Realignment of myocardial first-pass MR perfusion images using an automatic detection of the heart-lung interface

Comte, Alexandre; Lalande, Alain; Aho, Serge-Ludwig; Walker, Paul; Brunotte, François

doi:10.1016/j.mri.2004.01.058

Cited by 9 publications

(4 citation statements)

References 33 publications

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“…Motion artifacts are inevitable. Rigid body image registration (38–44) has been studied but these methods do not correct for local deformations. Some techniques compensate for non-rigid image motion semi-automatically (45) or automatically (46).…”

Section: Discussionmentioning

confidence: 99%

A Quantitative Pixel-Wise Measurement of Myocardial Blood Flow by Contrast-Enhanced First-Pass CMR Perfusion Imaging

Hsu

Groves

Aletras

et al. 2012

JACC: Cardiovascular Imaging

124

112

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Objectives The aim of this study was to evaluate fully quantitative myocardial blood flow (MBF) at a pixel level based on contrast-enhanced first-pass cardiac magnetic resonance (CMR) imaging in dogs and patients. Background Microspheres can quantify MBF in subgram regions of interest but CMR perfusion imaging may be able to quantify MBF and differentiate blood flow at much higher resolution. Methods First-pass CMR perfusion imaging was performed in a dog model with local hyperemia induced by intracoronary adenosine. Fluorescent microspheres were the reference standard for MBF validation. CMR perfusion imaging was also performed on patients with significant coronary artery disease (CAD) by invasive coronary angiography. Myocardial time-signal intensity curves of the images were quantified on a pixel-by-pixel basis using a model-constrained deconvolution analysis. Results Qualitatively, color CMR perfusion pixel maps were comparable to microsphere MBF bull’s-eye plots in all animals. Pixel-wise CMR MBF estimates correlated well against subgram (0.49 ± 0.14 g) microsphere measurements (r=0.87 to 0.90) but showed minor underestimation of MBF. To reduce bias due to misregistration and minimize issues related to repeated measures, one hyperemic and one remote sector per animal were compared to the microsphere MBF which improved the correlation (r=0.97 to 0.98) and the bias was close to zero. Sector-wise and pixel-wise CMR MBF estimates also correlated well (r=0.97). In patients, color CMR stress perfusion pixel maps showed regional blood flow decreases and transmural perfusion gradients in territories served by stenotic coronary arteries. MBF estimates in endocardial versus epicardial subsectors, and ischemic versus remote sectors, were all significantly different (p<0.001 and p<0.01). Conclusions Myocardial blood flow can be quantified at the pixel level (~32 microliters of myocardium) on CMR perfusion images and results compared well with microsphere measurements. High-resolution pixel-wise CMR perfusion maps can quantify transmural perfusion gradients in patients with CAD.

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Section: Discussionmentioning

confidence: 99%

A Quantitative Pixel-Wise Measurement of Myocardial Blood Flow by Contrast-Enhanced First-Pass CMR Perfusion Imaging

Hsu

Groves

Aletras

et al. 2012

JACC: Cardiovascular Imaging

124

112

View full text Add to dashboard Cite

show abstract

“…However, in quantitative myocardial perfusion studies segmentation is mostly done manually because of insufficient contrast between hypoperfused areas with no contrast agent (CA) uptake and surrounding tissue [21]. Several studies proposed to register the images of one series to make segmentation easier [21][22][23][24][25][26]. An upstream registration of the series is intended to allow segmentation of only one image and to apply the resulting segmentation to all images [22,23].…”

Section: Introductionmentioning

confidence: 99%

Automatic postprocessing for the assessment of quantitative human myocardial perfusion using MRI

et al. 2009

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“…[61][62][63][64] It is recognized the rapid contrast change during the bolus passage makes robust image registration difficult. 61 To overcome this issue, different strategies were proposed, including progressively applying registration on consecutive perfusion images, 48 detecting image features and tissue boundaries for registration, 65 and estimating model image to minimize contrast change. 61,62,64 Among the latest category to which the proposed algorithm belongs, independent component analysis was used in Milles et al 64 to separate LV, right ventricle, and myocardium and derive a motion-free reference.…”

Section: Mocomentioning

confidence: 99%

Automatic in‐line quantitative myocardial perfusion mapping: Processing algorithm and implementation

Xue

Brown

Nielles‐Vallespin

et al. 2019

Magnetic Resonance in Med

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Purpose: Quantitative myocardial perfusion mapping has advantages over qualitative assessment, including the ability to detect global flow reduction. However, it is not clinically available and remains a research tool. Building upon the previously described imaging sequence, this study presents algorithm and implementation of an automated solution for inline perfusion flow mapping with step by step performance characterization. Methods: Proposed workflow consists of motion correction (MOCO), arterial input function blood detection, intensity to gadolinium concentration conversion, and pixel-wise mapping. A distributed kinetics model, blood-tissue exchange model, is implemented, computing pixel-wise maps of myocardial blood flow (mL/min/g), permeability-surface-area product (mL/min/g), blood volume (mL/g), and interstitial volume (mL/g). Results: Thirty healthy subjects (11 men; 26.4 ± 10.4 years) were recruited and underwent adenosine stress perfusion cardiovascular MR. Mean MOCO quality score was 3.6 ± 0.4 for stress and 3.7 ± 0.4 for rest. Myocardial Dice similarity coefficients after MOCO were significantly improved (P < 1e-6), 0.87 ± 0.05 for stress and 0.86 ± 0.06 for rest. Arterial input function peak gadolinium concentration was 4.4 ± 1.3 mmol/L at stress and 5.2 ± 1.5 mmol/L at rest. Mean myocardial blood flow at stress and rest were 2.82 ± 0.47 mL/min/g and 0.68 ± 0.16 mL/min/g, respectively. The permeability-surface-area product was 1.32 ± 0.26 mL/min/g at stress and 1.09 ± 0.21 mL/min/g at rest (P < 1e-3). Blood volume was 12.0 ± 0.8 mL/100 g at stress and 9.7 ± 1.0 mL/100 g at rest (P < 1e-9), indicating good adenosine vasodilation response. Interstitial volume was 20.8 ± 2.5 mL/100 g at stress and 20.3 ± 2.9 mL/100 g at rest (P = 0.50). Conclusions: An inline perfusion flow mapping workflow is proposed and demonstrated on normal volunteers. Initial evaluation demonstrates this fully automated solution for the respiratory MOCO, arterial input function left ventricle mask detection, and pixel-wise mapping, from free-breathing myocardial perfusion imaging.

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Realignment of myocardial first-pass MR perfusion images using an automatic detection of the heart-lung interface

Cited by 9 publications

References 33 publications

A Quantitative Pixel-Wise Measurement of Myocardial Blood Flow by Contrast-Enhanced First-Pass CMR Perfusion Imaging

A Quantitative Pixel-Wise Measurement of Myocardial Blood Flow by Contrast-Enhanced First-Pass CMR Perfusion Imaging

Automatic postprocessing for the assessment of quantitative human myocardial perfusion using MRI

Automatic in‐line quantitative myocardial perfusion mapping: Processing algorithm and implementation

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