Design of iterative ROI transmission tomography reconstruction procedures and image quality analysis

Fourier-enhanced high-order total variation (FeHOT) iterative network for interior tomography

Objective. Determining a satisfactory solution for different computed tomography (CT) fields has been a long-standing challenge in the interior tomography, Traditional methods like FBP suffer from low contrast, while deep learning approaches often lack data consistency. The goal is to leverage high-order total variation (HOT) regularization and Fourier-based frequency domain enhancement to achieve high-precision reconstruction from truncated projection data while overcoming limitations such as slow convergence, over-smoothing, and loss of high-frequency details in existing methods.Approach. The proposed Fourier-enhanced HOT (FeHOT) network employs a coarse-to-fine strategy. First, a HOT-based unrolled iterative network accelerates coarse reconstruction using a learned primal-dual algorithm for data consistency and implicit high-order gradient constraints. Second, a Fourier-enhanced U-Net module selectively attenuates low-frequency components in skip connections while amplifying high-frequency features from filtered back-projection (FBP) results, preserving edge and texture details. Frequency-dependent scaling factors are introduced to balance spectral components during refinement.Main Results. Experiments on the AAPM and clinical medical datasets demonstrate FeHOT's superiority over competing methods (FBP, HOT, AG-Net, PD-Net). For the medical dataset, FeHOT achieved PSNR = 41.17 (noise-free) and 39.24 (noisy), outperforming PD-Net (33.42/31.08) and AG-Net (33.41/31.31). Meanwhile, For the AAPM dataset, where imaged objects exhibit piecewise constant properties, first-order total variation achieved satisfactory results. In contrast, for clinical medical datasets with non-piecewise-constant characteristics (e.g. complex anatomical structures), FeHOT's second-order regularization better aligned with the high-quality requirements of interior tomography. Ablation studies confirmed the necessity of Fourier enhancement, showing significant improvements in edge preservation (e.g. SSIM increased from 0.9877 to 0.9976 for noise-free cases). The method achieved high-quality reconstruction within five iterations, reducing computational costs.Significance. FeHOT represents a paradigm shift in interior tomography by: 1) Bridging classical HOT theory with deep learning through an iterative unrolling framework. 2) Introducing frequency-domain operations to overcome the limitations of polynomial/piecewise-constant assumptions in CT images. 3) Enabling high-quality reconstruction in just five iterations, balancing computational efficiency with accuracy. This method offers a promising solution for low-dose, precise imaging in clinical and industrial applications.

Multiresolution iterative reconstruction in high-resolution extremity cone-beam CT

Application of model-based iterative reconstruction (MBIR) to high resolution cone-beam CT (CBCT) is computationally challenging because of the very fine discretization (voxel size  <100 µm) of the reconstructed volume. Moreover, standard MBIR techniques require that the complete transaxial support for the acquired projections is reconstructed, thus precluding acceleration by restricting the reconstruction to a region-of-interest. To reduce the computational burden of high resolution MBIR, we propose a multiresolution penalized-weighted least squares (PWLS) algorithm, where the volume is parameterized as a union of fine and coarse voxel grids as well as selective binning of detector pixels. We introduce a penalty function designed to regularize across the boundaries between the two grids. The algorithm was evaluated in simulation studies emulating an extremity CBCT system and in a physical study on a test-bench. Artifacts arising from the mismatched discretization of the fine and coarse sub-volumes were investigated. The fine grid region was parameterized using 0.15 mm voxels and the voxel size in the coarse grid region was varied by changing a downsampling factor. No significant artifacts were found in either of the regions for downsampling factors of up to 4×. For a typical extremities CBCT volume size, this downsampling corresponds to an acceleration of the reconstruction that is more than five times faster than a brute force solution that applies fine voxel parameterization to the entire volume. For certain configurations of the coarse and fine grid regions, in particular when the boundary between the regions does not cross high attenuation gradients, downsampling factors as high as 10×  can be used without introducing artifacts, yielding a ~50×  speedup in PWLS. The proposed multiresolution algorithm significantly reduces the computational burden of high resolution iterative CBCT reconstruction and can be extended to other applications of MBIR where computationally expensive, high-fidelity forward models are applied only to a sub-region of the field-of-view.

Statistical iterative reconstruction to improve image quality for digital breast tomosynthesis

PURPOSE

Digital breast tomosynthesis (DBT) is a novel modality with the potential to improve early detection of breast cancer by providing three-dimensional (3D) imaging with a low radiation dose. 3D image reconstruction presents some challenges: cone-beam and flat-panel geometry, and highly incomplete sampling. A promising means of overcome these challenges is statistical iterative reconstruction (IR), since it provides the flexibility of accurate physics modeling and a general description of system geometry. The authors' goal was to develop techniques for applying statistical IR to tomosynthesis imaging data.

METHODS

These techniques include the following: a physics model with a local voxel-pair based prior with flexible parameters to fine-tune image quality; a precomputed parameter λ in the prior, to remove data dependence and to achieve a uniform resolution property; an effective ray-driven technique to compute the forward and backprojection; and an oversampled, ray-driven method to perform high resolution reconstruction with a practical region-of-interest technique. To assess the performance of these techniques, the authors acquired phantom data on the stationary DBT prototype system. To solve the estimation problem, the authors proposed an optimization-transfer based algorithm framework that potentially allows fewer iterations to achieve an acceptably converged reconstruction.

RESULTS

IR improved the detectability of low-contrast and small microcalcifications, reduced cross-plane artifacts, improved spatial resolution, and lowered noise in reconstructed images.

CONCLUSIONS

Although the computational load remains a significant challenge for practical development, the superior image quality provided by statistical IR, combined with advancing computational techniques, may bring benefits to screening, diagnostics, and intraoperative imaging in clinical applications.

Modelling the physics in the iterative reconstruction for transmission computed tomography

There is an increasing interest in iterative reconstruction (IR) as a key tool to improve quality and increase applicability of x-ray CT imaging. IR has the ability to significantly reduce patient dose; it provides the flexibility to reconstruct images from arbitrary x-ray system geometries and allows one to include detailed models of photon transport and detection physics to accurately correct for a wide variety of image degrading effects. This paper reviews discretization issues and modelling of finite spatial resolution, Compton scatter in the scanned object, data noise and the energy spectrum. The widespread implementation of IR with a highly accurate model-based correction, however, still requires significant effort. In addition, new hardware will provide new opportunities and challenges to improve CT with new modelling.

Modelling the physics in the iterative reconstruction for transmission computed tomography

There is an increasing interest in iterative reconstruction (IR) as a key tool to improve quality and increase applicability of x-ray CT imaging. IR has the ability to significantly reduce patient dose; it provides the flexibility to reconstruct images from arbitrary x-ray system geometries and allows one to include detailed models of photon transport and detection physics to accurately correct for a wide variety of image degrading effects. This paper reviews discretization issues and modelling of finite spatial resolution, Compton scatter in the scanned object, data noise and the energy spectrum. The widespread implementation of IR with a highly accurate model-based correction, however, still requires significant effort. In addition, new hardware will provide new opportunities and challenges to improve CT with new modelling.

Global and local hard X-ray tomography of a centimeter-size tumor vessel tree

The visualization of the vascular network in tumors down to the smallest vessels requires high spatial resolution and reasonable contrast. Stained corrosion casts of the microvasculature network guarantee superior X-ray absorption contrast and highest reproduction fidelity. Tomography of a centimeter-size tumor, however, is unfeasible at the spatial resolution needed to reveal the smallest vessels. Therefore, local tomography has been performed to visualize the smallest capillaries within the region of interest. These three-dimensional data show the detailed morphology, but the reconstructed absorption coefficients obtained in local tomography differ substantially from the absorption coefficients retrieved from the less detailed global tomography data. This paper deals with the adaptation of local tomography data using the global data and considers two-parameter histogram matching of the radiographs, sinogram extension, and multi-parameter cupping correction. It is demonstrated that two-parameter histogram matching of the radiographs already provides reasonable agreement. The change of the lens in front of the detector's camera, however, significantly affects the obtained local X-ray absorption coefficients in the tomograms predominantly owing to the dissimilar point-spread functions of the two configurations used, and much less to the fact that one of the data sets was acquired in a local geometry.