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10. Non-Rigid Volume to Surface Registration using a Data-Driven Biomechanical Model

Author

Pfeiffer, Micha, Riediger, Carina, Leger, Stefan, Kühn, Jens-Peter, Seppelt, Danilo, Hoffmann, Ralf-Thorsten, Weitz, Jürgen, and Speidel, Stefanie

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Graphics, Computer Science - Machine Learning
Abstract

Non-rigid registration is a key component in soft-tissue navigation. We focus on laparoscopic liver surgery, where we register the organ model obtained from a preoperative CT scan to the intraoperative partial organ surface, reconstructed from the laparoscopic video. This is a challenging task due to sparse and noisy intraoperative data, real-time requirements and many unknowns - such as tissue properties and boundary conditions. Furthermore, establishing correspondences between pre- and intraoperative data can be extremely difficult since the liver usually lacks distinct surface features and the used imaging modalities suffer from very different types of noise. In this work, we train a convolutional neural network to perform both the search for surface correspondences as well as the non-rigid registration in one step. The network is trained on physically accurate biomechanical simulations of randomly generated, deforming organ-like structures. This enables the network to immediately generalize to a new patient organ without the need to re-train. We add various amounts of noise to the intraoperative surfaces during training, making the network robust to noisy intraoperative data. During inference, the network outputs the displacement field which matches the preoperative volume to the partial intraoperative surface. In multiple experiments, we show that the network translates well to real data while maintaining a high inference speed. Our code is made available online., Comment: Provisionally accepted for MICCAI 2020

Published
2020

13. Linearity, Bias, and Precision of Hepatic Proton Density Fat Fraction Measurements by Using MR Imaging: A Meta-Analysis

Author

Yokoo, Takeshi, Serai, Suraj D, Pirasteh, Ali, Bashir, Mustafa R, Hamilton, Gavin, Hernando, Diego, Hu, Houchun H, Hetterich, Holger, Kühn, Jens-Peter, Kukuk, Guido M, Loomba, Rohit, Middleton, Michael S, Obuchowski, Nancy A, Song, Ji Soo, Tang, An, Wu, Xinhuai, Reeder, Scott B, Sirlin, Claude B, and Committee, For the RSNA-QI PDFF Biomarker

Subjects
Biomedical Imaging, Adipose Tissue, Adolescent, Adult, Aged, Aged, 80 and over, Child, Female, Humans, Liver, Magnetic Resonance Imaging, Magnetic Resonance Spectroscopy, Male, Middle Aged, Non-alcoholic Fatty Liver Disease, Protons, Publication Bias, Sensitivity and Specificity, Young Adult, RSNA-QIBA PDFF Biomarker Committee, Medical and Health Sciences, Nuclear Medicine & Medical Imaging
Abstract

Purpose To determine the linearity, bias, and precision of hepatic proton density fat fraction (PDFF) measurements by using magnetic resonance (MR) imaging across different field strengths, imager manufacturers, and reconstruction methods. Materials and Methods This meta-analysis was performed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A systematic literature search identified studies that evaluated the linearity and/or bias of hepatic PDFF measurements by using MR imaging (hereafter, MR imaging-PDFF) against PDFF measurements by using colocalized MR spectroscopy (hereafter, MR spectroscopy-PDFF) or the precision of MR imaging-PDFF. The quality of each study was evaluated by using the Quality Assessment of Studies of Diagnostic Accuracy 2 tool. De-identified original data sets from the selected studies were pooled. Linearity was evaluated by using linear regression between MR imaging-PDFF and MR spectroscopy-PDFF measurements. Bias, defined as the mean difference between MR imaging-PDFF and MR spectroscopy-PDFF measurements, was evaluated by using Bland-Altman analysis. Precision, defined as the agreement between repeated MR imaging-PDFF measurements, was evaluated by using a linear mixed-effects model, with field strength, imager manufacturer, reconstruction method, and region of interest as random effects. Results Twenty-three studies (1679 participants) were selected for linearity and bias analyses and 11 studies (425 participants) were selected for precision analyses. MR imaging-PDFF was linear with MR spectroscopy-PDFF (R2 = 0.96). Regression slope (0.97; P < .001) and mean Bland-Altman bias (-0.13%; 95% limits of agreement: -3.95%, 3.40%) indicated minimal underestimation by using MR imaging-PDFF. MR imaging-PDFF was precise at the region-of-interest level, with repeatability and reproducibility coefficients of 2.99% and 4.12%, respectively. Field strength, imager manufacturer, and reconstruction method each had minimal effects on reproducibility. Conclusion MR imaging-PDFF has excellent linearity, bias, and precision across different field strengths, imager manufacturers, and reconstruction methods. © RSNA, 2017 Online supplemental material is available for this article. An earlier incorrect version of this article appeared online. This article was corrected on October 2, 2017.

Published
2018

17. Lower muscular strength is associated with greater liver fat content and higher serum liver enzymes—"The Sedentary's Liver" The Study of Health in Pomerania.

Author

Mayer, Claudius, Ittermann, Till, Schipf, Sabine, Gross, Stefan, Kim, Simon, Schielke, Jan, Bülow, Robin, Kühn, Jens‐Peter, Lerch, Markus M., Völzke, Henry, Felix, Stephan Burkhard, Bahls, Martin, Targher, Giovanni, Dörr, Marcus, and Markus, Marcello Ricardo Paulista

Subjects
CROSS-sectional method, FATTY liver, RESEARCH funding, MULTIPLE regression analysis, SEDENTARY lifestyles, MAGNETIC resonance imaging, MUSCLE strength, GAMMA-glutamyltransferase, ODDS ratio, PHYSICAL fitness, CONFIDENCE intervals, GRIP strength, AMINOTRANSFERASES
Abstract

We investigated the associations of low handgrip strength (HGS, i.e., a marker of muscular fitness) with liver fat content (LFC) and serum liver enzymes in a population‐based setting. We used data from 2700 participants (51.7% women), aged 21–90 years, from two independent cohorts of the population‐based Study of Health in Pomerania (SHIP‐START‐2 and SHIP‐TREND‐0). Cross‐sectional, multivariable adjusted regression models were performed to examine the associations of HGS with LFC, measured by magnetic resonance imaging and serum liver enzymes. We found significant inverse associations of HGS with both LFC and serum liver enzymes. Specifically, a 10‐kg lower HGS was associated with a 0.59% (95% confidence interval [CI]: 0.24–0.94; p = 0.001) higher LFC, a 0.051 µkatal/L (95% CI: 0.005–0.097; p = 0.031) higher gamma‐glutamyltransferase (GGT) concentration and a 0.010 µkatal/L (95% CI: 0.001–0.020; p = 0.023) higher aspartate aminotransferase (AST) concentration. The adjusted odds‐ratio for prevalent hepatic steatosis (defined by a MRI‐PDFF ≥5.1%) per 10‐kg lower HGS was 1.21 (95% CI: 1.04–1.40; p = 0.014). When considering only obese individuals, those with low HGS had a 1.58% (95% CI: 0.18–2.98; p = 0.027) higher mean LFC and higher chance of prevalent hepatic steatosis (adjusted OR 1.74, 95% CI: 1.15–2.62; p = 0.009) compared to individuals with high HGS. We found similar associations in individuals with overweight, but not in those with normal weight. Lower HGS was strongly associated with both higher LFC and higher serum GGT and AST concentrations. Future studies might clarify whether these findings reflect adverse effects of a sedentary lifestyle or aging on the liver. Highlights: We used magnetic resonance imaging, the most accurate and sensitive noninvasive diagnostic tool for determination of hepatic steatosisLower handgrip strength was strongly associated with both higher liver fat content and higher serum GGT and AST concentrationsEspecially overweight and obese individuals with low handgrip strength had a significant higher risk of prevalent hepatic steatosis [ABSTRACT FROM AUTHOR]

Published
2024
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32. Quantification of Liver Iron Overload with MRI: Review and Guidelines from the ESGAR and SAR

Author

Reeder, Scott B., primary, Yokoo, Takeshi, additional, França, Manuela, additional, Hernando, Diego, additional, Alberich-Bayarri, Ángel, additional, Alústiza, José María, additional, Gandon, Yves, additional, Henninger, Benjamin, additional, Hillenbrand, Claudia, additional, Jhaveri, Kartik, additional, Karçaaltıncaba, Musturay, additional, Kühn, Jens-Peter, additional, Mojtahed, Amirkasra, additional, Serai, Suraj D., additional, Ward, Richard, additional, Wood, John C., additional, Yamamura, Jin, additional, and Martí-Bonmatí, Luis, additional

Published
2023
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35. Interactive Medical Miner: Interactively Exploring Subpopulations in Epidemiological Datasets

Author

Niemann, Uli, Spiliopoulou, Myra, Völzke, Henry, Kühn, Jens-Peter, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, Series editor, Tanaka, Yuzuru, Series editor, Wahlster, Wolfgang, Series editor, Siekmann, Jörg, Series editor, Calders, Toon, editor, Esposito, Floriana, editor, Hüllermeier, Eyke, editor, and Meo, Rosa, editor

Published
2014
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36. Mining Longitudinal Epidemiological Data to Understand a Reversible Disorder

Author

Hielscher, Tommy, Spiliopoulou, Myra, Völzke, Henry, Kühn, Jens-Peter, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Blockeel, Hendrik, editor, van Leeuwen, Matthijs, editor, and Vinciotti, Veronica, editor

Published
2014
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37. The Abdomen

Author

Kühn, Jens-Peter, Lühken, Christoph, Puls, Ralf, editor, and Hosten, Norbert, editor

Published
2014
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40. Prediction of clinically relevant postoperative pancreatic fistula using radiomic features and preoperative data

Author

Bhasker, Nithya, primary, Kolbinger, Fiona R., additional, Skorobohach, Nadiia, additional, Zwanenburg, Alex, additional, Löck, Steffen, additional, Weitz, Jürgen, additional, Hoffmann, Ralf-Thorsten, additional, Distler, Marius, additional, Speidel, Stefanie, additional, Leger, Stefan, additional, and Kühn, Jens-Peter, additional

Published
2022
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42. Fully Automatic Liver Volumetry Using 3D Level Set Segmentation for Differentiated Liver Tissue Types in Multiple Contrast MR Datasets

Author

Gloger, Oliver, Toennies, Klaus, Kuehn, Jens-Peter, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Heyden, Anders, editor, and Kahl, Fredrik, editor

Published
2011
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44. Subpopulation Discovery in Epidemiological Data with Subspace Clustering

Author

Niemann Uli, Spiliopoulou Myra, Völzke Henry, and Kühn Jens-Peter

Subjects
Electronic computers. Computer science, QA75.5-76.95
Abstract

A prerequisite of personalized medicine is the identification of groups of people who share specific risk factors towards an outcome. We investigate the potential of subspace clustering for finding such groups in epidemiological data. We propose a workflow that encompasses clusterability assessment before cluster discovery and quality assessment after learning the clusters. Epidemiological usually do not have a ground truth for the verification of clusters found in subspaces. Hence, we introduce quality assessment through juxtaposition of the learned models to “models-of-randomness”, i.e. models that do not reflect a true cluster structure. On the basis of this workflow, we select subspace clustering methods, compare and discuss their performance. We use a dataset with hepatic steatosis as outcome, but our findings apply on arbitrary epidemiological cohort data that have tenths of variables and exhibit class skew.

Published
2014
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