Revisiting [Formula: see text]-wavelet compressed-sensing MRI in the era of deep learning.

Following their success in numerous imaging and computer vision applications, deep-learning (DL) techniques have emerged as one of the most prominent strategies for accelerated MRI reconstruction. These methods have been shown to outperform conventional regularized methods based on compressed sensing (CS). However, in most comparisons, CS is implemented with two or three hand-tuned parameters, while DL methods enjoy a plethora of advanced data science tools. In this work, we revisit [Formula: see text]-wavelet CS reconstruction using these modern tools. Using ideas such as algorithm unrolling and advanced optimization methods over large databases that DL algorithms utilize, along with conventional insights from wavelet representations and CS theory, we show that [Formula: see text]-wavelet CS can be fine-tuned to a level close to DL reconstruction for accelerated MRI. The optimized [Formula: see text]-wavelet CS method uses only 128 parameters compared to >500,000 for DL, employs a convex reconstruction at inference time, and performs within <1% of a DL approach that has been used in multiple studies in terms of quantitative quality metrics.

Motion-robust free-running volumetric cardiovascular MRI.

PURPOSE: To present and assess an outlier mitigation method that makes free-running volumetric cardiovascular MRI (CMR) more robust to motion. METHODS: The proposed method, called compressive recovery with outlier rejection (CORe), models outliers in the measured data as an additive auxiliary variable. We enforce MR physics-guided group sparsity on the auxiliary variable, and jointly estimate it along with the image using an iterative algorithm. For evaluation, CORe is first compared to traditional compressed sensing (CS), robust regression (RR), and an existing outlier rejection method using two simulation studies. Then, CORe is compared to CS using seven three-dimensional (3D) cine, 12 rest four-dimensional (4D) flow, and eight stress 4D flow imaging datasets. RESULTS: Our simulation studies show that CORe outperforms CS, RR, and the existing outlier rejection method in terms of normalized mean square error and structural similarity index across 55 different realizations. The expert reader evaluation of 3D cine images demonstrates that CORe is more effective in suppressing artifacts while maintaining or improving image sharpness. Finally, 4D flow images show that CORe yields more reliable and consistent flow measurements, especially in the presence of involuntary subject motion or exercise stress. CONCLUSION: An outlier rejection method is presented and tested using simulated and measured data. This method can help suppress motion artifacts in a wide range of free-running CMR applications.

Accelerated motion correction with deep generative diffusion models.

PURPOSE: The aim of this work is to develop a method to solve the ill-posed inverse problem of accelerated image reconstruction while correcting forward model imperfections in the context of subject motion during MRI examinations. METHODS: The proposed solution uses a Bayesian framework based on deep generative diffusion models to jointly estimate a motion-free image and rigid motion estimates from subsampled and motion-corrupt two-dimensional (2D) k-space data. RESULTS: We demonstrate the ability to reconstruct motion-free images from accelerated two-dimensional (2D) Cartesian and non-Cartesian scans without any external reference signal. We show that our method improves over existing correction techniques on both simulated and prospectively accelerated data. CONCLUSION: We propose a flexible framework for retrospective motion correction of accelerated MRI based on deep generative diffusion models, with potential application to other forward model corruptions.

Accelerating breast MRI acquisition with generative AI models.

OBJECTIVES: To investigate the use of the score-based diffusion model to accelerate breast MRI reconstruction. MATERIALS AND METHODS: We trained a score-based model on 9549 MRI examinations of the female breast and employed it to reconstruct undersampled MRI images with undersampling factors of 2, 5, and 20. Images were evaluated by two experienced radiologists who rated the images based on their overall quality and diagnostic value on an independent test set of 100 additional MRI examinations. RESULTS: The score-based model produces MRI images of high quality and diagnostic value. Both T1- and T2-weighted MRI images could be reconstructed to a high degree of accuracy. Two radiologists rated the images as almost indistinguishable from the original images (rating 4 or 5 on a scale of 5) in 100% (radiologist 1) and 99% (radiologist 2) of cases when the acceleration factor was 2. This fraction dropped to 88% and 70% for an acceleration factor of 5 and to 5% and 21% with an extreme acceleration factor of 20. CONCLUSION: Score-based models can reconstruct MRI images at high fidelity, even at comparatively high acceleration factors, but further work on a larger scale of images is needed to ensure that diagnostic quality holds. CLINICAL RELEVANCE STATEMENT: The number of MRI examinations of the breast is expected to rise with MRI screening recommended for women with dense breasts. Accelerated image acquisition methods can help in making this examination more accessible. KEY POINTS: Accelerating breast MRI reconstruction remains a significant challenge in clinical settings. Score-based diffusion models can achieve near-perfect reconstruction for moderate undersampling factors. Faster breast MRI scans with maintained image quality could revolutionize clinic workflows and patient experience.

Stop moving: MR motion correction as an opportunity for artificial intelligence.

Subject motion is a long-standing problem of magnetic resonance imaging (MRI), which can seriously deteriorate the image quality. Various prospective and retrospective methods have been proposed for MRI motion correction, among which deep learning approaches have achieved state-of-the-art motion correction performance. This survey paper aims to provide a comprehensive review of deep learning-based MRI motion correction methods. Neural networks used for motion artifacts reduction and motion estimation in the image domain or frequency domain are detailed. Furthermore, besides motion-corrected MRI reconstruction, how estimated motion is applied in other downstream tasks is briefly introduced, aiming to strengthen the interaction between different research areas. Finally, we identify current limitations and point out future directions of deep learning-based MRI motion correction.

Multi-Device Parallel MRI Reconstruction: Efficient Partitioning for Undersampled 5D Cardiac CINE.

Cardiac CINE, a form of dynamic cardiac MRI, is indispensable in the diagnosis and treatment of heart conditions, offering detailed visualization essential for the early detection of cardiac diseases. As the demand for higher-resolution images increases, so does the volume of data requiring processing, presenting significant computational challenges that can impede the efficiency of diagnostic imaging. Our research presents an approach that takes advantage of the computational power of multiple Graphics Processing Units (GPUs) to address these challenges. GPUs are devices capable of performing large volumes of computations in a short period, and have significantly improved the cardiac MRI reconstruction process, allowing images to be produced faster. The innovation of our work resides in utilizing a multi-device system capable of processing the substantial data volumes demanded by high-resolution, five-dimensional cardiac MRI. This system surpasses the memory capacity limitations of single GPUs by partitioning large datasets into smaller, manageable segments for parallel processing, thereby preserving image integrity and accelerating reconstruction times. Utilizing OpenCL technology, our system offers adaptability and cross-platform functionality, ensuring wider applicability. The proposed multi-device approach offers an advancement in medical imaging, accelerating the reconstruction process and facilitating faster and more effective cardiac health assessment.

MEDL-Net: A model-based neural network for MRI reconstruction with enhanced deep learned regularizers.

PURPOSE: To improve the MRI reconstruction performance of model-based networks and to alleviate their large demand for GPU memory. METHODS: A model-based neural network with enhanced deep learned regularizers (MEDL-Net) was proposed. The MEDL-Net is separated into several submodules, each of which consists of several cascades to mimic the optimization steps in conventional MRI reconstruction algorithms. Information from shallow cascades is densely connected to latter ones to enrich their inputs in each submodule, and additional revising blocks (RB) are stacked at the end of the submodules to bring more flexibility. Moreover, a composition loss function was designed to explicitly supervise RBs. RESULTS: Network performance was evaluated on a publicly available dataset. The MEDL-Net quantitatively outperforms the state-of-the-art methods on different MR image sequences with different acceleration rates (four-fold and six-fold). Moreover, the reconstructed images showed that the detailed textures are better preserved. In addition, fewer cascades are required when achieving the same reconstruction results compared with other model-based networks. CONCLUSION: In this study, a more efficient model-based deep network was proposed to reconstruct MR images. The experimental results indicate that the proposed method improves reconstruction performance with fewer cascades, which alleviates the large demand for GPU memory.

On the shape of convolution kernels in MRI reconstruction: Rectangles versus ellipsoids.

PURPOSE: Many MRI reconstruction methods (including GRAPPA, SPIRiT, ESPIRiT, LORAKS, and convolutional neural network [CNN] methods) involve shift-invariant convolution models. Rectangular convolution kernel shapes are often chosen by default, although ellipsoidal kernel shapes have potentially appealing theoretical characteristics. In this work, we systematically investigate the differences between different kernel shape choices in several contexts. THEORY: It is well-understood that a rectangular region of k-space is associated with anisotropic spatial resolution, while ellipsoidal regions can be associated with more isotropic resolution. Further, for a fixed spatial resolution, ellipsoidal kernels are associated with substantially fewer parameters than rectangular kernels. These characteristics suggest that ellipsoidal kernels may have certain advantages over rectangular kernels. METHODS: We used real retrospectively undersampled k-space data to empirically study the characteristics of rectangular and ellipsoidal kernels in the context of seven methods (GRAPPA, SPIRiT, ESPIRiT, SAKE, LORAKS, AC-LORAKS, and CNN-based reconstructions). RESULTS: Empirical results suggest that both kernel shapes can produce reconstructed images with similar error metrics, although the ellipsoidal shape can often achieve this with reduced computation time and memory usage and/or fewer model parameters. CONCLUSION: Ellipsoidal kernel shapes may offer advantages over rectangular kernel shapes in various MRI applications.

MaxGIRF: Image reconstruction incorporating concomitant field and gradient impulse response function effects.

PURPOSE: To develop and evaluate an improved strategy for compensating concomitant field effects in non-Cartesian MRI at the time of image reconstruction. THEORY: We present a higher-order reconstruction method, denoted as MaxGIRF, for non-Cartesian imaging that simultaneously corrects off-resonance, concomitant fields, and trajectory errors without requiring specialized hardware. Gradient impulse response functions are used to predict actual gradient waveforms, which are in turn used to estimate the spatiotemporally varying concomitant fields based on analytic expressions. The result, in combination with a reference field map, is an encoding matrix that incorporates a correction for all three effects. METHODS: The MaxGIRF reconstruction is applied to noiseless phantom simulations, spiral gradient-echo imaging of an International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom, and axial and sagittal multislice spiral spin-echo imaging of a healthy volunteer at 0.55 T. The MaxGIRF reconstruction was compared against previously established concomitant field-compensation and image-correction methods. Reconstructed images are evaluated qualitatively and quantitatively using normalized RMS error. Finally, a low-rank approximation of MaxGIRF is used to reduce computational burden. The accuracy of the low-rank approximation is studied as a function of minimum rank. RESULTS: The MaxGIRF reconstruction successfully mitigated blurring artifacts both in phantoms and in vivo and was effective in regions where concomitant fields counteract static off-resonance, superior to the comparator method. A minimum rank of 8 and 30 for axial and sagittal scans, respectively, gave less than 2% error compared with the full-rank reconstruction. CONCLUSIONS: The MaxGIRF reconstruction simultaneously corrects off-resonance, trajectory errors, and concomitant field effects. The impact of this method is greatest when imaging with longer readouts and/or at lower field strength.

Multiple dimensions of radiographic reconstruction for the optimal operative strategy of sacral meningeal cysts.

BACKGROUND AND PURPOSE: The contents and subtypes of sacral cysts are sophisticated in many cases. We applied multiple dimensional magnetic resonance imaging (MRI) reconstruction to preoperatively clarify the specific subtype of sacral meningeal cysts. MATERIALS AND METHODS: We preoperatively used multimodal neural reconstruction MRI sequences to evaluate 76 patients with sacral cysts. The linear nerve roots were precisely traced based on sagittal or coronal images processed at various angles and levels which was conducive to the design of the operation strategy. RESULTS: Cysts with nerve passage were detected in 47 cases (62%, 47/76), whereas cysts without nerve roots were detected in 24 cases (32%, 24/76). Five patients had mixed cysts with or without nerve roots. Intraoperative exploration results proved the high accuracy of image reconstruction; only one cyst without a nerve root was misdiagnosed prior to surgery. CONCLUSION: MRI reconstruction based on the three-dimensional fast imaging employing steady-state acquisition T2 sequence precisely tracked the nerve roots of sacral cysts and guided the optimal strategy during surgery.

MADGAN: unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction.

BACKGROUND: Unsupervised learning can discover various unseen abnormalities, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a 2D/3D single medical image to detect outliers either in the learned feature space or from high reconstruction loss. However, without considering continuity between multiple adjacent slices, they cannot directly discriminate diseases composed of the accumulation of subtle anatomical anomalies, such as Alzheimer's disease (AD). Moreover, no study has shown how unsupervised anomaly detection is associated with either disease stages, various (i.e., more than two types of) diseases, or multi-sequence magnetic resonance imaging (MRI) scans. RESULTS: We propose unsupervised medical anomaly detection generative adversarial network (MADGAN), a novel two-step method using GAN-based multiple adjacent brain MRI slice reconstruction to detect brain anomalies at different stages on multi-sequence structural MRI: (Reconstruction) Wasserstein loss with Gradient Penalty + 100 [Formula: see text] loss-trained on 3 healthy brain axial MRI slices to reconstruct the next 3 ones-reconstructs unseen healthy/abnormal scans; (Diagnosis) Average [Formula: see text] loss per scan discriminates them, comparing the ground truth/reconstructed slices. For training, we use two different datasets composed of 1133 healthy T1-weighted (T1) and 135 healthy contrast-enhanced T1 (T1c) brain MRI scans for detecting AD and brain metastases/various diseases, respectively. Our self-attention MADGAN can detect AD on T1 scans at a very early stage, mild cognitive impairment (MCI), with area under the curve (AUC) 0.727, and AD at a late stage with AUC 0.894, while detecting brain metastases on T1c scans with AUC 0.921. CONCLUSIONS: Similar to physicians' way of performing a diagnosis, using massive healthy training data, our first multiple MRI slice reconstruction approach, MADGAN, can reliably predict the next 3 slices from the previous 3 ones only for unseen healthy images. As the first unsupervised various disease diagnosis, MADGAN can reliably detect the accumulation of subtle anatomical anomalies and hyper-intense enhancing lesions, such as (especially late-stage) AD and brain metastases on multi-sequence MRI scans.

Reliability of radiomics features due to image reconstruction using a standardized T2 -weighted pulse sequence for MR-guided radiotherapy: An anthropomorphic phantom study.

PURPOSE: To prospectively investigate the impact of image reconstruction on MRI radiomics features. METHODS: An anthropomorphic phantom was scanned at 1.5 T using a standardized sequence for MR-guided radiotherapy under SENSE and compressed-SENSE reconstruction settings. A total of 93 first-order and texture radiomics features in 10 volumes of interest were assessed based on (1) accuracy measured by the percentage deviation from the reference, (2) robustness on reconstruction in all volumes of interest measured by the intraclass correlation coefficient, and (3) repeatability measured by the coefficient of variance over the repetitive acquisitions. Finally, reliable and unreliable radiomics features were comprehensively determined based on their accuracy, robustness, and repeatability. RESULTS: Better accuracy and robustness of the radiomics features were achieved under SENSE than compressed-SENSE reconstruction. The feature accuracy under SENSE reconstruction was more affected by acceleration factor than direction, whereas under compressed-SENSE reconstruction, accuracy was substantially impacted by the increasing denoising levels. Feature repeatability was dependent more on feature types than on reconstruction. A total of 45 reliable features and 13 unreliable features were finally determined for SENSE, compared with 22 reliable and 26 unreliable features for compressed SENSE. First-order and gray-level co-occurrence matrix features were generally more reliable than other features. CONCLUSION: Radiomics features could be substantially affected by MRI reconstruction, so precautions need to be taken regarding their reliability for clinical use. This study helps the guidance of the preselection of reliable radiomics features and the preclusion of unreliable features in MR-guided radiotherapy.

Prospective Deployment of Deep Learning in MRI: A Framework for Important Considerations, Challenges, and Recommendations for Best Practices.

Artificial intelligence algorithms based on principles of deep learning (DL) have made a large impact on the acquisition, reconstruction, and interpretation of MRI data. Despite the large number of retrospective studies using DL, there are fewer applications of DL in the clinic on a routine basis. To address this large translational gap, we review the recent publications to determine three major use cases that DL can have in MRI, namely, that of model-free image synthesis, model-based image reconstruction, and image or pixel-level classification. For each of these three areas, we provide a framework for important considerations that consist of appropriate model training paradigms, evaluation of model robustness, downstream clinical utility, opportunities for future advances, as well recommendations for best current practices. We draw inspiration for this framework from advances in computer vision in natural imaging as well as additional healthcare fields. We further emphasize the need for reproducibility of research studies through the sharing of datasets and software. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 2.

Determination of the Geometric Parameters of Electrode Systems for Electrical Impedance Myography: A Preliminary Study.

The electrical impedance myography method is widely used in solving bionic control problems and consists of assessing the change in the electrical impedance magnitude during muscle contraction in real time. However, the choice of electrode systems sizes is not always properly considered when using the electrical impedance myography method in the existing approaches, which is important in terms of electrical impedance signal expressiveness and reproducibility. The article is devoted to the determination of acceptable sizes for the electrode systems for electrical impedance myography using the Pareto optimality assessment method and the electrical impedance signals formation model of the forearm area, taking into account the change in the electrophysical and geometric parameters of the skin and fat layer and muscle groups when performing actions with a hand. Numerical finite element simulation using anthropometric models of the forearm obtained by volunteers' MRI 3D reconstructions was performed to determine a sufficient degree of the forearm anatomical features detailing in terms of the measured electrical impedance. For the mathematical description of electrical impedance relationships, a forearm two-layer model, represented by the skin-fat layer and muscles, was reasonably chosen, which adequately describes the change in electrical impedance when performing hand actions. Using this model, for the first time, an approach that can be used to determine the acceptable sizes of electrode systems for different parts of the body individually was proposed.

Surgical injury and repair of hip external rotators in THA via posterior approach: a three-dimensional MRI-evident quantitative prospective study.

BACKGROUND: As one of the classical total hip arthroplasty (THA) approaches, the posterior approach is widely used. However, there is a lack of in-depth quantitative researches on the surgical-related injury to the hip external rotators. The purpose of this study is to quantificationally analyse the surgical injury of hip external rotators after posterior THA and explore the effect of the muscle repair on the muscle recovery using the MRI three-dimensional reconstruction technique combined with the clinical assessment. METHODS: Sixty five patients were eligible to receive a unilateral cementless THA via the posterior approach. During operation, the piriformis tendon was reattached but it was not applicable for the internal obturator muscle. We performed three-dimensional MRI reconstruction of bilateral piriformis and internal obturator muscle along with clinical assessment preoperatively, 6, 12 and 52 weeks postoperatively. RESULTS: Bilateral piriformis and internal obturator muscle were homogeneous preoperatively. Compared with the contralateral side, the volume atrophy and fat-muscle ratio of the piriformis on the operated side increased inconspicuously by 1.64%, 0.26% (p = 0.062, p = 0.071) at 6 weeks and 1.33%, 0.20% (p = 0.057, p = 0.058) at 12 weeks, while 7.28%, 2.09% and 15.71%, 5.14% for the internal obturator muscle (p < 0.01). Up to 52 weeks, the pirformis also showed significant muscle atrophy as well as fatty infiltration (increased by 7.79%, 4.21%; p < 0.01), and 24.18%, 11.91% for the internal obturator muscle (p < 0.01). CONCLUSION: The THA via posterior approach significantly harms the hip external rotators and the early hip external rotation function. The effective repair could be conducive to the early postoperative recovery of the hip external rotators. TRAIL REGISTRATION: The study has been registered in Chinese Clinical Trial Registry (ChiCTR) before the clical trial started, the Clinical Trial Registry Number is ChiCTR-IOR-17013007 . Registered 17 October 2017. The Trial registration is prospective registration.

Comparison of morphological changes of gluteus medius and abductor strength for total hip arthroplasty via posterior and modified direct lateral approaches.

BACKGROUND: It is a general belief among orthopedists that the muscle damage of the hip abductors after total hip arthroplasty (THA) is theoretically minimal via posterior approach. However, there is little data scientifically supporting the purported advantage. The purpose of this study was to quantifiably assess the injury to the gluteus medius (GMED) after THA via the minimally invasive (MIS) posterior and the modified direct lateral (mDL) approaches. METHODS: Sixty-four consecutive patients enrolled prospectively were randomly assigned to the MIS posterior and the mDL approach groups. Three-dimensional MRI reconstruction of bilateral GMED, abductor strength measurement as well as post-operative pain assessment were included in the analysis. Data were collected pre-operatively, six, 12, and 52 weeks post-operatively. RESULTS: Interestingly, in terms of the morphological changes of GMED, the MIS posterior approach showed a more significant degeneration caused by the surgical trauma compared with the mDL approach in both muscle volume atrophy and fatty infiltration from six to 52 weeks post-operatively. However, the improvement of abductor muscle strength on surgical side and VAS pain score were comparable between the two groups during the entire follow-up. CONCLUSION: The injury of hip abductors after THA via posterior approach cannot be neglected. And, the planned detachment of partial GMED tendon combined with the reconstruction in situ could also achieve the satisfactory muscle recovery. Moreover, the post-operative rehabilitation of abductor strength was the aggregated results of a battery of factors, especially the pain, not just determined by the muscular morphological changes.

Accelerated Dynamic MRI Using Kernel-Based Low Rank Constraint.

We present a novel reconstruction method for dynamic MR images from highly under-sampled k-space measurements. The reconstruction problem is posed as spectrally regularized matrix recovery problem, where kernel-based low rank constraint is employed to effectively utilize the non-linear correlations between the images in the dynamic sequence. Unlike other kernel-based methods, we use a single-step regularized reconstruction approach to simultaneously learn the kernel basis functions and the weights. The objective function is optimized using variable splitting and alternating direction method of multipliers. The framework can seamlessly handle additional sparsity constraints such as spatio-temporal total variation. The algorithm performance is evaluated on a numerical phantom and in vivo data sets and it shows significant improvement over the comparison methods.

Impact of radical hysterectomy on the transobturator sling pathway: a retrospective three-dimensional magnetic resonance imaging study.

INTRODUCTION AND HYPOTHESIS: Morphological and functional anomalies of the urethra may cause stress urinary incontinence after radical hysterectomy (RH). We introduce a novel three-dimensional (3D) magnetic resonance imaging (MRI) technique to assess the impact of RH on the transobturator sling pathway. METHODS: 3D-MRI reconstruction models were retrospectively developed for the measurement of various parameters before and after RH, including puncture angle, orientation and distance from the midurethral puncture site to the obturator membrane (DUO), in 31 patients with cervical cancer. Additionally, the correlations between DUO and body height and interspinal diameter were evaluated. RESULTS: No significant differences were noted between the preoperative and postoperative inclination angle (-7.1 ± 33.5° vs. -0.68 ± 23.9°, ranges -62.4 to 46.8° vs. -54.1 to 42.2°, respectively) or between the preoperative and postoperative left and right mean rotation angles (left 69.0 ± 8.0° vs. 67.8 ± 9.2°; right 65.1 ± 8.38° vs. 64.3 ± 10.5°). Similarly, there were no statistically or clinically significant differences between the preoperative and postoperative DUO, although slight differences were noted between the two sides before and after RH (P = 0.018 and P = 0.023, respectively). None of the parameters differed significantly between the groups with and without postoperative urodynamic stress incontinence. Further, there was no clinically significant correlation between DUO and height or interspinal diameter. CONCLUSIONS: The sling procedure via the transobturator approach is technically safe from a 3D anatomical standpoint. However, wide variability in the anatomical parameters must be taken into account when planning the procedure.

Jacobian weighted temporal total variation for motion compensated compressed sensing reconstruction of dynamic MRI.

PURPOSE: To eliminate the need of spatial intraframe regularization in a recently reported dynamic MRI compressed-sensing-based reconstruction method with motion compensation and to increase its performance. THEORY AND METHODS: We propose a new regularization metric based on the introduction of a spatial weighting measure given by the Jacobian of the estimated deformations. It shows convenient discretization properties and, as a byproduct, it also provides a theoretical support to a result reported by others based on an intuitive design. The method has been applied to the reconstruction of both short and long axis views of the heart of four healthy volunteers. Quantitative image quality metrics as well as straightforward visual assessment are reported. RESULTS: Short and long axis reconstructions of cardiac cine MRI sequences have shown superior results than previously reported methods both in terms of quantitative metrics and of visual assessment. Fine details are better preserved due to the lack of additional intraframe regularization, with no significant image artifacts even for an acceleration factor of 12. CONCLUSIONS: The proposed Jacobian Weighted temporal Total Variation results in better reconstructions of highly undersampled cardiac cine MRI than previously proposed methods and sets a theoretical ground for forward and backward predictors used elsewhere. Magn Reson Med 77:1208-1215, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Advanced MRI Techniques of the Shoulder Joint: Current Applications in Clinical Practice.

OBJECTIVE: We are fortunate to live in a time when real advances in medicine are happening at an increasingly rapid pace. This is especially true in the field of radiology, and keeping abreast of these advances is one of the main challenges of clinical practice. Traditionally, cutting edge techniques in our field have been researched and validated at major academic medical centers before slowly making their way into the armamentarium of routine clinical practice. However, the improved ability to communicate and disseminate information in our modern age has facilitated more rapid implementation of new techniques to allow us to better serve our patients. CONCLUSION: As such, this article aims to review the current standards for MRI of the shoulder used in routine practice. Furthermore, we will discuss some of the most recent advances in shoulder MRI, with particular emphasis on the applicability of an additional axial 3D T1-weighted FLASH sequence with Dixon-based water-fat separation in routine clinical practice that can be useful in characterizing several commonly encountered pathologic processes of the shoulder joint.