Dr. Philipp Fürnstahl Profile

Dr. Philipp Fürnstahl

at Univ Hospital Balgrist

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Publications (2)

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SPIE Journal Paper | 24 July 2023

Synthetic white balancing for intra-operative hyperspectral imaging

Anisha Bahl, Conor C. Horgan, Mirek Janatka, Oscar J. MacCormac, Philip Noonan, Yijing Xie, Jianrong Qiu, Nicola Cavalcanti, Philipp Fürnstahl, Michael Ebner, Mads S. Bergholt, Jonathan Shapey, Tom Vercauteren

JMI, Vol. 10, Issue 04, 046001, (July 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.4.046001

KEYWORDS: Hyperspectral imaging, Vignetting, Video, Light sources, Light sources and illumination, Cameras, Reconstruction algorithms, Light emitting diodes, Spectroscopy, RGB color model

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PurposeHyperspectral imaging shows promise for surgical applications to non-invasively provide spatially resolved, spectral information. For calibration purposes, a white reference image of a highly reflective Lambertian surface should be obtained under the same imaging conditions. Standard white references are not sterilizable and so are unsuitable for surgical environments. We demonstrate the necessity for in situ white references and address this by proposing a novel, sterile, synthetic reference construction algorithm.ApproachThe use of references obtained at different distances and lighting conditions to the subject were examined. Spectral and color reconstructions were compared with standard measurements qualitatively and quantitatively, using ΔE and normalized RMSE, respectively. The algorithm forms a composite image from a video of a standard sterile ruler, whose imperfect reflectivity is compensated for. The reference is modeled as the product of independent spatial and spectral components, and a scalar factor accounting for gain, exposure, and light intensity. Evaluation of synthetic references against ideal but non-sterile references is performed using the same metrics alongside pixel-by-pixel errors. Finally, intraoperative integration is assessed though cadaveric experiments.ResultsImproper white balancing leads to increases in all quantitative and qualitative errors. Synthetic references achieve median pixel-by-pixel errors lower than 6.5% and produce similar reconstructions and errors to an ideal reference. The algorithm integrated well into surgical workflow, achieving median pixel-by-pixel errors of 4.77% while maintaining good spectral and color reconstruction.ConclusionsWe demonstrate the importance of in situ white referencing and present a novel synthetic referencing algorithm. This algorithm is suitable for surgery while maintaining the quality of classical data reconstruction.

Proceedings Article | 16 March 2020 Presentation + Paper

Pivot calibration concept for sensor attached mobile c-arms

Sing Chun Lee, Matthias Seibold, Philipp Fürnstahl, Mazda Farshad, Nassir Navab

Proceedings Volume 11315, 1131503 (2020) https://doi.org/10.1117/12.2547581

KEYWORDS: Calibration, Sensors, X-rays, X-ray imaging, Imaging systems, Surgery, Cameras, Sensor calibration, Augmented reality, X-ray optics

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Medical augmented reality has been actively studied for decades and many methods have been proposed to revolutionize clinical procedures. One example is the camera augmented mobile C-arm (CAMC), which provides a real-time video augmentation onto medical images by rigidly mounting and calibrating a camera to the imaging device. Since then, several CAMC variations have been suggested by calibrating 2D/3D cameras, trackers, and more recently a Microsoft HoloLens to the C-arm. Different calibration methods have been applied to establish the correspondence between the rigidly attached sensor and the imaging device. A crucial step for these methods is the acquisition of X-Ray images or 3D reconstruction volumes; therefore, requiring the emission of ionizing radiation. In this work, we analyze the mechanical motion of the device and propose an alternative method to calibrate sensors to the C-arm without emitting any radiation. Given a sensor is rigidly attached to the device, we introduce an extended pivot calibration concept to compute the fixed translation from the sensor to the C-arm rotation center. The fixed relationship between the sensor and rotation center can be formulated as a pivot calibration problem with the pivot point moving on a locus. Our method exploits the rigid C-arm motion describing a Torus surface to solve this calibration problem. We explain the geometry of the C-arm motion and its relation to the attached sensor, propose a calibration algorithm and show its robustness against noise, as well as trajectory and observed pose density by computer simulations. We discuss this geometric-based formulation and its potential extensions to different C-arm applications.

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