DSpace Angular :: Browsing by Author "Prieto, Claudia"

Browsing by Author "Prieto, Claudia"

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3D Undersampled Golden-Radial Phase Encoding for DCE-MRA Using Inherently Regularized Iterative SENSE
(2010) Prieto, Claudia; Uribe Arancibia, Sergio A.; Razavi, Reza; Atkinson, David; Schaeffter, Tobias
A Spatial Off-Resonance Correction in Spirals for Magnetic Resonance Fingerprinting
(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021) Coronado, Ronal; Cruz, Gastao; Castillo Passi, Carlos; Tejos, Cristian; Uribe, Sergio; Prieto, Claudia; Irarrazaval, Pablo
In MR Fingerprinting (MRF), balanced Steady-State Free Precession (bSSFP) has advantages over unbalanced SSFP because it retains the spin history achieving a higher signal-to-noise ratio (SNR) and scan efficiency. However, bSSFP-MRF is not frequently used because it is sensitive to off-resonance, producing artifacts and blurring, and affecting the parametric map quality. Here we propose a novel Spatial Off-resonance Correction (SOC) approach for reducing these artifacts in bSSFP-MRF with spiral trajectories. SOC-MRF uses each pixel's Point Spread Function to create system matrices that encode both off-resonance and gridding effects. We iteratively compute the inverse of these matrices to reduce the artifacts. We evaluated the proposed method using brain simulations and actual MRF acquisitions of a standardized T1/T2 phantom and five healthy subjects. The results show that the off-resonance distortions in T1/T2 maps were considerably reduced using SOC-MRF. For T2, the Normalized Root Mean Square Error (NRMSE) was reduced from 17.3 to 8.3% (simulations) and from 35.1 to 14.9% (phantom). For T1, the NRMS was reduced from 14.7 to 7.7% (simulations) and from 17.7 to 6.7% (phantom). For in-vivo, the mean and standard deviation in different ROI in white and gray matter were significantly improved. For example, SOC-MRF estimated an average T2 for white matter of 77ms (the ground truth was 74ms) versus 50 ms of MRF. For the same example the standard deviation was reduced from 18 ms to 6ms. The corrections achieved with the proposed SOC-MRF may expand the potential applications of bSSFP-MRF, taking advantage of its better SNR property.
Accelerating dual cardiac phase images using phase encoding trajectories
(ELSEVIER SCIENCE INC, 2016) Letelier, Karis; Urbina, Jesus; Andia, Marcelo; Tejos, Cristian; Irarrazaval, Pablo; Prieto, Claudia; Uribe, Sergio
A three-dimensional dual-cardiac-phase (3D-DCP) scan has been proposed to acquire two data sets of the whole heart and great vessels during the end-diastolic and end-systolic cardiac phases in a single free-breathing scan. This method has shown accurate assessment of cardiac anatomy and function but is limited by long acquisition times. This work proposes to accelerate the acquisition and reconstruction of 3D-DCP scans by exploiting redundant information of the outer k-space regions of both cardiac phases. This is achieved using a modified radial-phase-encoding trajectory and gridding reconstruction with uniform coil combination. The end-diastolic acquisition trajectory was angularly shifted with respect to the end-systolic phase. Initially, a fully-sampled 3D-DCP scan was acquired to determine the optimal percentage of the outer k-space data that can be combined between cardiac phases. Thereafter, prospectively undersampled data were reconstructed based on this percentage. As gold standard images, the undersampled data were also reconstructed using iterative SENSE. To validate the method, image quality assessments and a cardiac volume analysis were performed. The proposed method was tested in thirteen healthy volunteers (mean age, 30 years). Prospectively undersampled data (R = 4) reconstructed with 50% combination led high quality images. There were no significant differences in the image quality and in the cardiac volume analysis between our method and iterative SENSE. In addition, the proposed approach reduced the reconstruction time from 40 min to 1 min. In conclusion, the proposed method obtains 3D-DCP scans with an image quality comparable to those reconstructed with iterative SENSE, and within a clinically acceptable reconstruction time. (C) 2016 Elsevier Inc. All rights reserved.
Artificial Intelligence in Cardiac MRI: Is Clinical Adoption Forthcoming?
(FRONTIERS MEDIA SA, 2022) Fotaki, Anastasia; Puyol Anton, Esther; Chiribiri, Amedeo; Botnar, Rene; Pushparajah, Kuberan; Prieto, Claudia
Artificial intelligence (AI) refers to the area of knowledge that develops computerised models to perform tasks that typically require human intelligence. These algorithms are programmed to learn and identify patterns from "training data," that can be subsequently applied to new datasets, without being explicitly programmed to do so. AI is revolutionising the field of medical imaging and in particular of Cardiovascular Magnetic Resonance (CMR) by providing deep learning solutions for image acquisition, reconstruction and analysis, ultimately supporting the clinical decision making. Numerous methods have been developed over recent years to enhance and expedite CMR data acquisition, image reconstruction, post-processing and analysis; along with the development of promising AI-based biomarkers for a wide spectrum of cardiac conditions. The exponential rise in the availability and complexity of CMR data has fostered the development of different AI models. Integration in clinical routine in a meaningful way remains a challenge. Currently, innovations in this field are still mostly presented in proof-of-concept studies with emphasis on the engineering solutions; often recruiting small patient cohorts or relying on standardised databases such as Multi-ethnic Study on atherosclerosis (MESA), UK Biobank and others. The wider incorporation of clinically valid endpoints such as symptoms, survival, need and response to treatment remains to be seen. This review briefly summarises the current principles of AI employed in CMR and explores the relevant prospective observational studies in cardiology patient cohorts. It provides an overview of clinical studies employing undersampled reconstruction techniques to speed up the scan encompassing cine imaging, whole-heart imaging, multi-parametric mapping and magnetic resonance fingerprinting along with the clinical utility of AI applications in image post-processing, and analysis. Specific focus is given to studies that have incorporated CMR-derived prediction models for prognostication in cardiac disease. It also discusses current limitations and proposes potential developments to enable multi-disciplinary collaboration for improved evidence-based medicine. AI is an extremely promising field and the timely integration of clinician's input in the ingenious technical investigator's paradigm holds promise for a bright future in the medical field.
Coronary Magnetic Resonance Angiography in Chronic Coronary Syndromes
(FRONTIERS MEDIA SA, 2021) Hajhosseiny, Reza; Munoz, Camila; Cruz, Gastao; Khamis, Ramzi; Kim, Won Yong; Prieto, Claudia; Botnar, Rene M.
Cardiovascular disease is the leading cause of mortality worldwide, with atherosclerotic coronary artery disease (CAD) accounting for the majority of cases. X-ray coronary angiography and computed tomography coronary angiography (CCTA) are the imaging modalities of choice for the assessment of CAD. However, the use of ionising radiation and iodinated contrast agents remain drawbacks. There is therefore a clinical need for an alternative modality for the early identification and longitudinal monitoring of CAD without these associated drawbacks. Coronary magnetic resonance angiography (CMRA) could be a potential alternative for the detection and monitoring of coronary arterial stenosis, without exposing patients to ionising radiation or iodinated contrast agents. Further advantages include its versatility, excellent soft tissue characterisation and suitability for repeat imaging. Despite the early promise of CMRA, widespread clinical utilisation remains limited due to long and unpredictable scan times, onerous scan planning, lower spatial resolution, as well as motion related image quality degradation. The past decade has brought about a resurgence in CMRA technology, with significant leaps in image acceleration, respiratory and cardiac motion estimation and advanced motion corrected or motion-resolved image reconstruction. With the advent of artificial intelligence, great advances are also seen in deep learning-based motion estimation, undersampled and super-resolution reconstruction promising further improvements of CMRA. This has enabled high spatial resolution (1 mm isotropic), 3D whole heart CMRA in a clinically feasible and reliable acquisition time of under 10 min. Furthermore, latest super-resolution image reconstruction approaches which are currently under evaluation promise acquisitions as short as 1 min. In this review, we will explore the recent technological advances that are designed to bring CMRA closer to clinical reality.
Evaluation of accelerated motion-compensated 3d water/fat late gadolinium enhanced MR for atrial wall imaging
(SPRINGER, 2021) Munoz, Camila; Sim, Iain; Neji, Radhouene; Kunze, Karl P.; Masci, Pier Giorgio; Schmidt, Michaela; O'Neill, Mark; Williams, Steven; Botnar, Rene M.; Prieto, Claudia
Objective 3D late gadolinium enhancement (LGE) imaging is a promising non-invasive technique for the assessment of atrial fibrosis. However, current techniques result in prolonged and unpredictable scan times and high rates of non-diagnostic images. The purpose of this study was to compare the performance of a recently proposed accelerated respiratory motion-compensated 3D water/fat LGE technique with conventional 3D LGE for atrial wall imaging. Materials and methods 18 patients (age: 55.7 +/- 17.1 years) with atrial fibrillation underwent conventional diaphragmatic navigator gated inversion recovery (IR)-prepared 3D LGE (dNAV) and proposed image-navigator motion-corrected water/fat IR-prepared 3D LGE (iNAV) imaging. Images were assessed for image quality and presence of fibrosis by three expert observers. The scan time for both techniques was recorded. Results Image quality scores were improved with the proposed compared to the conventional method (iNAV: 3.1 +/- 1.0 vs. dNAV: 2.6 +/- 1.0, p = 0.0012, with 1: Non-diagnostic to 4: Full diagnostic). Furthermore, scan time for the proposed method was significantly shorter with a 59% reduction is scan time (4.5 +/- 1.2 min vs. 10.9 +/- 3.9 min, p < 0.0001). The images acquired with the proposed method were deemed as inconclusive less frequently than the conventional images (expert 1/expert 2: 4/7 dNAV and 2/4 iNAV images inconclusive). Discussion The motion-compensated water/fat LGE method enables atrial wall imaging with diagnostic quality comparable to the current conventional approach with a significantly shorter scan of about 5 min.
Extended MRI-based PET motion correction for cardiac PET/MRI
(2024) Aizaz, Mueez; Van der Pol, Jochem A. J.; Schneider, Alina; Munoz, Camila; Holtackers, Robert J.; Van Cauteren, Yvonne; Van Langen, Herman; Meeder, Joan G.; Rahel, Braim M.; Wierts, Roel; Botnar, Rene M.; Prieto, Claudia; Moonen, Rik P. M.; Kooi, M. E.
Purpose: A 2D image navigator (iNAV) based 3D whole-heart sequence has been used to perform MRI and PET non-rigid respiratory motion correction for hybrid PET/MRI. However, only the PET data acquired during the acquisition of the 3D whole-heart MRI is corrected for respiratory motion. This study introduces and evaluates an MRI-based respiratory motion correction method of the complete PET data. Methods Twelve oncology patients scheduled for an additional cardiac 18F-Fluorodeoxyglucose (18F-FDG) PET/MRI and 15 patients with coronary artery disease (CAD) scheduled for cardiac 18F-Choline (18F-FCH) PET/MRI were included. A 2D iNAV recorded the respiratory motion of the myocardium during the 3D whole-heart coronary MR angiography (CMRA) acquisition (~ 10 min). A respiratory belt was used to record the respiratory motion throughout the entire PET/MRI examination (~ 30–90 min). The simultaneously acquired iNAV and respiratory belt signal were used to divide the acquired PET data into 4 bins. The binning was then extended for the complete respiratory belt signal. Data acquired at each bin was reconstructed and combined using iNAV-based motion fields to create a respiratory motion-corrected PET image. Motion-corrected (MC) and non-motion-corrected (NMC) datasets were compared. Gating was also performed to correct cardiac motion. The SUVmax and TBRmax values were calculated for the myocardial wall or a vulnerable coronary plaque for the 18F-FDG and 18F-FCH datasets, respectively. Results A pair-wise comparison showed that the SUVmax and TBRmax values of the motion corrected (MC) datasets were significantly higher than those for the non-motion-corrected (NMC) datasets (8.2 ± 1.0 vs 7.5 ± 1.0, p < 0.01 and 1.9 ± 0.2 vs 1.2 ± 0.2, p < 0.01, respectively). In addition, the SUVmax and TBRmax of the motion corrected and gated (MC_G) reconstructions were also higher than that of the non-motion-corrected but gated (NMC_G) datasets, although for the TBRmax this difference was not statistically significant (9.6 ± 1.3 vs 9.1 ± 1.2, p = 0.02 and 2.6 ± 0.3 vs 2.4 ± 0.3, p = 0.16, respectively). The respiratory motion-correction did not lead to a change in the signal to noise ratio. Conclusion The proposed respiratory motion correction method for hybrid PET/MRI improved the image quality of cardiovascular PET scans by increased SUVmax and TBRmax values while maintaining the signal-to-noise ratio. Trial registration METC162043 registered 01/03/2017.
Generalized low-rank nonrigid motion-corrected reconstruction for MR fingerprinting
(WILEY, 2021) Cruz, Gastao; Qi, Haikun; Jaubert, Olivier; Kuestner, Thomas; Schneider, Torben; Michael Botnar, Rene; Prieto, Claudia
Purpose: Develop a novel low-rank motion-corrected (LRMC) reconstruction for nonrigid motion-corrected MR fingerprinting (MRF).
MRI-Guided Motion-Corrected PET Image Reconstruction for Cardiac PET/MRI
(SOC NUCLEAR MEDICINE INC, 2021) Munoz, Camila; Ellis, Sam; Nekolla, Stephan G.; Kunze, Karl P.; Vitadello, Teresa; Neji, Radhouene; Botnar, Rene M.; Schnabel, Julia A.; Reader, Andrew J.; Prieto, Claudia
Simultaneous PET/MRI has shown potential for the comprehensive assessment of myocardial health from a single examination. Furthermore, MRI-derived respiratory motion information, when incorporated into the PET image reconstruction, has been shown to improve PET image quality. Separately, MRI-based anatomically guided PET image reconstruction has been shown to effectively denoise images, but this denoising has so far been demonstrated mainly in brain imaging. To date, the combined benefits of motion compensation and anatomic guidance have not been demonstrated for myocardial PET/MRI. This work addressed this lack by proposing a single cardiac PET/MRI image reconstruction framework that fully utilizes MRI-derived information to allow both motion compensation and anatomic guidance within the reconstruction. Methods: Fifteen patients underwent an F-18-FDG cardiac PET/MRI scan with a previously introduced acquisition framework. The MRI data processing and image reconstruction pipeline produces respiratory motion fields and a high-resolution respiratory motion-corrected MR image with good tissue contrast. This MRI-derived information was then included in a respiratory motion-corrected, cardiac-gated, anatomically guided image reconstruction of the simultaneously acquired PET data. Reconstructions were evaluated by measuring myocardial contrast and noise and were compared with images from several comparative intermediate methods using the components of the proposed framework separately. Results: Including respiratory motion correction, cardiac gating, and anatomic guidance significantly increased contrast. In particular, myocardiumto-blood pool contrast increased by 143% on average (P < 0.0001), compared with conventional uncorrected, non-guided PET images. Furthermore, anatomic guidance significantly reduced image noise, by 16.1%, compared with nonguided image reconstruction (P < 0.0001). Conclusion: The proposed framework for MRI-derived motion compensation and anatomic guidance of cardiac PET data significantly improved image quality compared with alternative reconstruction methods. Each component of the reconstruction pipeline had a positive impact on the final image quality. These improvements have the potential to improve clinical interpretability and diagnosis based on cardiac PET/MR images.
Myocardial T1, T2, T2*, and fat fraction quantification via low-rank motion-corrected cardiac MR fingerprinting
(WILEY, 2022) da Cruz, Gastao Jose Lima; Velasco, Carlos; Lavin, Begona; Jaubert, Olivier; Michael Botnar, Rene; Prieto, Claudia
Purpose Develop a novel 2D cardiac MR fingerprinting (MRF) approach to enable simultaneous T1, T2, T2*, and fat fraction (FF) myocardial tissue characterization in a single breath-hold scan. Methods Simultaneous, co-registered, multi-parametric mapping of T1, T2, and FF has been recently achieved with cardiac MRF. Here, we further incorporate T2* quantification within this approach, enabling simultaneous T1, T2, T2*, and FF myocardial tissue characterization in a single breath-hold scan. T2* quantification is achieved with an eight-echo readout that requires a long cardiac acquisition window. A novel low-rank motion-corrected (LRMC) reconstruction is exploited to correct for cardiac motion within the long acquisition window. The proposed T1/T2/T2*/FF cardiac MRF was evaluated in phantom and in 10 healthy subjects in comparison to conventional mapping techniques. Results The proposed approach achieved high quality parametric mapping of T1, T2, T2*, and FF with corresponding normalized RMS error (RMSE) T1 = 5.9%, T2 = 9.6% (T2 values <100 ms), T2* = 3.3% (T2* values <100 ms), and FF = 0.8% observed in phantom scans. In vivo, the proposed approach produced higher left-ventricular myocardial T1 values than MOLLI (1148 vs 1056 ms), lower T2 values than T2-GraSE (42.8 vs 50.6 ms), lower T2* values than eight-echo gradient echo (GRE) (35.0 vs 39.4 ms), and higher FF values than six-echo GRE (0.8 vs 0.3 %) reference techniques. The proposed approach achieved considerable reduction in motion artifacts compared to cardiac MRF without motion correction, improved spatial uniformity, and statistically higher apparent precision relative to conventional mapping for all parameters. Conclusion The proposed cardiac MRF approach enables simultaneous, co-registered mapping of T1, T2, T2*, and FF in a single breath-hold for comprehensive myocardial tissue characterization, achieving higher apparent precision than conventional methods.
Non-rigid motion-corrected free-breathing 3D myocardial Dixon LGE imaging in a clinical setting
(SPRINGER, 2022) Zeilinger, Martin Georg; Kunze, Karl Philipp; Munoz, Camila; Neji, Radhouene; Schmidt, Michaela; Croisille, Pierre; Heiss, Rafael; Wuest, Wolfgang; Uder, Michael; Botnar, Rene Michael; Treutlein, Christoph; Prieto, Claudia
Objectives To investigate the efficacy of an in-line non-rigid motion-compensated reconstruction (NRC) in an image-navigated high-resolution three-dimensional late gadolinium enhancement (LGE) sequence with Dixon water-fat separation, in a clinical setting. Methods Forty-seven consecutive patients were enrolled prospectively and examined with 1.5 T MRI. NRC reconstructions were compared to translational motion-compensated reconstructions (TC) of the same datasets in overall and different sub-category image quality scores, diagnostic confidence, contrast ratios, LGE pattern, and semiautomatic LGE quantification. Results NRC outperformed TC in all image quality scores (p < 0.001 to 0.016; e.g., overall image quality 5/5 points vs. 4/5). Overall image quality was downgraded in only 23% of NRC datasets vs. 53% of TC datasets due to residual respiratory motion. In both reconstructions, LGE was rated as ischemic in 11 patients and non-ischemic in 10 patients, while it was absent in 26 patients. NRC delivered significantly higher LGE-to-myocardium and blood-to-myocardium contrast ratios (median 6.33 vs. 5.96, p < 0.001 and 4.88 vs. 4.66, p < 0.001, respectively). Automatically detected LGE mass was significantly lower in the NRC reconstruction (p < 0.001). Diagnostic confidence was identical in all cases, with high confidence in 89% and probable in 11% datasets for both reconstructions. No case was rated as inconclusive. Conclusions The in-line implementation of a non-rigid motion-compensated reconstruction framework improved image quality in image-navigated free-breathing, isotropic high-resolution 3D LGE imaging with undersampled spiral-like Cartesian sampling and Dixon water-fat separation compared to translational motion correction of the same datasets. The sharper depictions of LGE may lead to more accurate measures of LGE mass.
Self-supervised learning-based diffeomorphic non-rigid motion estimation for fast motion-compensated coronary MR angiography
(ELSEVIER SCIENCE INC, 2022) Munoz, Camila; Qi, Haikun; Cruz, Gastao; Kuestner, Thomas; Botnar, Rene M.; Prieto, Claudia
Purpose: To accelerate non-rigid motion corrected coronary MR angiography (CMRA) reconstruction by developing a deep learning based non-rigid motion estimation network and combining this with an efficient implementation of the undersampled motion corrected reconstruction.
Simultaneous T-1, T-2, and T-1 rho cardiac magnetic resonance fingerprinting for contrast agent-free myocardial tissue characterization
(WILEY, 2021) Velasco, Carlos; Cruz, Gastao; Lavin, Begona; Hua, Alina; Fotaki, Anastasia; Botnar, Rene M.; Prieto, Claudia
Purpose: To develop a simultaneous T-1, T-2, and T-1 rho cardiac magnetic resonance fingerprinting (MRF) approach to enable comprehensive contrast agent-free myocardial tissue characterization in a single breath-hold scan.
Space-time variant weighted regularization in compressed sensing cardiac cine MRI
(2019) Godino-Moya, Alejandro ; Royuela-del-Val, Javier ; Usman, Muhammad; Menchón-Lara, Rosa María ; Martín-Fernández, Marcos ; Prieto, Claudia ; Alberola-López, Carlos
Synergistic multi-contrast cardiac magnetic resonance image reconstruction
(ROYAL SOC, 2021) Qi, Haikun; Cruz, Gastao; Botnar, Rene; Prieto, Claudia
Cardiac magnetic resonance imaging (CMR) is an important tool for the non-invasive diagnosis of a variety of cardiovascular diseases. Parametric mapping with multi-contrast CMR is able to quantify tissue alterations in myocardial disease and promises to improve patient care. However, magnetic resonance imaging is an inherently slow imaging modality, resulting in long acquisition times for parametric mapping which acquires a series of cardiac images with different contrasts for signal fitting or dictionary matching. Furthermore, extra efforts to deal with respiratory and cardiac motion by triggering and gating further increase the scan time. Several techniques have been developed to speed up CMR acquisitions, which usually acquire less data than that required by the Nyquist-Shannon sampling theorem, followed by regularized reconstruction to mitigate undersampling artefacts. Recent advances in CMR parametric mapping speed up CMR by synergistically exploiting spatial-temporal and contrast redundancies. In this article, we will review the recent developments in multi-contrast CMR image reconstruction for parametric mapping with special focus on low-rank and model-based reconstructions. Deep learning-based multi-contrast reconstruction has recently been proposed in other magnetic resonance applications. These developments will be covered to introduce the general methodology. Current technical limitations and potential future directions are discussed.
Whole-heart non-rigid motion corrected coronary MRA with autofocus virtual 3D iNAV
(ELSEVIER SCIENCE INC, 2022) Schneider, Alina; Cruz, Gastao; Munoz, Camila; Hajhosseiny, Reza; Kuestner, Thomas; Kunze, Karl P.; Neji, Radhouene; Botnar, Rene M.; Prieto, Claudia
Purpose: Respiratory motion-corrected coronary MR angiography (CMRA) has shown promise for assessing coronary disease. By incorporating coronal 2D image navigators (iNAVs), respiratory motion can be corrected for in a beat-to-beat basis using translational correction in the foot-head (FH) and right-left (RL) directions and in a bin-to-bin basis using non-rigid motion correction addressing the remaining FH, RL and anterior-posterior (AP) motion. However, with this approach beat-to-beat AP motion is not corrected for. In this work we investigate the effect of remaining beat-to-beat AP motion and propose a virtual 3D iNAV that exploits autofocus motion correction to enable beat-to-beat AP and improved RL intra-bin motion correction. Methods: Free-breathing 3D whole-heart CMRA was acquired using a 3-fold undersampled variable-density Cartesian trajectory. Beat-to-beat 3D translational respiratory motion was estimated from the 2D iNAVs in FH and RL directions, and in AP direction with autofocus assuming a linear relationship between FH and AP movement of the heart. Furthermore, motion in RL was also refined using autofocus. This virtual 3D (v3D) iNAV was incorporated in a non-rigid motion correction (NRMC) framework. The proposed approach was tested in 12 cardiac patients, and visible vessel length and vessel sharpness for the right (RCA) and left (LAD) coronary arteries were compared against 2D iNAV-based NRMC. Results: Average vessel sharpness and length in v3D iNAV NRMC was improved compared to 2D iNAV NRMC (vessel sharpness: RCA: 56 +/- 1% vs 52 +/- 11%, LAD: 49 +/- 8% vs 49 +/- 7%; visible vessel length: RCA: 5.98 +/- 1.37 cm vs 5.81 +/- 1.62 cm, LAD: 5.95 +/- 1.85 cm vs 4.83 +/- 1.56 cm), however these improvements were not statistically significant. Conclusion: The proposed virtual 3D iNAV NRMC reconstruction further improved NRMC CMRA image quality by reducing artefacts arising from residual AP motion, however the level of improvement was subject-dependent.

Browsing by Author "Prieto, Claudia"

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