Signal acquisition and processing for magnetic resonance imaging

HomeRadiologyVol. 231, No. 3 PreviousNext EditorialsMR Imaging: Its Development and the Recent Nobel PrizeStephen J. RiedererStephen J. RiedererAuthor Affiliations1From the MR Laboratory, Mayo Clinic, 200 First Street SW, Rochester, MN 55905. Received January 19, 2004; accepted January 21. Address correspondence to the author (e-mail: [email protected]).Stephen J. RiedererPublished Online:Jun 1 2004https://doi.org/10.1148/radiol.2313040114MoreSectionsFull textPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In References 1 Lauterbur PC. Image formation by induced local interactions: examples employing nuclear magnetic resonance. Nature 1973; 242:190-191. Crossref, Google Scholar2 USA MRI market Mountain View, Calif: Frost and Sullivan Market Research, 2002. Google Scholar3 Mansfield P, Grannell PK. NMR “diffraction” in solids? J Phys C Solid State Phys 1973; 6:L422. Crossref, Google Scholar4 Mansfield P. 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Computerized fluoroscopy in real time for noninvasive visualization of the cardiovascular system: preliminary studies. Radiology 1979; 130:49-57. Link, Google ScholarArticle HistoryPublished in print: June 2004 FiguresReferencesRelatedDetailsCited ByMedicine, Vol. 93, No. 28Skeletal Radiology, Vol. 40, No. 9Medicine - Programa de Formaci?n M?dica Continuada Acreditado, Vol. 9, No. 92Magnetic Resonance Imaging, Vol. 24, No. 4Applied Magnetic Resonance, Vol. 29, No. 1Pacing and Clinical Electrophysiology, Vol. 28, No. 10Magnetic Resonance Imaging, Vol. 22, No. 10Recommended Articles RSNA Education Exhibits RSNA Case Collection Vol. 231, No. 3 Metrics Altmetric Score PDF download

HighlightsWhat are the main findings?Cine phase contrast MRI for time-resolved assessment of leg muscle function during voluntary and electrically evoked motion was feasible in typically developing children.The success rates of cine phase contrast MRI during both voluntary and electrically evoked leg motion were reduced in pediatric patients with cerebral palsy.What is the implication of the main finding?Further optimization of the electrical muscle stimulation protocol, the voluntary motion task, and the MRI acquisition—particularly with regard to scan acceleration—is required.These improvements would enable cine phase-contrast MRI to serve as a feasible method for studying muscle dynamics and assessing treatment effects in pediatric patients with neuromuscular diseases.Background/Objectives: Magnetic resonance imaging (MRI) can be used to assess muscle function while performing a motion task within the scanner. Quantitative measures such as contraction velocity and strain can be derived from the images. Cine phase contrast (PC) MRI for time-resolved imaging of muscle function relies on the consistently repeated execution of the motion task for several minutes until data acquisition is complete. This may be difficult for patients with neuromuscular dysfunctions. To date, this approach has been applied only in adults, but not pediatric populations. The aim of this pilot study was to investigate the feasibility of PC MRI for assessing calf muscle function during electrically evoked and voluntary motion in children with cerebral palsy (CP) using open-source hardware and software. Methods: Cine PC MRI was performed at 3T in ambulatory pediatric patients with CP and typically developing children under electrical muscle stimulation (EMS) (n = 14/13) and during voluntary plantarflexion (n = 4/4) using a home-built pedal with a force sensor. A visual feedback software was developed to enable synchronized imaging of voluntary muscle contractions. Muscle contraction velocity and strain were calculated from the MRI data. Data quality was rated by two readers. Results: During EMS, the velocity data quality was rated as sufficient in 21% of scans in patients compared with 82% of scans in controls. During the voluntary task, all patients demonstrated increased compliance and greater generated force output than during EMS. Voluntary motion imaging was successful in all controls but none of the patients, as motion periodicity in patients was worse during voluntary than during stimulated contraction. Conclusions: Cine phase-contrast MRI combined with EMS or voluntary motion proved challenging in pediatric patients with CP, particularly in those with more severe baseline muscle dysfunction or reduced tolerance to stimulation. In contrast, the approach was successfully implemented in typically developing children. Although the scope of the patient-based findings is limited by data heterogeneity, the method demonstrates considerable potential as a tool for monitoring treatment-related changes in muscle function, particularly in less severely affected patients. Further refinement of the EMS and voluntary motion protocols, together with a reduction in MRI acquisition time, is required to improve motion periodicity, tolerability, and consequently the overall success rate in the intended pediatric patient cohort.

Super-resolution reconstruction of single anisotropic 3D MR images using residual convolutional neural network

Resolution ‘scaling law’ in MRI of articular cartilage

Computer vision has been applied to many medical imaging problems. Of the different medical imaging modalities, magnetic resonance (MR) imaging is a powerful and widely used technique for medical diagnosis and is attracting much medical image processing research effort. One use of MR imaging is the detection of benign tumours, called acoustic neuromas, which occur in the auditory canal. At present, these tumours are identified manually from MR images (slices) of the head, a task which is both costly and tedious. Here, a method for automating the detection and localisation of acoustic neuromas from head MR images, using computer vision, is described which comprises of three phases: a) a data driven initial segmentation; b) classification at the pixel level, using a neural network, to identify pixels from acoustic neuromas; c) fusion of the segmentation and the pixel level classification to identify segmented regions likely to belong to an acoustic neuroma. These three phases are discussed and the results of each phase presented. Current and future work is then outlined. The MR images of the head used are 256 /spl times/ 256 pixels in size, grey scale (8 bits per pixel) images.

Magnetic resonance imaging (MRI) is the keystone of modern musculoskeletal imaging; however, long pulse sequence acquisition times may restrict patient tolerability and access. Advances in MRI scanners, coil technology, and innovative pulse sequence acceleration methods enable 4-fold turbo spin echo pulse sequence acceleration in clinical practice; however, at this speed, conventional image reconstruction approaches the signal-to-noise limits of temporal, spatial, and contrast resolution. Novel deep learning image reconstruction methods can minimize signal-to-noise interdependencies to better advantage than conventional image reconstruction, leading to unparalleled gains in image speed and quality when combined with parallel imaging and simultaneous multislice acquisition. The enormous potential of deep learning-based image reconstruction promises to facilitate the 10-fold acceleration of the turbo spin echo pulse sequence, equating to a total acquisition time of 2-3 minutes for entire MRI examinations of joints without sacrificing spatial resolution or image quality. Current investigations aim for a better understanding of stability and failure modes of image reconstruction networks, validation of network reconstruction performance with external data sets, determination of diagnostic performances with independent reference standards, establishing generalizability to other centers, scanners, field strengths, coils, and anatomy, and building publicly available benchmark data sets to compare methods and foster innovation and collaboration between the clinical and image processing community. In this article, we review basic concepts of deep learning-based acquisition and image reconstruction techniques for accelerating and improving the quality of musculoskeletal MRI, commercially available and developing deep learning-based MRI solutions, superresolution, denoising, generative adversarial networks, and combined strategies for deep learning-driven ultra-fast superresolution musculoskeletal MRI. This article aims to equip radiologists and imaging scientists with the necessary practical knowledge and enthusiasm to meet this exciting new era of musculoskeletal MRI.

Mild traumatic brain injury (mTBI) sustained in early childhood affects the brain at a peak developmental period and may disrupt sensitive stages of skill acquisition, thereby compromising child functioning. However, due to the challenges of collecting non-sedated neuroimaging data in young children, the consequences of mTBI on young children's brains have not been systematically studied. In typically developing preschool children (of age 3-5years), a brief behavioral-play familiarization provides an effective alternative to sedation for acquiring awake magnetic resonance imaging (MRI) in a time- and resource-efficient manner. To date, no study has applied such an approach for acquiring non-sedated MRI in preschool children with mTBI who may present with additional MRI acquisition challenges such as agitation or anxiety. The present study aimed to compare the effectiveness of a brief behavioral-play familiarization for acquiring non-sedated MRI for research purposes between young children with and without mTBI, and to identify factors associated with successful MRI acquisition. Preschool children with mTBI (n=13) and typically developing children (n=24) underwent a 15-minutes behavioral-play MRI familiarization followed by a 35-minutes non-sedated MRI protocol. Success rate was compared between groups, MRI quality was assessed quantitatively, and factors predicting success were documented. Among the 37 participants, 15 typically developing children (63%) and 10 mTBI (77%) reached the MRI acquisition success criteria (i.e., completing the two first sequences). The success rate was not significantly different between groups (p=.48; 95% CI [-0.36 14.08]; Cramer's V=.15). The images acquired were of high-quality in 100% (for both groups) of the structural images, and 60% (for both groups) of the diffusion images. Factors associated with success included older child age (Β=0.73, p=.007, exp(B)=3.11, 95% CI [1.36 7.08]) and fewer parental concerns (Β=-1.56, p=.02, exp(Β)=0.21, 95% CI [0.05 0.82]) about the MRI procedure. Using brief behavioral-play familiarization allows acquisition of high-quality non-sedated MRI in young children with mTBI with success rates comparable to those of non-injured peers.

A nuclear magnetic resonance advance. Imaging fluorinated anesthetics in the brain.

Compressed sensing (CS) is extensively used to reduce magnetic resonance imaging (MRI) acquisition time. State-of-the-art deep learning-based methods have proven effective in obtaining fast, high-quality reconstruction of CS-MR images. However, they treat the inherently complex-valued MRI data as real-valued entities by extracting the magnitude content or concatenating the complex-valued data as two real-valued channels for processing. In both cases, the phase content is discarded. To address the fundamental problem of real-valued deep networks, i.e. their inability to process complex-valued data, we propose a complex-valued generative adversarial network (Co-VeGAN) framework, which is the first-of-its-kind generative model exploring the use of complex-valued weights and operations. Further, since real-valued activation functions do not generalize well to the complex-valued space, we propose a novel complex-valued activation function that is sensitive to the input phase and has a learnable profile. Extensive evaluation of the proposed approach <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> on different datasets demonstrates that it significantly outperforms the existing CS-MRI reconstruction techniques.

In many in vivo magnetic resonance imaging (MRI) acquisitions, the image resolution is low and induces considerable segmentation errors with a poor and limited visual interpretation. Acquiring high-resolution images reduces the patient comfort and results in considerable cost because of an increase of the acquisition time. We developed a progressive technique based on multidimensional dual kriging to interpolate 2D and 3D multi-parametric MRI data. The objectives were to (1) optimise kriging parameters for 2D images and 3D volumes interpolation, (2) validate the technique in increasing the image resolution and (3) provide a direct application to 3D non-parametric volumes interpolation and image filtering. We carried out two multi-parametric MRI acquisitions on a bovine tail segment including intervertebral discs from the second to the fourth caudal vertebrae with two different spatial resolutions. We adopted the dual kriging formulation and introduced the technique of progressive kriging, which consists in kriging the given Cartesian data region-by-region. Kriging parameters were optimised to minimise the relative errors. For each MRI sequence, kriging was compared with other interpolation techniques in terms of signal mean within the region of interest, including the zero-padding, the nearest-neighbour, the bilinear and the bicubic techniques. Optimal kriging parameters for transverse relaxation time maps interpolation were a cubic covariance function and a distance of influence of only 1–8 pixels. Our technique was found to be efficient to interpolate the longitudinal and transverse relaxation times, the magnetisation transfer ratio and the apparent diffusion coefficient maps, compared with zero-padding, nearest-neighbour, bilinear and bicubic techniques. Kriging provided relatively high performance and low error, whereas image edges tend to be corrected and better defined compared with other techniques. Progressive dual kriging is a flexible technique that can be optimised to interpolate multidimensional data based on the signal distribution and should open doors to many future applications in medical imaging such as diffusion MRI, image filtering and non-parametric 3D volume reconstruction.

To investigate the use of diffusion-weighted (DW) imaging for differentiating benign lesions from malignant pleural disease (MPD) and to retrospectively assess dynamic contrast material-enhanced (DCE) magnetic resonance (MR) imaging acquisitions to find out whether combining these measurements with DW imaging could improve the diagnostic value of DW imaging. This study was approved by the local ethics committee, and all patients provided written informed consent. Thirty-one consecutive patients with pleural abnormalities suspicious for MPD underwent whole-body positron emission tomography (PET)/computed tomography (CT) and thorax MR examinations. Diagnostic thoracoscopy with histopathologic analysis of pleural biopsies served as the reference standard. First-line evaluation of each suspicious lesion was performed by using the apparent diffusion coefficient (ADC) calculated from the DW image, and the optimal cutoff value was found by using receiver operating characteristic curve analysis. Afterward, DCE MR imaging data were used to improve the diagnosis in the range of ADCs where DW imaging results were equivocal. Sensitivity, specificity, and accuracy of PET/CT for diagnosis of MPD were 100%, 35.3%, and 64.5%. The optimal ADC threshold to differentiate benign lesions from MPD with DW MR imaging was 1.52 × 10(-3) mm(2)/sec, with sensitivity, specificity, and accuracy of 71.4%, 100%, and 87.1%, respectively. This result could be improved to 92.8%, 94.1%, and 93.5%, respectively, when DCE MR imaging data were included in those cases where ADC was between 1.52 and 2.00 × 10(-3) mm(2)/sec. A total of 20 patients had disease diagnosed correctly, nine had disease diagnosed incorrectly, and two cases were undetermined with PET/CT. DW imaging helped stage disease correctly in 27 patients and incorrectly in four. The undetermined cases at PET/CT were correctly diagnosed at MR imaging. DW imaging is a promising tool for differentiating MPD from benign lesions, with high accuracy, and supplementation with DCE MR imaging seems to further improve sensitivity.

The deep learning method is an efficient solution for improving the quality of undersampled magnetic resonance (MR) image reconstruction while reducing lengthy data acquisition. Most deep learning methods neglect the mutual constraints between the real and imaginary components of complex-valued k-space data. In this paper, a new complex-valued convolutional neural network, namely, Dense-U-Dense Net (DUD-Net), is proposed to interpolate the undersampled k-space data and reconstruct MR images. The proposed network comprises dense layers, U-Net, and other dense layers in sequence. The dense layers are used to simulate the mutual constraints between real and imaginary components, and U-Net performs feature sparsity and interpolation estimation for k-space data. Two MRI datasets were used to evaluate the proposed method: brain magnitude-only MR images and knee complex-valued k-space data. Several operations were conducted for data preprocessing. First, the complex-valued MR images were synthesized by phase modulation on magnitude-only images. Second, a radial trajectory based on the golden angle was used for k-space undersampling, whereby a reversible normalization method was proposed to balance the distribution of positive and negative values in k-space data. The optimal performance of DUD-Net was demonstrated based on a quantitative evaluation of inter-method and intra-method comparisons. When compared with other methods, significant improvements were achieved, PSNRs were increased by 10.78 and 5.74dB, whereas RMSEs were decreased by 71.53% and 30.31% for magnitude and phase image, respectively. It is concluded that DUD-Net significantly improves the performance of MR image reconstruction.

Dynamic magnetic resonance imaging (MRI) has been extensively utilized for enhancing medical living environment visualization, however, in clinical practice it often suffers from long data acquisition times. Dynamic imaging essentially reconstructs the visual image from raw (k,t)-space measurements, commonly referred to as big data. The purpose of this work is to accelerate big medical data acquisition in dynamic MRI by developing a non-convex minimization framework. In particular, to overcome the inherent speed limitation, both non-convex low-rank and sparsity constraints were combined to accelerate the dynamic imaging. However, the non-convex constraints make the dynamic reconstruction problem difficult to directly solve through the commonly-used numerical methods. To guarantee solution efficiency and stability, a numerical algorithm based on Alternating Direction Method of Multipliers (ADMM) is proposed to solve the resulting non-convex optimization problem. ADMM decomposes the original complex optimization problem into several simple sub-problems. Each sub-problem has a closed-form solution or could be efficiently solved using existing numerical methods. It has been proven that the quality of images reconstructed from fewer measurements can be significantly improved using non-convex minimization. Numerous experiments have been conducted on two in vivo cardiac datasets to compare the proposed method with several state-of-the-art imaging methods. Experimental results illustrated that the proposed method could guarantee the superior imaging performance in terms of quantitative and visual image quality assessments.

Ultra-high field (UHF) magnetic resonance (MR) systems are advancing in preclinical imaging offering the potential to enhance radiation research. However, system-dependent factors, such as magnetic field inhomogeneities ( ) and gradient non-linearity (GNL), induce geometric distortions compromising the sub-millimeter accuracy required for radiationresearch. This study tackles system-dependent distortions in 15.2T MR images by prospective shimming strategies optimization and comparing two imaging methods for voxel displacement correction. The methods were evaluated on a 3D-printed grid phantom and validated on in vivo mouse brain MR images. Additionally, a phantom-based displacement map was tested for GNL correction in mouse brainimages. Phantom MR and CT images were acquired with 200 resolution. In vivo mouse brain MR and CT images had 140 and 200 resolutions, respectively. Three shimming strategies were established to assess displacements ( ) in phantom MR images. was calculated using the acquired static field maps in three volumes of interest (VOIs) via Python script. A one-step distortion correction (1SDC) method, which simultaneously corrects and GNL distortions via non-rigid registration with CT, and a two-step distortion correction (2SDC) method, which corrects separately in two consecutive steps and GNL displacements, were assessed on phantom and in vivo images. For in vivo 2SDC validation, a phantom displacement map generated by MR to CT non-rigid registration was applied to correct GNL on the mouse brain. Total displacements ( ) were quantified in phantom VOIs and the in vivo skull region by measuring landmarks'positions. The in the phantom increased with distance from the VOI center and magnet isocenter. Shimming scenario-2 showed the lowest maximum displacement (0.26mm) for the largest VOI but required a longer acquisition time. Distortion correction methods were necessary for large VOIs (13-25mm, along the z-axis) in the phantom where 0.2mm. The 2SDC method outperformed 1SDC by achieving a 0.2mm accuracy in 100%, 92.1%, and 59.3% of the landmarks from the smallest to the largest VOI. Phantom dice scores confirmed the improvement in geometric precision after each correction step. In vivo results showed that 1SDC correction overcorrected MR images, increasing voxel displacements. The 2SDC exceeded the 1SDC, reducing by 85%, in accordance with the dice score analysis (0.97 2SDC vs. 0.84 1SDC). At 15.2T, in vivo MR images of even small regions (e.g., mouse brain) require geometric distortion correction for radiation research. The 2SDC method outperformed the 1SDC, emphasizing the need for separate and GNL corrections. Moreover, a phantom-based displacement map shows promise for in vivo GNL correction.

Functional magnetic resonance (MR) imaging is a complex, specialized examination that is able to noninvasively measure information critical to patient care such as hemispheric language lateralization ( 1 ). Diagnostic functional MR imaging requires extensive patient interaction as well as the coordinated efforts of the entire health care team. We observed in our practice at an academic center that the times to perform functional MR imaging examinations were excessively lengthy, making scheduling of the examination difficult. The purpose of our project was to reduce functional MR imaging acquisition times by increasing the efficiency of our workflow, using specific quality tools to drive improvement of functional MR imaging. We assembled a multidisciplinary team and retrospectively reviewed all functional MR imaging examinations performed at our institution from January 2013 to August 2015. We identified five key drivers: (a) streamlined protocols, (b) consistent patient monitoring, (c) clear visual slides and audio, (d) improved patient understanding, and (e) minimized patient motion. We then implemented four specific interventions over a period of 10 months: (a) eliminating intravenous contrast medium, (b) reducing repeated language paradigms, (c) updating technologist and physician checklists, and (d) updating visual slides and audio. Our mean functional MR imaging acquisition time was reduced from 76.3 to 53.2 minutes, while our functional MR imaging examinations remained of diagnostic quality. As a result, we reduced our routine scheduling time for functional MR imaging from 2 hours to 1 hour, improving patient comfort and satisfaction as well as saving time for additional potential MR imaging acquisitions. Our efforts to optimize functional MR imaging workflow constitute a practice quality improvement project that is beneficial for patient care and can be applied broadly to other functional MR imaging practices. ©RSNA, 2017.