Assessment of 3D DCE-MRI of the kidneys using non-rigid image registration and segmentation of voxel time courses

Zöllner, Frank G.; Sance, Rosario; Rogelj, Peter; Ledesma-Carbayo, María J.; Rørvik, Jarle; Santos, A.; Lundervold, Arvid

doi:10.1016/j.compmedimag.2008.11.004

Cited by 108 publications

(94 citation statements)

References 31 publications

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“…Since the only difference in calculating perfusion parameters from T1-and T2*-weighted dynamic MRI is the signal to concentration conversion [30], such an extension will be easily to implement in a future version of our plug-in. Also, when using the plug-in for perfusion analysis of moving organs like the heart or the kidneys, motion artifacts have to be corrected for independently, using image registration [21] or breathing, triggering, or navigation techniques during image acquisition [22,53].…”

Section: Discussionmentioning

confidence: 99%

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UMMPerfusion: an Open Source Software Tool Towards Quantitative MRI Perfusion Analysis in Clinical Routine

et al. 2012

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To develop a generic Open Source MRI perfusion analysis tool for quantitative parameter mapping to be used in a clinical workflow and methods for quality management of perfusion data. We implemented a classic, pixel-by-pixel deconvolution approach to quantify T1-weighted contrastenhanced dynamic MR imaging (DCE-MRI) perfusion data as an OsiriX plug-in. It features parallel computing capabilities and an automated reporting scheme for quality management. Furthermore, by our implementation design, it could be easily extendable to other perfusion algorithms. Obtained results are saved as DICOM objects and directly added to the patient study. The plug-in was evaluated on ten MR perfusion data sets of the prostate and a calibration data set by comparing obtained parametric maps (plasma flow, volume of distribution, and mean transit time) to a widely used reference implementation in IDL. For all data, parametric maps could be calculated and the plug-in worked correctly and stable. On average, a deviation of 0.032±0.02 ml/100 ml/ min for the plasma flow, 0.004±0.0007 ml/100 ml for the volume of distribution, and 0.037±0.03 s for the mean transit time between our implementation and a reference implementation was observed. By using computer hardware with eight CPU cores, calculation time could be reduced by a factor of 2.5. We developed successfully an Open Source OsiriX plugin for T1-DCE-MRI perfusion analysis in a routine quality managed clinical environment. Using model-free deconvolution, it allows for perfusion analysis in various clinical applications. By our plug-in, information about measured physiological processes can be obtained and transferred into clinical practice.

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Section: Discussionmentioning

confidence: 99%

“…evaluation, including sequence and contrast agent dose optimization [19], correct selection of the arterial input function [20], or correction for motion artifacts [21,22].…”

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confidence: 99%

UMMPerfusion: an Open Source Software Tool Towards Quantitative MRI Perfusion Analysis in Clinical Routine

et al. 2012

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“…Nonrigid iconic registration methods [2][3][4] are computationally demanding and rely on their similarity criterion. Standard choices such as mutual information are not effective in DCE-CT sequences [5].…”

Section: Related Work and Contributionsmentioning

confidence: 99%

Registration of Free-Breathing 3D+t Abdominal Perfusion CT Images via Co-segmentation

Prevost

Romain

Cuingnet

et al. 2013

Advanced Information Systems Engineering

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Abstract. Dynamic contrast-enhanced computed tomography (DCE-CT) is a valuable imaging modality to assess tissues properties, particularly in tumours, by estimating pharmacokinetic parameters from the evolution of pixels intensities in 3D+t acquisitions. However, this requires a registration of the whole sequence of volumes, which is challenging especially when the patient breathes freely. In this paper, we propose a generic, fast and automatic method to address this problem. As standard iconic registration methods are not robust to contrast intake, we rather rely on the segmentation of the organ of interest. This segmentation is performed jointly with the registration of the sequence within a novel co-segmentation framework. Our approach is based on implicit template deformation, that we extend to a co-segmentation algorithm which provides as outputs both a segmentation of the organ of interest in every image and stabilising transformations for the whole sequence. The proposed method is validated on 15 datasets acquired from patients with renal lesions and shows improvement in terms of registration and estimation of pharmacokinetic parameters over the state-of-the-art method.

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“…Some image registration solutions are based on intensity conservation and, therefore, inapplicable in contrast-enhanced imaging. Alternative solutions have been proposed, based either on noncontrast dependant similarity criteria (8,9) or contrast-changing reference image (10,11). A drawback of postprocessing registration techniques is that they deal only with magnitude images, whose irregular quality influences strongly the robustness and efficiency of the methods.…”

Section: Introductionmentioning

confidence: 99%

Motion compensated generalized reconstruction for free‐breathing dynamic contrast‐enhanced MRI

Filipovic

Vuissoz

Codreanu

et al. 2010

Magnetic Resonance in Med

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The analysis of abdominal and thoracic dynamic contrastenhanced MRI is often impaired by artifacts and misregistration caused by physiological motion. Breath-hold is too short to cover long acquisitions. A novel multipurpose reconstruction technique, entitled dynamic contrast-enhanced generalized reconstruction by inversion of coupled systems, is presented. It performs respiratory motion compensation in terms of both motion artefact correction and registration. It comprises motion modeling and contrast-change modeling. The method feeds on physiological signals and x-f space properties of dynamic series to invert a coupled system of linear equations. The unknowns solved for represent the parameters for a linear nonrigid motion model and the parameters for a linear contrast-change model based on B-splines. Performance is demonstrated on myocardial perfusion imaging, on six simulated data sets and six clinical exams. The main purpose consists in removing motion-induced errors from time-intensity curves, thus improving curve analysis and postprocessing in general. This method alleviates postprocessing difficulties in dynamic contrast-enhanced MRI and opens new possibilities for dynamic contrast-enhanced MRI analysis. Magn Reson Med 65:812-822,

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Assessment of 3D DCE-MRI of the kidneys using non-rigid image registration and segmentation of voxel time courses

Cited by 108 publications

References 31 publications

UMMPerfusion: an Open Source Software Tool Towards Quantitative MRI Perfusion Analysis in Clinical Routine

UMMPerfusion: an Open Source Software Tool Towards Quantitative MRI Perfusion Analysis in Clinical Routine

Registration of Free-Breathing 3D+t Abdominal Perfusion CT Images via Co-segmentation

Motion compensated generalized reconstruction for free‐breathing dynamic contrast‐enhanced MRI

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