Characterization of the Physiological Displacement of the Aortic Arch Using Non-Rigid Registration and MR Imaging

Nasr, Bahaa; Ven, Florent Le; Savéan, Joël; Salem, Douraı̈ed Ben; Nonent, Michel; Gouny, P; Visvikis, Dimitris; Fayad, Hadi

doi:10.1016/j.ejvs.2016.11.019

Cited by 9 publications

(7 citation statements)

References 33 publications

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“…Even in cases with non-tortuous anatomy, there may be a mismatch between the fused image and angiography, which in our opinion remains essential for validating the fusion (7 ml to 30 ml/s suffice at the proximal landing zone). As long as registration is based on bony structures (hence with rigid transformations), there will be a mismatch with arterial structures which by nature are soft and deformable, which therefore calls for approaches based on elastic registration (27), for example, or digital simulation approaches that predict deformations using a biomechanical model. We have already published several studies on this simulation approach (10,11), which is integrated into the…”

Section: Limitationsmentioning

confidence: 99%

Fusion Imaging for EVAR with Mobile C-arm

Kaladji

Villena

Pascot

et al. 2019

Annals of Vascular Surgery

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Section: Limitationsmentioning

confidence: 99%

Fusion Imaging for EVAR with Mobile C-arm

Kaladji

Villena

Pascot

et al. 2019

Annals of Vascular Surgery

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“…More knowledge and understanding of this new endovascular treatment technique seems requisite, since the longer term outcomes appear to be significantly inferior to other available treatment options. 5 While several studies have investigated the dynamic behaviour of the aorta, 2,16e23 including a recent study that employed deformable registration techniques for the aortic arch, 33 little has been reported on the dynamic behaviour of stent grafts. From a clinical point of view however, this information is of the utmost importance.…”

Section: Discussionmentioning

confidence: 99%

Quantitative Stent Graft Motion in ECG Gated CT by Image Registration and Segmentation: In Vitro Validation and Preliminary Clinical Results

Koenrades

Struijs

Klein³

et al. 2019

European Journal of Vascular and Endovascular Surgery

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WHAT THIS PAPER ADDS The present article establishes a novel method to quantify and visualise stent graft motion in multiphasic ECG gated computed tomography using image registration techniques. In vitro validation demonstrated adequate accuracy for quantifying abdominal aortic stent graft displacement. The methodology was successfully applied to clinical data of patients treated by endovascular aneurysm repair (EVAR) with different stent graft designs. This novel methodology enables provision of accurate data characterising the in situ motions of stent grafts in the endovascular environment. This may have a major impact on stent graft design and early prediction of failure in post-market evaluations of new or modified devices.Objectives: The dynamic endovascular environment of stent grafts may influence long term outcome after endovascular aneurysm repair (EVAR). The sealing and fixation of a stent graft to the aortic wall is challenged at every heartbeat, yet knowledge of the cardiac induced dynamics of stent grafts is sparse. Understanding the stenteartery interaction is crucial for device development and may aid the prediction of failure in the individual patient. The aim of this work was to establish quantitative stent graft motion in multiphasic electrocardiogram (ECG) gated computed tomography (CT) by image registration and segmentation techniques. Methods: Experimental validation was performed by evaluating a series of ECG gated CT scans of a stent graft moving at different amplitudes of displacement at different virtual heart rates using a motion generating device with synchronised ECG triggering. The methodology was further tested on clinical data of patients treated with EVAR devices with different stent graft designs. Displacement during the cardiac cycle was analysed for points on the fixating stent rings, the branches or fenestrations, and the spine. Results: Errors for the amplitude of displacement measured in vitro at individual points on the wire frame were at most 0.3 mm. In situ cardiac induced displacement of the devices was found to differ per location and also depended on the type of stent graft. Displacement during the cardiac cycle was greatest in a fenestrated device and smallest in a chimney graft sac anchoring endosystem, with maximum displacement varying from 0.0 to 1.4 mm. There was no substantial displacement measurable in the spine. Conclusions: A novel methodology to quantify and visualise stent graft motion in multiphasic ECG gated CT has been validated in vitro and tested in vivo. This methodology enables further exploration of in situ motion of different stent grafts and branch stents and their interaction with native vessels.

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“…[ 19 ] Misregistration is always randomly caused by peristalsis between SSTSE and TSE images even if registration techniques are applied. [ 20 , 21 ] Hence, it is difficult to optimize the training dataset of SSTSE and TSE images and accuracy cannot be gained. Instead, input images were generated from TSE images to simulate SSTSE images by contrast-adjustment, downsampling, adding noise, and blurring images with image processing software (ImageJ version 1.52a; National Institutes of Health, Bethesda, MD) as performed in several studies.…”

Section: Methodsmentioning

confidence: 99%

Image quality improvement of single-shot turbo spin-echo magnetic resonance imaging of female pelvis using a convolutional neural network

et al. 2020

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We have developed a deep learning-based approach to improve image quality of single-shot turbo spin-echo (SSTSE) images of female pelvis. We aimed to compare the deep learning-based single-shot turbo spin-echo (DL-SSTSE) images of female pelvis with turbo spin-echo (TSE) and conventional SSTSE images in terms of image quality. One hundred five and 21 subjects were used as training and test sets, respectively. We performed 6-fold cross validation. In the training process, low-quality images were generated from TSE images as input. TSE images were used as ground truth images. In the test process, the trained convolutional neural network was applied to SSTSE images. The output images were denoted as DL-SSTSE images. Apart from DL-SSTSE images, classical filtering methods were adopted to SSTSE images. Generated images were denoted as F-SSTSE images. Contrast ratio (CR) of gluteal fat and myometrium and signal-to-noise ratio (SNR) of gluteal fat were measured for all images. Two radiologists graded these images using a 5-point scale and evaluated the image quality with regard to overall image quality, contrast, noise, motion artifact, boundary sharpness of layers in the uterus, and the conspicuity of the ovaries. CRs, SNRs, and image quality scores were compared using the Steel-Dwass multiple comparison tests. CRs and SNRs were significantly higher in DL-SSTSE, F-SSTSE, and TSE images than in SSTSE images. Scores with regard to overall image quality, contrast, noise, and boundary sharpness of layers in the uterus were significantly higher on DL-SSTSE and TSE images than on SSTSE images. There were no significant differences in the CRs, SNRs, and respective scores between DL-SSTSE and TSE images. The score with regard to motion artifacts was significantly higher on DL-SSTSE, F-SSTSE, and SSTSE images than on TSE images. The score with regard to the conspicuity of ovaries was significantly higher on DL-SSTSE images than on F-SSTSE, SSTSE, and TSE images (P < .001). DL-SSTSE images showed higher image quality as compared with SSTSE images. In comparison with conventional TSE images, DL-SSTSE images had acceptable image quality while keeping the advantage of the motion artifact-robustness and acquisition time efficiency in SSTSE imaging.

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Characterization of the Physiological Displacement of the Aortic Arch Using Non-Rigid Registration and MR Imaging

Cited by 9 publications

References 33 publications

Fusion Imaging for EVAR with Mobile C-arm

Fusion Imaging for EVAR with Mobile C-arm

Quantitative Stent Graft Motion in ECG Gated CT by Image Registration and Segmentation: In Vitro Validation and Preliminary Clinical Results

Image quality improvement of single-shot turbo spin-echo magnetic resonance imaging of female pelvis using a convolutional neural network

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