Disclaimer: Some of the published work below originates from researchers of the IADI lab who co-founded Epsidy.

2024

Phantom evaluation of electrical conductivity mapping by MRI: Comparison to vector network analyzer measurements and spatial resolution assessment

He Z, (…) Odille F. Magn Reson Med. 2024 Jan 15. doi: 10.1002/mrm.30009. Online ahead of print.

Abstract

Purpose: To evaluate the performance of various MR electrical properties tomography (MR-EPT) methods at 3 T in terms of absolute quantification and spatial resolution limit for electrical conductivity.

Methods: Absolute quantification as well as spatial resolution performance were evaluated on homogeneous phantoms and a phantom with holes of different sizes, respectively. Ground-truth conductivities were measured with an open-ended coaxial probe connected to a vector network analyzer (VNA). Four widely used MR-EPT reconstruction methods were investigated: phase-based Helmholtz (PB), phase-based convection-reaction (PB-cr), image-based (IB), and generalized-image-based (GIB). These methods were compared using the same complex images from a 1 mm-isotropic UTE sequence. Alternative transceive phase acquisition sequences were also compared in PB and PB-cr.

Results: In large homogeneous phantoms, all methods showed a strong correlation with ground truth conductivities (r > 0.99); however, GIB was the best in terms of accuracy, spatial uniformity, and robustness to boundary artifacts. In the resolution phantom, the normalized root-mean-squared error of all methods grew rapidly (>0.40) when the hole size was below 10 mm, with simplified methods (PB and IB), or below 5 mm, with generalized methods (PB-cr and GIB).

Conclusion: VNA measurements are essential to assess the accuracy of MR-EPT. In this study, all tested MR-EPT methods correlated strongly with the VNA measurements. The UTE sequence is recommended for MR-EPT, with the GIB method providing good accuracy for structures down to 5 mm. Structures below 5 mm may still be detected in the conductivity maps, but with significantly lower accuracy.

2023

A Generative Adversarial Network to Synthesize 3D Magnetohydrodynamic Distortions for Electrocardiogram Analyses Applied to Cardiac Magnetic Resonance Imaging

Mehri M, Calmon G, Odille F, Oster J and Lalande A. Sensors 2023, 23(21), 8691; doi.org/10.3390/s23218691(registering DOI)

Abstract

Recently, deep learning (DL) models have been increasingly adopted for automatic analyses of medical data, including electrocardiograms (ECGs). Large, available ECG datasets, generally of high quality, often lack specific distortions, which could be helpful for enhancing DL-based algorithms. Synthetic ECG datasets could overcome this limitation. A generative adversarial network (GAN) was used to synthesize realistic 3D magnetohydrodynamic (MHD) distortion templates, as observed during magnetic resonance imaging (MRI), and then added to available ECG recordings to produce an augmented dataset. Similarity metrics, as well as the accuracy of a DL-based R-peak detector trained with and without data augmentation, were used to evaluate the effectiveness of the synthesized data. Three-dimensional MHD distortions produced by the proposed GAN were similar to the measured ones used as input. The precision of a DL-based R-peak detector, tested on actual unseen data, was significantly enhanced by data augmentation; its recall was higher when trained with augmented data. Using synthesized MHD-distorted ECGs significantly improves the accuracy of a DL-based R-peak detector, with a good generalization capacity. This provides a simple and effective alternative to collecting new patient data. DL-based algorithms for ECG analyses can suffer from bias or gaps in training datasets. Using a GAN to synthesize new data, as well as metrics to evaluate its performance, can overcome the scarcity issue of data availability.

A Deep Learning Architecture Using 3D Vectorcardiogram to Detect R-Peaks in ECG with Enhanced Precision

Mehri M, Calmon G, Odille F and Oster J. Sensors 2023, 23, 2288. doi:10.3390/s23042288

Abstract

Providing reliable detection of QRS complexes is key in automated analyses of electrocardiograms (ECG). Accurate and timely R-peak detections provide a basis for ECG-based diagnoses and to synchronize radiologic, electrophysiologic, or other medical devices. Compared with classical algorithms, deep learning (DL) architectures have demonstrated superior accuracy and high generalization capacity. Furthermore, they can be embedded on edge devices for real-time inference. 3D vectorcardiograms (VCG) provide a unifying framework for detecting R-peaks regardless of the acquisition strategy or number of ECG leads. In this article, a DL architecture was demonstrated to provide enhanced precision when trained and applied on 3D VCG, with no pre-processing nor post-processing steps. Experiments were conducted on four different public databases. Using the proposed approach, high F1-scores of 99.80% and 99.64% were achieved in leave-one-out cross-validation and cross-database validation protocols, respectively. False detections, measured by a precision of 99.88% or more, were significantly reduced compared with recent state-of-the-art methods tested on the same databases, without penalty in the number of missed peaks, measured by a recall of 99.39% or more. This approach can provide new applications for devices where precision, or positive predictive value, is essential, for instance cardiac magnetic resonance imaging.

2022

A hardware and software system for MRI applications requiring external device data

Isaieva K, Fauvel M, Weber N, Vuissoz PA, Felblinger J, Oster J, Odille F. Magn Res in Med, Sep;88(3):1406-1418. doi: 10.1002/mrm.29280

Abstract

Purpose: Numerous MRI applications require data from external devices. Such devices are often independent of the MRI system, so synchronizing these data with the MRI data is often tedious and limited to offline use. In this work, a hardware and software system is proposed for acquiring data from external devices during MR imaging, for use online (in real-time) or offline.

Methods: The hardware includes a set of external devices – electrocardiography (ECG) devices, respiration sensors, microphone, electronics of the MR system etc. – using various channels for data transmission (analog, digital, optical fibers), all connected to a server through a universal serial bus (USB) hub. The software is based on a flexible client–server architecture, allowing real-time processing pipelines to be configured and executed. Communication protocols and data formats are proposed, in particular for transferring the external device data to an open-source reconstruction software (Gadgetron), for online image reconstruction using external physiological data. The system performance is evaluated in terms of accuracy of the recorded signals and delays involved in the real-time processing tasks. Its flexibility is shown with various applications.

Results: The real-time system had low delays and jitters (on the order of 1 ms). Example MRI applications using external devices included: prospectively gated cardiac cine imaging, multi-modal acquisition of the vocal tract (image, sound, and respiration) and online image reconstruction with nonrigid motion correction.

Conclusion: The performance of the system and its versatile architecture make it suitable for a wide range of MRI applications requiring online or offline use of external device data.

In Vivo Super-Resolution Cardiac Diffusion Tensor MRI: A Feasibility Study

Le Bars AL, Moulin K, Ennis DB, Felblinger J, Chen B and Odille F. Diagnostics 2022, 12, 877. doi: 10.3390/diagnostics12040877

Abstract

A super-resolution (SR) technique is proposed for imaging myocardial fiber architecture with cardiac magnetic resonance. Images were acquired with a motion-compensated cardiac diffusion tensor imaging (cDTI) sequence. The heart left ventricle was covered with three stacks of thick slices, in short axis, horizontal and vertical long axes orientations, respectively. The three low-resolution stacks (2 × 2 × 8 mm3) were combined into an isotropic volume (2 × 2 × 2 mm3) by a super-resolution reconstruction. For in vivo measurements, each slice was acquired during a breath-hold period. Bulk motion was corrected by optimizing a similarity metric between intensity profiles from all intersecting slices in the dataset. The benefit of the proposed approach was evaluated using a numerical heart phantom, a physical helicoidal phantom with artificial fibers, and six healthy subjects. The SR technique showed improved results compared to the native scans, in terms of image quality and cDTI metrics. In particular, the myocardial helix angle (HA) was more accurately estimated in the physical phantom (HA = 41.5° ± 1.1°, with the ground truth being 42.0°). In vivo, it resulted in a sharper rate of change of HA across the myocardial wall (−0.993°/% ± 0.007°/% against −0.873°/% ± 0.010°/%).

2021

MR electrical properties imaging using a generalized image-based method

Soullié P, (…), Felblinger J, Odille F, Magn. Reson. Med., vol. 85, no. 2, pp. 762–776, Feb. 2021, doi: 10.1002/mrm.28458

Abstract

Purpose: To develop a fast and easy-to-use electrical properties tomography (EPT) method based on a single MR scan, avoiding both the need of a B1-map and transceive phase assumption, and that is robust against noise.

Theory: Derived from Maxwell’s equations, conductivity, and permittivity are reconstructed from a new partial differential equation involving the product of the RF fields and its derivatives. This also allows us to clarify and revisit the relevance of common assumptions of MREPT.

Methods: Our new governing equation is solved using a 3D finite-difference scheme and compared to previous frameworks. The benefits of our method over selected existing MREPT methods are demonstrated for different simulation models, as well as for both an inhomogeneous agar phantom gel and in vivo brain data at 3T.

Results: Simulation and experimental results are illustrated to highlight the merits of the proposed method over existing methods. We show the validity of our algorithm in versatile configurations, with many transition regions notably. Complex admittivity maps are also provided as a complementary MR contrast.

Conclusion: Because it avoids time-consuming RF field mapping and generalizes the use of standard MR image for electrical properties reconstruction, this contribution is promising as a new step forward for clinical applications.

Magnetic Resonance Imaging Screening for Postinfarct Life-Threatening Ventricular Arrhythmia

de Chillou C, (...), Felblinger J, Haïssaguerre M, Odille F. JACC Cardiovasc Imaging. 2021 Dec;14(12):2479-2481 doi: 10.1016/j.jcmg.2021.07.003

No abstract available

MR electrical properties imaging using a generalized image‐based method

Soullié P, Missoffe A, Ambarki K, Felblinger J, Odille F. Magn Reson Med. 2021. doi: 10.1002/mrm.28458

Abstract

Purpose: To develop a fast and easy-to-use electrical properties tomography (EPT) method based on a single MR scan, avoiding both the need of a B1 -map and transceive phase assumption, and that is robust against noise.

Theory: Derived from Maxwell's equations, conductivity, and permittivity are reconstructed from a new partial differential equation involving the product of the RF fields and its derivatives. This also allows us to clarify and revisit the relevance of common assumptions of MREPT.

Methods: Our new governing equation is solved using a 3D finite-difference scheme and compared to previous frameworks. The benefits of our method over selected existing MREPT methods are demonstrated for different simulation models, as well as for both an inhomogeneous agar phantom gel and in vivo brain data at 3T.

Results: Simulation and experimental results are illustrated to highlight the merits of the proposed method over existing methods. We show the validity of our algorithm in versatile configurations, with many transition regions notably. Complex admittivity maps are also provided as a complementary MR contrast.

Conclusion: Because it avoids time-consuming RF field mapping and generalizes the use of standard MR image for electrical properties reconstruction, this contribution is promising as a new step forward for clinical applications.

2020

A Paced-ECG Detector and Delineator for Automatic Multi-Parametric Catheter Mapping of Ventricular Tachycardia

Hoyland P, Hammache N , Battaglia A, Oster J, Felblinger J, De Chillou C, Odille F. IEEE Access ( Volume: 8) 223952 – 223960. doi: 10.1109/ACCESS.2020.3043542

Abstract

Ventricular tachycardia (VT) is a life-threatening arrhythmia, which can be treated by catheter intervention. Accurate identification of the underlying reentrant circuit is often challenging, yet it is key to successful ablation of the VT. In practice, the cardiologist often uses electrocardiography (ECG) data provided by various catheter mapping techniques, including parameters acquired during sinus rhythm (voltage maps, presence of fragmented/late potentials) and during controlled pacing from different sites of the ventricle, so-called pace-mapping. A novel method is presented here to automatically extract the key information from pace-mapping data with automated detection of paced heartbeats from the surface ECG signals, using wavelet detection of pacing spikes and combined time/energy criteria, and automated delineation of paced beats, QRS peak, and QRS onset. This allows the generation of correlation gradient maps (indicating QRS morphology changes as the catheter is moved) and stimulus-to-QRS maps (sQRS, indicating the delay between pacing and activation of the healthy myocardium). The delineator is shown to be in good agreement with manual annotations from experts in a retrospective study of 18 VT ablation procedures. Paced-QRS detection had 95.2% sensitivity and 98.4% positive predictive value. Resulting sQRS maps had a mean absolute error of 11.1 ms, which was in the same range as the inter-observer errors (9.7 ms). The automatic processing drastically reduces the need for manual annotations. Therefore it makes it feasible to process and visualize, during the procedure, all the relevant parametric maps, which can be analyzed jointly to identify VT circuits and corresponding ablation targets.

Broadband Electrocardiogram Acquisition for Improved Suppression of MRI Gradient Artifacts

Dos Reis JE, Odille F, et al. Physiol Meas. 2020 May 7;41(4):045004. doi: 10.1088/1361-6579/ab7b8e

Abstract

Objective: Despite being routinely acquired during MRI examinations for triggering or monitoring purposes, electrocardiogram (ECG) signal recording and analysis remain challenging due to the inherent magnetic environment of an MRI scanner. The ECG signals are particularly distorted by the induction of electrical fields in the body by the MRI gradients. In this study, we propose a new hardware and software solution for the acquisition of ECG signal during MRI up to 3 T.

Approach: Instead of restricting the sensor bandwidth to limit these gradient artifacts, the new sensor architecture has a higher bandwidth, higher sampling frequency and larger input dynamics, in order to acquire the ECG signals and the gradient artifacts more precisely. Signal processing based on a novel detection algorithm and blanking are then applied for improved artifact suppression.

Main results: The proposed sensor allows the gradient artifacts to be acquired more precisely, and these artifacts are recorded with peak-to-peak amplitudes two orders of magnitude larger than for QRS complexes. The proposed method outperforms a state-of-the-art approach both in terms of signal quality (+9% 'SNR') and accuracy of QRS detection (+11%).

Significance: The proposed hardware and software solutions open the way for the acquisition of high-quality of ECG gating in MRI, and improved diagnostic quality of ECG signals in MRI.

2019

Reconstruction of the 12-Lead ECG Using a Novel MR-Compatible ECG Sensor Network

Dos Reis JE, (...), Felblinger J, Odille F. Magn Reson Med. 2019 Nov;82(5):1929-1945. doi: 10.1002/mrm.27854

Abstract

Purpose: Current electrocardiography (ECG) devices in MRI use non-conventional electrode placement, have a narrow bandwidth, and suffer from signal distortions including magnetohydrodynamic (MHD) effects and gradient-induced artifacts. In this work a system is proposed to obtain a high-quality 12-lead ECG.

Methods: A network of N electrically independent MR-compatible ECG sensors was developed (N = 4 in this study). Each sensor uses a safe technology - short cables, preamplification/digitization close to the patient, and optical transmission - and provides three bipolar voltage leads. A matrix combination is applied to reconstruct a 12-lead ECG from the raw network signals. A subject-specific calibration is performed to identify the matrix coefficients, maximizing the similarity with a true 12-lead ECG, acquired with a conventional 12-lead device outside the scan room. The sensor network was subjected to radiofrequency heating phantom tests at 3T. It was then tested in four subjects, both at 1.5T and 3T.

Results: Radiofrequency heating at 3T was within the MR-compatibility standards. The reconstructed 12-lead ECG showed minimal MHD artifacts and its morphology compared well with that of the true 12-lead ECG, as measured by correlation coefficients above 93% (respectively, 84%) for the QRS complex shape during steady-state free precession (SSFP) imaging at 1.5T (respectively, 3T).

Conclusion: High-quality 12-lead ECG can be reconstructed by the proposed sensor network at 1.5T and 3T with reduced MHD artifacts compared to previous systems. The system might help improve patient monitoring and triggering and might also be of interest for interventional MRI and advanced cardiac MR applications.

Catheter Treatment of Ventricular Tachycardia: a Reference-Less Pace-Mapping Method to Identify Ablation Targets

Odille F, (...), Felblinger J. IEEE Trans Biomed Eng. 2019 Nov;66(11):3278-3287. doi: 10.1109/TBME.2019.2903631

Abstract

Objective: A novel method is developed to identify ablation targets for the catheter treatment of ventricular tachycardia (VT).

Methods: The method is based on pace-mapping, which is a validated technique to determine the catheter ablation targets. Conventionally, it consists of stimulating the heart ventricle from various sites and comparing the resulting activation pathways to that of a clinical VT by the analysis of surface electrocardiograms (ECG). In this paper, a novel pace-mapping method is presented, which does not require a reference ECG recording of the VT. A three-dimensional correlation gradient map is reconstructed by semiautomatic analysis of ECG morphological changes within the network of pace-mapping sites. In these maps, abnormal points are identified by high correlation gradient values (i.e., corresponding to slow propagation of the electric influx, as in the core of the reentrant VT circuit). The relation between the conventional and reference-less method is described theoretically and evaluated in a retrospective study including 24 VT ablation procedures.

Results: The "reference-less" method was able to identify normal points with a high accuracy (negative predictive value: NPV = 97%), and to detect more abnormal points, as predicted by the theory. Correlation gradients computed by the proposed method were significantly higher in ablation zones than in other zones of the ventricle (p < 10-12), indicating excellent prediction of the ablation targets.

Significance: The reference-less method might either be used in complement of the conventional method or to treat patients in whom VT cannot be induced during the intervention.

Electrocardiogram Acquisition During Remote Magnetic Catheter Navigation

Dos Reis JE, (...), Odille F, Felblinger J. Ann Biomed Eng. 2019 Apr;47(4):1141-1152. doi: 10.1007/s10439-019-02214-3

Abstract

Electrocardiogram (ECG) acquisition is required during catheter treatment of cardiac arrhythmias. The remote magnetic navigation technology allows the catheter to be moved automatically inside the heart chambers using large external magnets. Each change of position of the catheter requires fast motion of the magnets, therefore magnetic fluxes are created through the ECG cables, causing large distortions of the ECG signals. In this study a novel ECG sensor is proposed for reducing such distortions. The sensor uses short cables to connect the electrodes to the amplification and optical conversion circuit, using a technology similar to that used for magnetic resonance imaging. The proposed sensor was compared to the conventional 12-lead ECG device during various operation modes of the magnets. Quantitative morphological analysis of the different waves of the ECG was performed in two healthy subjects and on a conductivity phantom reproducing various cardiac pathologies. In healthy subjects the beat-to-beat correlation coefficients were improved with the proposed sensor for the PR interval (80-93% vs. 49-89%), QRS complex (93-96% vs. 74-94%), ST segment + T wave (95-98% vs. 67-99%), and whole PQRST wave (82-97% vs. 55-96%). Similar observations were made with the conductive gel in the whole PQRST wave in the pathological morphologies of the ECG for the VT (99% vs. 56-98%), AT (95% vs. 26-89%), STE (96-97% vs. 20-91%) and STD (96% vs. 28-90%). The new sensor might be used for better (uninterrupted) monitoring of the patient during catheter interventions using remote magnetic navigation. It has the potential to improve the robustness and/or duration of certain clinical procedures such as ventricular tachycardia ablation.

2017

Statistical Variations of Heart Orientation in Healthy Adults

Odille F, Liu S, van Dam, Felblinger J. Presented at the 2017 Computing in Cardiology Conference, Sep. 2017. doi: 10.22489/CinC.2017.225-058

Abstract

The orientation of the heart in the chest impacts the shape and amplitude of surface ECG signals. It is also key information in electrocardiographic imaging (ECGI), where a model of the heart and torso is required. In this study we seek to analyze statistical relations between heart orientation and several easily available patient characteristics. Heart orientation data were obtained from an MRI database of 185 healthy adults. Relations with sex, height, weight (collected from case reports forms) and chest circumference (extracted from the images) were analyzed using univariate and multivariate linear regression. Chest circumference was found to be the best single predictor of heart orientation, and simple formulas were determined for its estimation. The proposed heart orientation statistical model might be used for selecting a torso/heart model from an existing database; this approach might allow ECGI techniques to be integrated into an ECG device.

Design And Validation of a Novel MR-Compatible Sensor for Respiratory Motion Modeling and Correction

Chen B, (...), Odille F, Felblinger J. IEEE Trans Biomed Eng. 2017 Jan;64(1):123-133. doi: 10.1109/TBME.2016.2549272

Abstract

Goal: A novel magnetic resonance (MR) compatible accelerometer for respiratory motion sensing (MARMOT) is developed as a surrogate of the vendors' pneumatic belts. We aim to model and correct respiratory motion for free-breathing thoracic-abdominal MR imaging and to simplify patient installation.

Methods: MR compatibility of MARMOT sensors was assessed in phantoms and its motion modeling/correction efficacy was demonstrated on 21 subjects at 3 T. Respiration was modeled and predicted from MARMOT sensors and pneumatic belts, based on real-time images and a regression method. The sensor accuracy was validated by comparing motion errors in the liver/kidney. Sensor data were also exploited as inputs for motion-compensated reconstruction of free-breathing cardiac cine MR images. Multiple and single sensor placement strategies were compared.

Results: The new sensor is compatible with the MR environment. The average motion modeling and prediction errors with MARMOT sensors and with pneumatic belts were comparable (liver and kidney) and were below 2 mm with all tested configurations (belts, multiple/single MARMOT sensor). Motion corrected cardiac cine images were of improved image quality, as assessed by an entropy metric (p < 10-6), with all tested configurations. Expert readings revealed multiple MARMOT sensors were the best (p < 0.03) and the single MARMOT sensor was similar to the belts (nonsignificant in two of the three readers).

Conclusion: The proposed sensor can model and predict respiratory motion with sufficient accuracy to allow free-breathing MR imaging strategy.

Significance: It provides an alternative sensor solution for the respiratory motion problem during MR imaging and may improve the convenience of patient setup.

2016

Joint Reconstruction of Multiple Images and Motion in MRI: Application to Free-Breathing Myocardial T₂ Quantification.

Odille F, Menini A, (…), Felblinger J. IEEE Trans Med Imaging. 2016 Jan;35(1):197-207. doi: 10.1109/TMI.2015.2463088

Abstract

Exploiting redundancies between multiple images of an MRI examination can be formalized as the joint reconstruction of these images. The anatomy is preserved indeed so that specific constraints can be implemented (e.g. most of the features or spatial gradients should be in the same place in all these images) and only the contrast changes from one image to another need to be encoded. The application of this concept is particularly challenging in cardiovascular and body imaging due to the complex organ deformations, especially with the patient breathing. In this study a joint optimization framework is proposed for reconstructing multiple MR images together with a nonrigid motion model. The motion model takes into account both intra-image and inter-image motion and therefore can correct for most ghosting/blurring artifacts and misregistration between images. The framework was validated with free-breathing myocardial T 2 mapping experiments from nine heart transplant patients at 1.5 T. Results showed improved image quality and excellent image alignment with the multi-image reconstruction compared to the independent reconstruction of each image. Segment-wise myocardial T 2 values were in good agreement with the reference values obtained from multiple breath-holds (62.5 ± 11.1 ms against 62.2 ± 11.2 ms which was not significant with p=0.49).

2012

In Vivo Characterization of The Vestibulo-Cochlear Nerve Motion by MRI

Labrousse M, Hossu G, Calmon G, (…) Felblinger J, Braun M. Neuroimage. 2012 Jan 16;59(2):943-9. doi: 10.1016/j.neuroimage.2011.08.058

Abstract

The motion of the vestibulo-cochlear nerve (VCN) was quantified at the level of the cerebello-pontine angle in 28 healthy volunteers enrolled in a prospective study performed on a 3T MRI scanner. A phase contrast MRI (PCMRI) sequence was used. The VCN was divided into a cisternal part and a meatic part, both of which were measured for motion in the cranio-caudal (CC) and antero-posterior (AP) directions. Motion was cardiac-cycle-dependent in these two directions. The meatic VCN motion was delayed compared to the cisternal VCN motion. In the CC direction, the mean amplitude of the cisternal VCN motion was twice larger than the mean amplitude of the meatic VCN motion (0.37+/-0.14 mm versus 0.17+/-0.08 mm). In the AP direction, the mean amplitude of the cisternal VCN was 0.19+/-0.08 mm versus 0.16+/-0.14 mm for the meatic VCN. We used an "oscillating string" to explain the VCN motion. Reproducibility tests have shown small variations in measurements of the CC motion. PCMRI can be used to assess the VCN motion at the level of the cerebello-pontine angle.

2008

Generalized reconstruction by inversion of coupled systems (GRICS) applied to free-breathing MRI.

Odille F, Vuissoz PA, Marie PY, Felblinger J. Magn Reson Med. 2008 Jul;60(1):146-57. doi: 10.1002/mrm.21623

Abstract

A reconstruction strategy is proposed for physiological motion correction, which overcomes many limitations of existing techniques. The method is based on a general framework allowing correction for arbitrary motion–nonrigid or affine, making it suitable for cardiac or abdominal imaging, in the context of multiple coil, arbitrarily sampled acquisition. A model is required to predict motion in the field of view at each sample time point, based on prior knowledge provided by external sensors. A theoretical study is carried out to analyze the influence of motion prediction errors. Small errors are shown to propagate linearly in that reconstruction algorithm, and thus induce a reconstruction residue that is bounded (stability). Furthermore, optimization of the motion model is proposed in order to minimize this residue. This leads to reformulating reconstruction as two inverse problems which are coupled: motion-compensated reconstruction (known motion) and model optimization (known image). A fixed-point multiresolution scheme is described for inverting these two coupled systems. This framework is shown to allow fully autocalibrated reconstructions, as coil sensitivities and motion model coefficients are determined directly from the corrupted raw data. The theory is validated with real cardiac and abdominal data from healthy volunteers, acquired in free-breathing.

2007

Noise Cancellation Signal Processing Method and Computer System for Improved Real-Time Electrocardiogram Artifact Correction During MRI Data Acquisition

Odille F, Pasquier C, (…), Felblinger J. Trans Biomed Eng, Apr;54(4):630-640. doi: 10.1109/tbme.2006.889174

Abstract

A system was developed for real-time electrocardiogram (ECG) analysis and artifact correction during magnetic resonance (MR) scanning, to improve patient monitoring and triggering of MR data acquisitions. Based on the assumption that artifact production by magnetic field gradient switching represents a linear time invariant process, a noise cancellation (NC) method is applied to ECG artifact linear prediction. This linear prediction is performed using a digital finite impulse response (FIR) matrix, that is computed employing ECG and gradient waveforms recorded during a training scan. The FIR filters are used during further scanning to predict artifacts by convolution of the gradient waveforms. Subtracting the artifacts from the raw ECG signal produces the correction with minimal delay. Validation of the system was performed both off-line, using prerecorded signals, and under actual examination conditions. The method is implemented using a specially designed Signal Analyzer and Event Controller (SAEC) computer and electronics. Real-time operation was demonstrated at 1 kHz with a delay of only 1 ms introduced by the processing. The system opens the possibility of automatic monitoring algorithms for electrophysiological signals in the MR environment.