Statistically-driven 3D fiber reconstruction and denoising from multi-slice cardiac DTI using a Markov random field model

Lekadir, Karim; Lange, Matthias; Zimmer, Veronika A.; Hoogendoorn, Corné; Frangi, Alejandro F.

doi:10.1016/j.media.2015.03.006

Cited by 3 publications

(3 citation statements)

References 49 publications

(58 reference statements)

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“…The experiment was carried out on real data, and the denoising performance of the proposed approach was robust. Lekadir, et al [66] developed a denoising and fiber reconstruction method for multi-slice cardiac diffusion tensor images (DTIs) based on MRF. The MRF was combined with a statistical constraint for missing fiber and a consistency term to enable the obtained meshes continuous.…”

Section: Markov Random Field Based Methodsmentioning

confidence: 99%

Advances in Data Preprocessing for Biomedical Data Fusion: An Overview of the Methods, Challenges, and Prospects

et al. 2021

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Section: Markov Random Field Based Methodsmentioning

confidence: 99%

Advances in Data Preprocessing for Biomedical Data Fusion: An Overview of the Methods, Challenges, and Prospects

et al. 2021

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“…These approaches do not use specific diffusion measures of the specific heart, and rely on potential correlations of the shape and the fibers field. However, later in (Lekadir et al, 2016), the authors introduced a way to additionally take advantage of multi-slice diffusion data measured on a specific patient.…”

Section: A C C E P T E D Mmentioning

confidence: 99%

“…Finally, it is important to remark that these family of algorithms need user interaction for the definition of a number of free parameters (e.g. six in (Lekadir et al, 2016)).…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Maximum likelihood estimation of cardiac fiber bundle orientation from arbitrarily spaced diffusion weighted images

Nagler

Bertoglio

Stoeck

et al. 2017

Medical Image Analysis

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We propose an estimation scheme for local fiber bundle direction in the left ventricle directly from gray values of arbitrarily spaced cardiac diffusion weighted images (DWI). The approach is based on a parametric and space-dependent mathematical representation of the myocardial fiber bundle orientation and hence the diffusion tensor (DT) for the ventricular geometry. By solving a nonlinear inverse problem derived from a maximum likelihood estimator, the degrees of freedom of the fiber and DT model can be estimated from the measured gray values of the DWIs. The continuity of the DT model allows to relax the restriction to the individual DWIs to match spatially like for voxelwise DT calculation. Hence, the spatial misalignment between image slices with different diffusion encoding directions, that is encountered in-vivo cardiac imaging practice can be integrated into the estimation scheme. This feature results then in a negligible impact of the spatial misalignment on the reconstructed solution. We illustrate the methodology using synthetic data and compare it against a previously reported fiber bundle reconstruction technique. To show the potential for real data, we also present results for multi-slice data constructed from ex-vivo cardiac diffusion weighted measurements in both mono- and bi-ventricular configurations.

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Mathematical Modeling and Virtual Reality Simulation of Surgical Tool Interactions With Soft Tissue: A Review and Prospective

Malukhin

Ehmann

2018

Journal of Engineering and Science in Medical Diagnostics and Therapy

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This is an informed assessment of the state of the art and an extensive inventory of modeling approaches and methods for soft tissue/medical cutting tool interaction and of the associated medical processes and phenomena. Modeling and simulation through numerical, theoretical, computational, experimental, and other methods was discussed in comprehensive review sections each of which is concluded with a plausible prospective discussion biased toward the development of so-called virtual reality (VR) simulator environments. The finalized prospective section reflects on the future demands in the area of soft tissue cutting modeling and simulation mostly from a conceptual angle with emphasis on VR development requirements including real-time VR simulator response, cost-effective “close-to-reality” VR implementations, and other demands. The review sections that serve as the basis for the suggested prospective needs are categorized based on: (1) Major VR simulator applications including virtual surgery education, training, operation planning, intraoperative simulation, image-guided surgery, etc. and VR simulator types, e.g., generic, patient-specific and surgery-specific and (2) Available numerical, theoretical, and computational methods in terms of robustness, time effectiveness, computational cost, error control, and accuracy of modeling of certain types of virtual surgical interventions and their experimental validation, geared toward ethically driven artificial “phantom” tissue-based approaches. Digital data processing methods used in modeling of various feedback modalities in VR environments are also discussed.

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Statistically-driven 3D fiber reconstruction and denoising from multi-slice cardiac DTI using a Markov random field model

Cited by 3 publications

References 49 publications

Advances in Data Preprocessing for Biomedical Data Fusion: An Overview of the Methods, Challenges, and Prospects

Advances in Data Preprocessing for Biomedical Data Fusion: An Overview of the Methods, Challenges, and Prospects

Maximum likelihood estimation of cardiac fiber bundle orientation from arbitrarily spaced diffusion weighted images

Mathematical Modeling and Virtual Reality Simulation of Surgical Tool Interactions With Soft Tissue: A Review and Prospective

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