Improve Reconstruction Quality Research Articles

A half-covered helical cone-beam reconstruction algorithm based on the Radon inversion transformation

Compared with the traditional helical cone-beam computed tomography (CT), the field-of-view (FOV) half-covered cone-beam CT can almost double the FOV and thus image the large object by using a smaller panel detector. However, the projections are transversely truncated, resulting in truncation errors in reconstructed images if no correction measures are taken. In this paper, a half-covered cone-beam reconstruction algorithm based on the Radon inversion transformation is developed, in which the data filtering is performed in two steps. The first step is a local operation and can be carried out correctly even when the data is truncated. This performance of local operation makes the original data closer to zero, so the continuity of data is improved. And this also can restrain the truncation errors caused by the following global operation. Numerical simulations and experimental results are presented to demonstrate the algorithm and to compare it with existing algorithms. Preliminary results indicate that the proposed algorithm can well restrain the truncation errors and improves reconstruction quality.

Light Field Reconstruction Using Sparsity in the Continuous Fourier Domain

Sparsity in the Fourier domain is an important property that enables the dense reconstruction of signals, such as 4D light fields, from a small set of samples. The sparsity of natural spectra is often derived from continuous arguments, but reconstruction algorithms typically work in the discrete Fourier domain. These algorithms usually assume that sparsity derived from continuous principles will hold under discrete sampling. This article makes the critical observation that sparsity is much greater in the continuous Fourier spectrum than in the discrete spectrum. This difference is caused by a windowing effect. When we sample a signal over a finite window, we convolve its spectrum by an infinite sinc, which destroys much of the sparsity that was in the continuous domain. Based on this observation, we propose an approach to reconstruction that optimizes for sparsity in the continuous Fourier spectrum. We describe the theory behind our approach and discuss how it can be used to reduce sampling requirements and improve reconstruction quality. Finally, we demonstrate the power of our approach by showing how it can be applied to the task of recovering non-Lambertian light fields from a small number of 1D viewpoint trajectories.

Apparent Ultra-High b-Value Diffusion-Weighted Image Reconstruction via Hidden Conditional Random Fields.

A promising, recently explored, alternative to ultra-high b-value diffusion weighted imaging (UHB-DWI) is apparent ultra-high b-value diffusion-weighted image reconstruction (AUHB-DWR), where a computational model is used to assist in the reconstruction of apparent DW images at ultra-high b -values. Firstly, we present a novel approach to AUHB-DWR that aims to improve image quality. We formulate the reconstruction of an apparent DW image as a hidden conditional random field (HCRF) in which tissue model diffusion parameters act as hidden states in this random field. The second contribution of this paper is a new generation of fully connected conditional random fields, called the hidden stochastically fully connected conditional random fields (HSFCRF) that allows for efficient inference with significantly reduced computational complexity via stochastic clique structures. The proposed AUHB-DWR algorithms, HCRF and HSFCRF, are evaluated quantitatively in nine different patient cases using Fisher's criteria, probability of error, and coefficient of variation metrics to validate its effectiveness for the purpose of improving intensity delineation between expert identified suspected cancerous and healthy tissue within the prostate gland. The proposed methods are also examined using a prostate phantom, where the apparent ultra-high b-value DW images reconstructed using the tested AUHB-DWR methods are compared with real captured UHB-DWI. The results illustrate that the proposed AUHB-DWR methods has improved reconstruction quality and improved intensity delineation compared with existing AUHB-DWR approaches.

Rebinned Filtered Back-Projection Reconstruction From Truncated Data for Half-Covered Helical Cone-Beam Computed Tomography

For applications of imaging large objects by small planar detectors in helical cone-beam computed tomography (CT), the field-of-view (FOV) half-covered scanning technique is a preferred choice as it can almost double the FOV with only about half the cross-section of an object irradiated with x-rays at all projection views. However, the projections are transversely truncated, resulting in truncation errors in reconstructed images if no compensatory measures are taken. In this paper, the half-covered single slice rebinning method is developed, in which images are reconstructed by performing two steps of rebinning followed by a three-dimensional (3D) filtered-backprojection (FBP) -based algorithm. The algorithm rebins the half-covered cone-beam projections into parallel-beam data which covers the whole cross-section of the object, thus resulting in nearly no truncation errors in the reconstructed images. Numerical simulations and experimental results are presented to demonstrate the algorithm and to compare it with existing algorithms. Preliminary results indicate the proposed half-covered single slice rebinning algorithm improves reconstruction quality.

L1 regularization for reconstruction of a non-equilibrium Ising model

The couplings in a sparse asymmetric, asynchronous Ising network are reconstructed using an exact learning algorithm. L1 regularization is used to remove the spurious weak connections that would otherwise be found by simply maximizing the log likelihood of a finite data set. In order to see how L1 regularization works in detail, we perform the calculation in several ways including (1) by iterative minimization of a cost function equal to minus the log likelihood of the data plus an L1 penalty term, and (2) an approximate scheme based on a quadratic expansion of the cost function around its minimum. In these schemes, we track how connections are pruned as the strength of the L1 penalty is increased from zero to large values. The performance of the methods for various coupling strengths is quantified using receiver operating characteristic curves, showing that increasing the coupling strength improves reconstruction quality.

Accelerating sequences in the presence of metal by exploiting the spatial distribution of off‐resonance

To demonstrate feasibility of exploiting the spatial distribution of off-resonance surrounding metallic implants for accelerating multispectral imaging techniques. Multispectral imaging (MSI) techniques perform time-consuming independent three-dimensional acquisitions with varying radio frequency offsets to address the extreme off-resonance from metallic implants. Each off-resonance bin provides a unique spatial sensitivity that is analogous to the sensitivity of a receiver coil and, therefore, provides a unique opportunity for acceleration. Fully sampled MSI was performed to demonstrate retrospective acceleration. A uniform sampling pattern across off-resonance bins was compared with several adaptive sampling strategies using a total hip replacement phantom. Monte Carlo simulations were performed to compare noise propagation of two of these strategies. With a total knee replacement phantom, positive and negative off-resonance bins were strategically sampled with respect to the B0 field to minimize aliasing. Reconstructions were performed with a parallel imaging framework to demonstrate retrospective acceleration. An adaptive sampling scheme dramatically improved reconstruction quality, which was supported by the noise propagation analysis. Independent acceleration of negative and positive off-resonance bins demonstrated reduced overlapping of aliased signal to improve the reconstruction. This work presents the feasibility of acceleration in the presence of metal by exploiting the spatial sensitivities of off-resonance bins.

Reconstruction of Hyperspectral Imagery From Random Projections Using Multihypothesis Prediction

Reconstruction of hyperspectral imagery from spectral random projections is considered. Specifically, multiple predictions drawn for a pixel vector of interest are made from spatially neighboring pixel vectors within an initial non-predicted reconstruction. A two-phase hypothesis-generation procedure based on partitioning and merging of spectral bands according to the correlation coefficients between bands is proposed to fine-tune the hypotheses. The resulting prediction is used to generate a residual in the projection domain. This residual being typically more compressible than the original pixel vector leads to improved reconstruction quality. To appropriately weight the hypothesis predictions, a distance-weighted Tikhonov regularization to an ill-posed least-squares optimization is proposed. Experimental results demonstrate that the proposed reconstruction significantly outperforms alternative strategies not employing multihypothesis prediction.

Adaptive zooming in X-ray computed tomography

In computed tomography (CT), the source-detector system commonly rotates around the object in a circular trajectory. Such a trajectory does not allow to exploit a detector fully when scanning elongated objects. Increase the spatial resolution of the reconstructed image by optimal zooming during scanning. A new approach is proposed, in which the full width of the detector is exploited for every projection angle. This approach is based on the use of prior information about the object's convex hull to move the source as close as possible to the object, while avoiding truncation of the projections. Experiments show that the proposed approach can significantly improve reconstruction quality, producing reconstructions with smaller errors and revealing more details in the object. The proposed approach can lead to more accurate reconstructions and increased spatial resolution in the object compared to the conventional circular trajectory.

Phase reconstruction from velocity-encoded MRI measurements – A survey of sparsity-promoting variational approaches

In recent years there has been significant developments in the reconstruction of magnetic resonance velocity images from sub-sampled k-space data. While showing a strong improvement in reconstruction quality compared to classical approaches, the vast number of different methods, and the challenges in setting them up, often leaves the user with the difficult task of choosing the correct approach, or more importantly, not selecting a poor approach. In this paper, we survey variational approaches for the reconstruction of phase-encoded magnetic resonance velocity images from sub-sampled k-space data. We are particularly interested in regularisers that correctly treat both smooth and geometric features of the image. These features are common to velocity imaging, where the flow field will be smooth but interfaces between the fluid and surrounding material will be sharp, but are challenging to represent sparsely. As an example we demonstrate the variational approaches on velocity imaging of water flowing through a packed bed of solid particles. We evaluate Wavelet regularisation against Total Variation and the relatively recent second order Total Generalised Variation regularisation. We combine these regularisation schemes with a contrast enhancement approach called Bregman iteration. We verify for a variety of sampling patterns that Morozov’s discrepancy principle provides a good criterion for stopping the iterations. Therefore, given only the noise level, we present a robust guideline for setting up a variational reconstruction scheme for MR velocity imaging.

Compressive video sensing with limited measurements

Compressive sensing (CS) is an innovative technology, allowing us to capture signals with significantly fewer samples than those required by classical Nyquist theory. We propose a novel adaptive video compressive sensing algorithm to exploit the potential of CS in video acquisition. Each frame is divided into blocks to take advantage of its inhomogeneity. We first classify the blocks into one of three types based on their texture complexity and their temporal difference from neighboring frames based on which we determine the number of required measurements. In the reconstruction process, we use the measurements made for the later frames to assist the recovery of previous ones, thus ensuring improved reconstruction quality even when the number of measurements for each frame is limited. Our experimental results demonstrate that we not only obtain significant visual quality improvement but also achieve at least 2.5 dB gain in peak signal-to-noise ratio compared with the existing video compressive sensing algorithms.

Vector reconstruction of multicomponent seismic data

A multicomponent seismic record was transformed to the frequency-wavenumber domain using the quaternion Fourier transform. This transform was integrated into the projection onto convex sets algorithm to allow for the reconstruction of vector signals. The method took advantage of the spectral overlap of components in the frequency-wavenumber domain. The results of the method were compared with standard component-by-component reconstruction. Results were found for synthetic and real data including an example of 5D reconstruction of a converted-wave data set acquired over a heavy oil reservoir. We found an improvement in reconstruction quality producing fully sampled radial and transverse offset-azimuth gathers with a preserved vector relationship.

Reconstruction of magnetic resonance imaging by three‐dimensional dual‐dictionary learning

To improve the magnetic resonance imaging (MRI) data acquisition speed while maintaining the reconstruction quality, a novel method is proposed for multislice MRI reconstruction from undersampled k-space data based on compressed-sensing theory using dictionary learning. There are two aspects to improve the reconstruction quality. One is that spatial correlation among slices is used by extending the atoms in dictionary learning from patches to blocks. The other is that the dictionary-learning scheme is used at two resolution levels; i.e., a low-resolution dictionary is used for sparse coding and a high-resolution dictionary is used for image updating. Numerical experiments are carried out on in vivo 3D MR images of brains and abdomens with a variety of undersampling schemes and ratios. The proposed method (dual-DLMRI) achieves better reconstruction quality than conventional reconstruction methods, with the peak signal-to-noise ratio being 7 dB higher. The advantages of the dual dictionaries are obvious compared with the single dictionary. Parameter variations ranging from 50% to 200% only bias the image quality within 15% in terms of the peak signal-to-noise ratio. Dual-DLMRI effectively uses the a priori information in the dual-dictionary scheme and provides dramatically improved reconstruction quality.

Structural reliability of road accidents reconstruction

Reconstruction of road accidents combines objective and subjective action. The former concerns science, the latter assessment of human behavior in the context of objective findings. It is not uncommon for experts equipped with an arsenal of tools to obtain similar results of calculations, but to present radically different conclusions about the cause of the accident. The use of sophisticated methods of uncertainty analysis does not guarantee improvement in quality of reconstruction, because, increasingly, the most serious source of reduced reliability of reconstruction is problems in logical inference. In the article the structure of uncertainty and reliability of accident reconstruction was described. A definition of reliability of road accident reconstruction based on the theory of conditional probability and Bayesian network, as a function of modeling, data and expert reliability (defined in the text) was proposed. The uncertainty of reconstruction was made dependent only on the uncertainty of the data. This separation makes it possible to conduct a qualitative and quantitative analysis of reconstruction reliability and to analyze its sensitivity to component parameters, independently of the uncertainty analysis. An example of calculation was presented. The proposed formalism constitutes a tool helpful to explain, among other things, the paradox of reliable reconstruction despite its uncertain results or unreliable reconstruction despite high precision of results. This approach is of great importance in the reconstruction of road accidents, which goes far beyond the analysis of a single, homogeneous subsystem.

Improved Reconstruction Quality of Bioluminescent Images by Combining SP3Equations and Bregman Iteration Method

Bioluminescence tomography (BLT) has a great potential to provide a powerful tool for tumor detection, monitoring tumor therapy progress, and drug development; developing new reconstruction algorithms will advance the technique to practical applications. In the paper, we propose a BLT reconstruction algorithm by combining SP3 equations and Bregman iteration method to improve the quality of reconstructed sources. The numerical results for homogeneous and heterogeneous phantoms are very encouraging and give significant improvement over the algorithms without the use of SP3 equations and Bregman iteration method.

Accelerated three-dimensional free-breathing first pass cardiac perfusion at 1.5T

Background Perfusion CMR provides an important diagnostic tool for the detection of myocardial ischemia in patients with known or suspected CAD [1]. Conventionally, 2D multislice imaging with 2-4 short-axis slices is often used for evaluation of left ventricular perfusion. 3D CMR perfusion has been proposed for its superior coverage and higher SNR [2]. However, accelerated imaging is required for adequate spatio-temporal resolution. Recent studies in accelerated CMR have shown that utilizing local information in the imaging volume significantly improves reconstruction quality [3,4]. However, such highly accelerated k-t based techniques used for 3D perfusion [3] require breath-hold acquisitions, which may be difficult following physical stress or for some patients. In this study, we sought to develop a compressed-sensing (CS) based image reconstruction technique utilizing local imaging features for highly-accelerated 3D free-breathing perfusion CMR.

MAP estimation with structural priors for fluorescence molecular tomography

Fluorescence molecular tomography (FMT) is an attractive imaging tool for quantitatively and three-dimensionally resolving fluorophore distributions in small animals, but it suffers from low spatial resolution due to its inherent ill-posed nature. Structural priors obtained from a secondary modality system such as x-ray computed tomography or magnetic resonance imaging can help to improve FMT reconstruction results. However, challenge remains in how to fully take advantage of the structural priors while effectively avoid undesirable influence caused by an immoderate usage. In this paper, we propose a new method to resolve the FMT inverse problem based on maximum a posteriori (MAP) estimation with structural priors (MAP-SP) in a Bayesian framework. Instead of imposing the structural priors directly on the reconstruction results, the MAP-SP method utilizes them to constrain the unknown hyperparameters of the prior information model which is essential for the Bayesian framework. Then, a low dimensional inverse problem and an alternating optimization scheme are used to automatically calculate the unknown hyperparameters, which make the FMT reconstruction process self-adaptive. Simulation and phantom results show that the proposed MAP-SP method can effectively make use of the structural priors and leads to improvements in reconstruction quality as compared with traditional regularization methods.

Non-uniform optical transfer functions in particle imaging: calibration and application to tomographic reconstruction

A new approach to the weighting function, which describes particle imaging in tomographic reconstruction, is introduced. Instead of assuming a spatially homogeneous mapping function of voxels to the images, a variable optical transfer function (OTF) is applied. By this method, the negative effects of optical distortions on the reconstruction can be reduced considerably. The effects of these improvements in reconstruction quality on the methods of tomographic particle imaging velocimetry, as well as 3D particle tracking are investigated. A method to calibrate the OTF to experimental circumstances is proposed as an additional step to the volume self-calibration. It is shown that this kind of calibration is able to capture the predominant particle imaging both for simulated as well as experimental data. The most common distortions of particle imaging are blurring due to a small depth of field and astigmatism due to imaging optics. The effects of both of these distortions on reconstruction and correlation quality are investigated via simulated data. In both cases, a strong influence on relevant parameters can be seen. Reconstructions using a spatially varying OTF, calibrated to the imaging conditions, show a significant improvement in reconstruction quality and the accuracy of the particle peak position, as well as in the accuracy of the gained displacement vector field when using two time steps. Evaluation of experimental data by PTV methods shows a reduction in ghost particle intensity and improvements in peak position accuracy. A computationally efficient method of applying the OTF to tomographic reconstruction is introduced.

A generalization for optimized phase retrieval algorithms

In this work, we demonstrate an improved method for iterative phase retrieval with application to coherent diffractive imaging. By introducing additional operations inside the support term of existing iterated projection algorithms, we demonstrate improved convergence speed, higher success rate and, in some cases, improved reconstruction quality. New algorithms take a particularly simple form with the introduction of a generalized projection-based reflector. Numerical simulations verify that these new algorithms surpass the current standards without adding complexity to the reconstruction process. Thus the introduction of this new class of algorithms offers a new array of methods for efficiently deconvolving intricate data.

A regularisation scheme for electromagnetic inverse problems: application to crack detection in civil structures

A new regularisation scheme is proposed for the solution of the inverse scattering problem arising in electromagnetic imaging. The aim is to efficiently reconstruct defects and cracks with sharp boundaries when inspecting civil structures. The approach is developed in Banach spaces and tends to alleviate the over-smoothing effects of the usually adopted regularisation schemes in Hilbert spaces. Preliminary numerical results concerning the tomographic reconstruction of defects in cylindrical models made of cement paste are reported and discussed. The improved reconstruction quality (in terms of the reduction in the smoothing effect) is proven in those cases.

Superior Reconstruction Quality Improvement of CT Image for Bias Correction Variance Measures

Image segmentation algorithms based on ROI typically rely on the homogeneity of image intensities. CT scanner is dedicated as research Scanner which has been developed in view of imaging applications. A key Feature of the work is to use Empirical system to achieve resolution recovery with novel region based method. This method identifies local intensity cluster with local clustering criterion function with respect to neighborhood center. Reconstruction quality is analyzed quantitatively in terms bias field correction for intensity inhomogenity correction. This method is valid on synthetic images of various imaging modalities. A significant improvement in reconstruction quality can be realized by faster and more accurate visual quality quantitative measures where Reconstruction quality is analyzed quantitatively in terms of bias-variance measures (bar phantom) and mean square error (lesion phantom). However, with the inclusion of the empirical kernel, the iterative algorithms provide superior reconstructions compared to FBP, both in terms of visual quality and quantitative measures. Simulated results show improved tumor bias and variance characteristics with the proposed algorithm.