A robust and extendible framework for medical image registration focused on rapid clinical application deployment

Boehler, Tobias; Straaten, D. van; Wirtz, Stefan; Peitgen, Heinz‐Otto

doi:10.1016/j.compbiomed.2011.03.011

Cited by 20 publications

(12 citation statements)

References 14 publications

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“…The resultant volumes and MRI data were imported into MeVisLab software (version 2.2.1; MeVis Medical Solutions AG, Bremen, Germany), where the T 1 W MRI was automatically registered to the water, fat, and R2* maps from the four‐point Dixon sequence using the MERIT framework within MeVisLab . MRI data were up‐sampled by a scale of 10, and 3D histology volume was manually registered with the T 1 W MRI by matching the lumen in the 3D histology volume with the lumen within the MRI throughout the volume (Fig.…”

Section: Methodsmentioning

confidence: 99%

Quantitative fat and R2* mapping in vivo to measure lipid‐rich necrotic core and intraplaque hemorrhage in carotid atherosclerosis

Koppal

Warntjes

Swann

et al. 2016

Magnetic Resonance in Med

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Purpose The aim of this work was to quantify the extent of lipid‐rich necrotic core (LRNC) and intraplaque hemorrhage (IPH) in atherosclerotic plaques. Methods Patients scheduled for carotid endarterectomy underwent four‐point Dixon and T1‐weighted magnetic resonance imaging (MRI) at 3 Tesla. Fat and R2* maps were generated from the Dixon sequence at the acquired spatial resolution of 0.60 × 0.60 × 0.70 mm voxel size. MRI and three‐dimensional (3D) histology volumes of plaques were registered. The registration matrix was applied to segmentations denoting LRNC and IPH in 3D histology to split plaque volumes in regions with and without LRNC and IPH. Results Five patients were included. Regarding volumes of LRNC identified by 3D histology, the average fat fraction by MRI was significantly higher inside LRNC than outside: 12.64 ± 0.2737% versus 9.294 ± 0.1762% (mean ± standard error of the mean [SEM]; P < 0.001). The same was true for IPH identified by 3D histology, R2* inside versus outside IPH was: 71.81 ± 1.276 s−1 versus 56.94 ± 0.9095 s−1 (mean ± SEM; P < 0.001). There was a strong correlation between the cumulative fat and the volume of LRNC from 3D histology (R2 = 0.92) as well as between cumulative R2* and IPH (R2 = 0.94). Conclusion Quantitative mapping of fat and R2* from Dixon MRI reliably quantifies the extent of LRNC and IPH. Magn Reson Med 78:285–296, 2017. © 2016 International Society for Magnetic Resonance in Medicine

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Section: Methodsmentioning

confidence: 99%

Quantitative fat and R2* mapping in vivo to measure lipid‐rich necrotic core and intraplaque hemorrhage in carotid atherosclerosis

Koppal

Warntjes

Swann

et al. 2016

Magnetic Resonance in Med

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“…Rigid volumetric image registrations were performed using the MERIT toolbox (Boehler et al 2011) within the MevisLab software framework (Heckel et al 2009). US volumes were streamed in real-time using the iU22’s Digital Navigation Link and synchronized with optical tracking data using customized MevisLab modules written in C++.…”

Section: Methodsmentioning

confidence: 99%

Automatic 3D ultrasound calibration for image guided therapy using intramodality image registration

Schlosser¹,

Kirmizibayrak²,

Shamdasani³

et al. 2013

Phys. Med. Biol.

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Many real time ultrasound (US) guided therapies can benefit from management of motion-induced anatomical changes with respect to a previously acquired computerized anatomy model. Spatial calibration is a prerequisite to transforming US image information to the reference frame of the anatomy model. We present a new method for calibrating 3D US volumes using intramodality image registration, derived from the “hand eye” calibration technique. The method is fully automated by implementing data rejection based on sensor displacements, automatic registration over overlapping image regions, and a self-consistency error metric evaluated continuously during calibration. We also present a novel method for validating US calibrations based on measurement of physical phantom displacements within US images. Both calibration and validation can be performed on arbitrary phantoms. Results indicate that normalized mutual information and localized cross correlation produce the most accurate 3D US registrations for calibration. Volumetric image alignment is more accurate and reproducible than point selection for validating the calibrations, yielding <1.5 mm root mean square error, a significant improvement relative to previously reported hand eye US calibration results. Comparison of two different phantoms for calibration and for validation revealed significant differences for validation (p=0.003) but not for calibration (p=0.795).

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“…This was robust to cases where no valid correspondence was found. Pairwise non-rigid registrations (Modersitzki, 2004) were performed and implemented as part of a software (Boehler et al, 2011). Prescott et al (2006) paired features using the area only.…”

Section: Correspondences and Spatial Transformationsmentioning

confidence: 99%

A Survey of Methods for 3D Histology Reconstruction

Pichat

Iglesias

Yousry³

et al. 2018

Medical Image Analysis

170

179

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Histology permits the observation of otherwise invisible structures of the internal topography of a specimen. Although it enables the investigation of tissues at a cellular level, it is invasive and breaks topology due to cutting. Three-dimensional (3D) reconstruction was thus introduced to overcome the limitations of single-section studies in a dimensional scope. 3D reconstruction finds its roots in embryology, where it enabled the visualisation of spatial relationships of developing systems and organs, and extended to biomedicine, where the observation of individual, stained sections provided only partial understanding of normal and abnormal tissues. However, despite bringing visual awareness, recovering realistic reconstructions is elusive without prior knowledge about the tissue shape. 3D medical imaging made such structural ground truths available. In addition, combining non-invasive imaging with histology unveiled invaluable opportunities to relate macroscopic information to the underlying microscopic properties of tissues through the establishment of spatial correspondences; image registration is one technique that permits the automation of such a process and we describe reconstruction methods that rely on it. It is thereby possible to recover the original topology of histology and lost relationships, gain insight into what affects the signals used to construct medical images (and characterise them), or build high resolution anatomical atlases. This paper reviews almost three decades of methods for 3D histology reconstruction from serial sections, used in the study of many different types of tissue. We first summarise the process that produces digitised sections from a tissue specimen in order to understand the peculiarity of the data, the associated artefacts and some possible ways to minimise them. We then describe methods for 3D histology reconstruction with and without the help of 3D medical imaging, along with methods of validation and some applications. We finally attempt to identify the trends and challenges that the field is facing, many of which are derived from the cross-disciplinary nature of the problem as it involves the collaboration between physicists, histolopathologists, computer scientists and physicians.

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A robust and extendible framework for medical image registration focused on rapid clinical application deployment

Cited by 20 publications

References 14 publications

Quantitative fat and R2* mapping in vivo to measure lipid‐rich necrotic core and intraplaque hemorrhage in carotid atherosclerosis

Quantitative fat and R2* mapping in vivo to measure lipid‐rich necrotic core and intraplaque hemorrhage in carotid atherosclerosis

Automatic 3D ultrasound calibration for image guided therapy using intramodality image registration

A Survey of Methods for 3D Histology Reconstruction

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