@article{FahrigDixonPayneetal., author = {Fahrig, Rebecca and Dixon, Robert and Payne, Thomas and Morin, Richard L. and Ganguly, Arundhuti and Strobel, Robert}, title = {Dose and Image Quality for a Cone-Beam C-arm CT System}, series = {Medical Physics}, volume = {33}, journal = {Medical Physics}, number = {12}, doi = {10.1118/1.2370508}, pages = {4541 -- 4550}, abstract = {We assess dose and image quality of a state-of-the-art angiographic C-arm system (Axiom Artis dTA, Siemens Medical Solutions, Forchheim, Germany) for three-dimensional neuro-imaging at various dose levels and tube voltages and an associated measurement method. Unlike conventional CT, the beam length covers the entire phantom, hence, the concept of computed tomography dose index (CTDI) is not the metric of choice, and one can revert to conventional dosimetry methods by directly measuring the dose at various points using a small ion chamber. This method allows us to define and compute a new dose metric that is appropriate for a direct comparison with the familiar CTDIw of conventional CT. A perception study involving the CATPHAN 600 indicates that one can expect to see at least the 9 mm inset with 0.5\% nominal contrast at the recommended head-scan dose (60 mGy) when using tube voltages ranging from 70 kVp to 125 kVp. When analyzing the impact of tube voltage on image quality at a fixed dose, we found that lower tube voltages gave improved low contrast detectability for small-diameter objects. The relationships between kVp, image noise, dose, and contrast perception are discussed.}, language = {en} } @article{SzeStrobelFahrigetal., author = {Sze, Daniel Y. and Strobel, Norbert and Fahrig, Rebecca and Moore, Teri and Busque, Stephan and Frisoli, Joan K.}, title = {Transjugular intrahepatic portosystemic shunt creation in a polycystic liver facilitated by hybrid cross-sectional/angiographic imaging}, series = {Journal of Vascular and Interventional Radiology (JVIR)}, volume = {17}, journal = {Journal of Vascular and Interventional Radiology (JVIR)}, number = {4}, doi = {10.1097/01.rvi.0000208984.17697.58}, pages = {711 -- 715}, abstract = {Polycystic liver disease (PCLD) has long been considered to represent a contraindication to transjugular intrahepatic portosystemic shunt (TIPS) creation, primarily because of the risk of hemorrhage. Three-dimensional (3D) navigation within the enlarged and potentially disorienting parenchyma can now be performed during the procedure with the development of C-arm cone-beam computed tomography, which relies on the same equipment already used for angiography. Such a hybrid 3D reconstruction-enabled angiography system was used for safe image guidance of a TIPS procedure in a patient with PCLD. This technology has the potential to expedite any image-guided procedure that requires 3D navigation.}, language = {en} } @article{BourierFahrigWangetal., author = {Bourier, Felix and Fahrig, Rebecca and Wang, Paul and Santangeli, Pasquale and Kurzidim, Klaus and Strobel, Norbert and Moore, Teri and Hinkel, Cameron and Al-Ahmad, Amin}, title = {Accuracy assessment of catheter guidance technology in electrophysiology procedures: a comparison of a new 3D-based fluoroscopy navigation system to current electroanatomic mapping system}, series = {Journal of Cardiovascular Electrophysiology}, volume = {25}, journal = {Journal of Cardiovascular Electrophysiology}, number = {1}, doi = {10.1111/jce.12264}, pages = {74 -- 83}, abstract = {Background: With increasing complexity in electrophysiology (EP) procedures, the use of electroanatomic mapping systems (EAMS) as a supplement to fluoroscopy has become common practice. This is the first study that evaluates spatial and point localization accuracy for 2 current EAMS, CARTO3(®) (Biosense Webster, Diamond Bar, CA, USA) and EnSite Velocity(®) (St. Jude Medical Inc., St. Paul, MN, USA), and for a novel overlay guidance (OG) software (Siemens AG, Forchheim, Germany) in a phantom experiment. Methods and results: A C-arm CT scan was performed on an acrylic phantom containing holes and location markers. Spatial accuracy was assessed for each system using distance measurements involving known markers inside the phantom and properly placed catheters. Anatomical maps of the phantom were acquired by each EAMS, whereas the 3D-based OG software superimposed an overlay image of the phantom, segmented from the C-arm CT data set, onto biplane fluoroscopy. Registration processes and landmark measurements quantitatively assessed the spatial accuracy of each technology with respect to the ground truth phantom. Point localization performance was 0.49 ± 0.25 mm in OG, 0.46 ± 0.17 mm in CARTO3(®) and 0.79 ± 0.83 mm in EnSite(®) . The registration offset between virtual visualization and reality was 1.10 ± 0.52 mm in OG, 1.62 ± 0.77 mm in CARTO3(®) and 2.02 ± 1.21 mm in EnSite(®) . The offset to phantom C-arm CT landmark measurements was 0.30 ± 0.26 mm in OG, 0.24 ± 0.21 mm in CARTO3(®) and 1.32 ± 0.98 mm in EnSite(®) . Conclusions: Each of the evaluated EP guidance systems showed a high level of accuracy; the observed offsets between the virtual 3D visualization and the real phantom were below a clinically relevant threshold of 3 mm.}, language = {en} } @article{MaierWigstromHofmannetal., author = {Maier, Andreas and Wigstrom, Lars and Hofmann, Hannes G and Hornegger, Joachim and Zhu, Lei and Strobel, Norbert and Fahrig, Rebecca}, title = {Three-dimensional anisotropic adaptive filtering of projection data for noise reduction in cone beam CT}, series = {Medical Physic}, volume = {38}, journal = {Medical Physic}, number = {11}, doi = {10.1118/1.3633901}, pages = {5896 -- 5909}, abstract = {Purpose: The combination of quickly rotating C-arm gantry with digital flat panel has enabled the acquisition of three-dimensional data (3D) in the interventional suite. However, image quality is still somewhat limited since the hardware has not been optimized for CT imaging. Adaptive anisotropic filtering has the ability to improve image quality by reducing the noise level and therewith the radiation dose without introducing noticeable blurring. By applying the filtering prior to 3D reconstruction, noise-induced streak artifacts are reduced as compared to processing in the image domain. Methods: 3D anisotropic adaptive filtering was used to process an ensemble of 2D x-ray views acquired along a circular trajectory around an object. After arranging the input data into a 3D space (2D projections + angle), the orientation of structures was estimated using a set of differently oriented filters. The resulting tensor representation of local orientation was utilized to control the anisotropic filtering. Low-pass filtering is applied only along structures to maintain high spatial frequency components perpendicular to these. The evaluation of the proposed algorithm includes numerical simulations, phantom experiments, and in-vivo data which were acquired using an AXIOM Artis dTA C-arm system (Siemens AG, Healthcare Sector, Forchheim, Germany). Spatial resolution and noise levels were compared with and without adaptive filtering. A human observer study was carried out to evaluate low-contrast detectability. Results: The adaptive anisotropic filtering algorithm was found to significantly improve low-contrast detectability by reducing the noise level by half (reduction of the standard deviation in certain areas from 74 to 30 HU). Virtually no degradation of high contrast spatial resolution was observed in the modulation transfer function (MTF) analysis. Although the algorithm is computationally intensive, hardware acceleration using Nvidia's CUDA Interface provided an 8.9-fold speed-up of the processing (from 1336 to 150 s). Conclusions: Adaptive anisotropic filtering has the potential to substantially improve image quality and/or reduce the radiation dose required for obtaining 3D image data using cone beam CT.}, language = {en} } @inproceedings{HariharanKaethnerStrobeletal., author = {Hariharan, Sai Gokul and Kaethner, Christian and Strobel, Norbert and Kowarschik, Markus and Albarqouni, Shadi and Fahrig, Rebecca and Navab, Nassir}, title = {Learning-based X-ray image denoising utilizing model-based image simulations}, series = {International Conference on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2019, 22nd International Conference Shenzhen, China, October 13-17, 2019 Proceedings, Part VI}, booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2019, 22nd International Conference Shenzhen, China, October 13-17, 2019 Proceedings, Part VI}, publisher = {Springer International Publishing}, address = {Cham}, doi = {10.1007/978-3-030-32226-7_61}, pages = {549 -- 557}, language = {en} } @inproceedings{HariharanKaethnerStrobeletal., author = {Hariharan, Sai Gokul and Kaethner, Christian and Strobel, Norbert and Kowarschik, Markus and DiNitto, Julie and Fahrig, Rebecca and Navab, Nassir}, title = {Model-based motion artifact correction in digital subtraction angiography using optical-flow}, series = {Bildverarbeitung f{\"u}r die Medizin 2019: Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 17. bis 19. M{\"a}rz 2019 in L{\"u}beck}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2019: Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 17. bis 19. M{\"a}rz 2019 in L{\"u}beck}, publisher = {Springer Fachmedien}, address = {Wiesbaden}, doi = {10.1007/978-3-658-25326-4_31}, pages = {146 -- 151}, language = {en} } @inproceedings{RoserZhongBirkholdetal., author = {Roser, Philipp and Zhong, Xia and Birkhold, Annette and Preuhs, Alexander and Syben, Christopher and Hoppe, Elisabeth and Strobel, Norbert and Kowarschik, Markus and Fahrig, Rebecca and Maier, Andreas}, title = {Simultaneous estimation of X-ray back-scatter and forward-scatter using multi-task learning}, series = {Medical Image Computing and Computer Assisted Intervention-MICCAI 2020: 23rd International Conference, Lima, Peru, October 4-8, 2020, Proceedings, Part II 23}, booktitle = {Medical Image Computing and Computer Assisted Intervention-MICCAI 2020: 23rd International Conference, Lima, Peru, October 4-8, 2020, Proceedings, Part II 23}, publisher = {Springer International Publishing}, doi = {10.1007/978-3-030-59713-9_20}, pages = {199 -- 208}, language = {en} } @inproceedings{ZhongRoserBayeretal., author = {Zhong, Xia and Roser, Philipp and Bayer, Siming and Ravikumar, Nishant and Strobel, Norbert and Birkhold, Annette and Horz, Tim and Kowarschik, Markus and Fahrig, Rebecca and Maier, Andreas}, title = {Pediatric Patient Surface Model Atlas Generation and X-Ray Skin Dose Estimation}, series = {Bildverarbeitung f{\"u}r die Medizin 2019: Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 17. bis 19. M{\"a}rz 2019 in L{\"u}beck}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2019: Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 17. bis 19. M{\"a}rz 2019 in L{\"u}beck}, publisher = {Springer Fachmedien}, address = {Wiesbaden}, doi = {10.1007/978-3-658-25326-4_27}, pages = {122 -- 127}, language = {en} } @inproceedings{RoserBirkholdPreuhsetal., author = {Roser, Philipp and Birkhold, Annette and Preuhs, Alexander and Syben, Christopher and Strobel, Norbert and Kowarschik, Markus and Fahrig, Rebecca and Maier, Andreas}, title = {Deep scatter splines: Learning-based medical X-ray scatter estimation using B-splines}, series = {The 6th International Conference on Image Formation in X-Ray Computed Tompgraphy}, booktitle = {The 6th International Conference on Image Formation in X-Ray Computed Tompgraphy}, language = {en} } @article{HariharanKaethnerStrobeletal., author = {Hariharan, Sai Gokul and Kaethner, Christian and Strobel, Norbert and Kowarschik, Markus and Fahrig, Rebecca and Navab, Nassir}, title = {Robust learning-based x-ray image denoising—potential pitfalls, their analysis and solutions}, series = {Biomedical Physics \& Engineering Express}, volume = {8}, journal = {Biomedical Physics \& Engineering Express}, number = {3}, doi = {10.1088/2057-1976/ac3489}, language = {en} } @article{ZhongAmrehnRavikumaretal., author = {Zhong, Xia and Amrehn, Mario and Ravikumar, Nishant and Chen, Shuqing and Strobel, Norbert and Birkhold, Annette and Kowarschik, Markus and Fahrig, Rebecca and Maier, Andreas}, title = {Deep action learning enables robust 3D segmentation of body organs in various CT and MRI images}, series = {Scientific Reports}, volume = {11}, journal = {Scientific Reports}, number = {1}, doi = {10.1038/s41598-021-82370-6}, language = {en} } @article{RoserBirkholdPreuhsetal., author = {Roser, Philipp and Birkhold, Annette and Preuhs, Alexander and Stimpel, Bernhard and Syben, Christopher and Strobel, Norbert and Kowarschik, Markus and Fahrig, Rebecca and Maier, Andreas}, title = {Fully-automatic CT data preparation for interventional X-ray skin dose simulation}, series = {Bildverarbeitung f{\"u}r die Medizin 2020: Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 15. bis 17. M{\"a}rz 2020 in Berlin}, journal = {Bildverarbeitung f{\"u}r die Medizin 2020: Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 15. bis 17. M{\"a}rz 2020 in Berlin}, publisher = {Springer Fachmedien}, address = {Wiesbaden}, doi = {10.1007/978-3-658-29267-6_26}, pages = {125 -- 130}, language = {en} } @article{RoserBirkholdPreuhsetal., author = {Roser, Philipp and Birkhold, Annette and Preuhs, Alexander and Ochs, Philipp and Stepina, Elizaveta and Strobel, Norbert and Kowarschik, Markus and Fahrig, Rebecca and Maier, Andreas}, title = {XDose: toward online cross-validation of experimental and computational X-ray dose estimation}, series = {International Journal of Computer Assisted Radiology and Surgery}, volume = {16}, journal = {International Journal of Computer Assisted Radiology and Surgery}, number = {2}, doi = {10.1007/s11548-020-02298-6}, language = {en} } @article{RoserBirkholdPreuhsetal., author = {Roser, Philipp and Birkhold, Annette and Preuhs, Alexander and Syben, Christopher and Felsner, Lina and Hoppe, Elisabeth and Strobel, Norbert and Kowarschik, Markus and Fahrig, Rebecca and Maier, Andreas}, title = {X-ray Scatter Estimation Using Deep Splines}, series = {IEEE Transactions on Medical Imaging}, volume = {40}, journal = {IEEE Transactions on Medical Imaging}, number = {9}, pages = {2272 -- 2283}, language = {en} } @article{HariharanStrobelKaethneretal., author = {Hariharan, Sai Gokul and Strobel, Norbert and Kaethner, Christian and Kowarschik, Markus and Fahrig, Rebecca and Navab, Nassir}, title = {Data-driven estimation of noise variance stabilization parameters for low-dose x-ray images}, series = {Physics in Medicine and Biology}, volume = {65}, journal = {Physics in Medicine and Biology}, number = {22}, doi = {10.1088/1361-6560/abbc82}, language = {en} } @article{ZhongStrobelBirkholdetal., author = {Zhong, Xia and Strobel, Norbert and Birkhold, Annette and Kowarschik, Markus and Fahrig, Rebecca and Maier, Andreas}, title = {A machine learning pipeline for internal anatomical landmark embedding based on a patient surface model}, series = {International Journal of Computer Assisted Radiology and Surgery}, volume = {14}, journal = {International Journal of Computer Assisted Radiology and Surgery}, number = {1}, doi = {10.1007/s11548-018-1871-y}, pages = {53 -- 61}, language = {en} }