Recent Advances in CT Image Reconstruction

Hsieh, Jiang; Nett, Brian E.; Zhou, Yu; Sauer, Ken D.; Thibault, Jean‐Baptiste; Bouman, Charles A.

doi:10.1007/s40134-012-0003-7

Cited by 151 publications

(130 citation statements)

References 83 publications

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“…There are two main categories for Image Reconstruction (IR) algorithms in CT, SPECT and PET: analytical reconstructions and iterative reconstructions [124][125][126]. Analytical approaches are based on a closed-form equation that directly yields one solution based on the input (Figure 3A), while iterative techniques are based on a more accurate description of the imaging process, thereby resulting in a more complicated mathematical solution that requires multiple steps to achieve an image.…”

Section: Image Acquisition and Reconstruction In Ct Pet And Spectmentioning

confidence: 99%

Hybrid Imaging: Instrumentation and Data Processing

et al. 2018

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State-of-the-art patient management frequently requires the use of non-invasive imaging methods to assess the anatomy, function or molecular-biological conditions of patients or study subjects. Such imaging methods can be singular, providing either anatomical or molecular information, or they can be combined, thus, providing "anato-metabolic" information. Hybrid imaging denotes image acquisitions on systems that physically combine complementary imaging modalities for an improved diagnostic accuracy and confidence as well as for increased patient comfort. The physical combination of formerly independent imaging modalities was driven by leading innovators in the field of clinical research and benefited from technological advances that permitted the operation of PET and MR in close physical proximity, for example. This review covers milestones of the development of various hybrid imaging systems for use in clinical practice and small-animal research. Special attention is given to technological advances that helped the adoption of hybrid imaging, as well as to introducing methodological concepts that benefit from the availability of complementary anatomical and biological information, such as new types of image reconstruction and data correction schemes. The ultimate goal of hybrid imaging is to provide useful, complementary and quantitative information during patient work-up. Hybrid imaging also opens the door to multi-parametric assessment of diseases, which will help us better understand the causes of various diseases that currently contribute to a large fraction of healthcare costs.

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Section: Image Acquisition and Reconstruction In Ct Pet And Spectmentioning

confidence: 99%

Hybrid Imaging: Instrumentation and Data Processing

et al. 2018

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“…This is acceptable since the MSCT scanner geometry [5] has an aperture length of 700 mm, and a range of reconstruction field-of-view (FOV) between 50 -500 mm. With these dimensions, the head Multi-slice detectors have proved to be advantageous due to its simplicity and robustness especially in reducing the radiation dose during scanning [9]. However, the cone beam angle from this geometry has its limitations such as image artefacts arising due to several factors, such as 'axial truncation' that limits the FOV, since the object is not entirely covered in the longitudinal direction.…”

Section: Image Geometrymentioning

confidence: 99%

Modelling the Siemens SOMATOM Sensation 64 Multi-Slice CT (MSCT) Scanner

Amin

Rahni

2017

J. Phys.: Conf. Ser.

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Abstract.Reconstructing large volumetric 3D images with minimal radiation dosage exposure with reduced scanning time has been one of the main objectives in the advancement of CT development. One of its advancement is the introduction of multi-slice arc detector geometry from a cone-beam source in third generation scanners. In solving this complex geometry, apart from the known vast computations in CT image reconstruction due to large CT images, iterative reconstruction methods are preferred compared to analytic methods due to its flexibility in image reconstruction. A scanner of interest that has this type of geometry is the Siemens SOMATOM Sensation 64 Multi-Slice CT (MSCT) Scanner, which has a total of 32 slices with 672 detector elements on each slice. In this paper, the scanner projection is modelled via the intersecting lengths between each ray (exhibited from the source to the detector elements) with the scanned image voxels, which are evaluated using the classical Siddon's algorithm to generate the system matrix, H. This is a prerequisite to perform various iterative reconstruction methods, which involves solving the inverse problem arising from the linear equation: S = H·I; where S is the projections produced from the image, I. Due to the 'cone-beam geometry' along the z-axis, the effective field-of-view (FOV) with voxel dimensions (0.4×0.4×0.4) mm 3 is 512×512×32 voxels. The scanner model is demonstrated by reconstructing an image from simulated projections using the analytic Feldkamp-Davis-Kress (FDK) method against basic iterative image reconstruction methods.

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“…limited angular coverage), image model, forward model, noise model and prior information into the reconstruction (Hsieh et al, 2013). The image model mainly deals with the representation of the volume to be reconstructed.…”

Section: Preliminariesmentioning

confidence: 99%

Reconstruction of coronary arteries from X-ray angiography: A review

Çimen

Gooya

Graß

et al. 2016

Medical Image Analysis

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Despite continuous progress in X-ray angiography systems, X-ray coronary angiography is fundamentally limited by its 2D representation of moving coronary arterial trees, which can negatively impact assessment of coronary artery disease and guidance of percutaneous coronary intervention. To provide clinicians with 3D/3D+time information of coronary arteries, methods computing reconstructions of coronary arteries from X-ray angiography are required. Because of several aspects (e.g. cardiac and respiratory motion, type of X-ray system), reconstruction from X-ray coronary angiography has led to vast amount of research and it still remains as a challenging and dynamic research area. In this paper, we review the state-of-the-art approaches on reconstruction of high-contrast coronary arteries from X-ray angiography. We mainly focus on the theoretical features in model-based (modelling) and tomographic reconstruction of coronary arteries, and discuss the evaluation strategies. We also discuss the potential role of reconstructions in clinical decision making and interventional guidance, and highlight areas for future research.

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Recent Advances in CT Image Reconstruction

Cited by 151 publications

References 83 publications

Hybrid Imaging: Instrumentation and Data Processing

Hybrid Imaging: Instrumentation and Data Processing

Modelling the Siemens SOMATOM Sensation 64 Multi-Slice CT (MSCT) Scanner

Reconstruction of coronary arteries from X-ray angiography: A review

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