A Gauss–Newton approach to joint image registration and intensity correction

Ebrahimi, Mehran; Lausch, Anthony; Martel, Anne L.

doi:10.1016/j.cmpb.2013.07.026

Cited by 9 publications

(3 citation statements)

References 37 publications

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“…Third, we investigated the dynamic image alignment based on a sparse representation in principal components of the intervolume correlation matrix [22] and variance of intensities of spatially corresponding voxels over time [32]. Fourth, we implemented a dynamic version of [33] in extended state space (morphology and bolus flux) with regularization based on the elastic potential of the deformations and the sparsity-promoting total variation transform on the intensity changes. Registration performance was visually evaluated (morphology, motion, artifacts, deformation field, time intensity curves).…”

Section: Methodsmentioning

confidence: 99%

“…The dual presence of deformation and tracer flux is the core decision problem for the registration of contrast-enhanced perfusion sequences. Without additional information on cardiac anatomy or haemodynamic, this has been addressed, for example, by state space extension [33], sparseness assumptions [25], variations of PCA [20,21,23,24], or explicit spatiotemporal regularization in order to separate motion components from contrast enhancement. The assumption underlying ASTRA4D and [22] is that intensity changes can be captured by a low dimensional linear acquisition model.…”

Section: Motion Compensationmentioning

confidence: 99%

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Noise reduction and motion elimination in low-dose 4D myocardial computed tomography perfusion (CTP): preliminary clinical evaluation of the ASTRA4D algorithm

et al. 2019

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Objectives. To propose and evaluate a four-dimensional (4D) algorithm for joint motion elimination and spatiotemporal noise reduction in low-dose dynamic myocardial computed tomography perfusion (CTP).Methods. Thirty patients with suspected or confirmed coronary artery disease were prospectively included und underwent dynamic contrast-enhanced 320-row CTP. The presented deformable image registration method ASTRA4D identifies a low-dimensional linear model of contrast propagation (by principal component analysis, PCA) of the ex-ante temporally smoothed time-intensity curves (by local polynomial regression). Quantitative (standard deviation, signal-to-noise ratio (SNR), temporal variation, volumetric deformation) and qualitative (motion, contrast, contour sharpness; 1, poor; 5, excellent) measures of CTP quality were assessed for the original and motion-compensated volumes (without and with temporal filtering, PCA/ASTRA4D). Following visual myocardial perfusion deficit detection by two readers, diagnostic accuracy was evaluated using 1.5T magnetic resonance (MR) myocardial perfusion imaging as the reference standard in 15 patients.Results. Registration using ASTRA4D was successful in all 30 patients and resulted in comparison with the benchmark PCA in significantly (p<0.001) reduced noise over time (-83%, 178.5 vs 29.9) and spatially (-34%, 21.4 vs 14.1) as well as improved SNR (+47%, 3.6 vs 5.3) and subjective image quality (motion, contrast, contour sharpness: +1.0, +1.0, +0.5). ASTRA4D resulted in significantly improved per-segment sensitivity of 91% (58/64) and similar specificity of 96% (429/446) compared with PCA (52%, 33/64; 98%, 435/446; p=0.011) and the original sequence (45%, 29/64; 98%, 438/446; p=0.003) in the visual detection of perfusion deficits. Conclusions.The proposed functional approach to temporal denoising and morphologic alignment was shown to improve quality metrics and sensitivity of 4D CTP in the detection of myocardial ischemia. 4 Abstrakt (Deutsch) Zielsetzung. Die Entwicklung und Bewertung einer Methode zur simultanen Rauschreduktion und Bewegungskorrektur für niedrig dosierte dynamische CT Myokardperfusion. Methoden. Dreißig prospektiv eingeschlossene Patienten mit vermuteter oder bestätigter koronarer Herzkrankheit wurden einer dynamischen CT Myokardperfusionsuntersuchung unterzogen. Die präsentierte Registrierungsmethode ASTRA4D ermittelt ein niedrigdimensionales Modell des Kontrastmittelflusses (mittels einer Hauptkomponentenanalyse, PCA) der vorab zeitlich geglätteten Intensitätskurven (mittels lokaler polynomialer Regression). Quantitative (Standardabweichung, Signal-Rausch-Verhältnis (SNR), zeitliche Schwankung, räumliche Verformung) und qualitative (Bewegung, Kontrast, Kantenschärfe; 1, schlecht; 5, ausgezeichnet) Kennzahlen der unbearbeiteten und bewegungskorrigierten Perfusionsdatensätze (ohne und mit zeitlicher Glättung PCA/ASTRA4D) wurden ermittelt. Nach visueller Beurteilung von myokardialen Perfusionsdefiziten durch zwei Radiologen wurde die diagnostische Genauigkeit im Ver...

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Section: Methodsmentioning

confidence: 99%

Section: Motion Compensationmentioning

confidence: 99%

Noise reduction and motion elimination in low-dose 4D myocardial computed tomography perfusion (CTP): preliminary clinical evaluation of the ASTRA4D algorithm

et al. 2019

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“…This method used a non-rigid joint motion and intensity correction algorithm, introduced in [36]. This algorithm integrates changes in intensity to compensate motion artifacts.…”

Section: Methodsmentioning

confidence: 99%

An Open Benchmark Challenge for Motion Correction of Myocardial Perfusion MRI

Pontré

Cowan

DiBella

et al. 2017

IEEE J. Biomed. Health Inform.

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Cardiac magnetic resonance perfusion examinations enable non-invasive quantification of myocardial blood flow. However, motion between frames due to breathing must be corrected for quantitative analysis. Although several methods have been proposed, there is a lack of widely available benchmarks to compare different algorithms. We sought to compare many algorithms from several groups in an open benchmark challenge. Nine clinical studies from two different centers comprising normal and diseased myocardium at both rest and stress were made available for this study. The primary validation measure was regional myocardial blood flow based on the transfer coefficient (Ktrans), which was computed using a compartment model and the myocardial perfusion reserve (MPR) index. The ground truth was calculated using contours drawn manually on all frames by a single observer, and visually inspected by a second observer. Six groups participated and 19 different motion correction algorithms were compared. Each method used one of three different motion models: rigid, global affine or local deformation. The similarity metric also varied with methods employing either sum-of-squared differences, mutual information or cross-correlation. There were no significant differences in Ktrans or MPR compared across different motion models or similarity metrics. Compared with the ground truth, only Ktrans for the sum of squared differences metric, and for local deformation motion models, had significant bias. In conclusion, the open benchmark enabled evaluation of clinical perfusion indices over a wide range of methods. In particular, there was no benefit of non-rigid registration techniques over the other methods evaluated in this study. The benchmark data and results are available from the Cardiac Atlas Project (www.cardiacatlas.org).

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An Unbiased Groupwise Registration Algorithm for Correcting Motion in Dynamic Contrast-Enhanced Magnetic Resonance Images

Mojica

Ebrahimi

2018

Image Analysis for Moving Organ, Breast, and Thoracic Images

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A Gauss–Newton approach to joint image registration and intensity correction

Cited by 9 publications

References 37 publications

Noise reduction and motion elimination in low-dose 4D myocardial computed tomography perfusion (CTP): preliminary clinical evaluation of the ASTRA4D algorithm

Noise reduction and motion elimination in low-dose 4D myocardial computed tomography perfusion (CTP): preliminary clinical evaluation of the ASTRA4D algorithm

An Open Benchmark Challenge for Motion Correction of Myocardial Perfusion MRI

An Unbiased Groupwise Registration Algorithm for Correcting Motion in Dynamic Contrast-Enhanced Magnetic Resonance Images

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