‡We describe the use of singular value decomposition in transforming genome-wide expression data from genes 3 arrays space to reduced diagonalized ‘‘eigengenes’’ 3 ‘‘eigenarrays’’ space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.

Singular Value Decomposition for Genome-Wide Expression Data Processing and Modeling

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Magnetic Resonance Imaging Physical Principles And Sequence Design

Demonstrate a novel fast method for reconstruction of multi-dimensional MR fingerprinting (MRF) data using deep learning methods.A neural network (NN) is defined using the TensorFlow framework and trained on simulated MRF data computed with the extended phase graph formalism. The NN reconstruction accuracy for noiseless and noisy data is compared to conventional MRF template matching as a function of training data size and is quantified in simulated numerical brain phantom data and International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom data measured on 1.5T and 3T scanners with an optimized MRF EPI and MRF fast imaging with steady state precession (FISP) sequences with spiral readout. The utility of the method is demonstrated in a healthy subject in vivo at 1.5T.Network training required 10 to 74 minutes; once trained, data reconstruction required approximately 10 ms for the MRF EPI and 76 ms for the MRF FISP sequence. Reconstruction of simulated, noiseless brain data using the NN resulted in a RMS error (RMSE) of 2.6 ms for T1 and 1.9 ms for T2 . The reconstruction error in the presence of noise was less than 10% for both T1 and T2 for SNR greater than 25 dB. Phantom measurements yielded good agreement (R2  = 0.99/0.99 for MRF EPI T1 /T2 and 0.94/0.98 for MRF FISP T1 /T2 ) between the T1 and T2 estimated by the NN and reference values from the International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom.Reconstruction of MRF data with a NN is accurate, 300- to 5000-fold faster, and more robust to noise and dictionary undersampling than conventional MRF dictionary-matching.

MR fingerprinting Deep RecOnstruction NEtwork (DRONE)

Purpose
To introduce a two-dimensional MR fingerprinting (MRF) technique for quantification of T1, T2, and M0 in myocardium.

Methods
An electrocardiograph-triggered MRF method is introduced for mapping myocardial T1, T2, and M0 during a single breath-hold in as short as four heartbeats. The pulse sequence uses variable flip angles, repetition times, inversion recovery times, and T2 preparation dephasing times. A dictionary of possible signal evolutions is simulated for each scan that incorporates the subject's unique variations in heart rate. Aspects of the sequence design were explored in simulations, and the accuracy and precision of cardiac MRF were assessed in a phantom study. In vivo imaging was performed at 3 Tesla in 11 volunteers to generate native parametric maps.

Results
T1 and T2 measurements from the proposed cardiac MRF sequence correlated well with standard spin echo measurements in the phantom study (R2 > 0.99). A Bland-Altman analysis revealed good agreement for myocardial T1 measurements between MRF and MOLLI (bias 1 ms, 95% limits of agreement −72 to 72 ms) and T2 measurements between MRF and T2-prepared balanced steady-state free precession (bias, −2.6 ms; 95% limits of agreement, −8.5 to 3.3 ms).

Conclusion
MRF can provide quantitative single slice T1, T2, and M0 maps in the heart within a single breath-hold. Magn Reson Med 77:1446–1458, 2017. © 2016 International Society for Magnetic Resonance in Medicine

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MR fingerprinting for rapid quantification of myocardial T1 , T2 , and proton spin density.

A rapid technique for quantitative abdominal imaging was developed by using a fast imaging with steady-state free precession MR fingerprinting acquisition in combination with the Bloch-Siegert B1 mapping method, allowing simultaneous quantification of T1 and T2 in the abdomen within a 19-second breath hold.

https://pubs.rsna.org/pb-assets/Radiology/podcasts/transcripts/2016/2016-04-transcript.pdf

MR Fingerprinting for Rapid Quantitative Abdominal Imaging

Magnetic resonance (MR) fingerprinting is a technique for acquiring and processing MR data that simultaneously provides quantitative maps of different tissue parameters through a pattern recognition algorithm. A predefined dictionary models the possible signal evolutions simulated using the Bloch equations with different combinations of various MR parameters and pattern recognition is completed by computing the inner product between the observed signal and each of the predicted signals within the dictionary. Though this matching algorithm has been shown to accurately predict the MR parameters of interest, one desires a more efficient method to obtain the quantitative images. We propose to compress the dictionary using the singular value decomposition, which will provide a low-rank approximation. By compressing the size of the dictionary in the time domain, we are able to speed up the pattern recognition algorithm, by a factor of between 3.4-4.8, without sacrificing the high signal-to-noise ratio of the original scheme presented previously.

SVD Compression for Magnetic Resonance Fingerprinting in the Time Domain

Purpose To reduce the acquisition time needed to obtain reliable parametric maps with Magnetic Resonance Fingerprinting. Methods An iterative-denoising algorithm is initialized by reconstructing the MRF image series at low image resolution. For subsequent iterations, the method enforces pixel-wise fidelity to the best-matching dictionary template then enforces fidelity to the acquired data at slightly higher spatial resolution. After convergence, parametric maps with desirable spatial resolution are obtained through template matching of the final image series. The proposed method was evaluated on phantom and in vivo data using the highly undersampled, variable-density spiral trajectory and compared with the original MRF method. The benefits of additional sparsity constraints were also evaluated. When available, gold standard parameter maps were used to quantify the performance of each method. Results The proposed approach allowed convergence to accurate parametric maps with as few as 300 time points of acquisition, as compared to 1000 in the original MRF work. Simultaneous quantification of T1, T2, proton density (PD), and B0 field variations in the brain was achieved in vivo for a 256 × 256 matrix for a total acquisition time of 10.2 s, representing a three-fold reduction in acquisition time. Conclusion The proposed iterative multiscale reconstruction reliably increases MRF acquisition speed and accuracy. Magn Reson Med 75:2481-2492, 2016. © 2015 Wiley Periodicals, Inc.

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Multiscale reconstruction for MR fingerprinting.

Background Conventional MRI can be limited in detecting subtle epileptic lesions or identifying active/epileptic lesions among widespread, multifocal lesions. Purpose We developed a high-resolution 3D MR fingerprinting (MRF) protocol to simultaneously provide quantitative T1 , T2 , proton density, and tissue fraction maps for detection and characterization of epileptic lesions. Study type Prospective. Population National Institute of Standards and Technology (NIST) / International Society for Magnetic Resonance in Medicine (ISMRM) phantom, five healthy volunteers and 15 patients with medically intractable epilepsy undergoing presurgical evaluation with noninvasive or invasive electroclinical data. Field strength/sequence 3D MRF scans and routine clinical epilepsy MR protocols were acquired at 3 T. Assessment The accuracy of the T1 and T2 values were first evaluated using the NIST/ISMRM phantom. The repeatability was then estimated with both phantom and volunteers based on the coefficient of variance (CV). For epilepsy patients, all the maps were qualitatively reviewed for lesion detection by three independent reviewers (S.E.J., M.L., I.N.) blinded to clinical data. Region of interest (ROI) analysis was performed on T1 and T2 maps to quantify the multiparametric signal differences between lesion and normal tissues. Findings from qualitative review and quantitative ROI analysis were compared with patients' electroclinical data to assess concordance. Statistical tests Phantom results were compared using R-squared, and patient results were compared using linear regression models. Results The phantom study showed high accuracy with the standard values, with an R2 of 0.99. The volunteer study showed high repeatability, with an average CV of 4.3% for T1 and T2 in various tissue regions. For the 15 patients, MRF showed additional findings in four patients, with the remaining 11 patients showing findings consistent with conventional MRI. The additional MRF findings were highly concordant with patients' electroclinical presentation. Data conclusion The 3D MRF protocol showed potential to identify otherwise inconspicuous epileptogenic lesions from the patients with negative conventional MRI diagnosis, as well as to correlate with different levels of epileptogenicity when widespread lesions were present. Level of evidence 3. Technical Efficacy Stage: 3. J. Magn. Reson. Imaging 2019;49:1333-1346.

Development of high-resolution 3D MR fingerprinting for detection and characterization of epileptic lesions.

Purpose

Unpleasant acoustic noise is a drawback of almost every MRI scan. Instead of reducing acoustic noise to improve patient comfort, we propose a technique for mitigating the noise problem by producing musical sounds directly from the switching magnetic fields while simultaneously quantifying multiple important tissue properties.



Theory and Methods

MP3 music files were converted to arbitrary encoding gradients, which were then used with varying flip angles and repetition times in a two- and three-dimensional magnetic resonance fingerprinting (MRF) examination. This new acquisition method, named MRF-Music, was used to quantify T1, T2, and proton density maps simultaneously while providing pleasing sounds to the patients.



Results

MRF-Music scans improved patient comfort significantly during MRI examinations. The T1 and T2 values measured from phantom are in good agreement with those from the standard spin echo measurements. T1 and T2 values from the brain scan are also close to previously reported values.



Conclusions

MRF-Music sequence provides significant improvement in patient comfort compared with the MRF scan and other fast imaging techniques such as echo planar imaging and turbo spin echo scans. It is also a fast and accurate quantitative method that quantifies multiple relaxation parameters simultaneously. Magn Reson Med, 2015. © 2015 Wiley Periodicals, Inc.

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Music‐based magnetic resonance fingerprinting to improve patient comfort during MRI examinations

Purpose The purpose of this study is to increase the robustness of MR fingerprinting (MRF) toward subject motion. Methods A novel reconstruction algorithm, MOtion insensitive MRF (MORF), was developed, which uses an iterative reconstruction based retrospective motion correction approach. Each iteration loops through the following steps: pattern recognition, metric based identification of motion corrupted frames, registration based motion estimation, and motion compensated data consistency verification. The proposed algorithm was validated using in vivo 2D brain MRF data with retrospective in-plane motion introduced at different stages of the acquisition. The validation was performed using qualitative and quantitative comparisons between results from MORF, the iterative multi-scale (IMS) algorithm, and with the IMS results using data without motion for a ground truth comparison. Additionally, the MORF algorithm was evaluated in prospectively motion corrupted in vivo 2D brain MRF datasets. Results For datasets corrupted by in-plane motion both prospectively and retrospectively, MORF noticeably reduced motion artifacts compared with iterative multi-scale and closely resembled the results from data without motion, even when ∼54% of data was motion corrupted during different parts of the acquisition. Conclusions MORF improves the insensitivity of MRF toward rigid-body motion occurring during any part of the MRF acquisition.

/pdf/image-reconstruction-algorithm-for-motion-insensitive-mr-1qidoz5xql.pdf

Eric Y. Pierre

Papers

SVD Compression for Magnetic Resonance Fingerprinting in the Time Domain

Multiscale reconstruction for MR fingerprinting.

Development of high-resolution 3D MR fingerprinting for detection and characterization of epileptic lesions.

Music‐based magnetic resonance fingerprinting to improve patient comfort during MRI examinations

Image reconstruction algorithm for motion insensitive MR Fingerprinting (MRF): MORF