Challenges For Image Reconstruction Research Articles

Improved Balanced Steady-State Free Precession Based MR Fingerprinting with Deep Autoencoders.

Magnetic Resonance (MR) Fingerprinting is an emerging transient-state imaging paradigm, which enables the quantization of multiple MR tissue parameters in a single experiment. Balanced steady-state free precession (bSSFP)-based MR Fingerprinting has excellent signal-to-noise characteristics and also allows for acquiring both tissue parameter maps and field inhomogeneity maps. However, field inhomogeneity often results in complex magnetization evolutions in bSSFP-based MR Fingerprinting, which creates significant challenges in image reconstruction. In this paper, we introduce a new method to address the image reconstruction problem. The proposed method incorporates a low-dimensional nonlinear manifold learned from an ensemble of magnetization evolutions using a deep autoencoder. It provides much better representation power than a low-dimensional linear subspace in capturing complex magnetization evolutions. We formulate the image reconstruction problem with this nonlinear model and solve the resulting optimization problem using an algorithm based on variable splitting and the alternating direction method of multipliers. We evaluate the performance of the proposed method using numerical experiments and demonstrate that it significantly outperforms the state-of-art method using a linear subspace model.

A new inversion method for reconstruction of plasmaspheric He<sup>+</sup> density from EUV images

The Computer Tomography (CT) method is used for remote sensing the Earth’s plasmasphere. One challenge for image reconstruction is insufficient projection data, mainly caused by limited projection angles. In this study, we apply the Algebraic Reconstruction Technique (ART) and the minimization of the image Total Variation (TV) method, with a combination of priori knowledge of north–south symmetry, to reconstruct plasmaspheric He⁺ density from simulated EUV images. The results demonstrate that incorporating priori assumption can be particularly useful when the projection data is insufficient. This method has good performance even with a projection angle of less than 150 degrees. The method of our study is expected to have applications in the Soft X-ray Imager (SXI) reconstruction for the Solar wind–Magnetosphere–Ionosphere Link Explorer (SMILE) mission.

Sampled limited memory methods for massive linear inverse problems

In many modern imaging applications the desire to reconstruct high resolution images, coupled with the abundance of data from acquisition using ultra-fast detectors, have led to new challenges in image reconstruction. A main challenge is that the resulting linear inverse problems are massive. The size of the forward model matrix exceeds the storage capabilities of computer memory, or the observational dataset is enormous and not available all at once. Row-action methods that iterate over samples of rows can be used to approximate the solution while avoiding memory and data availability constraints. However, their overall convergence can be slow. In this paper, we introduce a sampled limited memory row-action method for linear least squares problems, where an approximation of the global curvature of the underlying least squares problem is used to speed up the initial convergence and to improve the accuracy of iterates. We show that this limited memory method is a generalization of the damped block Kaczmarz method, and we prove linear convergence of the expectation of the iterates and of the error norm up to a convergence horizon. Numerical experiments demonstrate the benefits of these sampled limited memory row-action methods for massive 2D and 3D inverse problems in tomography applications.

Sparse optimization for image reconstruction in Electrical Impedance Tomography

Abstract Electrical Impedance Tomography (EIT) can be used to obtain phase boundaries and gas holdups in multiphase flows. The main challenge in image reconstruction using EIT is the low spatial resolution. In this paper, a reconstruction algorithm using sparse optimization techniques is presented. For multiphase flows, gradients in the conductivity vector are sparse. Therefore, the reconstruction problem is formulated as identification of this sparse vector given the current-voltage measurements. A new iterative algorithm is proposed to estimate the conductivity values. The accuracy of the proposed method is demonstrated with the help of several examples and comparison with an existing technique.

Quantitative Study of LSCF and LSM-YSZ Cathode Microstructure by FIB/SEM Tomography

The mixed ionic-electronic conducting cathode (La, Sr)(Co, Fe)O3-δ (LSCF) is utilized worldwide for solid oxide fuel cells (SOFCs) operated at intermediate temperatures [1] because of its excellent oxygen reduction reaction rate. The challenge of using LSCF as cathode material is the complex nature of the interface to Zirconia-based electrolytes, which may counteract this advantage [2, 3]. The composite cathode (La, Sr)MnO3 (LSM) – Y2O3 doped ZrO2(YSZ) is an adequate alternative for higher temperatures only. LSM-YSZ offers fewer regions of oxygen reduction (triple phase boundaries) due to spatially separated ionically (YSZ) and electronically (LSM) conductive phases. If electrochemical and microstructural parameters of both types of electrodes are quantified by appropriate techniques, the performance can be modeled and compared for different operating conditions. Advanced imaging techniques such as focused ion beam/scanning electron microscopy (FIB/SEM) or X-ray tomography are promising techniques for microstructure quantification enabling 3D reconstructions of µm- and sub-µm-scaled multiphase electrodes [4, 5, 6]. Reconstructing the composite cathode, in particular, comprises the challenge of overcoming the weak material contrast between LSM and YSZ using scanning electron microscopy. This contribution will show how FIB/SEM tomography can help to identify different microstructural parameters such as porosity, particle size, surface area and tortuosity. We will compare LSCF and LSM-YSZ cathodes and enlighten new challenges in image reconstruction, parameter acquisition and parameter interpretation for the different material sets. [1] H. Yokokawa, et al., J. Power Sources, 182, p. 400 (2008) [2] J. Szasz, et al., ECS Trans., 66(2), p. 79 (2015) [3] Yokokawa, et al., Solid State Ionics, 262, p. 454 (2014) [4] J.R. Wilson, et al., Nature Materials 5(17), p. 541 (2006) [5] J. Joos, et al., Electrochim. Acta 82, p. 268 (2012) [6] Y. C. K. Chen-Wiegart, et al., J. Power Sources, 218, p. 348 (2012) Figure 1

High speed imaging of dynamic processes with a switched source x-ray CT system

Conventional x-ray computed tomography (CT) scanners are limited in their scanning speed by the mechanical constraints of their rotating gantries and as such do not provide the necessary temporal resolution for imaging of fast-moving dynamic processes, such as moving fluid flows. The Real Time Tomography (RTT) system is a family of fast cone beam CT scanners which instead use multiple fixed discrete sources and complete rings of detectors in an offset geometry. We demonstrate the potential of this system for use in the imaging of such high speed dynamic processes and give results using simulated and real experimental data. The unusual scanning geometry results in some challenges in image reconstruction, which are overcome using algebraic iterative reconstruction techniques and explicit regularisation. Through the use of a simple temporal regularisation term and by optimising the source firing pattern, we show that temporal resolution of the system may be increased at the expense of spatial resolution, which may be advantageous in some situations. Results are given showing temporal resolution of approximately 500 µs with simulated data and 3 ms with real experimental data.

Simulation and experimental studies of three-dimensional (3D) image reconstruction from insufficient sampling data based on compressed-sensing theory for potential applications to dental cone-beam CT

In practical applications of three-dimensional (3D) tomographic imaging, there are often challenges for image reconstruction from insufficient sampling data. In computed tomography (CT), for example, image reconstruction from sparse views and/or limited-angle (<360°) views would enable fast scanning with reduced imaging doses to the patient. In this study, we investigated and implemented a reconstruction algorithm based on the compressed-sensing (CS) theory, which exploits the sparseness of the gradient image with substantially high accuracy, for potential applications to low-dose, high-accurate dental cone-beam CT (CBCT). We performed systematic simulation works to investigate the image characteristics and also performed experimental works by applying the algorithm to a commercially-available dental CBCT system to demonstrate its effectiveness for image reconstruction in insufficient sampling problems. We successfully reconstructed CBCT images of superior accuracy from insufficient sampling data and evaluated the reconstruction quality quantitatively. Both simulation and experimental demonstrations of the CS-based reconstruction from insufficient data indicate that the CS-based algorithm can be applied directly to current dental CBCT systems for reducing the imaging doses and further improving the image quality.

Recent advancement in transcranial brain imaging using photoacoustic computed tomography

Photoacoustic computed tomography (PACT) holds great promise for transcranial brain imaging. However, the strong reflection, scattering, and attenuation of acoustic waves by the skull present significant challenges for image reconstruction. In this talk, we will review our recent progress on transcranial PACT image reconstruction. Our contributions include the following: (1) development of a methodology to establish a discrete transcranial PACT image model by use of adjunct X-ray CT data; (2) development of image reconstruction methods that can compensate for speed-of-sound and density variations within the skull; and (3) a detailed investigation of the role of shear waves in transcranial PACT. Computer-simulated and experimental data are employed to demonstrate the feasibility of transcranial PACT.

Experimental study on the application of a compressed-sensing (CS) algorithm to dental cone-beam CT (CBCT) for accurate, low-dose image reconstruction

In practical applications of three-dimensional (3D) tomographic imaging, there are often challenges for image reconstruction from insufficient data. In computed tomography (CT); for example, image reconstruction from few views would enable fast scanning with reduced doses to the patient. In this study, we investigated and implemented an efficient reconstruction method based on a compressed-sensing (CS) algorithm, which exploits the sparseness of the gradient image with substantially high accuracy, for accurate, low-dose dental cone-beam CT (CBCT) reconstruction. We applied the algorithm to a commercially-available dental CBCT system (Expert7™, Vatech Co., Korea) and performed experimental works to demonstrate the algorithm for image reconstruction in insufficient sampling problems. We successfully reconstructed CBCT images from several undersampled data and evaluated the reconstruction quality in terms of the universal-quality index (UQI). Experimental demonstrations of the CS-based reconstruction algorithm appear to show that it can be applied to current dental CBCT systems for reducing imaging doses and improving the image quality.

THREE-DIMENSIONAL NONLINEAR INVERSION OF ELECTRICAL CAPACITANCE TOMOGRAPHY DATA USING A COMPLETE SENSOR MODEL

Electrical Capacitance Tomography (ECT) is a non- invasive and non-destructive imaging technique that uses electrical capacitance measurements at the periphery of an object to generate map of dielectric permittivity of the object. This visualization method is a relatively mature industrial process tomography technique, especially in 2D imaging mode. Volumetric ECT is a new method that poses major computational challenges in image reconstruction and new challenges in sensor design. This paper shows a nonlinear image reconstruction method for 3D ECT based on a validated forward model. The method is based on the flnite element approximation for the complete sensor model and the solution of the inverse problem with nonlinear iterative reconstruction. The nonlinear algorithm has been tested against some complicated experimental test cases, and it has been demonstrated that by using an improved forward model and nonlinear inversion method, very complex shaped samples can be reconstructed. The reconstruction of very complex geometry with objects in the shape of letters H, A, L and T is extremely promising for the applications of 3D ECT.

TU‐D‐L100J‐04: Respiration Motion Correction in Helical Tomotherapy Imaging

Purpose: In helical tomotherapy, Megavoltage CT (MVCT) images are acquired at a rate of one transverse slice per five seconds. The slow acquisition of raw data results in CT images with varying degrees of motion‐induced artifacts. In addition, the high energy of the x‐ray beam (4–6 MV) presents unique challenges for image reconstruction. The purpose of this work was to correct for motion in the superior‐inferior (S/I) direction using retrospective manipulation of the unprocessed sinogram data. Method and Materials: A respiratory motion platform with a S/I motion range of 3‐cm, and periods of 2, 4 and 6 seconds was used to move an ACR CT accreditation phantom and a anthropomorphic thorax phantom in the superior‐inferior direction. The motion of the respiratory platform was measured using the Varian respiratory gating system. Sinogram data was acquired axially with the couch stationary for 5 complete rotations of the gantry. The respiration cycle was synchronized to the projections in the raw sinogram so that the position of the moving object was known in each projection. Motion correction was performed by identifying and extracting projections occurring within a specified respiration phase, correcting for missing projections, and then reconstructing the CT images. Results: Images acquired with the ACR CT phantom showed considerable improvement with motion correction using temporal re‐binning over the uncorrected images. Images acquired with the RANDO phantom suffered from greater image artifacts than the ACR phantom because of the larger variation in the phantom's anatomy over the 3‐cm range of S/I motion. Improvement was seen for each period, however only slight improvement was seen for the 2‐second period of motion. Conclusion: Temporal re‐binning can successfully reduce S/I motion artifacts in reconstructed MVCT images. Conflict of Interest: This research is supported by TomoTherapy, Inc.

Recent advances in image reconstruction, coil sensitivity calibration, and coil array design for SMASH and generalized parallel MRI.

Parallel magnetic resonance imaging (MRI) techniques use spatial information from arrays of radiofrequency (RF) detector coils to accelerate imaging. A number of parallel MRI techniques have been described in recent years, and numerous clinical applications are currently being explored. The advent of practical parallel imaging presents various challenges for image reconstruction and RF system design. Recent advances in tailored SiMultaneous Acquisition of Spatial Harmonics (SMASH) image reconstructions are summarized. These advances enable robust SMASH imaging in arbitrary image planes with a wide range of coil array geometries. A generalized formalism is described which may be used to understand the relations between SMASH and SENSE, to derive typical implementations of each as special cases, and to form hybrid techniques combining some of the advantages of both. Accurate knowledge of coil sensitivities is crucial for parallel MRI, and errors in calibration represent one of the most common and the most pernicious sources of error in parallel image reconstructions. As one example, motion of the patient and/or the coil array between the sensitivity reference scan and the accelerated acquisition can lead to calibration errors and reconstruction artifacts. Self-calibrating parallel MRI approaches that address this problem by eliminating the need for external sensitivity references are reviewed. The ultimate achievable signal-to-noise ratio (SNR) for parallel MRI studies is closely tied to the geometry and sensitivity patterns of the coil arrays used for spatial encoding. Several parallel imaging array designs that depart from the traditional model of overlapped adjacent loop elements are described.