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BACKGROUND Dixon cardiac magnetic resonance fingerprinting (MRF) has been recently introduced to simultaneously provide water T1 , water T2 , and fat fraction (FF) maps. PURPOSE To assess Dixon cardiac MRF repeatability in healthy subjects and its clinical feasibility in a cohort of patients with cardiovascular disease. POPULATION T1MES phantom, water-fat phantom, 11 healthy subjects and 19 patients with suspected cardiovascular disease. STUDY TYPE Prospective. FIELD STRENGTH/SEQUENCE 1.5T, inversion recovery spin echo (IRSE), multiecho spin echo (MESE), modified Look-Locker inversion recovery (MOLLI), T2 gradient spin echo (T2 -GRASE), 6-echo gradient rewound echo (GRE), and Dixon cardiac MRF. ASSESSMENT Dixon cardiac MRF precision was assessed through repeated scans against conventional MOLLI, T2 -GRASE, and PDFF in phantom and 11 healthy subjects. Dixon cardiac MRF native T1 , T2 , FF, postcontrast T1 and synthetic extracellular volume (ECV) maps were assessed in 19 patients in comparison to conventional sequences. Measurements in patients were performed in the septum and in late gadolinium enhanced (LGE) areas and assessed using mean value distributions, correlation, and Bland-Altman plots. Image quality and diagnostic confidence were assessed by three experts using 5-point scoring scales. STATISTICAL TESTS Paired Wilcoxon rank signed test and paired t-tests were applied. Statistical significance was indicated by *(P

Since its inception in the early 1970s [1], magnetic resonance imaging (MRI) has revolutionized radiology and medicine. Apart from high-quality data acquisition, image reconstruction is an important step to guarantee high image quality in MRI. Although the very first MR images were obtained from data resembling radial projections of the imaged object by applying an iterative reconstruction algorithm [1], non-Cartesian acquisition and iterative reconstruction techniques were not adopted in clinical MRI for many years, and, even today, their use is very limited. The reason for this is twofold. First, the underlying assumption that the measured data are radial projections of the imaged object fails in the presence of B0 field inhomogeneity and/or gradient waveform imperfections. Second, the long reconstruction times associated with iterative reconstruction algorithms limit their practical application.

The articles in this special section focus on computational magnetic resonance imaging (MRI) using compressed sensing applications. Presents recent developments in computational MRI. These developments are pushing the frontier of computational imaging beyond CS. Similar to CS, most of these algorithms rely on image representation in one form or another. However, the common recent thread is the departure from handcrafted image representations to learning-based image representations. These learned representations are seamlessly combined with clever measurement strategies to significantly advance the state of the art in a number of areas. Several exciting applications including significantly improved spatial and temporal resolution, a considerable reduction in scan time, measurement of biophysical parameters directly from highly undersampled data, and direct measurement of very high-dimensional data are reviewed in this special issue of SPM. This issue describes key ideas underlying the computational approaches used in MRI. These approaches range from CS algorithms that rely on fixed transforms or dictionaries, to adaptive or shallow-learning algorithms that adapt the image representation to the data to recent deep-learning methods that learn a highly nonlinear representation from exemplar data. The articles provide insight into the capabilities of the current algorithms, their limitations, and their utility in challenging MRI problems.

PURPOSE Cardiac magnetic resonance fingerprinting (cMRF) has been recently introduced to simultaneously provide T1 , T2 , and M0 maps. Here, we develop a 3-point Dixon-cMRF approach to enable simultaneous water specific T1 , T2 , and M0 mapping of the heart and fat fraction (FF) estimation in a single breath-hold scan. METHODS Dixon-cMRF is achieved by combining cMRF with several innovations that were previously introduced for other applications, including a 3-echo GRE acquisition with golden angle radial readout and a high-dimensional low-rank tensor constrained reconstruction to recover the highly undersampled time series images for each echo. Water-fat separation of the Dixon-cMRF time series is performed to allow for water- and fat-specific T1 , T2 , and M0 estimation, whereas FF estimation is extracted from the M0 maps. Dixon-cMRF was evaluated in a standardized T1 -T2 phantom, in a water-fat phantom, and in healthy subjects in comparison to current clinical standards: MOLLI, SASHA, T2 -GRASE, and 6-point Dixon proton density FF (PDFF) mapping. RESULTS Dixon-cMRF water T1 and T2 maps showed good agreement with reference T1 and T2 mapping techniques (R2 > 0.99 and maximum normalized RMSE ~5%) in a standardized phantom. Good agreement was also observed between Dixon-cMRF FF and reference PDFF (R2 > 0.99) and between Dixon-cMRF water T1 and T2 and water selective T1 and T2 maps (R2 > 0.99) in a water-fat phantom. In vivo Dixon-cMRF water T1 values were in good agreement with MOLLI and water T2 values were slightly underestimated when compared to T2 -GRASE. Average myocardium septal T1 values were 1129 ± 38 ms, 1026 ± 28 ms, and 1045 ± 32 ms for SASHA, MOLLI, and the proposed water Dixon-cMRF. Average T2 values were 51.7 ± 2.2 ms and 42.8 ± 2.6 ms for T2 -GRASE and water Dixon-cMRF, respectively. Dixon-cMRF FF maps showed good agreement with in vivo PDFF measurements (R2 > 0.98) and average FF in the septum was measured at 1.3%. CONCLUSION The proposed Dixon-cMRF allows to simultaneously quantify myocardial water T1 , water T2 , and FF in a single breath-hold scan, enabling multi-parametric T1 , T2 , and fat characterization. Moreover, reduced T1 and T2 quantification bias caused by water-fat partial volume was demonstrated in phantom experiments.

PURPOSE: Magnetic Resonance Fingerprinting (MRF) with spiral readout enables rapid quantification of tissue relaxation times. However, it is prone to blurring due to off-resonance effects. Hence, fat blurring into adjacent regions might prevent identification of small tumors by their quantitative T1 and T2 values. This study aims to correct for the blurring artifacts, thereby enabling fast quantitative mapping in the female breast. METHODS: The impact of fat blurring on spiral MRF results was first assessed by simulations. Then, MRF was combined with 3-point Dixon water-fat separation and spiral blurring correction based on conjugate phase reconstruction. The approach was assessed in phantom experiments and compared to Cartesian reference measurements, namely inversion recovery (IR), multi-echo spin echo (MESE) and Cartesian MRF, by normalized root mean square error (NRMSE) and standard deviation (STD) calculations. Feasibility is further demonstrated in-vivo for quantitative breast measurements of 6 healthy female volunteers, age range 24-31 years. RESULTS: In the phantom experiment, the blurring correction reduced the NRMSE per phantom vial on average from 16% to 8% for T1 and from 18% to 11% for T2 when comparing spiral MRF to IR/MESE sequences. When comparing to Cartesian MRF, the NRMSE reduced from 15% to 8% for T1 and from 12% to 7% for T2. Furthermore, STDs decreased. In-vivo, the blurring correction removed fat bias on T1/T2 from a rim of about 7-8 mm width adjacent to fatty structures. CONCLUSION: The blurring correction for spiral MRF yields improved quantitative maps in the presence of water and fat., Submitted to Magnetic Resonance in Medicine. 13 pages, 7 figures, 3 tables

Demyelination is the key pathological process in multiple sclerosis (MS). The extent of demyelination can be quantified with magnetic resonance imaging by assessing the myelin water fraction (MWF). However, long computation times and high noise sensitivity hinder the translation of MWF imaging to clinical practice. In this work, we introduce a more efficient and noise robust method to determine the MWF using a joint sparsity constraint and a pre-computed B1+-T2 dictionary. A single component analysis with this dictionary is used in an initial step to obtain a B1+ map. The T2 distribution is then determined from a reduced dictionary corresponding to the estimated B1+ map using a combination of a non-negativity and a joint sparsity constraint. The non-negativity constraint ensures that a feasible solution with non-negative contribution of each T2 component is obtained. The joint sparsity constraint restricts the T2 distribution to a small set of T2 relaxation times shared between all voxels and reduces the noise sensitivity. The applied Sparsity Promoting Iterative Joint NNLS (SPIJN) algorithm can be implemented efficiently, reducing the computation time by a factor of 50 compared to the commonly used regularized non-negative least squares algorithm. The proposed method was validated in simulations and in 8 healthy subjects with a 3D multi-echo gradient- and spin echo scan at 3 ​T. In simulations, the absolute error in the MWF decreased from 0.031 to 0.013 compared to the regularized NNLS algorithm for SNR ​= ​250. The in vivo results were consistent with values reported in literature and improved MWF-quantification was obtained especially in the frontal white matter. The maximum standard deviation in mean MWF in different regions of interest between subjects was smaller for the proposed method (0.0193) compared to the regularized NNLS algorithm (0.0266). In conclusion, the proposed method for MWF estimation is less computationally expensive and less susceptible to noise compared to state of the art methods. These improvements might be an important step towards clinical translation of MWF measurements.

Magnetic resonance fingerprinting (MRF) offers rapid quantitative imaging but may be subject to confounding effects (CE) if these are not included in the model-based reconstruction. This study characterizes the influence of in-planeSimulations were used to predict the influence of each CE on the accuracy of MRF and to investigate the influence of electronic noise and spiral aliasing artefacts. The experimentally observed bias in regions of fibroglandular tissue (FGT) and fatty tissue (FT) was analyzed for undersampled spiral breast MRF data of 6 healthy volunteers by performing MRF reconstruction with and without a CE.Theoretic analysis predicts TUnderstanding CEs and their relative significance for an MRF sequence is important when defining an MRF signal model for accurate parameter mapping.

PURPOSE To develop a novel respiratory motion compensated three-dimensional (3D) cardiac magnetic resonance fingerprinting (cMRF) approach for whole-heart myocardial T1 and T2 mapping from a free-breathing scan. METHODS Two-dimensional (2D) cMRF has been recently proposed for simultaneous, co-registered T1 and T2 mapping from a breath-hold scan; however, coverage is limited. Here we propose a novel respiratory motion compensated 3D cMRF approach for whole-heart myocardial T1 and T2 tissue characterization from a free-breathing scan. Variable inversion recovery and T2 preparation modules are used for parametric encoding, respiratory bellows driven localized autofocus is proposed for beat-to-beat translation motion correction and a subspace regularized reconstruction is employed to accelerate the scan. The proposed 3D cMRF approach was evaluated in a standardized T1 /T2 phantom in comparison with reference spin echo values and in 10 healthy subjects in comparison with standard 2D MOLLI, SASHA and T2 -GraSE mapping techniques at 1.5 T. RESULTS 3D cMRF T1 and T2 measurements were generally in good agreement with reference spin echo values in the phantom experiments, with relative errors of 2.9% and 3.8% for T1 and T2 (T2 < 100 ms), respectively. in vivo left ventricle (LV) myocardial T1 values were 1054 ± 19 ms for MOLLI, 1146 ± 20 ms for SASHA and 1093 ± 24 ms for the proposed 3D cMRF; corresponding T2 values were 51.8 ± 1.6 ms for T2-GraSE and 44.6 ± 2.0 ms for 3D cMRF. LV coefficients of variation were 7.6 ± 1.6% for MOLLI, 12.1 ± 2.7% for SASHA and 5.8 ± 0.8% for 3D cMRF T1 , and 10.5 ± 1.4% for T2-GraSE and 11.7 ± 1.6% for 3D cMRF T2 . CONCLUSION The proposed 3D cMRF can provide whole-heart, simultaneous and co-registered T1 and T2 maps with accuracy and precision comparable to those of clinical standards in a single free-breathing scan of about 7 min.

Adaptive intelligence aims at empowering machine learning techniques with the additional use of domain knowledge. In this work, we present the application of adaptive intelligence to accelerate MR acquisition. Starting from undersampled k-space data, an iterative learning-based reconstruction scheme inspired by compressed sensing theory is used to reconstruct the images. We developed a novel deep neural network to refine and correct prior reconstruction assumptions given the training data. The network was trained and tested on a knee MRI dataset from the 2019 fastMRI challenge organized by Facebook AI Research and NYU Langone Health. All submissions to the challenge were initially ranked based on similarity with a known groundtruth, after which the top 4 submissions were evaluated radiologically. Our method was evaluated by the fastMRI organizers on an independent challenge dataset. It ranked #1, shared #1, and #3 on respectively the $8\times $ accelerated multi-coil, the $4\times $ multi-coil, and the $4\times $ single-coil tracks. This demonstrates the superior performance and wide applicability of the method.

PurposeTo develop an efficient algorithm for multi‐component analysis of magnetic resonance fingerprinting (MRF) data without making a priori assumptions about the exact number of tissues or their relaxation properties.MethodsDifferent tissues or components within a voxel are potentially separable in MRF because of their distinct signal evolutions. The observed signal evolution in each voxel can be described as a linear combination of the signals for each component with a non‐negative weight. An assumption that only a small number of components are present in the measured field of view is usually imposed in the interpretation of multi‐component data. In this work, a joint sparsity constraint is introduced to utilize this additional prior knowledge in the multi‐component analysis of MRF data. A new algorithm combining joint sparsity and non‐negativity constraints is proposed and compared to state‐of‐the‐art multi‐component MRF approaches in simulations and brain MRF scans of 11 healthy volunteers.ResultsSimulations and in vivo measurements show reduced noise in the estimated tissue fraction maps compared to previously proposed methods. Applying the proposed algorithm to the brain data resulted in 4 or 5 components, which could be attributed to different brain structures, consistent with previous multi‐component MRF publications.ConclusionsThe proposed algorithm is faster than previously proposed methods for multi‐component MRF and the simulations suggest improved accuracy and precision of the estimated weights. The results are easier to interpret compared to voxel‐wise methods, which combined with the improved speed is an important step toward clinical evaluation of multi‐component MRF.

Purpose To develop and validate a new algorithm called "dictionary-based electric properties tomography" (dbEPT) for deriving tissue electric properties from measured B1 maps. Methods Inspired by Magnetic Resonance fingerprinting, dbEPT uses a dictionary of local patterns ("atoms") of B1 maps and corresponding electric properties distributions, derived from electromagnetic field simulations. For reconstruction, a pattern from a measured B1 map is compared with the B1 atoms of the dictionary. The B1 atom showing the best match with the measured B1 pattern yields the optimum electric properties pattern that is chosen for reconstruction. Matching was performed through machine learning algorithms. Two dictionaries, using transmit and transceive phases, were evaluated. The spatial distribution of local matching distance between optimal atom and measured pattern yielded a reconstruction reliability map. The method was applied to reconstruct conductivity of 4 volunteers' brains. A conventional, Helmholtz-based Electric properties tomography (EPT) reconstruction was performed for reference. Noise performance was studied through phantom simulations. Results Quantitative values of conductivity agree with literature values. Results of the 2 dictionaries exhibit only minor differences. Somewhat larger differences are visible between dbEPT and Helmholtz-based EPT. Quantified by the correlation between conductivity and anatomic images, dbEPT depicts brain details more clearly than Helmholtz-based EPT. Matching distance is minimal in homogeneous brain ventricles and increases with tissue heterogeneity. Central processing unit time was approximately 2 minutes per dictionary training and 3 minutes per brain conductivity reconstruction using standard hardware equipment. Conclusion A new, dictionary-based approach for reconstructing electric properties is presented. Its conductivity reconstruction is able to overcome the EPT transceive-phase problem.

Demyelination is the key pathological process in multiple sclerosis (MS). The extent of demyelination can be quantified with magnetic resonance imaging by assessing the myelin water fraction (MWF). However, long computation times and high noise sensitivity hinder the translation of MWF imaging to clinical practice. In this work, we introduce a more efficient and noise robust method to determine the MWF using a joint sparsity constraint and a pre-computed B

An iterative reconstruction method for undersampled magnetic resonance fingerprinting data is presented. The method performs the reconstruction entirely in k-space and is related to low rank matrix completion methods. A low dimensional data subspace is estimated from a small number of k-space locations fully sampled in the temporal direction and used to reconstruct the missing k-space samples before MRF dictionary matching. Performing the iterations in k-space eliminates the need for applying a forward and an inverse Fourier transform in each iteration required in previously proposed iterative reconstruction methods for undersampled MRF data. A projection onto the low dimensional data subspace is performed as a matrix multiplication instead of a singular value thresholding typically used in low rank matrix completion, further reducing the computational complexity of the reconstruction. The method is theoretically described and validated in phantom and in-vivo experiments. The quality of the parameter maps can be significantly improved compared to direct matching on undersampled data.

Several very rare forms of dementia are associated with characteristic focal atrophy predominantly of the frontal and/or temporal lobes and currently lack imaging solutions to monitor disease. Magnetic resonance fingerprinting (MRF) is a recently developed technique providing quantitative relaxivity maps and images with various tissue contrasts out of a single sequence acquisition. This pilot study explores the utility of MRF-based T1 and T2 mapping to discover focal differences in relaxation times between patients with frontotemporal lobe degenerative dementia and healthy controls. 8 patients and 30 healthy controls underwent a 3 T MRI including an axial 2D spoiled gradient echo MRF sequence. T1 and T2 relaxation maps were generated based on an extended phase graphs algorithm-founded dictionary involving inner product pattern matching. A region of interest (ROI)-based analysis of T1 and T2 relaxation times was performed with FSL and ITK-SNAP. Depending on the brain region analyzed, T1 relaxation times were up to 10.28% longer in patients than in controls reaching significant differences in cortical gray matter (P = .047) and global white matter (P = .023) as well as in both hippocampi (P = .001 left; P = .027 right). T2 relaxation times were similarly longer in the hippocampus by up to 19.18% in patients compared with controls. The clinically most affected patient had the most control-deviant relaxation times. There was a strong correlation of T1 relaxation time in the amygdala with duration of the clinically manifest disease (Spearman Rho = .94; P = .001) and of T1 relaxation times in the left hippocampus with disease severity (Rho = .90, P = .002). In conclusion, MRF-based relaxometry is a promising and time-saving new MRI tool to study focal cerebral alterations and identify patients with frontotemporal lobe degeneration. To validate the results of this pilot study, MRF is worth further exploration as a diagnostic tool in neurodegenerative diseases.

Purpose To develop and evaluate a novel non-ECG triggered 2D magnetic resonance fingerprinting (MRF) sequence allowing for simultaneous myocardial T1 and T2 mapping and cardiac Cine imaging. Methods Cardiac MRF (cMRF) has been recently proposed to provide joint T1/T2 myocardial mapping by triggering the acquisition to mid-diastole and relying on a subject-dependent dictionary of MR signal evolutions to generate the maps. In this work, we propose a novel “free-running” (non-ECG triggered) cMRF framework for simultaneous myocardial T1 and T2 mapping and cardiac Cine imaging in a single scan. Free-running cMRF is based on a transient state bSSFP acquisition with tiny golden angle radial readouts, varying flip angle and multiple adiabatic inversion pulses. The acquired data is retrospectively gated into several cardiac phases, which are reconstructed with an approach that combines parallel imaging, low rank modelling and patch-based high-order tensor regularization. Free-running cMRF was evaluated in a standardized phantom and ten healthy subjects. Comparison with reference spin-echo, MOLLI, SASHA, T2-GRASE and Cine was performed. Results T1 and T2 values obtained with the proposed approach were in good agreement with reference phantom values (ICC(A,1) > 0.99). Reported values for myocardium septum T1 were 1043 ± 48 ms, 1150 ± 100 ms and 1160 ± 79 ms for MOLLI, SASHA and free-running cMRF respectively and for T2 of 51.7 ± 4.1 ms and 44.6 ± 4.1 ms for T2-GRASE and free-running cMRF respectively. Good agreement was observed between free-running cMRF and conventional Cine 2D ejection fraction (bias = −0.83%). Conclusion The proposed free-running cardiac MRF approach allows for simultaneous assessment of myocardial T1 and T2 and Cine imaging in a single scan.

Sequence optimization and appropriate sequence selection is still an unmet need in magnetic resonance fingerprinting (MRF). The main challenge in MRF sequence design is the lack of an appropriate measure of the sequence's encoding capability. To find such a measure, three different candidates for judging the encoding capability have been investigated: local and global dot-product-based measures judging dictionary entry similarity as well as a Monte Carlo method that evaluates the noise propagation properties of an MRF sequence. Consistency of these measures for different sequence lengths as well as the capability to predict actual sequence performance in both phantom and in vivo measurements was analyzed. While the dot-product-based measures yielded inconsistent results for different sequence lengths, the Monte Carlo method was in a good agreement with phantom experiments. In particular, the Monte Carlo method could accurately predict the performance of different flip angle patterns in actual measurements. The proposed Monte Carlo method provides an appropriate measure of MRF sequence encoding capability and may be used for sequence optimization.

Purpose The aim of this study was to investigate a technique for improving the performance of Magnetic Resonance Fingerprinting (MRF) in repetitive sampling schemes, in particular for 3D MRF acquisition, by shortening relaxation intervals between MRF pulse train repetitions. Material and methods A calculation method for MRF dictionaries adapted to short relaxation intervals and non-relaxed initial spin states is presented, based on the concept of stationary fingerprints. The method is applicable to many different k-space sampling schemes in 2D and 3D. For accuracy analysis, T1 and T2 values of a phantom are determined by single-slice Cartesian MRF for different relaxation intervals and are compared with quantitative reference measurements. The relevance of slice profile effects is also investigated in this case. To further illustrate the capabilities of the method, an application to in-vivo spiral 3D MRF measurements is demonstrated. Results The proposed computation method enables accurate parameter estimation even for the shortest relaxation intervals, as investigated for different sampling patterns in 2D and 3D. In 2D Cartesian measurements, we achieved a scan acceleration of more than a factor of two, while maintaining acceptable accuracy: The largest T1 values of a sample set deviated from their reference values by 0.3% (longest relaxation interval) and 2.4% (shortest relaxation interval). The largest T2 values showed systematic deviations of up to 10% for all relaxation intervals, which is discussed. The influence of slice profile effects for multislice acquisition is shown to become increasingly relevant for short relaxation intervals. In 3D spiral measurements, a scan time reduction of 36% was achieved, maintaining the quality of in-vivo T1 and T2 maps. Conclusions Reducing the relaxation interval between MRF sequence repetitions using stationary fingerprint dictionaries is a feasible method to improve the scan efficiency of MRF sequences. The method enables fast implementations of 3D spatially resolved MRF.

Purpose To develop an efficient 3D affine respiratory motion compensation framework for Cartesian whole-heart coronary magnetic resonance angiography (MRA). Materials and Methods The proposed method achieves 100% scan efficiency by estimating the affine respiratory motion from the data itself and correcting the acquired data in the reconstruction process. For this, a golden-step Cartesian sampling with spiral profile ordering was performed to enable reconstruction of respiratory resolved images at any breathing position and with different respiratory window size. Affine motion parameters were estimated from image-based registration of 3D undersampled respiratory resolved images reconstructed with iterative SENSE and motion correction was performed directly in the reconstruction using a multiple-coils generalized matrix formulation method. This approach was tested on healthy volunteers and compared against a conventional diaphragmatic navigator-gated acquisition using quantitative and qualitative image quality assessment. Results The proposed approach achieved 47 ± 12% and 59 ± 6% vessel sharpness for the right (RCA) and left (LAD) coronary arteries, respectively. Also, good quality visual scores of 2.4 ± 0.74 and 2.44 ± 0.86 were observed for the RCA and LAD (scores from 0, no to 4, excellent coronary vessel delineation). A not statically significant difference (P = 0.05) was found between the proposed method and an 8-mm navigator-gated and tracked scan, although scan efficiency increased from 61 ± 10% to 100%. Conclusion We demonstrate the feasibility of a new 3D affine respiratory motion correction technique for Cartesian whole-heart CMRA that achieves 100% scan efficiency and therefore a predictable acquisition time. This approach yields image quality comparable to that of an 8-mm navigator-gated acquisition with lower scan efficiency. Further evaluation of this technique in patients is now warranted to determine its clinical use. J. Magn. Reson. Imaging 2015;41:738–746. © 2014 Wiley Periodicals, Inc.

Objective To compare a high acceleration three-dimensional (3D) T1-weighted gradient-recalled-echo (GRE) sequence using the combined compressed sensing (CS)-sensitivity encoding (SENSE) method with a conventional 3D GRE sequence using SENSE, with respect to image quality and detectability of solid focal liver lesions (FLLs) in the hepatobiliary phase (HBP) of gadoxetic acid-enhanced liver MRI. Materials and Methods A total of 217 patients with gadoxetic acid-enhanced liver MRI at 3T (54 in the preliminary study and 163 in the main study) were retrospectively included. In the main study, HBP imaging was done twice using the standard mDixon-3D-GRE technique with SENSE (acceleration factor [AF]: 2.8, standard mDixon-GRE) and the high acceleration mDixon-3D GRE technique using the combined CS-SENSE technique (CS-SENSE mDixon-GRE). Two abdominal radiologists assessed the two MRI data sets for image quality in consensus. Three other abdominal radiologists independently assessed the diagnostic performance of each data set and its ability to detect solid FLLs in 117 patients with 193 solid nodules and compared them using jackknife alternative free-response receiver operating characteristics (JAFROC). Results There was no significant difference in the overall image quality. CS-SENSE mDixon-GRE showed higher image noise, but lesser motion artifact levels compared with the standard mDixon-GRE (all p < 0.05). In terms of lesion detection, reader-averaged figures-of-merit estimated with JAFROC was 0.918 for standard mDixon-GRE, and 0.953 for CS-SENSE mDixon-GRE (p = 0.142). The non-inferiority of CS-SENSE mDixon-GRE over standard mDixon-GRE was confirmed (difference: 0.064 [−0.012, 0.081]). Conclusion The CS-SENSE mDixon-GRE HBP sequence provided comparable overall image quality and non-inferior solid FFL detectability compared with the standard mDixon-GRE sequence, with reduced acquisition time.

Multi echo chemical shift-based water-fat separation methods allow for uniform fat suppression in the presence of main field inhomogeneities. However, these methods require additional scan time for chemical shift encoding. This work presents a method for water-fat separation from undersampled data (CS-WF), which combines compressed sensing and chemical shift-based water-fat separation. Undersampling was applied in the k-space and in the chemical shift encoding dimension to reduce the total scanning time. The method can reconstruct high quality water and fat images in 2D and 3D applications from undersampled data. As an extension, multipeak fat spectral models were incorporated into the CS-WF reconstruction to improve the water-fat separation quality. In 3D MRI, reduction factors of above three can be achieved, thus fully compensating the additional time needed in three-echo water-fat imaging. The method is demonstrated on knee and abdominal in vivo data.

Compressed sensing (CS) holds considerable promise to accelerate the data acquisition in magnetic resonance imaging by exploiting signal sparsity. Prior knowledge about the signal can be exploited in some applications to choose an appropriate sparsifying transform. This work presents a CS reconstruction for magnetic resonance (MR) parameter mapping, which applies an overcomplete dictionary, learned from the data model to sparsify the signal. The approach is presented and evaluated in simulations and in in vivo T(1) and T(2) mapping experiments in the brain. Accurate T(1) and T(2) maps are obtained from highly reduced data. This model-based reconstruction could also be applied to other MR parameter mapping applications like diffusion and perfusion imaging.

Purpose Breath-holding is an established strategy for reducing motion artifacts in abdominal imaging. However, the breath-holding capabilities of patients are often overstrained by scans with large coverage and high resolution. In this work, a new strategy for coping with resulting incomplete breath-holds in abdominal imaging is suggested. Methods A sampling pattern is designed to support image reconstruction from undersampled data acquired up to any point in time using compressed sensing and parallel imaging. In combination with a navigator-based detection of the onset of respiration, it allows scan termination and thus reconstruction only from consistent data, which suppresses motion artifacts. The spatial resolution is restricted by a lower bound of the sampling density and is increased over the scan, to strike a compromise with the signal-to-noise ratio and undersampling artifacts for any breath-hold duration. Results The sampling pattern is optimized in phantom experiments and is successfully applied in abdominal gradient-echo imaging including water-fat separation on volunteers. Conclusions The new strategy provides images in which motion artifacts are minimized independent of the breath-holding capabilities of patients, and which enhance in terms of spatial resolution, signal-to-noise ratio, and undersampling artifacts with the a priori unknown breath-hold duration actually achieved in a particular scan. Magn Reson Med 71:1733–1742, 2014. © 2013 Wiley Periodicals, Inc.

To develop an efficient 3D affine respiratory motion compensation framework for Cartesian whole-heart coronary magnetic resonance angiography (MRA).The proposed method achieves 100% scan efficiency by estimating the affine respiratory motion from the data itself and correcting the acquired data in the reconstruction process. For this, a golden-step Cartesian sampling with spiral profile ordering was performed to enable reconstruction of respiratory resolved images at any breathing position and with different respiratory window size. Affine motion parameters were estimated from image-based registration of 3D undersampled respiratory resolved images reconstructed with iterative SENSE and motion correction was performed directly in the reconstruction using a multiple-coils generalized matrix formulation method. This approach was tested on healthy volunteers and compared against a conventional diaphragmatic navigator-gated acquisition using quantitative and qualitative image quality assessment.The proposed approach achieved 47 ± 12% and 59 ± 6% vessel sharpness for the right (RCA) and left (LAD) coronary arteries, respectively. Also, good quality visual scores of 2.4 ± 0.74 and 2.44 ± 0.86 were observed for the RCA and LAD (scores from 0, no to 4, excellent coronary vessel delineation). A not statically significant difference (P = 0.05) was found between the proposed method and an 8-mm navigator-gated and tracked scan, although scan efficiency increased from 61 ± 10% to 100%.We demonstrate the feasibility of a new 3D affine respiratory motion correction technique for Cartesian whole-heart CMRA that achieves 100% scan efficiency and therefore a predictable acquisition time. This approach yields image quality comparable to that of an 8-mm navigator-gated acquisition with lower scan efficiency. Further evaluation of this technique in patients is now warranted to determine its clinical use.

Compressed sensing (CS) holds considerable promise to accelerate the data acquisition in magnetic resonance imaging by exploiting signal sparsity. Prior knowledge about the signal can be exploited in some applications to choose an appropriate sparsifying transform. This work presents a CS reconstruction for magnetic resonance (MR) parameter mapping, which applies an overcomplete dictionary, learned from the data model to sparsify the signal. The approach is presented and evaluated in simulations and in in vivo T(1) and T(2) mapping experiments in the brain. Accurate T(1) and T(2) maps are obtained from highly reduced data. This model-based reconstruction could also be applied to other MR parameter mapping applications like diffusion and perfusion imaging.

Coil arrays with large number of receive elements allow improved imaging performance and higher signal-to-noise-ratio. The MR systems supporting these arrays have to handle an increased amount of data and higher reconstruction burden. To overcome these problems, data reduction techniques need to be applied, realized either by linear combination of the original coil data prior to reconstruction or by discarding particular data from unimportant coil elements.This work focuses on the latter approach and presents an efficient algorithm for automatic coil selection applicable to SENSE imaging. A singular value decomposition (SVD)-based coil selection is proposed that performs a coil element ranking quantifying the contribution of each coil element to the image reconstruction allowing appropriate coil selection. This approach makes use of the coil sensitivity information and takes reduction factor and phase encoding direction into account.Simulations, phantom and in vivo experiments were performed to validate the SVD-based coil selection algorithm. The proposed approach proved to be computationally efficient without remarkable image quality degradation.The SVD-based approach offers the opportunity for fast automatic coil selection. This could simplify clinical workflow and may, furthermore, pave the way for various 2D real-time and interventional applications.

Purpose Magnetic resonance fingerprinting (MRF) offers rapid quantitative imaging but may be subject to confounding effects (CE) if these are not included in the model-based reconstruction. This study characterizes the influence of in-plane B 1 + , slice profile and diffusion effects on T1 and T2 estimation in the female breast at 1.5T. Methods Simulations were used to predict the influence of each CE on the accuracy of MRF and to investigate the influence of electronic noise and spiral aliasing artefacts. The experimentally observed bias in regions of fibroglandular tissue (FGT) and fatty tissue (FT) was analyzed for undersampled spiral breast MRF data of 6 healthy volunteers by performing MRF reconstruction with and without a CE. Results Theoretic analysis predicts T1 under-/T2 overestimation if the nominal flip angles are underestimated and inversely, T1 under-/T2 overestimation if omitting slice profile correction, and T1 under-/T2 underestimation if omitting diffusion in the signal model. Averaged over repeated signal simulations, including spiral aliasing artefacts affected precision more than accuracy. Strong in-plane B 1 + effects occurred in vivo, causing T2 left-right inhomogeneity between both breasts. Their correction decreased the T2 difference from 29 to 5 ms in FGT and from 29 to 9 ms in FT. Slice profile correction affected FGT T2 most strongly, resulting in -22% smaller values. For the employed spoiler gradient strengths, diffusion did not affect the parameter maps, corresponding well with theoretic predictions. Conclusion Understanding CEs and their relative significance for an MRF sequence is important when defining an MRF signal model for accurate parameter mapping.