Advances and challenges in deformable image registration: From image fusion to complex motion modelling

Schnabel, Julia A.; Heinrich, Mattias P.; Papież, Bartłomiej W.; Brady, Sir Michael

doi:10.1016/j.media.2016.06.031

Cited by 55 publications

(31 citation statements)

References 16 publications

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“…Image registration (matching) is the task of transforming a template image so that it matches a target image. This arises in many fields, such as quality control in industrial manufacturing [27], various applications in remote sensing [25], face recognition [67,51], robotic navigation [14], and medical imaging [57,53,60], etc. The variant that is considered here is indirect image registration, i.e., when the template image is registered against a target that is known only through indirect noisy observations, such as in tomographic imaging.…”

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confidence: 99%

Indirect Image Registration with Large Diffeomorphic Deformations

Chen

Öktem

2018

SIAM J. Imaging Sci.

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The paper adapts the large deformation diffeomorphic metric mapping framework for image registration to the indirect setting where a template is registered against a target that is given through indirect noisy observations. The registration uses diffeomorphisms that transform the template through a (group) action. These diffeomorphisms are generated by solving a flow equation that is defined by a velocity field with certain regularity. The theoretical analysis includes a proof that indirect image registration has solutions (existence) that are stable and that converge as the data error tends so zero, so it becomes a well-defined regularization method. The paper concludes with examples of indirect image registration in 2D tomography with very sparse and/or highly noisy data.

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confidence: 99%

Indirect Image Registration with Large Diffeomorphic Deformations

Chen

Öktem

2018

SIAM J. Imaging Sci.

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“…In the past years, a large number of image registration algorithms have been proposed in the literature, which could greatly benefit the patients and clinicians in the operating room (OR). However, validation and comparison of these methods with real clinical data have been challenging, and thus posing the difficulty in transferring these potentially beneficial registration algorithms into the operating room.…”

Section: Purposementioning

confidence: 99%

“…In contrast to the surgical judgement by direct visual comparison between pre-and intra-operative images, automatic image registration can offer more intuitive and potentially more accurate clinical assessments of tumor removal while avoiding displaced vital structures, such as blood vessels, the ventricles, and critical motor and sensory cortex. 9 In the past years, a large number of image registration algorithms [10][11][12] have been proposed in the literature, which could greatly benefit the patients and clinicians in the operating room (OR). However, validation and comparison of these methods with real clinical data have been challenging, and thus posing the difficulty in transferring these potentially beneficial registration algorithms into the operating room.…”

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confidence: 99%

REtroSpective Evaluation of Cerebral Tumors (RESECT): A clinical database of pre-operative MRI and intra-operative ultrasound in low-grade glioma surgeries

et al. 2017

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Purpose: The advancement of medical image processing techniques, such as image registration, can effectively help improve the accuracy and efficiency of brain tumor surgeries. However, it is often challenging to validate these techniques with real clinical data due to the rarity of such publicly available repositories. Acquisition and validation methods: Pre-operative magnetic resonance images (MRI), and intraoperative ultrasound (US) scans were acquired from 23 patients with low-grade gliomas who underwent surgeries at St. Olavs University Hospital between 2011 and 2016. Each patient was scanned by Gadolinium-enhanced T1w and T2-FLAIR MRI protocols to reveal the anatomy and pathology, and series of B-mode ultrasound images were obtained before, during, and after tumor resection to track the surgical progress and tissue deformation. Retrospectively, corresponding anatomical landmarks were identified across US images of different surgical stages, and between MRI and US, and can be used to validate image registration algorithms. Quality of landmark identification was assessed with intra-and inter-rater variability. Data format and access: In addition to co-registered MRIs, each series of US scans are provided as a reconstructed 3D volume. All images are accessible in MINC2 and NIFTI formats, and the anatomical landmarks were annotated in MNI tag files. Both the imaging data and the corresponding landmarks are available online as the RESECT database at https://archive.norstore.no (search for "RESECT"). Potential impact: The proposed database provides real high-quality multi-modal clinical data to validate and compare image registration algorithms that can potentially benefit the accuracy and efficiency of brain tumor resection. Furthermore, the database can also be used to test other image processing methods and neuro-navigation software platforms.

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“…Efforts have been made to address some of the technical issues in multi‐modality image registration . For example, instead of directly matching the image voxel values, mutual information is used to determine the image similarity based on joint entropy .…”

Section: Introductionmentioning

confidence: 99%

“…Efforts have been made to address some of the technical issues in multi-modality image registration. 17,18 For example, instead of directly matching the image voxel values, mutual information is used to determine the image similarity based on joint entropy. 17 However, mutual information based on an image histogram cannot resolve tissue types with similar image intensities, such as the bones and air cavities in MR and various soft tissues in CT. 2 The problem is further complicated by the common presence of MR shading and susceptibility artifacts.…”

Section: Introductionmentioning

confidence: 99%

Multimodality image registration in the head‐and‐neck using a deep learning‐derived synthetic CT as a bridge

et al. 2020

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Purpose To develop and demonstrate the efficacy of a novel head‐and‐neck multimodality image registration technique using deep‐learning‐based cross‐modality synthesis. Methods and Materials Twenty‐five head‐and‐neck patients received magnetic resonance (MR) and computed tomography (CT) (CTaligned) scans on the same day with the same immobilization. Fivefold cross validation was used with all of the MR‐CT pairs to train a neural network to generate synthetic CTs from MR images. Twenty‐four of 25 patients also had a separate CT without immobilization (CTnon‐aligned) and were used for testing. CTnon‐aligned's were deformed to the synthetic CT, and compared to CTnon‐aligned registered to MR. The same registrations were performed from MR to CTnon‐aligned and from synthetic CT to CTnon‐aligned. All registrations used B‐splines for modeling the deformation, and mutual information for the objective. Results were evaluated using the 95% Hausdorff distance among spinal cord contours, landmark error, inverse consistency, and Jacobian determinant of the estimated deformation fields. Results When large initial rigid misalignment is present, registering CT to MRI‐derived synthetic CT aligns the cord better than a direct registration. The average landmark error decreased from 9.8 ± 3.1 mm in MR→CTnon‐aligned to 6.0 ± 2.1 mm in CTsynth→CTnon‐aligned deformable registrations. In the CT to MR direction, the landmark error decreased from 10.0 ± 4.3 mm in CTnon‐aligned→MR deformable registrations to 6.6 ± 2.0 mm in CTnon‐aligned→CTsynth deformable registrations. The Jacobian determinant had an average value of 0.98. The proposed method also demonstrated improved inverse consistency over the direct method. Conclusions We showed that using a deep learning‐derived synthetic CT in lieu of an MR for MR→CT and CT→MR deformable registration offers superior results to direct multimodal registration.

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Advances and challenges in deformable image registration: From image fusion to complex motion modelling

Cited by 55 publications

References 16 publications

Indirect Image Registration with Large Diffeomorphic Deformations

Indirect Image Registration with Large Diffeomorphic Deformations

REtroSpective Evaluation of Cerebral Tumors (RESECT): A clinical database of pre-operative MRI and intra-operative ultrasound in low-grade glioma surgeries

Multimodality image registration in the head‐and‐neck using a deep learning‐derived synthetic CT as a bridge

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