Publications | Dr. Oliver Zettinig

2024

Reliability of Manual Measurements Versus Semiautomated Software for Glenoid Bone Loss Quantification in Patients With Anterior Shoulder Instability

Katrin Karpinski, Doruk Akguen, Henry Gebauer, Alp Paksoy, Mattia Lupetti, Viktoria Markova, Oliver Zettinig, and Philipp Moroder

Orthopaedic Journal of Sports Medicine, Feb 2024

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Background: The presence of glenoid bone defects is indicative in the choice of treatment for patients with anterior shoulder instability. In contrast to traditional linear- and area-based measurements, techniques such as the consideration of glenoid concavity have been proposed and validated. Purpose: To compare the reliability of linear (1-dimensional [1D]), area (2-dimensional [2D]), and concavity (3-dimensional [3D]) measurements to quantify glenoid bone loss performed manually and to analyze how automated measurements affect reliability. Study Design: Cohort study (diagnosis); Level of evidence, 3. Methods: Computed tomography images of 100 patients treated for anterior shoulder instability with differently sized glenoid defects were evaluated independently by 2 orthopaedic surgeons manually using conventional software (OsiriX; Pixmeo) as well as automatically with a dedicated prototype software program (ImFusion Suite; ImFusion). Parameters obtained included 1D (defect diameter, best-fit circle diameter), 2D (defect area, best-fit circle area), and 3D (bony shoulder stability ratio) measurements. Mean values and reliability as expressed by the intraclass correlation coefficient [ICC]) were compared between the manual and automated measurements. Results: When manually obtained, the measurements showed almost perfect agreement for 1D parameters (ICC = 0.83), substantial agreement for 2D parameters (ICC = 0.79), and moderate agreement for the 3D parameter (ICC = 0.48). When measurements were aided by automated software, the agreement between raters was almost perfect for all parameters (ICC = 0.90 for 1D, 2D, and 3D). There was a significant difference in mean values between manually versus automatically obtained measurements for 1D, 2D, and 3D parameters ( P \textless .001 for all). Conclusion: While more advanced measurement techniques that take glenoid concavity into account are more accurate in determining the biomechanical relevance of glenoid bone loss, our study showed that the reliability of manually performed, more complex measurements was moderate.
The Global Track Concept for Assessment of Engaging Hill-Sachs Defects in Anterior Shoulder Instability

Philipp Moroder, Doruk Akguen, Alp Paksoy, Henry Gebauer, Nicolas Barthod-Tonnot, Mattia Lupetti, Viktoria Markova, Oliver Zettinig, Stephen Parada, and Katrin Karpinski

The American Journal of Sports Medicine, Apr 2024

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Background: The glenoid track concept is used to determine preoperatively whether a Hill-Sachs defect is engaging or not. Currently, the glenoid track concept relies on measurements of bony structures as well as on the confines and elasticity of the rotator cuff as a reference point, which varies extensively among individuals and therefore limits the reliability and accuracy of this concept. Purpose: To evaluate the reliability of the global track concept, which determines the angular distance of the Hill-Sachs defect from the center of the articular surface of the humeral head as a new reference point with the help of an automated image analysis software and 3-dimensional analysis of the humeral head. Study Design: Controlled laboratory study. Methods: Computed tomography scans of 100 patients treated for anterior shoulder instability with different sizes of Hill-Sachs defects were evaluated manually by 2 orthopaedic surgeons independently using the software OsiriX as well as automatically by using a dedicated prototype software (ImFusion). Obtained manual and automated measurements included the Hill-Sachs length, Hill-Sachs width, and Hill-Sachs depth of the defect; the Hill-Sachs interval (HSI); and the glenoid width for the glenoid track concept, as well as the angular distance of the Hill-Sachs defect from the center of the articular surface of the humeral head (global track concept). The reliability of the different measurement techniques was compared by calculating intraclass correlation coefficients (ICCs). Results: There was a significant difference for all obtained parameters comparing manual and automatic measurements. For manually obtained parameters, measurements referring to bony boundaries (glenoid width, Hill-Sachs length, and Hill-Sachs width) showed good to excellent agreement (ICC, 0.86, 0.82, and 0.62, respectively), while measurements referring to soft tissue boundaries (HSI and glenoid track; ICC, 0.56 and 0.53, respectively) or not directly identifiable reference points (center of articular surface and global track) only showed fair reliability (ICC middle excursion, 0.42). When the same parameters were measured with the help of an automated software, good reliability for the glenoid track concept and excellent reliability for the global track concept in the middle excursion were achieved. Conclusion: The present study showed that the more complex global track measurements of humeral defects are more reliable than the current standard HSI and glenoid track measurements. However, this is only true when automated software is used to perform the measurements. Clinical Relevance: Future studies using the new proposed method in combination with an automated software need to be conducted to determine critical threshold values for defects prone to engagement.

2023

Femur Reconstruction in 3D Ultrasound for Orthopedic Surgery Planning

Christian Gebhardt, Lara Göttling, Lukas Buchberger, Christian Ziegler, Felix Endres, Quirin Wuermeling, Boris M. Holzapfel, Wolfgang Wein, Ferdinand Wagner, and Oliver Zettinig

International Journal of Computer Assisted Radiology and Surgery, Jun 2023

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DISA: DIfferentiable Similarity Approximation for Universal Multimodal Registration

Matteo Ronchetti, Wolfgang Wein, Nassir Navab, Oliver Zettinig, and Raphael Prevost

In Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, Sep 2023

Series Title: Lecture Notes in Computer Science

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2022

Orientation Estimation of Abdominal Ultrasound Images with Multi-Hypotheses Networks

Timo Horstmann, Oliver Zettinig, Wolfgang Wein, and Raphael Prevost

In Proceedings of The 5th International Conference on Medical Imaging with Deep Learning, Jul 2022

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Ultrasound imaging can provide valuable information to clinicians during interventions, in particular when fused with other modalities. Multi-modal image registration algorithms however require a somewhat accurate initialization, which is particularly difficult to estimate for ultrasound images as their orientation is arbitrary and their content ambiguous (limited field of view, artifacts, etc.). In this work, we not only train neural networks to predict the absolute orientation of ultrasound frames, but also to produce a confidence for each prediction. This allows us to select only the most confident frames in the clip. Our networks are trained to produce multiple hypotheses using a simple yet overlooked meta-loss that is specifically designed to capture the ambiguity of the input data. We show on several abdominal ultrasound datasets that multi-hypotheses networks provide better uncertainty estimates than Monte-Carlo dropout while being more efficient than network ensembling. Generic, easy to implement and able to quantify both data ambiguity and out-of-distribution samples, they represent a preferable alternative to traditional baselines for uncertainty estimation. On a clinical test our method produces estimates within 20^∘ of the true orientation, which we can use to improve the accuracy of a subsequent registration algorithm down to less than 10^∘.
Standardized Evaluation of Current Ultrasound Bone Segmentation Algorithms on Multiple Datasets

Prashant Pandey, Benjamin Hohlmann, Peter Broessner, Ilker Hacihaliloglu, Keiran Barr, Tamas Ungi, Oliver Zettinig, Raphael Prevost, Guillaume Dardenne, Zian Fanti, Wolfgang Wein, Eric Stindel, Fernando Arambula Cosio, Pierre Guy, Gabor Fichtinger, Klaus Radermacher, and Antony Hodgson

In CAOS 2022. The 20th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery, Jun 2022

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Global Multi-modal 2D/3D Registration via Local Descriptors Learning

Viktoria Markova, Matteo Ronchetti, Wolfgang Wein, Oliver Zettinig, and Raphael Prevost

In Medical Image Computing and Computer Assisted Intervention – MICCAI 2022, Sep 2022

Series Title: Lecture Notes in Computer Science

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PRO-TIP: Phantom for RObust Automatic Ultrasound Calibration by TIP Detection

Matteo Ronchetti, Julia Rackerseder, Maria Tirindelli, Mehrdad Salehi, Nassir Navab, Wolfgang Wein, and Oliver Zettinig

In Medical Image Computing and Computer Assisted Intervention – MICCAI 2022, Sep 2022

Series Title: Lecture Notes in Computer Science

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2020

Three-Dimensional Thyroid Assessment from Untracked 2D Ultrasound Clips

Wolfgang Wein, Mattia Lupetti, Oliver Zettinig, Simon Jagoda, Mehrdad Salehi, Viktoria Markova, Dornoosh Zonoobi, and Raphael Prevost

In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, Sep 2020

Series Title: Lecture Notes in Computer Science

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2019

Smart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image Analysis. First International Workshop, SUSI 2019, and 4th International Workshop, PIPPI 2019, Held in Conjunction with MICCAI 2019. Preface SUSI 2019.

Alberto Gomez, Kristin McLeod, Veronika Zimmer, and Oliver Zettinig

Oct 2019

Publication Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ISSN: 16113349

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2018

Robotic ultrasound-guided facet joint insertion

Javier Esteban, Walter Simson, Sebastian Requena Witzig, Anna Rienmüller, Salvatore Virga, Benjamin Frisch, Oliver Zettinig, Drazen Sakara, Yu-Mi Ryang, Nassir Navab, and Christoph Hennersperger

International Journal of Computer Assisted Radiology and Surgery, Jun 2018

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Purpose: Facet joint insertion is a common treatment of chronic pain in the back and spine. This procedure is often performed under fluoroscopic guidance, where the staff’s repetitive radiation exposure remains an unsolved problem. Robotic ultrasound (rUS) has the potential to reduce or even eliminate the use of radiation by using ultrasound with a robotic-guided needle insertion. This work presents first clinical data of rUS-based needle insertions extending previous work of our group. Methods: Our system implements an automatic US acquisition protocol combined with a calibrated needle targeting system. This approach assists the physician by positioning the needle holder on a trajectory selected in a 3D US volume of the spine. Results: By the time of submission, nine facets were treated with our approach as first data from an ongoing clinical study. The insertion success rate was shown to be comparable to current clinical practice. Furthermore, US imaging offers additional anatomical context for needle trajectory planning. Conclusion: This work shows first clinical data for robotic ultrasound-assisted facet joint insertion as a promising solution that can easily be incorporated into the clinical workflow. Presented results show the clinical value of such a system.
3D freehand ultrasound without external tracking using deep learning

Raphael Prevost, Mehrdad Salehi, Simon Jagoda, Navneet Kumar, Julian Sprung, Alexander Ladikos, Robert Bauer, Oliver Zettinig, and Wolfgang Wein

Medical Image Analysis, Aug 2018

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This work aims at creating 3D freehand ultrasound reconstructions from 2D probes with image-based tracking, therefore not requiring expensive or cumbersome external tracking hardware. Existing model-based approaches such as speckle decorrelation only partially capture the underlying complexity of ultrasound image formation, thus producing reconstruction accuracies incompatible with current clinical requirements. Here, we introduce an alternative approach that relies on a statistical analysis rather than physical models, and use a convolutional neural network (CNN) to directly estimate the motion of successive ultrasound frames in an end-to-end fashion. We demonstrate how this technique is related to prior approaches, and derive how to further improve its predictive capabilities by incorporating additional information such as data from inertial measurement units (IMU). This novel method is thoroughly evaluated and analyzed on a dataset of 800 in vivo ultrasound sweeps, yielding unprecedentedly accurate reconstructions with a median normalized drift of 5.2%. Even on long sweeps exceeding 20 cm with complex trajectories, this allows to obtain length measurements with median errors of 3.4%, hence paving the way toward translation into clinical routine.
Recent Advances in Point-of-Care Ultrasound Using the ImFusion Suite for Real-Time Image Analysis

Oliver Zettinig, Mehrdad Salehi, Raphael Prevost, and Wolfgang Wein

In MICCAI 2018 Workshop Point-of-Care Ultrasound. POCUS 2018, BIVPCS 2018, CuRIOUS 2018, CPM 2018: Simulation, Image Processing, and Ultrasound Systems for Assisted Diagnosis and Navigation, Oct 2018

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Medical ultrasound is rapidly advancing both through more powerful hardware and software; in combination these allow the modality to become an ever more indispensable point-of-care tool. In this paper, we summarize some recent developments on the image analysis side that are enabled through the proprietary ImFusion Suite software and corresponding software development kit (SDK). These include 3D reconstruction of arbitrary untracked 2D US clips, image filtering and classification, speed-of-sound calibration and live acquisition parameter tuning in a visual servoing fashion.

2017

Towards MRI-Based Autonomous Robotic US Acquisitions: A First Feasibility Study

Christoph Hennersperger, Bernhard Fuerst, Salvatore Virga, Oliver Zettinig, Benjamin Frisch, Thomas Neff, and Nassir Navab

IEEE Transactions on Medical Imaging, Oct 2017

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On the reproducibility of expert-operated and robotic ultrasound acquisitions

Risto Kojcev, Ashkan Khakzar, Bernhard Fuerst, Oliver Zettinig, Carole Fahkry, Robert DeJong, Jeremy Richmon, Russell Taylor, Edoardo Sinibaldi, and Nassir Navab

International Journal of Computer Assisted Radiology and Surgery, Jun 2017

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Preconditioned intensity-based prostate registration using statistical deformation models

Oliver Zettinig, Julia Rackerseder, Beatrice Lentes, Tobias Maurer, Kay Westenfelder, Matthias Eiber, Benjamin Frisch, and Nassir Navab

In 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Apr 2017

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3D ultrasound registration-based visual servoing for neurosurgical navigation

Oliver Zettinig, Benjamin Frisch, Salvatore Virga, Marco Esposito, Anna Rienmüller, Bernhard Meyer, Christoph Hennersperger, Yu-Mi Ryang, and Nassir Navab

International Journal of Computer Assisted Radiology and Surgery, Sep 2017

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PhD Thesis

Advanced Ultrasound Imaging Techniques for Computer Assisted Interventions

Oliver Zettinig

Technische Universität München. PhD thesis , Mar 2017

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2016

Collaborative Robotic Ultrasound: Towards Clinical Application

Benjamin Frisch, Oliver Zettinig, Bernhard Fuerst, Salvatore Virga, Christoph Hennersperger, and Nassir Navab

In Radiological Society of North America (RSNA) Annual Meeting, Dec 2016

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Dual-robot ultrasound-guided needle placement: closing the planning-imaging-action loop

Risto Kojcev, Bernhard Fuerst, Oliver Zettinig, Javad Fotouhi, Sing Chun Lee, Benjamin Frisch, Russell Taylor, Edoardo Sinibaldi, and Nassir Navab

International Journal of Computer Assisted Radiology and Surgery, Jun 2016

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Purpose: Precise needle placement is an important task during several medical procedures. Ultrasound imaging is often used to guide the needle toward the target region in soft tissue. This task remains challenging due to the user’s dependence on image quality, limited field of view, moving target, and moving needle. In this paper, we present a novel dual-robot framework for robotic needle insertions under robotic ultrasound guidance. Method: We integrated force-controlled ultrasound image acquisition, registration of preoperative and intraoperative images, vision-based robot control, and target localization, in combination with a novel needle tracking algorithm. The framework allows robotic needle insertion to target a preoperatively defined region of interest while enabling real-time visualization and adaptive trajectory planning to provide safe and quick interactions. We assessed the framework by considering both static and moving targets embedded in water and tissue-mimicking gelatin. Results: The presented dual-robot tracking algorithms allow for accurate needle placement, namely to target the region of interest with an error around 1 mm. Conclusion: To the best of our knowledge, we show the first use of two independent robots, one for imaging, the other for needle insertion, that are simultaneously controlled using image processing algorithms. Experimental results show the feasibility and demonstrate the accuracy and robustness of the process.
Automatic force-compliant robotic ultrasound screening of abdominal aortic aneurysms

Salvatore Virga, Oliver Zettinig, Marco Esposito, Karin Pfister, Benjamin Frisch, Thomas Neff, Nassir Navab, and Christoph Hennersperger

In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct 2016

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Ultrasound (US) imaging is commonly employed for the diagnosis and staging of abdominal aortic aneurysms (AAA), mainly due to its non-invasiveness and high availability. High inter-operator variability and a lack of repeatability of current US image acquisition impair the implementation of extensive screening programs for affected patient populations. However, this opens the way to a possible automation of the procedure, and recent works have exploited the use of robotic platforms for US applications, both in diagnostic and interventional scenarios. In this work, we propose a system for autonomous robotic US acquisitions aimed at the quantitative assessment of patients’ vessel diameter for abdominal aortic aneurysm screening. Using a probabilistic measure of the US quality, we introduce an automatic estimation of the optimal pressure to be applied during the acquisition, and an online optimization of the out-of-plane rotation of the US probe to maximize the visibility of the aorta. We evaluate our method on healthy volunteers and compare the results to manual acquisitions performed by a clinical expert, demonstrating the feasibility of the presented system for AAA screening.
Toward real-time 3D ultrasound registration-based visual servoing for interventional navigation

Oliver Zettinig, Bernhard Fuerst, Risto Kojcev, Marco Esposito, Mehrdad Salehi, Wolfgang Wein, Julia Rackerseder, Edoardo Sinibaldi, Benjamin Frisch, and Nassir Navab

In 2016 IEEE International Conference on Robotics and Automation (ICRA), May 2016

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2015

PET/MRI/TRUS image fusion guided prostate biopsy: Development of a research platform and initial clinical results

Benjamin Frisch, Enno Storz, Oliver Zettinig, Amit Shah, Hubert Kübler, Nassir Navab, Hans-Jürgen Wester, Markus Schwaiger, Matthias Eiber, and Tobias Maurer

Journal of Nuclear Medicine, May 2015

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Objectives The gold standard for prostate biopsy, transrectal ultrasound (TRUS) guided random biopsy (RB), suffers from low sensitivity. Recent advancements in prostate PET imaging with 68Ga-HBED-PSMA (Prostate Specific Membrane Antigen), combined with multi-parametric MR, provide improved identification of suspicious areas. The development of a dedicated framework for PET/MR to TRUS image fusion guided biopsy (IFGB) might improve prostate cancer (PCa) detection rates. Methods We propose a platform with minimal overhead in the clinical workflow that provides both rigid PET/MR to TRUS registration based on the manual selection of landmarks and elastic registration supported by automatically segmented images. The distance between the surfaces of manual and automatic segmentations and registration errors (RE) on manually selected landmarks evaluate the technical performance. The initial clinical study includes 16 patients who had at least one prior negative RB. A targeted biopsy was carried out following a systematic 10-core RB. All samples were histologically analyzed. Results The mean surface distance between manual and automatic segmentation is below 2 mm. The average RE for the rigid and elastic registration are 4.6 mm and 2.5 mm. On PSMA-PET/MR, 7 prostates revealed at least one PCa-suspicious lesion, 5 were described as equivocal and 4 as inconspicuous. The histological analysis indicated PCa in 8 of the patients, out of which 6 had suspicious and 2 equivocal findings on PSMA-PET/MR. Conclusions PSMA-PET/MR to TRUS IFGB could be a valuable tool for PCa detection in patients with prior negative RB. Further developments and studies are necessary to improve and evaluate its full potential.
Challenges in Multimodal Image-guided Targeted Prostate Biopsy

Amit Shah, Oliver Zettinig, Enno Storz, Tobias Maurer, Matthias Eiber, Nassir Navab, and Benjamin Frisch

In Hamlyn Symposium on Medical Robotics, London, UK, Jun 2015

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PSMA-PET/MRI-guided transrectal fusion biopsy for the detection of prostate cancer

Enno Storz, Amit Shah, Oliver Zettinig, Matthias Eiber, Hans-Jürgen Wester, Hubert Kübler, Jürgen E Gschwend, Markus Schwaiger, Benjamin Frisch, and Tobias Maurer

European Urology Supplements, Apr 2015

Publisher: European Association of Urology

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INTRODUCTION & OBJECTIVES: Despite its low sensitivity transrectal ultrasound (TRUS)-guided random biopsy (RB) represents still the gold standard for the diagnosis of prostate cancer (PCa). However, with the advent of modern fusion biopsy systems enabling fusion of preinterventional MR imaging of the prostate to TRUS and thus targeting suspicious areas more accurately increased detection rates are reported. Recently, PET imaging using a novel 68Gallium-labelled ligand of the prostate-specific membrane antigen (PSMA) has been introduced in the diagnostic work-up of PCa. Therefore, the aim of this initial study was to determine the accuracy of PCa detection by PSMA-PET/MR imaging using a newly established open source framework for fusion biopsy guidance. MATERIAL & METHODS: 16 patients (age 45-75 years; median PSA 7.82 ng/ml (range 4.0 - 13.2 ng/ml)) who had at least one prior negative prostate biopsy were included in this study. Every patient underwent multiparametric PSMA-PET/MR imaging of the prostate. All patients received a systematic 10-core random biopsy as well as fusion-guided transrectal biopsy of suspicious lesions on PSMA-PET/MR in an outpatient setting. Results of imaging and histological analysis of prostate biopsies were compared per patient and per prostate sextant (apical, medial, basal). RESULTS: On PSMA-PET/MRI 44% (7/16) of the prostates revealed at least one PCa-suspicious lesion, 31% (5/16) were described as equivocal and in 25% (4/16) no suspicious lesion was present. The pathological analysis revealed PCa in 50% (8/16) of all patients - six patients with suspicious findings and two with equivocal findings on PSMA-PET/MRI. Of note, histological analysis did not show PCa in any of the patients with inconspicuous PSMA-PET/MRI. On a sextant basis, 23% (22/96) were suspicious on PSMA-PET/MRI. In 16% of all sextants (15/96) PCa was histologically proven. 80% (12/15) of these areas, where PCa was found, were also suspect on PSMA-PET/MRI. CONCLUSIONS: In this initial analysis, PSMA-PET/MRI in the combination with a newly developed fusion biopsy system proved as valuable tool for the detection of PCa in patients after prior negative prostate biopsy. However, greater patient cohorts are necessary to establish the exact clinical role of PSMA-PET/MRI-guided fusion biopsy.
Multimodal image-guided prostate fusion biopsy based on automatic deformable registration

Oliver Zettinig, Amit Shah, Christoph Hennersperger, Matthias Eiber, Christine Kroll, Hubert Kübler, Tobias Maurer, Fausto Milletarì, Julia Rackerseder, Christian Berge, Enno Storz, Benjamin Frisch, and Nassir Navab

International Journal of Computer Assisted Radiology and Surgery, Dec 2015

Publisher: Springer Verlag

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PURPOSE: Transrectal ultrasound (TRUS)-guided random prostate biopsy is, in spite of its low sensitivity, the gold standard for the diagnosis of prostate cancer. The recent advent of PET imaging using a novel dedicated radiotracer, [Formula: see text]-labeled prostate-specific membrane antigen (PSMA), combined with MRI provides improved pre-interventional identification of suspicious areas. This work proposes a multimodal fusion image-guided biopsy framework that combines PET-MRI images with TRUS, using automatic segmentation and registration, and offering real-time guidance.}n}nMETHODS: The prostate TRUS images are automatically segmented with a Hough transform-based random forest approach. The registration is based on the Coherent Point Drift algorithm to align surfaces elastically and to propagate the deformation field calculated from thin-plate splines to the whole gland.}n}nRESULTS: The method, which has minimal requirements and temporal overhead in the existing clinical workflow, is evaluated in terms of surface distance and landmark registration error with respect to the clinical ground truth. Evaluations on agar-gelatin phantoms and clinical data of 13 patients confirm the validity of this approach.}n}nCONCLUSION: The system is able to successfully map suspicious regions from PET/MRI to the interventional TRUS image.

2014

3D Velocity Field and Flow Profile Reconstruction from Arbitrarily Sampled Doppler Ultrasound Data

Oliver Zettinig, Christoph Hennersperger, Christian Berge, Maximilian Baust, and Nassir Navab

In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014, Oct 2014

Series Title: Lecture Notes in Computer Science

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A Framework for the Pre-clinical Validation of LBM-EP for the Planning and Guidance of Ventricular Tachycardia Ablation

Tommaso Mansi, Roy Beinart, Oliver Zettinig, Saikiran Rapaka, Bogdan Georgescu, Ali Kamen, Yoav Dori, M. Muz Zviman, Daniel A. Herzka, Henry R. Halperin, and Dorin Comaniciu

In Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges, Jan 2014

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This manuscript presents a framework for the pre-clinical validation of LBM-EP, a fast cardiac electrophysiology model based on the lattice-Boltzmann method (LBM). The overarching goal is to assess whether the model is able to predict ventricular tachycardia (VT) induction given lead location and stimulation protocol. First, the random-walk algorithm is used to interactively segment the heart ventricles from delayed-enhancement magnetic resonance images (DE-MRI). Scar and border zone are visually delineated using image thresholding. Then, a detailed anatomical model is generated, comprising fiber architecture and spatial distribution of action potential duration. That information is rasterized to a Cartesian grid, and the cardiac potentials are computed. The framework is illustrated on one swine data, for which two different pacing protocols at four different sites were tested. Each of the protocols were then virtually tested by computing seven seconds of heart beat. Model predictions in terms of VT induction were compared with what was observed in the animal. Our parallel implementation on graphics processing units required a total computation time of about two minutes at an isotropic grid resolution of 0.8 mm (21s at a resolution of 1.5 mm), thus enabling interactive VT testing.
Patent

Method and System for Interactive Computation of Cardiac Electromechanics

Tommaso Mansi, Oliver Zettinig, Bogdan Georgescu, Ali Kamen, Dorin Comaniciu, and Saikiran Rapaka

Jan 2014

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An Open Source Multimodal Image-Guided Prostate Biopsy Framework

Amit Shah, Oliver Zettinig, Tobias Maurer, Cristina Precup, Christian Berge, Jakob Weiss, Benjamin Frisch, and Nassir Navab

In Clinical Image-Based Procedures. Translational Research in Medical Imaging, Sep 2014

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Data-driven estimation of cardiac electrical diffusivity from 12-lead ECG signals

Oliver Zettinig, Tommaso Mansi, Dominik Neumann, Bogdan Georgescu, Saikiran Rapaka, Philipp Seegerer, Elham Kayvanpour, Farbod Sedaghat-Hamedani, Ali Amr, Jan Haas, Henning Steen, Hugo Katus, Benjamin Meder, Nassir Navab, Ali Kamen, and Dorin Comaniciu

Medical Image Analysis, Dec 2014

Publisher: Elsevier

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2013

Towards Real-Time Cardiac Electrophysiology Computations Using GP-GPU Lattice-Boltzmann Method

Bogdan Georgescu, Saikiran Rapaka, Tommaso Mansi, Oliver Zettinig, Ali Kamen, and Dorin Comaniciu

In MICCAI Workshop on High Performance Computing for Biomedical Image Analysis–HPC-MICCAI, Sep 2013

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Fast Data-Driven Calibration of a Cardiac Electrophysiology Model from Images and ECG

Oliver Zettinig, Tommaso Mansi, Bogdan Georgescu, Elham Kayvanpour, Farbod Sedaghat-Hamedani, Ali Amr, Jan Haas, Henning Steen, Benjamin Meder, Hugo Katus, Nassir Navab, Ali Kamen, and Dorin Comaniciu

In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2013, Sep 2013

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MICCAI Young Scientist Award 2013
From Medical Images to Fast Computational Models of Heart Electromechanics: An Integrated Framework towards Clinical Use

Oliver Zettinig, Tommaso Mansi, Bogdan Georgescu, Saikiran Rapaka, Ali Kamen, Jan Haas, Karen S Frese, Farbod Sedaghat-Hamedani, Elham Kayvanpour, Ali Amr, Stefan Hardt, Derliz Mereles, Henning Steen, Andreas Keller, Hugo A Katus, Benjamin Meder, Nassir Navab, and Dorin Comaniciu

In Functional Imaging and Modeling of the Heart, Jun 2013

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Efficient and Robust Patient-Specific Model of the Heart Function based on MRI Images

Oliver Zettinig

Technische Universität München. Master thesis , Aug 2013

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