MR‐based attenuation correction for hybrid PET‐MR brain imaging systems using deformable image registration

Schreibmann, Eduard; Nye, Jonathon A.; Schuster, David M.; Martín, Diego R.; Votaw, John R.; Fox, Tim

doi:10.1118/1.3377774

Cited by 102 publications

(79 citation statements)

References 22 publications

(11 reference statements)

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“…Despite the few degrees of freedom included, the method resulted in accurate segmentations which compared favorably with both a method based on mathematical morphology [7] and a method based on deformable registration [9]. The proposed method is however highly dependent on an accurate registration between the patient's MR and the MR used as reference.…”

Section: Discussionmentioning

confidence: 86%

“…4 that for more than 90% of the voxels segmented as bone in the CT the distance to the closest voxel segmented as bone in the MR is less than 3 mm. This can be compared with [9] where the corresponding fraction was about 80%.…”

Section: Discussionmentioning

confidence: 99%

“…To compare with [9] we will also consider the distribution of distances from voxels segmented as bone in the CT to the closest voxel segmented as bone in the MR. However, this is not an entirely satisfactory measure since a perfect score could be achieved by, trivially, classifying every voxel as bone.…”

Section: Performance Measuresmentioning

confidence: 99%

“…Attempts relying purely on intensity information have been made with some success, despite the problem of distinguishing air from bone [7]. In recent publications techniques based on deformable atlas registration are the predominant ones [8], [9]. Furthermore, a method which combines this with pattern recognition has shown promising results [10].…”

Section: Introductionmentioning

confidence: 99%

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Skull Segmentation in MRI by a Support Vector Machine Combining Local and Global Features

Sjölund

Järlideni

Andersson

et al. 2014

2014 22nd International Conference on Pattern Recognition

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Abstract-Magnetic resonance (MR) images lack information about radiation transport-a fact which is problematic in applications such as radiotherapy planning and attenuation correction in combined PET/MR imaging. To remedy this, a crude but common approach is to approximate all tissue properties as equivalent to those of water. We improve upon this using an algorithm that automatically identifies bone tissue in MR. More specifically; we focus on segmenting the skull prior to stereotactic neurosurgery, where it is common that only MR images are available.In the proposed approach, a machine learning algorithm known as a support vector machine is trained on patients for which both a CT and an MR scan are available. As input, a combination of local and global information is used. The latter is needed to distinguish between bone and air as this is not possible based only on the local image intensity.A whole skull segmentation is achievable in minutes. In a comparison with two other methods, one based on mathematical morphology and the other on deformable registration, the proposed method was found to yield consistently better segmentations.

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Section: Discussionmentioning

confidence: 86%

Section: Discussionmentioning

confidence: 99%

Section: Performance Measuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Skull Segmentation in MRI by a Support Vector Machine Combining Local and Global Features

Sjölund

Järlideni

Andersson

et al. 2014

2014 22nd International Conference on Pattern Recognition

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“…Either one can directly register an atlas CT to the target MR image (Schreibmann, Nye, Schuster, Martin, Votaw & Fox 2010) or one can relate them through an atlas MR that has previously been registered to a CT (Dowling, Lambert, Parker, Salvado, Fripp, Capp, Wratten, Denham & Greer 2012). When the latter approach is used for segmentation it is often referred to as atlas-based segmentation (Stanescu, Jans, Pervez, Stavrev & Fallone 2008).…”

Section: Introductionmentioning

confidence: 99%

Generating patient specific pseudo-CT of the head from MR using atlas-based regression

et al. 2015

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Abstract. Radiotherapy planning and attenuation correction of PET images require simulation of radiation transport. The necessary physical properties are typically derived from computed tomography (CT) images, but in some cases, including stereotactic neurosurgery and combined PET/MR imaging, only magnetic resonance (MR) images are available. With these applications in mind, we describe how a realistic, patient-specific, pseudo-CT of the head can be derived from anatomical MR images. We refer to the method as atlas-based regression, because of its similarity to atlas-based segmentation.Given a target MR and an atlas database comprising MR and CT pairs, atlas-based regression works by registering each atlas MR to the target MR, applying the resulting displacement fields to the corresponding atlas CTs and, finally, fusing the deformed atlas CTs into a single pseudo-CT.We use a deformable registration algorithm known as the Morphon and augment it with a certainty mask that allows a tailoring of the influence certain regions are allowed to have on the registration. Moreover, we propose a novel method of fusion, wherein the collection of deformed CTs is iteratively registered to their joint mean, and find that the resulting mean CT becomes more similar to the target CT. However, the voxelwise median provided even better results; at least as good as earlier work that required special MR imaging techniques. This makes atlas-based regression a good candidate for clinical use.

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From simultaneous to synergistic MR‐PET brain imaging: A review of hybrid MR‐PET imaging methodologies

Chen

Jamadar

et al. 2018

Human Brain Mapping

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Simultaneous Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) scanning is a recent major development in biomedical imaging. The full integration of the PET detector ring and electronics within the MR system has been a technologically challenging design to develop but provides capacity for simultaneous imaging and the potential for new diagnostic and research capability. This article reviews state-of-the-art MR-PET hardware and software, and discusses future developments focusing on neuroimaging methodologies for MR-PET scanning. We particularly focus on the methodologies that lead to an improved synergy between MRI and PET, including optimal data acquisition, PET attenuation and motion correction, and joint image reconstruction and processing methods based on the underlying complementary and mutual information. We further review the current and potential future applications of simultaneous MR-PET in both systems neuroscience and clinical neuroimaging research. We demonstrate a simultaneous data acquisition protocol to highlight new applications of MR-PET neuroimaging research studies.

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MR‐based attenuation correction for hybrid PET‐MR brain imaging systems using deformable image registration

Cited by 102 publications

References 22 publications

Skull Segmentation in MRI by a Support Vector Machine Combining Local and Global Features

Skull Segmentation in MRI by a Support Vector Machine Combining Local and Global Features

Generating patient specific pseudo-CT of the head from MR using atlas-based regression

From simultaneous to synergistic MR‐PET brain imaging: A review of hybrid MR‐PET imaging methodologies

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