A Forward Regridding Method With Minimal Oversampling for Accurate and Efficient Iterative Tomographic Algorithms

Improving image quality with deep learning image reconstruction in double-low-dose head CT angiography compared with standard dose and adaptive statistical iterative reconstruction

OBJECTIVE

To demonstrate similar image quality with deep learning image reconstruction (DLIR) in reduced contrast medium (CM) and radiation dose (double-low-dose) head CT angiography (CTA), in comparison with standard-dose and adaptive statistical iterative reconstruction-Veo (ASIR-V).

METHODS

A prospective study was performed in 63 patients who under head CTA using 256-slice CT. Patients were randomized into either the standard-dose group (n = 38) with 40 ml of Iopromide (370 mgI ml-1 at 4.5 ml s-1); or a double-low-dose group (n = 25) with CM of 25 ml at 3.0 ml s-1. For image reconstruction, the double-low-dose group used DLIR-M and DLIR-H strength, and the standard-dose group used ASIR-V with 50% strength. The CT value and standard deviation, signal-to-noise ratio and contrast-to-noise ratio of posterior fossa, neck muscles, carotid, vertebral and middle cerebral arteries were measured. The image noise, vessel edge and structure blurring and overall image quality were assessed by using a 5-grade method. The double-low-dose group reduced CM dose by 37.5% and CT dose index by 41% compared with the standard-dose group. DLIR further reduced the standard deviation value of the middle cerebral artery and posterior fossa and provided better overall subjective image quality (p < 0.05).

CONCLUSION

DLIR significantly reduces image noise and provides higher overall image quality in the double-low-dose CTA.

ADVANCES IN KNOWLEDGE

It is feasible to reduce CM dose by 37.5% and volume CT dose index by 41% with the combination of 80 kVp and DLIR in head CTA. Compared with ASIR-V, DLIR further reduces image noise and achieves better image quality with reduced contrast and radiation dose.

Computationally Efficient System Matrix Calculation Techniques in Computed Tomography Iterative Reconstruction

Background:

Relative to classical methods in computed tomography, iterative reconstruction techniques enable significantly improved image qualities and/or lowered patient doses. However, the computational speed is a major concern for these iterative techniques. In the present study, we present a method for fast system matrix calculation based on the line integral model (LIM) to speed up the computations without compromising the image quality. In addition, we develop a hybrid line–area integral model (AIM) that highlights the advantages of both LIM and AIMs.

Methods:

The contributing detectors for a given pixel and a given projection view, and the length of corresponding intersection lines with pixels, are calculated using our proposed algorithm. For the hybrid method, the respective narrow-angle fan beam was modeled by multiple equally spaced lines. The computed system matrix was evaluated in the context of reconstruction using the simultaneous algebraic reconstruction technique (SART) as well as maximum likelihood expectation maximization (MLEM).

Results:

The proposed LIM offers a considerable reduction in calculation times compared to the standard Siddon algorithm: 2.9 times faster. Differences in root mean square error and peak signal-to-noise ratio were not significant between the proposed LIM and the Siddon algorithm for both SART and MLEM reconstruction methods (P > 0.05). Meanwhile, the proposed hybrid method resulted in significantly improved image qualities relative to LIM and the Siddon algorithm (P < 0.05), though computations were 4.9 times more intensive than the proposed LIM.

Conclusion:

We have proposed two fast algorithms to calculate the system matrix. The first is based on LIM and was faster than the Siddon algorithm, with matched image quality, whereas the second method is a hybrid LIM–AIM that achieves significantly improved images though with its computational requirements.

A Backprojection Slice Theorem for Tomographic Reconstruction

Fast image reconstruction techniques are becoming important with the increasing number of scientific cases in high resolution micro and nano tomography. The processing of the large scale 3D data demands new mathematical tools for the tomographic reconstruction. Due to the high computational complexity of most current algorithms, big data sizes demands powerful hardware and more sophisticated numerical techniques. Several reconstruction algorithms are dependent on a mathematical tool called backprojection (a transposition process). A conventional implementation of the backprojection operator has cubic computational complexity. In the present manuscript we propose a new fast backprojection operator for the processing of tomographic data, providing a low-cost algorithm for this task. We compare our formula against other fast transposition techniques, using real and simulated large data sets.

Fast projection/backprojection and incremental methods applied to synchrotron light tomographic reconstruction

Iterative methods for tomographic image reconstruction have the computational cost of each iteration dominated by the computation of the (back)projection operator, which take roughly O(N³) floating point operations (flops) for N × N pixels images. Furthermore, classical iterative algorithms may take too many iterations in order to achieve acceptable images, thereby making the use of these techniques unpractical for high-resolution images. Techniques have been developed in the literature in order to reduce the computational cost of the (back)projection operator to O(N²logN) flops. Also, incremental algorithms have been devised that reduce by an order of magnitude the number of iterations required to achieve acceptable images. The present paper introduces an incremental algorithm with a cost of O(N²logN) flops per iteration and applies it to the reconstruction of very large tomographic images obtained from synchrotron light illuminated data.

Fast iterative reconstruction of data in full interior tomography

This paper introduces two novel strategies for iterative reconstruction of full interior tomography (FINT) data, i.e. when the field of view is entirely inside the object support and knowledge of the object support itself or the attenuation coefficients inside specific regions of interest are not available. The first approach is based on data edge-padding. The second technique creates an intermediate virtual sinogram, which is, then, reconstructed by a standard iterative algorithm. Both strategies are validated in the framework of the alternate direction method of multipliers plug-and-play with gridding projectors that provide a speed-up of three orders of magnitude with respect to standard operators implemented in real space. The proposed methods are benchmarked on synchrotron-based X-ray tomographic microscopy datasets of mouse lung alveoli. Compared with analytical techniques, the proposed methods substantially improve the reconstruction quality for FINT underconstrained datasets, facilitating subsequent post-processing steps.

The MAX IV imaging concept

The MAX IV Laboratory is currently the synchrotron X-ray source with the beam of highest brilliance. Four imaging beamlines are in construction or in the project phase. Their common characteristic will be the high acquisition rates of phase-enhanced images. This high data flow will be managed at the local computing cluster jointly with the Swedish National Computing Infrastructure. A common image reconstruction and analysis platform is being designed to offer reliable quantification of the multidimensional images acquired at all the imaging beamlines at MAX IV.

Fast gridding projectors for analytical and iterative tomographic reconstruction of differential phase contrast data

This paper introduces new gridding projectors designed to efficiently perform analytical and iterative tomographic reconstruction, when the forward model is represented by the derivative of the Radon transform. This inverse problem is tightly connected with an emerging X-ray tube- and synchrotron-based imaging technique: differential phase contrast based on a grating interferometer. This study shows, that the proposed projectors, compared to space-based implementations of the same operators, yield high quality analytical and iterative reconstructions, while improving the computational efficiency by few orders of magnitude.

Fast 3D reconstruction method for differential phase contrast X-ray CT

We present a fast algorithm for fully 3D regularized X-ray tomography reconstruction for both traditional and differential phase contrast measurements. In many applications, it is critical to reduce the X-ray dose while producing high-quality reconstructions. Regularization is an excellent way to do this, but in the differential phase contrast case it is usually applied in a slice-by-slice manner. We propose using fully 3D regularization to improve the dose/quality trade-off beyond what is possible using slice-by-slice regularization. To make this computationally feasible, we show that the two computational bottlenecks of our iterative optimization process can be expressed as discrete convolutions; the resulting algorithms for computation of the X-ray adjoint and normal operator are fast and simple alternatives to regridding. We validate this algorithm on an analytical phantom as well as conventional CT and differential phase contrast measurements from two real objects. Compared to the slice-by-slice approach, our algorithm provides a more accurate reconstruction of the analytical phantom and better qualitative appearance on one of the two real datasets.