Fabian Gigengack

University of Münster

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Co-Authors

Xiaoyi Jiang

Shandong Marine Resource and Environment Research Institute

Klaus Schäfers

University of Münster

Mohammad Dawood

CECOS University

Martin Burger

Deutsches Elektronen-Synchrotron DESY

Lars Ruthotto

Emory University

Michael Fieseler

University of Münster

Carsten H. Wolters

University of Münster

Jan Modersitzki

University of Lübeck

Thomas Kösters

University of Münster

Sven Hermann

University of Münster

Cooperative Institutions

University of Münster

University Hospital Münster

Friedrich-Alexander-Universität Erlangen-Nürnberg

University of Tübingen

Universitätsklinik für Strahlentherapie

Klinik für Schlafmedizin

Klinik und Poliklinik für Strahlentherapie und Radioonkologie

Blackberry (United States)

Klinik und Poliklinik für Nuklearmedizin

West German Heart and Vascular Center Essen

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A Simplified Pipeline for Motion Correction in Dual Gated Cardiac PET

Informatik aktuell (2012)

Lars Ruthotto Fabian Gigengack Martin Burger Carsten H. Wolters Xiaoyi Jiang

Cardiac PET

Correction for attenuation

10.1007/978-3-642-28502-8_11

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Citations (6)

Motion Correction of Whole-Body PET Data with a Joint PET-MRI Registration Functional

BioMedical Engineering OnLine (2014)

Michael Fieseler Fabian Gigengack Xiaoyi Jiang Klaus Schäfers

Respiratory motion is known to degrade image quality in PET imaging. The necessary acquisition time of several minutes per bed position will inevitably lead to a blurring effect due to organ motion. A lot of research has been done with regards to motion correction of PET data. As full-body PET-MRI became available recently, the anatomical data provided by MRI is a promising source of motion information. Current PET-MRI-based motion correction approaches, however, do not take into account the available information provided by PET data. PET data, though, may add valuable additional information to increase motion estimation robustness and precision.

PET Imaging

Whole body imaging

Robustness

Cardiac PET

10.1186/1475-925x-13-s1-s2

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Citations (34)

Motion correction in PET-MRI: A human torso phantom study

IEEE Nuclear Science Symposium conference record (2011)

Michael Fieseler Thomas Kösters Fabian Gigengack Harald Braun Harald H. Quick

Respiratory and cardiac motion as degrading factors in PET imaging have been tackled by gating and subsequent PET-based motion detection techniques. The limitation of these approaches, however, is that only regions with high uptake yield sufficient motion information. Motion detection for tiny structures, e.g. arteriosclerotic plaques or small tumors, can prove difficult. In PET-MRI, the simultaneously acquired MR data are a promising source of motion information. In the present work, we give preliminary results for motion correction of PET data using information derived from MR data. A human torso phantom capable of simulating cardiac and respiratory motion was used to generate realistic data. Our preliminary results show that motion correction in PET-MRI is a promising approach.

Torso

Real-time MRI

10.1109/nssmic.2011.6153673

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Citations (5)

A dynamic thorax phantom for the assessment of cardiac and respiratory motion correction in PET/MRI: A preliminary evaluation

Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment (2012)

Michael Fieseler Harald Kugel Fabian Gigengack Thomas Kösters Florian Büther

Torso

Cardiac PET

Thorax (insect anatomy)

PET Imaging

10.1016/j.nima.2012.09.039

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Citations (33)

OccFlowNet: Towards Self-supervised Occupancy Estimation via Differentiable Rendering and Occupancy Flow

arXiv (Cornell University) (2024)

Simon Boeder Fabian Gigengack Benjamin Risse

Semantic occupancy has recently gained significant traction as a prominent 3D scene representation. However, most existing methods rely on large and costly datasets with fine-grained 3D voxel labels for training, which limits their practicality and scalability, increasing the need for self-monitored learning in this domain. In this work, we present a novel approach to occupancy estimation inspired by neural radiance field (NeRF) using only 2D labels, which are considerably easier to acquire. In particular, we employ differentiable volumetric rendering to predict depth and semantic maps and train a 3D network based on 2D supervision only. To enhance geometric accuracy and increase the supervisory signal, we introduce temporal rendering of adjacent time steps. Additionally, we introduce occupancy flow as a mechanism to handle dynamic objects in the scene and ensure their temporal consistency. Through extensive experimentation we demonstrate that 2D supervision only is sufficient to achieve state-of-the-art performance compared to methods using 3D labels, while outperforming concurrent 2D approaches. When combining 2D supervision with 3D labels, temporal rendering and occupancy flow we outperform all previous occupancy estimation models significantly. We conclude that the proposed rendering supervision and occupancy flow advances occupancy estimation and further bridges the gap towards self-supervised learning in this domain.

Occupancy

10.48550/arxiv.2402.12792

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Citations (1)

Motion Correction

Springer briefs in electrical and computer engineering (2014)

Fabian Gigengack Xiaoyi Jiang Mohammad Dawood Klaus Schäfers

Structure from Motion

Motion field

Quarter-pixel motion

10.1007/978-3-319-08392-6_3

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Mass-preserving motion correction of PET: Displacement field vs. spline transformation

IEEE Nuclear Science Symposium conference record (2011)

Fabian Gigengack Lars Ruthotto Martin Burger Carsten H. Wolters Xiaoyi Jiang

In Positron Emission Tomography (PET), motion due to the cardiac and respiratory cycle causes blurred images. Different approaches for motion correction in PET vary in the general concept (optical flow or image registration) or, e.g., in the discretization of motion. Given our mass-preserving transformation model, we evaluate different motion models in this work: dense displacement field (compute for each voxel an individual displacement) vs. spline transformation (i.e. free-form deformation). Thereby a focus is put on the parametrization of the spline transformations where we optimize the number of spline coefficients and the regularization parameter. We make a quantitative comparison of the motion estimates of the different motion models based on data of the established XCAT software phantom. For both motion models (Displacement Field (DF) and Spline Transformation (ST)) the registration results are evaluated by 1) the total processing time and 2) the Euclidean distance to the ground-truth vectors provided by the XCAT phantom. We found that the spline transformation model is superior to the displacement field strategy in terms of processing time and accuracy.

Spline (mechanical)

Displacement field

Ground truth

10.1109/nssmic.2011.6152559

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Citations (2)

Motion Estimation

Springer briefs in electrical and computer engineering (2014)

Fabian Gigengack Xiaoyi Jiang Mohammad Dawood Klaus Schäfers

10.1007/978-3-319-08392-6_2

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Citations (0)