Interior micro-CT with an offset detector

Bilateral extended FDK: An improved weighting method for static CT imaging

BACKGROUND

Improving imaging speed has always been the focus of research in CT technology, which is related to the radiation dose and imaging quality of moving organs, including heart and blood vessels. However, it is difficult to achieve further improvement by increasing the rotation speed of the gantry due to its structural strength limitation. Differing from the conventional CTs, the static CT employs dozens of ray sources to acquire projection data from different angular ranges, and each source only needs to be rotated in a small range to finish a full 360° scan, thus greatly increasing the scanning speed.

PURPOSE

As sources of static CT need to be evenly distributed over 360°, the sources and detectors have to be arranged on two parallel rings independently. Such a geometry can be considered as a special case of CT systems with a significantly large cone angle, that is, a part of the detector is missing in the vicinity of the mid-plane. Due to restriction of upper and lower bounds of the cone angle of the static CT, there are uneven projection data varying in each portion of the reconstruction volume, the conventional analytical or iterative reconstruction methods may introduce artifacts in the reconstructed outcomes.

METHODS

Following the weighting approach extended FDK (xFDK) by Grimmer et al., we propose an improved bilateral xFDK algorithm (bixFDK), which focuses on the reconstruction of the expanded volume. With the same philosophy as xFDK in terms of weighting function design, bixFDK takes the longitudinal offset of the detector with respect to the source into consideration, making our method applicable to a wide range of CT geometries, especially for the static CT. Based on the proposed bixFDK, a new iterative scheme bixFDK-IR is also constructed to extend the applications to a wide range of scan protocols such as sparse-view scan.

RESULTS

The proposed method has been validated with the simulated phantom data and the actual clinical data of the static CT, and demonstrates that it can ensure good image quality and enlarge the reconstruction volume in z-direction of the static CT.

CONCLUSIONS

The bixFDK algorithm is an ideal reconstruction approach for static CT geometry, and the iterative scheme of bixFDK-IR is applicable to a wide range of CT geometries and scan protocols, thus providing a wide range of application scenarios.

End-to-end memory-efficient reconstruction for cone beam CT

BACKGROUND

Cone beam computed tomography (CBCT) plays an important role in many medical fields nowadays. Unfortunately, the potential of this imaging modality is hampered by lower image quality compared to the conventional CT, and producing accurate reconstructions remains challenging. A lot of recent research has been directed towards reconstruction methods relying on deep learning, which have shown great promise for various imaging modalities. However, practical application of deep learning to CBCT reconstruction is complicated by several issues, such as exceedingly high memory costs of deep learning methods when working with fully 3D data. Additionally, deep learning methods proposed in the literature are often trained and evaluated only on data from a specific region of interest, thus raising concerns about possible lack of generalization to other regions.

PURPOSE

In this work, we aim to address these limitations and propose LIRE: a learned invertible primal-dual iterative scheme for CBCT reconstruction.

METHODS

LIRE is a learned invertible primal-dual iterative scheme for CBCT reconstruction, wherein we employ a U-Net architecture in each primal block and a residual convolutional neural network (CNN) architecture in each dual block. Memory requirements of the network are substantially reduced while preserving its expressive power through a combination of invertible residual primal-dual blocks and patch-wise computations inside each of the blocks during both forward and backward pass. These techniques enable us to train on data with isotropic 2 mm voxel spacing, clinically-relevant projection count and detector panel resolution on current hardware with 24 GB video random access memory (VRAM).

RESULTS

Two LIRE models for small and for large field-of-view (FoV) setting were trained and validated on a set of 260 + 22 thorax CT scans and tested using a set of 142 thorax CT scans plus an out-of-distribution dataset of 79 head and neck CT scans. For both settings, our method surpasses the classical methods and the deep learning baselines on both test sets. On the thorax CT set, our method achieves peak signal-to-noise ratio (PSNR) of 33.84 ± 2.28 for the small FoV setting and 35.14 ± 2.69 for the large FoV setting; U-Net baseline achieves PSNR of 33.08 ± 1.75 and 34.29 ± 2.71 respectively. On the head and neck CT set, our method achieves PSNR of 39.35 ± 1.75 for the small FoV setting and 41.21 ± 1.41 for the large FoV setting; U-Net baseline achieves PSNR of 33.08 ± 1.75 and 34.29 ± 2.71 respectively. Additionally, we demonstrate that LIRE can be finetuned to reconstruct high-resolution CBCT data with the same geometry but 1 mm voxel spacing and higher detector panel resolution, where it outperforms the U-Net baseline as well.

CONCLUSIONS

Learned invertible primal-dual schemes with additional memory optimizations can be trained to reconstruct CBCT volumes directly from the projection data with clinically-relevant geometry and resolution. Such methods can offer better reconstruction quality and generalization compared to classical deep learning baselines.

SHCT: segmented helical computed tomography based on multiple slant source-translation

Micro-computed tomography (Micro-CT) is inevitably required to inspect long large objects with high resolution. It is well known that helical CT solves the so-called "long object" problem, but it requires that the measured object be strictly located in the lateral field of view (FOV). Therefore, developing a novel scanning method to extend the FOV in both the lateral and axial directions (i.e., the large helical FOV) is necessary. Recently, due to the application of linearly distributed source arrays and the characteristics of easy extension of the FOV and engineering implementation, straight-line scanning systems have attracted much attention. In this paper, we propose a segmented helical computed tomography (SHCT) based on multiple slant source-translation. SHCT can readily extend the helical FOV by adjusting the source slant translation (SST) length, pitch (or elevation of the SST trajectory), and number of scanning circles. In SHCT, each projection view is truncated laterally and axially, but the projection data set within the cylindrical FOV region is complete. To ensure reconstruction efficiency and avoid the lateral truncation, we propose a generalized backprojection-filtration (G-BPF) algorithm for SHCT approximate reconstruction. Experimental results verify the effectiveness of the proposed SHCT methods for imaging large and long objects. As the pitch decreases, the proposed SHCT methods can reconstruct competitive, high-quality volumes.

Analytical reconstructions of full-scan multiple source-translation computed tomography under large field of views

This paper is to investigate the high-quality analytical reconstructions of multiple source-translation computed tomography (mSTCT) under an extended field of view (FOV). Under the larger FOVs, the previously proposed backprojection filtration (BPF) algorithms for mSTCT, including D-BPF and S-BPF (their differences are different derivate directions along the detector and source, respectively), make some errors and artifacts in the reconstructed images due to a backprojection weighting factor and the half-scan mode, which deviates from the intention of mSTCT imaging. In this paper, to achieve reconstruction with as little error as possible under the extremely extended FOV, we combine the full-scan mSTCT (F-mSTCT) geometry with the previous BPF algorithms to study the performance and derive a suitable redundancy-weighted function for F-mSTCT. The experimental results indicate FS-BPF can get high-quality, stable images under the extremely extended FOV of imaging a large object, though it requires more projections than FD-BPF. Finally, for different practical requirements in extending FOV imaging, we give suggestions on algorithm selection.

Isometric spiracular scaling in scarab beetles: implications for diffusive and advective oxygen transport

The scaling of respiratory structures has been hypothesized to be a major driving factor in the evolution of many aspects of animal physiology. Here, we provide the first assessment of the scaling of the spiracles in insects using 10 scarab beetle species differing 180× in mass, including some of the most massive extant insect species. Using X-ray microtomography, we measured the cross-sectional area and depth of all eight spiracles, enabling the calculation of their diffusive and advective capacities. Each of these metrics scaled with geometric isometry. Because diffusive capacities scale with lower slopes than metabolic rates, the largest beetles measured require 10-fold higher P_O2 gradients across the spiracles to sustain metabolism by diffusion compared to the smallest species. Large beetles can exchange sufficient oxygen for resting metabolism by diffusion across the spiracles, but not during flight. In contrast, spiracular advective capacities scale similarly or more steeply than metabolic rates, so spiracular advective capacities should match or exceed respiratory demands in the largest beetles. These data illustrate a general principle of gas exchange: scaling of respiratory transport structures with geometric isometry diminishes the potential for diffusive gas exchange but enhances advective capacities; combining such structural scaling with muscle-driven ventilation allows larger animals to achieve high metabolic rates when active.

Multi source translation based projection completion for interior region of interest tomography with CBCT

Interior tomography by rotary computed tomography (RCT) is an effective method to improve the detection efficiency and achieve high-resolution imaging for the region of interest (ROI) within a large-scale object. However, because only the X-rays through the ROI can be received by detector, the projection data is inevitably truncated, resulting in truncation artifacts in the reconstructed image. When the ROI is totally within the object, the solution of the problem is not unique, which is named interior problem. Fortunately, projection completion (PC) is an effective technique to solve the interior problem. In this study, we proposed a multi source translation CT based PC method (mSTCT-PC) to cope with the interior problem. Firstly, mSTCT-PC employs multi-source translation to sparsely obtain the global projection which covered the whole object. Secondly, the sparse global projection is utilized to fill up the truncated projection of ROI. The global projection and truncated projection are obtained under the same geometric parameters. Therefore, it omits the registration of projection. To verify the feasibility of this method, simulation and practical experiments were implemented. Compared with the results of ROI reconstructed by filtered back-projection (FBP), simultaneous iterative reconstruction technique-total variation (SIRT-TV) and the multi-resolution based method (mR-PC), the proposed mSTCT-PC is good at mitigating truncation artifacts, preserving details and improving the accuracy of ROI images.

Extended imaging volume in cone-beam x-ray tomography using the weighted simultaneous iterative reconstruction technique

An issue in computerized x-ray tomography is the limited size of available detectors relative to objects of interest. A solution was provided in the past two decades by positioning the detector in a lateral offset position, increasing the effective field of view (FOV) and thus the diameter of the reconstructed volume. However, this introduced artifacts in the obtained reconstructions, caused by projection truncation and data redundancy. These issues can be addressed by incorporating an additional data weighting step in the reconstruction algorithms, known as redundancy weighting. In this work, we present an implementation of redundancy weighting in the widely-used simultaneous iterative reconstruction technique (SIRT), yielding the weighted SIRT (W-SIRT) method. The new technique is validated using geometric phantoms and a rabbit specimen, by performing both simulation studies as well as physical experiments. The experiments are carried out in a highly flexible stereoscopic x-ray system equipped with x-ray image intensifiers (XRIIs). The simulations showed that higher values of contrast-to-noise ratio could be obtained using the W-SIRT approach as compared to a weighted implementation of the simultaneous algebraic reconstruction technique (SART). The convergence rate of the W-SIRT was accelerated by including a relaxation parameter in the W-SIRT algorithm, creating the aW-SIRT algorithm. This allowed to obtain the same results as the W-SIRT algorithm, but at half the number of iterations, yielding a much shorter computation time. The aW-SIRT algorithm has proven to perform well for both large as well as small regions of overlap, outperforming the pre-convolutional Feldkamp-David-Kress algorithm for small overlap regions (or large detector offsets). The experiments confirmed the results of the simulations. Using the aW-SIRT algorithm, the effective FOV was increased by >75%, only limited by experimental constraints. Although an XRII is used in this work, the method readily applies to flat-panel detectors as well.

Interior tomographic imaging of mouse heart in a carbon nanotube micro-CT

BACKGROUND

The relatively high radiation dose from micro-CT is a cause for concern in preclinical research involving animal subjects. Interior region-of-interest (ROI) imaging was proposed for dose reduction, but has not been experimentally applied in micro-CT.

OBJECTIVE

Our aim is to implement interior ROI imaging in a carbon nanotube (CNT) x-ray source based micro-CT, and present the ROI image quality and radiation dose reduction for interior cardiac micro-CT imaging of a mouse heart in situ.

METHODS

An aperture collimator was mounted at the source-side to induce a small-sized cone beam (10 mm width) at the isocenter. Interior in situ micro-CT scans were conducted on a mouse carcass and several micro-CT phantoms. A GPU-accelerated hybrid iterative reconstruction algorithm was employed for volumetric image reconstruction. Radiation dose was measured for the same system operated at the interior and global micro-CT modes.

RESULTS

Visual inspection demonstrated comparable image quality between two scan modes. Quantitative evaluation demonstrated high structural similarity index (up to 0.9614) with improved contrast-noise-ratio (CNR) on interior micro-CT mode. Interior micro-CT mode yielded significant reduction (up to 83.9%) for dose length product (DLP).

CONCLUSIONS

This work demonstrates the applicability of using CNT x-ray source based interior micro-CT for preclinical imaging with significantly reduced radiation dose.