A dictionary learning approach with overlap for the low dose computed tomography reconstruction and its vectorial application to differential phase tomography

Optimization of a customized simultaneous algebraic reconstruction technique algorithm for phase-contrast breast computed tomography

Objective. To introduce the optimization of a customized GPU-based simultaneous algebraic reconstruction technique (cSART) in the field of phase-contrast breast computed tomography (bCT). The presented algorithm features a 3D bilateral regularization filter that can be tuned to yield optimal performance for clinical image visualization and tissues segmentation. Approach. Acquisitions of a dedicated test object and a breast specimen were performed at Elettra, the Italian synchrotron radiation (SR) facility (Trieste, Italy) using a large area CdTe single-photon counting detector. Tomographic images were obtained at 5 mGy of mean glandular dose, with a 32 keV monochromatic x-ray beam in the free-space propagation mode. Three independent algorithms parameters were optimized by using contrast-to-noise ratio (CNR), spatial resolution, and noise texture metrics. The results obtained with the cSART algorithm were compared with conventional SART and filtered back projection (FBP) reconstructions. Image segmentation was performed both with gray scale-based and supervised machine-learning approaches. Main results. Compared to conventional FBP reconstructions, results indicate that the proposed algorithm can yield images with a higher CNR (by 35% or more), retaining a high spatial resolution while preserving their textural properties. Alternatively, at the cost of an increased image ‘patchiness’, the cSART can be tuned to achieve a high-quality tissue segmentation, suggesting the possibility of performing an accurate glandularity estimation potentially of use in the realization of realistic 3D breast models starting from low radiation dose images. Significance. The study indicates that dedicated iterative reconstruction techniques could provide significant advantages in phase-contrast bCT imaging. The proposed algorithm offers great flexibility in terms of image reconstruction optimization, either toward diagnostic evaluation or image segmentation.

High-dynamic-range X-ray CT imaging method based on energy self-adaptation between scanning angles

High-dynamic-range (HDR) X-ray CT imaging is effective in detecting some complex structures. For previous low-dynamic-range (LDR) imaging detectors, multi-energy LDR image sequence fusion can extend the dynamic range, but the efficiency is decreased. However, with the application of HDR imaging devices, traditional fixed-energy X-ray imaging can cause incongruity within energy, dynamic range, and the equivalent thickness of the workpiece at different projection angles. Then, the projection has a blurred edge, and the CT image quality is poor because of scattering and the inadequate dose. In this paper, a new HDR X-ray CT imaging method with energy self-adaptation between scanning angles for HDR imaging devices is studied. Low-energy prescanning is used to determine the initial scanning energy and obtain the edge contour information with an attenuating effect on scattering. By establishing a mathematical model between the gray level of the projection and the transmission voltage, the transmission energy at each angle is adjusted adaptively. Then, the prescanning and energy self-adaption scanning projections are fused to obtain the complete projection of the complex workpiece. Finally, a conventional reconstruction algorithm is used to reconstruct the HDR CT image. The experimental results show that the proposed imaging method can achieve HDR CT imaging of complex structures with high reconstruction quality, clear edge details, and high completeness.

Low-dose x-ray tomography through a deep convolutional neural network

Synchrotron-based X-ray tomography offers the potential for rapid large-scale reconstructions of the interiors of materials and biological tissue at fine resolution. However, for radiation sensitive samples, there remain fundamental trade-offs between damaging samples during longer acquisition times and reducing signals with shorter acquisition times. We present a deep convolutional neural network (CNN) method that increases the acquired X-ray tomographic signal by at least a factor of 10 during low-dose fast acquisition by improving the quality of recorded projections. Short-exposure-time projections enhanced with CNNs show signal-to-noise ratios similar to long-exposure-time projections. They also show lower noise and more structural information than low-dose short-exposure acquisitions post-processed by other techniques. We evaluated this approach using simulated samples and further validated it with experimental data from radiation sensitive mouse brains acquired in a tomographic setting with transmission X-ray microscopy. We demonstrate that automated algorithms can reliably trace brain structures in low-dose datasets enhanced with CNN. This method can be applied to other tomographic or scanning based X-ray imaging techniques and has great potential for studying faster dynamics in specimens.

A Novel and Sensitive Approach for the Evaluation of Liver Ischemia-Reperfusion Injury After Liver Transplantation

OBJECTIVES

The purpose of our study was to evaluate the potential of x-ray propagation-based phase-contrast imaging (PCI) computed tomography (CT) for the detection and characterization of early changes after ischemia-reperfusion (IR) in a standardized rat liver transplantation (LTx) model.

MATERIALS AND METHODS

Syngeneic orthotopic liver transplantation was performed in male Lewis rats. Ischemia-reperfusion injury (IRI)-induced changes of liver parenchyma were investigated in a time-dependent manner (2, 16, 24, and 32 hours). X-ray phase-contrast images of formalin-fixated liver specimens were acquired in CT mode by using a voxel size of 8 × 8 × 8 μm. Necrapoptotic cell death was visualized with the TdT-mediated dUTP-biotin nick end labeling technique, and alterations of liver graft microhemodynamics, that is, acinar and sinusoidal perfusion failure, were evaluated by in vivo fluorescence microscopy.

RESULTS

Acquired and reconstructed PCI-CT images showed an increase in necrotic liver parenchyma dependent on cold storage time, measuring 5.7% ± 1.6% after 2 hours (comparable to 2.6% ± 0.4% for sham livers), 11.5% ± 2.1% (16 hours; P < 0.05 vs control), 23.0% ± 0.5% (24 hours; P < 0.001 vs control), and 31.3% ± 2.2% (32 hours; P < 0.001 vs control). There were a significant lower number of perfused acini in dependence on increasing cold storage time. The acinar perfusion index reached 0.970 ± 0.006 after 2 hours of cold ischemia (comparable to 0.960 ± 0.009 for sham livers) and declined continuously after 16, 24, and 32 hours cold ischemia (0.58 ± 0.03, 0.49 ± 0.02, 0.41 ± 0.03, each P < 0.0001 vs controls). Comparable results were found for sinusoidal perfusion, reaching 1.8% ± 0.4% of nonperfused sinusoids for 2 hours of cold ischemia and 8.2% ± 0.8% after 16 hours, 18.8% ± 1.4% after 24 hours, and 39.0% ± 2.4% after 32 hours (each P < 0.0001 vs controls). Prolonged cold ischemia was associated with an increasing number of TdT-mediated dUTP-biotin nick end labeling-positive cells (hepatocytes and sinusoidal lining cells), reaching 0.4 ± 0.1 (sham), 0.7 ± 0.4 (2 hours), 6.4 ± 1.1 (16 hours), 2.1 ± 0.3 (24 hours), and 14.7 ± 3.5 (32 hours; P = 0.002) for hepatocytes.

CONCLUSIONS

X-ray PCI of histological liver specimens can detect IR-induced tissue necrosis and can provide detailed complementary 3-dimensional information to standard histopathologic findings.

Clinical application of low-dose phase contrast breast CT: methods for the optimization of the reconstruction workflow

Results are presented of a feasibility study of three-dimensional X-ray tomographic mammography utilising in-line phase contrast. Experiments were performed at SYRMEP beamline of Elettra synchrotron. A specially designed plastic phantom and a mastectomy sample containing a malignant lesion were used to study the reconstructed image quality as a function of different image processing operations. Detailed evaluation and optimization of image reconstruction workflows have been carried out using combinations of several advanced computed tomography algorithms with different pre-processing and post-processing steps. Special attention was paid to the effect of phase retrieval on the diagnostic value of the reconstructed images. A number of objective image quality indices have been applied for quantitative evaluation of the results, and these were compared with subjective assessments of the same images by three experienced radiologists and one pathologist. The outcomes of this study provide practical guidelines for the optimization of image processing workflows in synchrotron-based phase-contrast mammo-tomography.