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

Dorn, Sabrina; Chen, Shuqing; Sawall, Stefan; Maier, Joscha; Knaup, Michael; Uhrig, Monika; Schlemmer, Heinz Peter; Maier, Andreas; Lell, Michael; Kachelrieß, Marc

doi:10.1002/mp.13127

Cited by 6 publications

(5 citation statements)

References 36 publications

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“…Let us consider CT images normalized to air at 0 and water at 1 [ 22 ]. Given the reconstructed image of the lower energy bin f Low and the image of the high energy bin f High , one might compute a water image f W , i.e., a VNC image, as …”

Section: Methodsmentioning

confidence: 99%

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Threshold-dependent iodine imaging and spectral separation in a whole-body photon-counting CT system

et al. 2021

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Objective To evaluate the dual-energy (DE) performance and spectral separation with respect to iodine imaging in a photon-counting CT (PCCT) and compare it to dual-source CT (DSCT) DE imaging. Methods A semi-anthropomorphic phantom extendable with fat rings equipped with iodine vials is measured in an experimental PCCT. The system comprises a PC detector with two energy bins (20 keV, T) and (T, eU) with threshold T and tube voltage U. Measurements using the PCCT are performed at all available tube voltages (80 to 140 kV) and threshold settings (50–90 keV). Further measurements are performed using a conventional energy-integrating DSCT. Spectral separation is quantified as the relative contrast media ratio R between the energy bins and low/high images. Image noise and dose-normalized contrast-to-noise ratio (CNRD) are evaluated in resulting iodine images. All results are validated in a post-mortem angiography study. Results R of the PC detector varies between 1.2 and 2.6 and increases with higher thresholds and higher tube voltage. Reference R of the EI DSCT is found as 2.20 on average overall phantoms. Maximum CNRD in iodine images is found for T = 60/65/70/70 keV for 80/100/120/140 kV. The highest CNRD of the PCCT is obtained using 140 kV and is decreasing with decreasing tube voltage. All results could be confirmed in the post-mortem angiography study. Conclusion Intrinsically acquired DE data are able to provide iodine images similar to conventional DSCT. However, PCCT thresholds should be chosen with respect to tube voltage to maximize image quality in retrospectively derived image sets. Key Points • Photon-counting CT allows for the computation of iodine images with similar quality compared to conventional dual-source dual-energy CT. • Thresholds should be chosen as a function of the tube voltage to maximize iodine contrast-to-noise ratio in derived image sets. • Image quality of retrospectively computed image sets can be maximized using optimized threshold settings.

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Section: Methodsmentioning

confidence: 99%

“…It is sometimes also referred to as CT number ratio or dual-energy ratio [ 24 ]. I.e., given two water-iodine mixtures of unknown mixing ratio, one can show that R is independent of the mixing ratio and is only a function of the CT values of iodine in these images as [ 22 ] …”

Section: Methodsmentioning

confidence: 99%

Threshold-dependent iodine imaging and spectral separation in a whole-body photon-counting CT system

et al. 2021

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“…To avoid multiple images, the need exists for a single image to integrate all of the relevant information from multiple reconstructions. Dorn et al [1] have trained a deep-learning neural network to automatically generate such a single image, namely, the organ-specific context-sensitive reconstruction, from a stack of FBP images. Their network is able to combine mutually exclusive CT image properties that are from different reconstructions into a single context-sensitive image.…”

Section: Introductionmentioning

confidence: 99%

“…Basically, we employ a DCNN to reconstruct the context-sensitive image. Unlike [1], our network only requires a single FBP image as input and directly outputs the desired image. It does not need multiple FBPs or MBIRs and hence the processing is much faster.…”

Section: Introductionmentioning

confidence: 99%

Feature-Aware Deep-Learning Reconstruction for Context-Sensitive X-ray Computed Tomography

Matsuura

Zhou

Akino

et al. 2021

IEEE Trans. Radiat. Plasma Med. Sci.

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It is common for CT images to be reconstructed differently for different clinical examination purposes. It is difficult for conventional filtered backprojection (FBP) methods and standard model-based iterative reconstruction (MBIR) methods to produce a single context-sensitive image without multiple reconstructions. In this article, we address this challenge by leveraging the power of deep learning. We propose to train a deep convolution neural network to reconstruct a universal image from one FBP image. We present a new data argumentation method that generates the specific training target image, specifically the feature-aware target. The resulting method, called feature-aware deep-learning reconstruction (DLR), requires only one FBP image as input and is much faster than MBIR. In experiments, we investigate one application of low-dose CT feature-aware DLR which aims to achieve noise and resolution consistency across different body parts. We evaluate the performance of the proposed method using both simulated and real clinical low-dose CT scans. The results show that our feature-aware DLR outperforms both FBP and standard MBIR by producing improved CT image quality potentially suitable for a broad range of clinical diagnoses.

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“…Recent works show great potential of the incorporation of DECT information into the clinical workflow. With the anatomical information gained through appropriate multi‐organ segmentation, novel medical techniques, such as segmentation‐assisted material decomposition, organ‐specific context‐sensitive DECT imaging, and segmentation‐based computation of bone mineral density, would be enabled. As such, these novel approaches highlight the importance of adequate multi‐organ segmentation in DECT and the need for reliable DECT multi‐organ segmentation.…”

Section: Introductionmentioning

confidence: 99%

Automatic multi‐organ segmentation in dual‐energy CT (DECT) with dedicated 3D fully convolutional DECT networks

Chen

Zhong

et al. 2020

Medical Physics

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Purpose Dual‐energy computed tomography (DECT) has shown great potential in many clinical applications. By incorporating the information from two different energy spectra, DECT provides higher contrast and reveals more material differences of tissues compared to conventional single‐energy CT (SECT). Recent research shows that automatic multi‐organ segmentation of DECT data can improve DECT clinical applications. However, most segmentation methods are designed for SECT, while DECT has been significantly less pronounced in research. Therefore, a novel approach is required that is able to take full advantage of the extra information provided by DECT. Methods In the scope of this work, we proposed four three‐dimensional (3D) fully convolutional neural network algorithms for the automatic segmentation of DECT data. We incorporated the extra energy information differently and embedded the fusion of information in each of the network architectures. Results Quantitative evaluation using 45 thorax/abdomen DECT datasets acquired with a clinical dual‐source CT system was investigated. The segmentation of six thoracic and abdominal organs (left and right lungs, liver, spleen, and left and right kidneys) were evaluated using a fivefold cross‐validation strategy. In all of the tests, we achieved the best average Dice coefficients of 98% for the right lung, 98% for the left lung, 96% for the liver, 92% for the spleen, 95% for the right kidney, 93% for the left kidney, respectively. The network architectures exploit dual‐energy spectra and outperform deep learning for SECT. Conclusions The results of the cross‐validation show that our methods are feasible and promising. Successful tests on special clinical cases reveal that our methods have high adaptability in the practical application.

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

Cited by 6 publications

References 36 publications

Threshold-dependent iodine imaging and spectral separation in a whole-body photon-counting CT system

Threshold-dependent iodine imaging and spectral separation in a whole-body photon-counting CT system

Feature-Aware Deep-Learning Reconstruction for Context-Sensitive X-ray Computed Tomography

Automatic multi‐organ segmentation in dual‐energy CT (DECT) with dedicated 3D fully convolutional DECT networks

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