Alpha image reconstruction (AIR): A new iterative CT image reconstruction approach using voxel-wise alpha blending

Hofmann, Christian; Sawall, Stefan; Knaup, Michael; Kachelrieß, Marc

doi:10.1118/1.4875975

Cited by 6 publications

(8 citation statements)

References 27 publications

(31 reference statements)

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“…A loss of detail in the α-images does not necessarily lead to a loss of detail in the AIR-image. 2 The cost function can be optimized using a gradient descent as recently proposed. 2 …”

Section: Alpha-image Reconstruction (Air)mentioning

confidence: 99%

“…1 As an alternative we recently proposed an iterative reconstruction method, the alpha-image reconstruction (AIR), providing well-defined image quality metrics on a per-voxel basis. 2 In particular, the AIR algorithm seeks to find weighting images, the alpha-images, that are used to blend between basis images with mutually exclusive image properties. The result is an image with highest diagnostic quality that provides a high spatial resolution and a low noise level.…”

Section: Introductionmentioning

confidence: 99%

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Optimization and image quality assessment of the alpha-image reconstruction algorithm: iterative reconstruction with well-defined image quality metrics

et al. 2015

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“…A loss of detail in the α-images does not necessarily lead to a loss of detail in the AIR-image. 2 The cost function can be optimized using a gradient descent as recently proposed. 2 …”

Section: Alpha-image Reconstruction (Air)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization and image quality assessment of the alpha-image reconstruction algorithm: iterative reconstruction with well-defined image quality metrics

et al. 2015

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“…It is possible to reconstruct the basis images using varying reconstruction methods, for instance an analytical reconstruction with varying kernels or an iterative reconstruction, resulting in images with desired competing properties. 1,13,14 For reconstruction, we use the weighted filtered backprojection (wFBP) 15 that is available at our scanner (Somatom Definition Flash, Siemens Healthineers, Forchheim, Germany). The basis images are reconstructed with different reconstruction kernels leading to various resolution levels.…”

Section: C1 Context-sensitive Spatial Resolution (Csr)mentioning

confidence: 99%

Towards context‐sensitive CT imaging — organ‐specific image formation for single (SECT) and dual energy computed tomography (DECT)

et al. 2018

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This work provides a proof of concept of CS imaging. Since radiologists are not aware of the presented method and the tool is not yet implemented in everyday clinical practice, a comprehensive clinical evaluation in a large cohort might be topic of future research. Nonetheless, the presented method has potential to facilitate workflow in clinical routine and could potentially improve diagnostic accuracy by improving sensitivity for incidental findings. It is a potential step toward the presentation of evermore increasingly complex information in CT and toward improving the radiologists workflow significantly since dealing with multiple CT reconstructions may no longer be necessary. The method can be readily generalized to multienergy data and also to other modalities.

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“…Furthermore, it can be shown that the effect of ray modeling in CT can also be converted to an image domain operation, given that this filtering is applied to a master image with rather small voxels. 93,94 These two observations justify minimizing the number of (computationally demanding) iterations between image domain and raw data domain in favor of performing more (computationally efficient) iterations in image domain ( Fig. 4; Table 2).…”

Section: Image Reconstructionmentioning

confidence: 99%

Evolution in Computed Tomography

Lell¹,

Wildberger²,

Alkadhi³

et al. 2015

Investigative Radiology

139

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The advent of computed tomography (CT) has revolutionized radiology. Starting as head-only scanners, modern CT systems are now capable of performing whole-body examinations within a couple of seconds in isotropic resolution. Technical advancements of scanner hardware and image reconstruction techniques are reviewed and discussed in their clinical context. These improvements have led to a steady increase of CT examinations in all age groups for a number of reasons. On the one hand, it is very easy today to obtain whole-body data for oncologic staging and follow-up or for trauma imaging. On the other hand, new examinations such as cardiac imaging, virtual colonoscopy, gout imaging, and whole-organ perfusion imaging have widened the application profile of CT. The increasing awareness of risks associated with radiation exposure triggered the development of a variety of dose reduction techniques. Effective dose values below 1 mSv, less than the annual natural background radiation (3.1 mSv/year on average in the United States), are now routinely possible for a number of dedicated examinations, even for coronary CT angiography.

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Alpha image reconstruction (AIR): A new iterative CT image reconstruction approach using voxel-wise alpha blending

Cited by 6 publications

References 27 publications

Optimization and image quality assessment of the alpha-image reconstruction algorithm: iterative reconstruction with well-defined image quality metrics

Optimization and image quality assessment of the alpha-image reconstruction algorithm: iterative reconstruction with well-defined image quality metrics

Towards context‐sensitive CT imaging — organ‐specific image formation for single (SECT) and dual energy computed tomography (DECT)

Evolution in Computed Tomography

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