of 4D-CBCT is greatly improved by the SMEIR algorithm in both phantom and patient studies. When all projections are used to reconstruct a 3D-CBCT by FDK, motion-blurring artifacts are present, leading to a 24.4% relative reconstruction error in the NACT phantom. View aliasing artifacts are present in 4D-CBCT reconstructed by FDK from 20 projections, with a relative error of 32.1%. When total variation minimization is used to reconstruct 4D-CBCT, the relative error is 18.9%. Image quality of 4D-CBCT is substantially improved by using the SMEIR algorithm and relative error is reduced to 7.6%. The maximum error (MaxE) of tumor motion determined from the DVF obtained by demons registration on a FDK-reconstructed 4D-CBCT is 3.0, 2.3, and 7.1 mm along left-right (L-R), anterior-posterior (A-P), and superior-inferior (S-I) directions, respectively. From the DVF obtained by demons registration on 4D-CBCT reconstructed by total variation minimization, the MaxE of tumor motion is reduced to 1.5, 0.5, and 5.5 mm along L-R, A-P, and S-I directions. From the DVF estimated by SMEIR algorithm, the MaxE of tumor motion is further reduced to 0.8, 0.4, and 1.5 mm along L-R, A-P, and S-I directions, respectively. The proposed SMEIR algorithm is able to estimate a motion model and reconstruct motion-compensated 4D-CBCT. The SMEIR algorithm improves image reconstruction accuracy of 4D-CBCT and tumor motion trajectory estimation accuracy as compared to conventional sequential 4D-CBCT reconstruction and motion estimation.

Compressed-sensing (CS)-based 3D image reconstruction in cone-beam CT (CBCT) for low-dose, high-quality dental X-ray imaging

The most popular reconstruction algorithm for cone-beam computed tomography (CBCT) is based on the computationally-inexpensive filtered-backprojection (FBP) method. However, that method usually requires dense projections over the Nyquist samplings, which imposes severe restrictions on the imaging doses. Moreover, the algorithm tends to produce cone-beam artifacts as the cone angle is increased. Several variants of the FBP-based algorithm have been developed to overcome these difficulties, but problems with the cone-beam reconstruction still remain. In this study, we considered a compressed-sensing (CS)-based reconstruction algorithm for low-dose, high-quality dental CBCT images that exploited the sparsity of images with substantially high accuracy. We implemented the algorithm and performed systematic simulation works to investigate the imaging characteristics. CBCT images of high quality were successfully reconstructed by using the built-in CS-based algorithm, and the image qualities were evaluated quantitatively in terms of the universal-quality index (UQI) and the slice-profile quality index (SPQI).We expect the reconstruction algorithm developed in the work to be applicable to current dental CBCT systems, to reduce imaging doses, and to improve the image quality further.

Efficient Ring Artifact Remove Scheme of Cone-Beam CT Image

The element response inconsistency of flat-panel detector may result in ring artefacts in cone-beam CT image reconstruction. Ring artefacts severely affect the quality of CT reconstructed image, thus removing ring artefacts is very meaningful for image analysis and subsequent processing. This paper presents an algorithm for processing reconstructed image, and has achieved a result through simulating. This approach proves very effective in removing ring artefacts from images.

TH‐C‐103‐06: Non‐Coplanar Cone Beam CT Reconstruction with Limited Angle of Projections Using Rigid Image Registration Facilitated Prior Image Constrained Compressed Sensing

Purpose: The purpose of this work is to develop a non‐coplanar cone beam CT (CBCT) image reconstruction method from limited angle of projections. Methods: Planning CT or CBCT acquired at neutral position (without couch rotation/translation) serves as the prior images for successive reconstruction. During the course of radiation therapy, subsequent CBCT scans are acquired from limited angle of projections. An iterative reconstruction algorithm, using prior image constrained compressed sensing (PICCS) framework and rigid image registration, is incorporated to reconstruct the patient image under non‐coplanar geometry. The prior reconstructed image is rotated/translated according to the nominal couch rotation/translation to serve as the prior input of the compressed sensing based iterative optimization. First, The PICCS framework can be solved iteratively using the total variation minimization step and the iterative image reconstruction step with simultaneous algebraic reconstruction technique (SART). Then, rigid image registration between the reconstructed image and the rotated/translated prior image is applied to further calibrate the rotation/translation of the prior image. The iterations of PICCS algorithm and rigid image registration continue until the registration results are below the predetermined threshold. The proposed reconstruction algorithm is evaluated with both digital phantom simulations and experimental data. Results: The proposed CBCT reconstruction algorithm significantly improved the image quality from projections with angular range as small as 72 degrees. It can reduce the rigid translational setup errors under from 8 mms to below 1 mm and the rigid rotational setup error from 5 degrees to below 1 degree. Compared with the original PICCS algorithm alone, the rigid registration step helps to improve the convergence of algorithm significantly. Conclusions: The proposed algorithm provides an accurate remedy for solving the problem of non‐coplanar CBCT reconstruction from limited angle of projections by taking advantage of the combination of PICCS framework and rigid image registration.

Volume-of-interest cone-beam CT using a 2.35 MV beam generated with a carbon target

This is a proof-of-concept study addressing volume of interest (VOI) cone beam CT (CBCT) imaging using an x-ray beam produced by 2.35 MeV electrons incident on a carbon linear accelerator target. Methodology is presented relevant to VOI CBCT image acquisition and reconstruction. Sample image data are given to demonstrate and compare two approaches to minimizing artifacts arising from reconstruction with truncated projections. Dosimetric measurements quantify the potential dose reduction of VOI acquisition relative to full-field CBCT. The dependence of contrast-to-noise ratio (CNR) on VOI dimension is investigated. A paradigm is presented linking the treatment planning process with the imaging technique, allowing definition of an imaging VOI to be tailored to the geometry of the patient. Missing data in truncated projection images are completed using a priori information in the form of digitally reconstructed radiographs (DRRs) generated from the planning CT set. This method is compared to a simpler technique of extrapolating truncated projection data prior to reconstruction. The utility of these approaches is shown through imaging of a geometric phantom and the head-and-neck section of a lamb. The total scatter factor of the 2.35 MV∕carbon beam on field size is measured and compared to a standard therapeutic beam to estimate the comparative dose reduction inside the VOI. Thermoluminescent dosimeters and Gafchromic film measurements are used to compare the imaging dose distributions for the 2.35 MV∕carbon beam between VOI and full-field techniques. The dependence of CNR on VOI dimension is measured for VOIs ranging from 4 to 15 cm diameter. Without compensating for missing data outside of truncated projections prior to reconstruction, pronounced boundary artifacts are present, in three dimensions, within 2-3 cm of the edges of the VOI. These artifacts, as well as cupping inside the VOI, can be reduced substantially using either the DRR filling or extrapolation techniques presented. Compared to 6 MV, the 2.35 MV∕carbon beam shows a substantially greater dependence of total scatter factor on field size, indicating a comparative advantage of the VOI approach when combined with the low-Z target beam. Dosimetric measurements in the anthropomorphic head phantom demonstrate a dose reduction by up to 15% and 75% inside and outside of the VOI, respectively, compared to full-field imaging. For the 2.35 MV∕carbon beam, CNR was shown to be approximately invariant with VOI dimension for bone and lung objects. The low-Z target, VOI CBCT technique appears to be feasible and combines the desirable characteristics of the low-Z target beam with regard to CNR, with the capacity to localize the imaging dose to the anatomy relevant to the image guidance task.

Iterative image reconstruction in helical cone-beam x-ray CT using a stored system matrix approach

We present a stored system matrix (SM) approach for iterative x-ray CT image reconstruction with helical cone-beam geometry. Because of the symmetry of a helical source trajectory, it is sufficient to calculate and store the SM entries for one transaxial slice only and for all source positions illuminating the slice. This is made possible by (1) selecting the reconstruction slice thickness to be an integer multiple of the source translation per projection view, and (2) discretizing the 3D reconstruction volume on a rotated stack of slices. Using the proposed method, the memory requirement for reconstructing a full field-of-view of clinical scanners is manageable on current computing platforms. The same storage principle can be generalized and applied to volume-of-interest (VOI) image reconstruction for helical cone-beam CT. In this case, the stored SM entries correspond to a partial- or full-ring region on one transaxial slice, and for all source positions illuminating the ring. The size and location of the ring depend on the size and the location of the VOI and the scan geometry. We demonstrate by both computer simulations and clinical patient data the speed and efficacy of iterative image reconstruction using the stored SM approach.

Effects of the penalty on the penalized weighted least-squares image reconstruction for low-dose CBCT

Statistical iterative reconstruction (SIR) algorithms have shown potential to substantially improve low-dose cone-beam CT (CBCT) image quality. The penalty term plays an important role in determining the performance of SIR algorithms. In this work, we quantitatively evaluate the impact of the penalties on the performance of a statistics-based penalized weighted least-squares (PWLS) iterative reconstruction algorithm for improving the image quality of low-dose CBCT. Three different edge-preserving penalty terms, exponential form anisotropic quadratic (AQ) penalty (PWLS-Exp), inverse square form AQ penalty (PWLS-InverseSqr) and total variation penalty (PWLS-TV), were compared against the conventional isotropic quadratic form penalty (PWLS-Iso) using both computer simulation and experimental studies. Noise in low-dose CBCT can be substantially suppressed by the PWLS reconstruction algorithm and edges are well preserved by both AQ- and TV-based penalty terms. The noise-resolution tradeoff measurement shows that the PWLS-Exp exhibits the best spatial resolution of all the three anisotropic penalty terms at matched noise level for reconstructing high-contrast objects. For the reconstruction of low-contrast objects, the TV-based penalty outperforms the AQ-based one with better resolution preservation at matched noise levels. Different penalty terms may be used for better edge preservation at different targeted contrast levels.

SU-DD-A4-06: Penalized Weighted Lease-Squares Image Reconstruction for Low-Dose CBCT: A Comparison Study of Different Edge-Preserving Penalties

Purpose: The penalty term plays an important role in determining the performance of statistics‐based iterative reconstruction algorithms. In this work, we qualitatively evaluate the performance of a total variation (TV)‐based penalized weighted lease‐squares (PWLS) algorithm with two anisotropic quadratic (AQ) penalties in improving the image quality of low‐dose cone‐beam CT(CBCT).Method and Materials: Low‐dose CBCT projection data of a quality assurance CatPhan 600 phantom was acquired by a Varian Acuity simulator (Varian Medical Systems, Palo Alto, CA) using a protocol of 10mA/10ms per projection. CBCTimages were reconstructed by minimizing an objective function based on the PWLS criterion with three penalty terms. The first one is the TV of the image. The other two are anisotropic penalties in quadratic form. The anisotropic quadratic penalty incorporates the gradient of image intensity of the reconstructed image by introducing an anisotropic coefficient for each neighbor of the quadratic penalty. In this work, two forms of anisotropic coefficient were investigated: one is in the inverse square form, and the other one is in the exponential form. In the anisotropic penalty, smoothness is discouraged between neighbors associated with a large gradient. We used a noise‐resolution tradeoff measure to quantitatively evaluate the performance of PWLS with three penalty terms. Results:Image improvement, in the terms of noise suppression and edge‐preservation, is achieved by all of the three PWLS algorithms. At a noise‐level comparable to a high‐dose FDK reconstructedCBCTimage, exponential‐weighted anisotropic penalty shows the best spatial resolution of all the three. At a lower noise level, the TV‐based term outperforms the AQ‐based terms. Conclusion:Noise in low‐dose CBCT can be substantially suppressed by PWLS reconstruction algorithm and edges are well‐preserved by both TV‐ and AQ‐based penalty term. Different penalty terms should be used for better edge‐preservation at different target noise levels.

A dual modality of cone beam CT and electrical impedance tomography for lung imaging

Electrical impedance tomography (EIT) is a new and emerging tomographic imaging system, which is able to provide high temporal resolution with low spatial resolution. In past few years, the EIT has been extensively studied for lung monitoring. Cone beam CT (CBCT) is an imaging system used in radiation therapy unit. The CBCT suffers from motion artefacts, making treatment planning inaccurate. This problem is very severe in lung cancer therapy. In this study a dual modality of EIT-CBCT has been proposed. Feasibility of such a dual system has been investigated using preliminary simulated phantom data. The motion information is extracted from the EIT images and has been used to compensate for the motion in CBCT through our recently developed iterative image reconstruction for CBCT. The results show that blurring artefacts around a tumour can be reduced for the motion compensated images using this dual modality combination. The dual EIT-CBCT imaging benefits from complementary high spatial resolution of CBCT and high temporal EIT resolution.