Context-sensitive organspecific evaluation and analysis of dual energy computed tomography (DECT) - Dorn, Sabrina; Chen, Shuqing; Pisana, Francesco; Özdemir, Mahmut; Maier, Joscha; Sawall, Stefan; Knaup, Michael; Maier, Andreas; Lell, Michael; Kachelriess, Marc (European Congress of Radiology (ECR), 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.contex

Organ-specific context-sensitive CT image reconstruction and display - Dorn, Sabrina; Chen, Shuqing; Sawall, Stefan; Simons, David; May, Matthias; Maier, Joscha; Knaup, Michael; Schlemmer, Heinz-Peter; Maier, Andreas; Lell, Michael; Kachelriess, Marc (Proc.SPIE, 2018)
In this work, we present a novel method to combine mutually exclusive CT image properties that emerge from different reconstruction kernels and display settings into a single organ-specific image reconstruction and display. We propose a context-sensitive reconstruction that locally emphasizes desired image properties by exploiting prior anatomical knowledge. Furthermore, we introduce an organ-specific windowing and display method that aims at providing a superior image visualization. Using a coarse-to-fine hierarchical 3D fully convolutional network (3D U-Net), the CT data set is segmented and classified into different organs, e.g. the heart, vasculature, liver, kidney, spleen and lung, as well as into the tissue types bone, fat, soft tissue and vessels. Reconstruction and display parameters most suitable for the organ, tissue type, and clinical indication are chosen automatically from a predefined set of reconstruction parameters on a per-voxel basis. The approach is evaluated using patient data acquired with a dual source CT system. The final context-sensitive images simultaneously link the indication-specific advantages of different parameter settings and result in images joining tissue-related desired image properties. A comparison with conventionally reconstructed and displayed images reveals an improved spatial resolution in highly attenuating objects and air while maintaining a low noise level in soft tissue in the compound image. The images present significantly more information to the reader simultaneously and dealing with multiple volumes may no longer be necessary. The presented method is useful for the clinical workflow and bears the potential to increase the rate of incidental findings.
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.organs

Organ-specific context-sensitive single and dual energy CT (DECT) image reconstruction, display and analysis - Dorn, Sabrina; Chen, Shuqing; Pisana, Francesco; Maier, Joscha; Knaup, Michael; Sawall, Stefan; Maier, Andreas; Lell, Michael; Kachelriess, Marc (n 103rd Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA), 2017)
UnivIS-Import:2018-09-06:Pub.2017.tech.IMMD.IMMD5.organs

Prior-based multi material decomposition (PBMMD) for dual energy CT - Dorn, Sabrina; Chen, Shuqing; Sawall, Stefan; Maier, Joscha; Knaup, Michael; Maier, Andreas; Lell, Michael; Kachelriess, Marc (the fifth edition of The International Conference on Image Formation in X-ray Computed Tomography, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.priorb

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 (Medical Physics, 2018)
PurposeThe purpose of this study was to establish a novel paradigm to facilitate radiologists' workflow combining mutually exclusive CT image properties that emerge from different reconstructions, display settings and organ-dependent spectral evaluation methods into a single context-sensitive imaging by exploiting prior anatomical information.MethodsThe CT dataset is segmented and classified into different organs, for example, the liver, left and right kidney, spleen, aorta, and left and right lung as well as into the tissue types bone, fat, soft tissue, and vessels using a cascaded three-dimensional fully convolutional neural network (CNN) consisting of two successive 3D U-nets. The binary organ and tissue masks are transformed to tissue-related weighting coefficients that are used to allow individual organ-specific parameter settings in each anatomical region. Exploiting the prior knowledge, we develop a novel paradigm of a context-sensitive (CS) CT imaging consisting of a prior-based spatial resolution (CSR), display (CSD), and dual energy evaluation (CSDE). The CSR locally emphasizes desired image properties. On a per-voxel basis, the reconstruction most suitable for the organ, tissue type, and clinical indication is chosen automatically. Furthermore, an organ-specific windowing and display method is introduced that aims at providing superior image visualization. The CSDE analysis allows to simultaneously evaluate multiple organs and to show organ-specific DE overlays wherever appropriate. The ROIs that are required for a patient-specific calibration of the algorithms are automatically placed into the corresponding anatomical structures. The DE applications are selected and only applied to the specific organs based on the prior knowledge. The approach is evaluated using patient data acquired with a dual source CT system. The final CS images simultaneously link the indication-specific advantages of different parameter settings and result in images combining tissue-related desired image properties.ResultsA comparison with conventionally reconstructed images reveals an improved spatial resolution in highly attenuating objects and in air while the compound image maintains a low noise level in soft tissue. Furthermore, the tissue-related weighting coefficients allow for the combination of varying settings into one novel image display. We are, in principle, able to automate and standardize the spectral analysis of the DE data using prior anatomical information. Each tissue type is evaluated with its corresponding DE application simultaneously.ConclusionThis 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.
Keywords: CT; CNN segmentation; dual energy; image display; image formation