Attenuation Correction Synthesis for Hybrid PET-MR Scanners

Burgos, Ninon; Cardoso, M. Jorge; Modat, Marc; Pedemonte, Stefano; Dickson, John; Barnes, Anna; Duncan, John S.; Atkinson, David; Arridge, Simon; Hutton, Brian F.; Ourselin, Sébastien

doi:10.1007/978-3-642-40811-3_19

Cited by 40 publications

(50 citation statements)

References 17 publications

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“…A way of mitigating this is to use a database of atlas patients that are separately registered. Just as in any estimation procedure the use of several independent measurements allows a more accurate estimate, which has been verified in several studies (Heckemann, Hajnal, Aljabar, Rueckert & Hammers 2006, Burgos, Cardoso, Modat, Pedemonte, Dickson, Barnes, Duncan, Atkinson, Arridge, Hutton et al 2013, Uh, Merchant, Li, Li & Hua 2014. Both how to select the atlas patients (Aljabar, Heckemann, Hammers, Hajnal & Rueckert 2009) and how to fuse the segmentations (Artaechevarria, Munoz-Barrutia & Ortiz-de-Solorzano 2009) are areas that deserve additional attention.…”

Section: Introductionmentioning

confidence: 82%

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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Section: Introductionmentioning

confidence: 82%

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

et al. 2015

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“…The same scheme was used by Burgos et al (2013 ) for pseudo-CT generation in the head region. As such, a ranking scheme is proposed whereby the similarity measure value for each transformed atlas is ranked across all atlases.…”

Section: Sdmentioning

confidence: 99%

“…(15) or ( 16 ) to perform the final segmentation step. The same local weighting atlas fusion strategy was exploited by Burgos et al for attenuation map synthesis in brain PET/MRI ( Burgos et al, 2013( Burgos et al, , 2014.…”

Section: Lnc C Dmentioning

confidence: 99%

Comparison of atlas-based techniques for whole-body bone segmentation

Arabi

Zaidi

2017

Medical Image Analysis

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a b s t r a c tWe evaluate the accuracy of whole-body bone extraction from whole-body MR images using a number of atlas-based segmentation methods. The motivation behind this work is to find the most promising approach for the purpose of MRI-guided derivation of PET attenuation maps in whole-body PET/MRI. To this end, a variety of atlas-based segmentation strategies commonly used in medical image segmentation and pseudo-CT generation were implemented and evaluated in terms of whole-body bone segmentation accuracy. Bone segmentation was performed on 23 whole-body CT/MR image pairs via leave-one-out cross validation procedure. The evaluated segmentation techniques include: (i) intensity averaging (IA), (ii) majority voting (MV), (iii) global and (iv) local (voxel-wise) weighting atlas fusion frameworks implemented utilizing normalized mutual information (NMI), normalized cross-correlation (NCC) and mean square distance (MSD) as image similarity measures for calculating the weighting factors, along with other atlas-dependent algorithms, such as (v) shape-based averaging (SBA) and (vi) Hofmann's pseudo-CT generation method. The performance evaluation of the different segmentation techniques was carried out in terms of estimating bone extraction accuracy from whole-body MRI using standard metrics, such as Dice similarity (DSC) and relative volume difference (RVD) considering bony structures obtained from intensity thresholding of the reference CT images as the ground truth. Considering the Dice criterion, global weighting atlas fusion methods provided moderate improvement of whole-body bone segmentation (DSC = 0.65 ± 0.05) compared to non-weighted IA (DSC = 0.60 ± 0.02). The local weighed atlas fusion approach using the MSD similarity measure outperformed the other strategies by achieving a DSC of 0.81 ± 0.03 while using the NCC and NMI measures resulted in a DSC of 0.78 ± 0.05 and 0.75 ± 0.04, respectively. Despite very long computation time, the extracted bone obtained from both SBA (DSC = 0.56 ± 0.05) and Hofmann's methods (DSC = 0.60 ± 0.02) exhibited no improvement compared to non-weighted IA. Finding the optimum parameters for implementation of the atlas fusion approach, such as weighting factors and image similarity patch size, have great impact on the performance of atlas-based segmentation approaches. The voxel-wise atlas fusion approach exhibited excellent performance in terms of cancelling out the non-systematic registration errors leading to accurate and reliable segmentation results. Denoising and normalization of MR images together with optimization of the involved parameters play a key role in improving bone extraction accuracy.

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“…The closed skull synthetic CT image was constructed from the T1-weighted iMRI image following the method described by Burgos et al [10]. The method relies on a database consisting of 6 pairs of co-registered T1-weighted MR / CT images from healthy subjects.…”

Section: Air-tissue Segmentationmentioning

confidence: 99%

Simulated Field Maps: Toward Improved Susceptibility Artefact Correction in Interventional MRI

Kochan

Daga

Burgos

et al. 2014

Information Processing in Computer-Assisted Interventions

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Abstract. Intraoperative MRI is a powerful modality for acquiring structural and functional images of the brain to enable precise imageguided neurosurgery. In this paper, we propose a novel method for simulating main magnetic field inhomogeneity maps during intraoperative MRI-guided neurosurgery. Our method relies on an air-tissue segmentation of intraoperative patient specific data, which is used as an input to a subsequent field simulation step. The generated simulation can then be used to enhance the precision of image-guidance. We report results of our method on 12 patient datasets acquired during image-guided neurosurgery for anterior lobe resection for surgical management of focal temporal lobe epilepsy. We find a close agreement between the field inhomogeneity maps acquired as part of the imaging protocol and the simulated field inhomogeneity maps generated by the proposed method.

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Attenuation Correction Synthesis for Hybrid PET-MR Scanners

Cited by 40 publications

References 17 publications

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

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

Comparison of atlas-based techniques for whole-body bone segmentation

Simulated Field Maps: Toward Improved Susceptibility Artefact Correction in Interventional MRI

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