Noise estimation in parallel MRI: GRAPPA and SENSE

Local abnormal white matter microstructure in the spinothalamic tract in people with chronic neck and shoulder pain

Objective

To investigate differences in the microstructure of the spinothalamic tract (STT) white matter in people with chronic neck and shoulder pain (CNSP) using diffusion tensor imaging, and to assess its correlation with pain intensity and duration of the pain.

Materials and methods

A 3.0T MRI scanner was used to perform diffusion tensor imaging scans on 31 people with CNSP and 24 healthy controls (HCs), employing the Automatic Fiber Segmentation and Quantification (AFQ) method to extract the STT and quantitatively analyze the fractional anisotropy (FA) and mean diffusivity (MD), reflecting the microstructural integrity of nerve fibers. Correlations of these differences with duration of pain and visual analog scale (VAS) scores were analyzed.

Results

No significant differences in the mean FA or MD values of the bilateral STT were observed between people with CNSP and HCs (p > 0.05), as indicated by the two-sample t test. Further point-by-point comparison along 100 equidistant nodes within the STT pathway revealed significant reductions in FA values in the left (segments 12-18, 81-89) and right (segments 9-19, 76-80) STT in the CNSP group compared to HCs; significant increases in MD values were observed in the left (segments 1-13, 26-30, 71-91) and right (segments 8-17, 76-91) STT (p < 0.05, FWE corrected). Partial correlation analysis indicates that in people with CNSP, the FA values of the STT in regions with damaged white matter structure show a negative correlation with VAS scores and duration of pain, whereas MD values show a positive correlation with VAS scores and duration of pain.

Conclusion

This study found that people with CNSP exhibit white matter microstructural abnormalities in the specific segments of STT. These abnormalities are associated with the patient's pain intensity and disease duration. The findings offer a new neuroimaging perspective on the pathophysiological basis of chronic pain in the ascending conduction process and its potential role in developing targeted intervention strategies. However, due to the limited sample size and the lack of statistical significance when analyzing the entire spinothalamic tract, these conclusions should be interpreted with caution. Further research with larger cohorts is necessary to validate these results.

4D flow MRI velocity uncertainty quantification

PURPOSE

An automatic method is presented for estimating 4D flow MRI velocity measurement uncertainty in each voxel. The velocity distance (VD) metric, a statistical distance between the measured velocity and local error distribution, is introduced as a novel measure of 4D flow MRI velocity measurement quality.

METHODS

The method uses mass conservation to assess the local velocity error variance and the standardized difference of means (SDM) velocity to estimate the velocity error correlations. VD is evaluated as the Mahalanobis distance between the local velocity measurement and the local error distribution. The uncertainty model is validated synthetically and tested in vitro under different flow resolutions and noise levels. The VD's application is demonstrated on two in vivo thoracic vasculature 4D flow datasets.

RESULTS

Synthetic results show the proposed uncertainty quantification method is sensitive to aliased regions across various velocity-to-noise ratios and assesses velocity error correlations in four- and six-point acquisitions with correlation errors at or under 3.2%. In vitro results demonstrate the method's sensitivity to spatial resolution, venc settings, partial volume effects, and phase wrapping error sources. Applying VD to assess in vivo 4D flow MRI in the aorta demonstrates the expected increase in measured velocity quality with contrast administration and systolic flow.

CONCLUSION

The proposed 4D flow MRI uncertainty quantification method assesses velocity measurement error owing to sources including noise, intravoxel phase dispersion, and velocity aliasing. This method enables rigorous comparison of 4D flow MRI datasets obtained in longitudinal studies, across patient populations, and with different MRI systems.

MRI denoising with a non-blind deep complex-valued convolutional neural network

MR images with high signal-to-noise ratio (SNR) provide more diagnostic information. Various methods for MRI denoising have been developed, but the majority of them operate on the magnitude image and neglect the phase information. Therefore, the goal of this work is to design and implement a complex-valued convolutional neural network (CNN) for MRI denoising. A complex-valued CNN incorporating the noise level map (non-blindℂ $$ \mathbb{C} $$ DnCNN) was trained with ground truth and simulated noise-corrupted image pairs. The proposed method was validated using both simulated and in vivo data collected from low-field scanners. Its denoising performance was quantitively and qualitatively evaluated, and it was compared with the real-valued CNN and several other algorithms. For the simulated noise-corrupted testing dataset, the complex-valued models had superior normalized root-mean-square error, peak SNR, structural similarity index, and phase ABSD. By incorporating the noise level map, the non-blindℂ $$ \mathbb{C} $$ DnCNN showed better performance in dealing with spatially varying parallel imaging noise. For in vivo low-field data, the non-blindℂ $$ \mathbb{C} $$ DnCNN significantly improved the SNR and visual quality of the image. The proposed non-blindℂ $$ \mathbb{C} $$ DnCNN provides an efficient and effective approach for MRI denoising. This is the first application of non-blindℂ $$ \mathbb{C} $$ DnCNN to medical imaging. The method holds the potential to enable improved low-field MRI, facilitating enhanced diagnostic imaging in under-resourced areas.

Enhancing diffusion-weighted prostate MRI through self-supervised denoising and evaluation

Diffusion-weighted imaging (DWI) is a magnetic resonance imaging (MRI) technique that provides information about the Brownian motion of water molecules within biological tissues. DWI plays a crucial role in stroke imaging and oncology, but its diagnostic value can be compromised by the inherently low signal-to-noise ratio (SNR). Conventional supervised deep learning-based denoising techniques encounter challenges in this domain as they necessitate noise-free target images for training. This work presents a novel approach for denoising and evaluating DWI scans in a self-supervised manner, eliminating the need for ground-truth data. By leveraging an adapted version of Stein's unbiased risk estimator (SURE) and exploiting a phase-corrected combination of repeated acquisitions, we outperform both state-of-the-art self-supervised denoising methods and conventional non-learning-based approaches. Additionally, we demonstrate the applicability of our proposed approach in accelerating DWI scans by acquiring fewer image repetitions. To evaluate denoising performance, we introduce a self-supervised methodology that relies on analyzing the characteristics of the residual signal removed by the denoising approaches.

Accelerating FLAIR imaging via deep learning reconstruction: potential for evaluating white matter hyperintensities

PURPOSE

To evaluate deep learning-reconstructed (DLR)-fluid-attenuated inversion recovery (FLAIR) images generated from undersampled data, compare them with fully sampled and rapidly acquired FLAIR images, and assess their potential for white matter hyperintensity evaluation.

MATERIALS AND METHODS

We examined 30 patients with white matter hyperintensities, obtaining fully sampled FLAIR images (standard FLAIR, std-FLAIR). We created accelerated FLAIR (acc-FLAIR) images using one-third of the fully sampled data and applied deep learning to generate DLR-FLAIR images. Three neuroradiologists assessed the quality (amount of noise and gray/white matter contrast) in all three image types. The reproducibility of hyperintensities was evaluated by comparing a subset of 100 hyperintensities in acc-FLAIR and DLR-FLAIR images with those in the std-FLAIR images. Quantitatively, similarities and errors of the entire image and the focused regions on white matter hyperintensities in acc-FLAIR and DLR-FLAIR images were measured against std-FLAIR images using structural similarity index measure (SSIM), regional SSIM, normalized root mean square error (NRMSE), and regional NRMSE values.

RESULTS

All three neuroradiologists evaluated DLR-FLAIR as having significantly less noise and higher image quality scores compared with std-FLAIR and acc-FLAIR (p < 0.001). All three neuroradiologists assigned significantly higher frontal lobe gray/white matter visibility scores for DLR-FLAIR than for acc-FLAIR (p < 0.001); two neuroradiologists attributed significantly higher scores for DLR-FLAIR than for std-FLAIR (p < 0.05). Regarding white matter hyperintensities, all three neuroradiologists significantly preferred DLR-FLAIR (p < 0.0001). DLR-FLAIR exhibited higher similarity to std-FLAIR in terms of visibility of the hyperintensities, with 97% of the hyperintensities rated as nearly identical or equivalent. Quantitatively, DLR-FLAIR demonstrated significantly higher SSIM and regional SSIM values than acc-FLAIR, with significantly lower NRMSE and regional NRMSE values (p < 0.0001).

CONCLUSIONS

DLR-FLAIR can reduce scan time and generate images of similar quality to std-FLAIR in patients with white matter hyperintensities. Therefore, DLR-FLAIR may serve as an effective method in traditional magnetic resonance imaging protocols.

Ultrafast Brain MRI at 3 T for MS: Evaluation of a 51-Second Deep Learning-Enhanced T2-EPI-FLAIR Sequence

In neuroimaging, there is no equivalent alternative to magnetic resonance imaging (MRI). However, image acquisitions are generally time-consuming, which may limit utilization in some cases, e.g., in patients who cannot remain motionless for long or suffer from claustrophobia, or in the event of extensive waiting times. For multiple sclerosis (MS) patients, MRI plays a major role in drug therapy decision-making. The purpose of this study was to evaluate whether an ultrafast, T2-weighted (T2w), deep learning-enhanced (DL), echo-planar-imaging-based (EPI) fluid-attenuated inversion recovery (FLAIR) sequence (FLAIRUF) that has targeted neurological emergencies so far might even be an option to detect MS lesions of the brain compared to conventional FLAIR sequences. Therefore, 17 MS patients were enrolled prospectively in this exploratory study. Standard MRI protocols and ultrafast acquisitions were conducted at 3 tesla (T), including three-dimensional (3D)-FLAIR, turbo/fast spin-echo (TSE)-FLAIR, and FLAIRUF. Inflammatory lesions were grouped by size and location. Lesion conspicuity and image quality were rated on an ordinal five-point Likert scale, and lesion detection rates were calculated. Statistical analyses were performed to compare results. Altogether, 568 different lesions were found. Data indicated no significant differences in lesion detection (sensitivity and positive predictive value [PPV]) between FLAIRUF and axially reconstructed 3D-FLAIR (lesion size ≥3 mm × ≥2 mm) and no differences in sensitivity between FLAIRUF and TSE-FLAIR (lesion size ≥3 mm total). Lesion conspicuity in FLAIRUF was similar in all brain regions except for superior conspicuity in the occipital lobe and inferior conspicuity in the central brain regions. Further findings include location-dependent limitations of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) as well as artifacts such as spatial distortions in FLAIRUF. In conclusion, FLAIRUF could potentially be an expedient alternative to conventional methods for brain imaging in MS patients since the acquisition can be performed in a fraction of time while maintaining good image quality.

Compressed SENSitivity Encoding (SENSE): Qualitative and Quantitative Analysis

BACKGROUND

This study aimed to qualitatively and quantitatively evaluate T1-TSE, T2-TSE and 3D FLAIR sequences obtained with and without Compressed-SENSE technique by assessing the contrast (C), the contrast-to-noise ratio (CNR) and the signal-to-noise ratio (SNR).

METHODS

A total of 142 MRI images were acquired: 69 with Compressed-SENSE and 73 without Compressed-SENSE. All the MRI images were contoured, spatially aligned and co-registered using 3D Slicer Software. Two radiologists manually drew 12 regions of interests on three different structures of CNS: white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF).

RESULTS

C values were significantly higher in Compressed-SENSE T1-TSE compared to No Compressed-SENSE T1-TSE for three different structures of the CNS. C values were also significantly lower for Compressed-SENSE 3D FLAIR and Compressed-SENSE T2-TSE compared to the corresponding No Compressed-SENSE scans. While CNR values did not significantly differ in GM-WM between Compressed-SENSE and No Compressed-SENSE for the 3D FLAIR and T1-TSE sequences, the differences in GM-CSF and WM-CSF were always statistically significant.

CONCLUSION

Compressed-SENSE for 3D T2 FLAIR, T1w and T2w sequences enables faster MRI acquisition, reducing scan time and maintaining equivalent image quality. Compressed-SENSE is very useful in specific medical conditions where lower SAR levels are required without sacrificing the acquisition of helpful diagnostic sequences.

Effect of deep learning-based reconstruction on high-resolution three-dimensional T2-weighted fast asymmetric spin-echo imaging in the preoperative evaluation of cerebellopontine angle tumors

PURPOSE

We aimed to evaluate the effect of deep learning-based reconstruction (DLR) on high-spatial-resolution three-dimensional T2-weighted fast asymmetric spin-echo (HR-3D T2-FASE) imaging in the preoperative evaluation of cerebellopontine angle (CPA) tumors.

METHODS

This study included 13 consecutive patients who underwent preoperative HR-3D T2-FASE imaging using a 3 T MRI scanner. The reconstruction voxel size of HR-3D T2-FASE imaging was 0.23 × 0.23 × 0.5 mm. The contrast-to-noise ratios (CNRs) of the structures were compared between HR-3D T2-FASE images with and without DLR. The observers' preferences based on four categories on the tumor side on HR-3D T2-FASE images were evaluated. The facial nerve in relation to the tumor on HR-3D T2-FASE images was assessed with reference to intraoperative findings.

RESULTS

The mean CNR between the tumor and trigeminal nerve and between the cerebrospinal fluid and trigeminal nerve was significantly higher for DLR images than non-DLR-based images (14.3 ± 8.9 vs. 12.0 ± 7.6, and 66.4 ± 12.0 vs. 53.9 ± 8.5, P < 0.001, respectively). The observer's preference for the depiction and delineation of the tumor, cranial nerves, vessels, and location relation on DLR HR-3D T2FASE images was superior to that on non-DLR HR-3D T2FASE images in 7 (54%), 6 (46%), 6 (46%), and 6 (46%) of 13 cases, respectively. The facial nerves around the tumor on HR-3D T2-FASE images were visualized accurately in five (38%) cases with DLR and in four (31%) without DLR.

CONCLUSION

DLR HR-3D T2-FASE imaging is useful for the preoperative assessment of CPA tumors.

The feasibility of half-dose contrast-enhanced scanning of brain tumours at 5.0 T: a preliminary study

PURPOSE

This study investigated and compared the effects of Gd enhancement on brain tumours with a half-dose of contrast medium at 5.0 T and with a full dose at 3.0 T.

METHODS

Twelve subjects diagnosed with brain tumours were included in this study and underwent MRI after contrast agent injection at 3.0 T (full dose) or 5.0 T (half dose) with a 3D T1-weighted gradient echo sequence. The postcontrast images were compared by two independent neuroradiologists in terms of the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and subjective image quality score on a ten-point Likert scale. Quantitative indices and subjective quality ratings were compared with paired Student's t tests, and interreader agreement was assessed with the intraclass correlation coefficient (ICC).

RESULTS

A total of 16 enhanced tumour lesions were detected. The SNR was significantly greater at 5.0 T than at 3.0 T in grey matter, white matter and enhanced lesions (p < 0.001). The CNR was also significantly greater at 5.0 T than at 3.0 T for grey matter/tumour lesions, white matter/tumour lesions, and grey matter/white matter (p < 0.001). Subjective evaluation revealed that the internal structure and outline of the tumour lesions were more clearly displayed with a half-dose at 5.0 T (Likert scale 8.1 ± 0.3 at 3.0 T, 8.9 ± 0.3 at 5.0 T, p < 0.001), and the effects of enhancement in the lesions were comparable to those with a full dose at 3.0 T (7.8 ± 0.3 at 3.0 T, 8.7 ± 0.4 at 5.0 T, p < 0.001). All subjective scores were good to excellent at both 5.0 T and 3.0 T.

CONCLUSION

Both quantitative and subjective evaluation parameters suggested that half-dose enhanced scanning via 5.0 T MRI might be feasible for meeting clinical diagnostic requirements, as the image quality remains optimal. Enhanced scanning at 5.0 T with a half-dose of contrast agents might benefit patients with conditions that require less intravenous contrast agent, such as renal dysfunction.

The Effects of Free Breathing on Cerebral Venous Flow: A Real-Time Phase Contrast MRI Study in Healthy Adults

Quantifying the effects of free breathing on cerebral venous flow is crucial for understanding cerebral circulation mechanisms and clinical applications. Unlike conventional cine phase-contrast MRI sequences (CINE-PC), real-time phase-contrast MRI sequences (RT-PC) can provide a continuous beat-to-beat flow signal that makes it possible to quantify the effect of breathing on cerebral venous flow. In this study, we examined 28 healthy human participants, comprising of 14 males and 14 females. Blood flows in the right/left internal jugular veins in the extracranial plane and the superior sagittal sinus (SSS) and straight sinus in the intercranial plane were quantified using CINE-PC and RT-PC. The first objective of this study was to determine the accuracy of RT-PC in quantifying cerebral venous flow, relative to CINE-PC. The second, and main objective, was to quantify the effect of free breathing on cerebral venous flow, using a time-domain multiparameter analysis method. Our results showed that RT-PC can accurately quantify cerebral venous flow with a 2 × 2 mm2 spatial resolution and 75 ms/image time resolution. The mean flow rate, amplitude, stroke volume, and cardiac period of cerebral veins were significantly higher from the mid-end phase of expiration to the mid-end phase of inspiration. Breathing affected the mean flow rates in the jugular veins more than those in the SSS and straight sinus. Furthermore, the effects of free breathing on the flow rate of the left and right jugular veins were not synchronous. These new findings provide a useful reference for better understanding the mechanisms of cerebral circulation.

Self-supervised MRI denoising: leveraging Stein's unbiased risk estimator and spatially resolved noise maps

Thermal noise caused by the imaged object is an intrinsic limitation in magnetic resonance imaging (MRI), resulting in an impaired clinical value of the acquisitions. Recently, deep learning (DL)-based denoising methods achieved promising results by extracting complex feature representations from large data sets. Most approaches are trained in a supervised manner by directly mapping noisy to noise-free ground-truth data and, therefore, require extensive paired data sets, which can be expensive or infeasible to obtain for medical imaging applications. In this work, a DL-based denoising approach is investigated which operates on complex-valued reconstructed magnetic resonance (MR) images without noise-free target data. An extension of Stein's unbiased risk estimator (SURE) and spatially resolved noise maps quantifying the noise level with pixel accuracy were employed during the training process. Competitive denoising performance was achieved compared to supervised training with mean squared error (MSE) despite optimizing the model without noise-free target images. The proposed DL-based method can be applied for MR image enhancement without requiring noise-free target data for training. Integrating the noise maps as an additional input channel further enables the regulation of the desired level of denoising to adjust to the preference of the radiologist.

Momentum-Net: Fast and Convergent Iterative Neural Network for Inverse Problems

Iterative neural networks (INN) are rapidly gaining attention for solving inverse problems in imaging, image processing, and computer vision. INNs combine regression NNs and an iterative model-based image reconstruction (MBIR) algorithm, often leading to both good generalization capability and outperforming reconstruction quality over existing MBIR optimization models. This paper proposes the first fast and convergent INN architecture, Momentum-Net, by generalizing a block-wise MBIR algorithm that uses momentum and majorizers with regression NNs. For fast MBIR, Momentum-Net uses momentum terms in extrapolation modules, and noniterative MBIR modules at each iteration by using majorizers, where each iteration of Momentum-Net consists of three core modules: image refining, extrapolation, and MBIR. Momentum-Net guarantees convergence to a fixed-point for general differentiable (non)convex MBIR functions (or data-fit terms) and convex feasible sets, under two asymptomatic conditions. To consider data-fit variations across training and testing samples, we also propose a regularization parameter selection scheme based on the "spectral spread" of majorization matrices. Numerical experiments for light-field photography using a focal stack and sparse-view computational tomography demonstrate that, given identical regression NN architectures, Momentum-Net significantly improves MBIR speed and accuracy over several existing INNs; it significantly improves reconstruction quality compared to a state-of-the-art MBIR method in each application.

dtiRIM: A generalisable deep learning method for diffusion tensor imaging

Diffusion weighted MRI is an indispensable tool for routine patient screening and diagnostics of pathology. Recently, several deep learning methods have been proposed to quantify diffusion parameters, but poor generalisation to new data prevents broader use of these methods, as they require retraining of the neural network for each new scan protocol. In this work, we present the dtiRIM, a new deep learning method for Diffusion Tensor Imaging (DTI) based on the Recurrent Inference Machines. Thanks to its ability to learn how to solve inverse problems and to use the diffusion tensor model to promote data consistency, the dtiRIM can generalise to variations in the acquisition settings. This enables a single trained network to produce high quality tensor estimates for a variety of cases. We performed extensive validation of our method using simulation and in vivo data, and compared it to the Iterated Weighted Linear Least Squares (IWLLS), the approach of the state-of-the-art MRTrix3 software, and to an implementation of the Maximum Likelihood Estimator (MLE). Our results show that dtiRIM predictions present low dependency on tissue properties, anatomy and scanning parameters, with results comparable to or better than both IWLLS and MLE. Further, we demonstrate that a single dtiRIM model can be used for a diversity of data sets without significant loss in quality, representing, to our knowledge, the first generalisable deep learning based solver for DTI.

Microstructural parameters from DW-MRI for tumour characterization and local recurrence prediction in particle therapy of skull-base chordoma

BACKGROUND

Quantitative imaging such as Diffusion-Weighted MRI (DW-MRI) can be exploited to non-invasively derive patient-specific tumor microstructure information for tumor characterization and local recurrence risk prediction in radiotherapy.

PURPOSE

To characterize tumor microstructure according to proliferative capacity and predict local recurrence through microstructural markers derived from pre-treatment conventional DW-MRI, in skull-base chordoma (SBC) patients treated with proton (PT) and carbon ion (CIRT) radiotherapy.

METHODS

Forty-eight patients affected by SBC, who underwent conventional DW-MRI before treatment and were enrolled for CIRT (n = 25) or PT (n = 23), were retrospectively selected. Clinically verified local recurrence information (LR) and histological information (Ki-67, proliferation index) were collected. Apparent diffusion coefficient (ADC) maps were calculated from pre-treatment DW-MRI and, from these, a set of microstructural parameters (cellular radius R, volume fraction vf, diffusion D) were derived by applying a fine-tuning procedure to a framework employing Monte Carlo simulations on synthetic cell substrates. In addition, apparent cellularity (ρ_app ) was estimated from vf and R for an easier clinical interpretation. Histogram-based metrics (mean, median, variance, entropy) from estimated parameters were considered to investigate differences (Mann-Whitney U-test, α = 0.05) in estimated tumor microstructure in SBCs characterized by low or high cell proliferation (Ki-67). Recurrence-free survival analyses were also performed to assess the ability of the microstructural parameters to stratify patients according to the risk of local recurrence (Kaplan-Meier curves, log-rank test α = 0.05).

RESULTS

Refined microstructural markers revealed optimal capabilities in discriminating patients according to cell proliferation, achieving best results with mean values (p-values were 0.0383, 0.0284, 0.0284, 0.0468, and 0.0088 for ADC, R, vf, D, and ρ_app, respectively). Recurrence-free survival analyses showed significant differences between populations at high and low risk of local recurrence as stratified by entropy values of estimated microstructural parameters (p = 0.0110).

CONCLUSION

Patient-specific microstructural information was non-invasively derived providing potentially useful tools for SBC treatment personalization and optimization in particle therapy.

Denoising of three-dimensional fast spin echo magnetic resonance images of knee joints using spatial-variant noise-relevant residual learning of convolution neural network

PURPOSE

Two-dimensional (2D) fast spin echo (FSE) techniques play a central role in the clinical magnetic resonance imaging (MRI) of knee joints. Moreover, three-dimensional (3D) FSE provides high-isotropic-resolution magnetic resonance (MR) images of knee joints, but it has a reduced signal-to-noise ratio compared to 2D FSE. Deep-learning denoising methods are a promising approach for denoising MR images, but they are often trained using synthetic noise due to challenges in obtaining true noise distributions for MR images. In this study, inherent true noise information from two number of excitations (2-NEX) acquisition was used to develop a deep-learning model based on residual learning of convolutional neural network (CNN), and this model was used to suppress the noise in 3D FSE MR images of knee joints.

METHODS

A deep learning-based denoising method was developed. The proposed CNN used two-step residual learning over parallel transporting and residual blocks and was designed to comprehensively learn real noise features from 2-NEX training data.

RESULTS

The results of an ablation study validated the network design. The new method achieved improved denoising performance of 3D FSE knee MR images compared with current state-of-the-art methods, based on the peak signal-to-noise ratio and structural similarity index measure. The improved image quality after denoising using the new method was verified by radiological evaluation.

CONCLUSION

A deep CNN using the inherent spatial-varying noise information in 2-NEX acquisitions was developed. This method showed promise for clinical MRI assessments of the knee, and has potential applications for the assessment of other anatomical structures.

To shift or to rotate? Comparison of acquisition strategies for multi-slice super-resolution magnetic resonance imaging

Multi-slice (MS) super-resolution reconstruction (SRR) methods have been proposed to improve the trade-off between resolution, signal-to-noise ratio and scan time in magnetic resonance imaging. MS-SRR consists in the estimation of an isotropic high-resolution image from a series of anisotropic MS images with a low through-plane resolution, where the anisotropic low-resolution images can be acquired according to different acquisition schemes. However, it is yet unclear how these schemes compare in terms of statistical performance criteria, especially for regularized MS-SRR. In this work, the estimation performance of two commonly adopted MS-SRR acquisition schemes based on shifted and rotated MS images respectively are evaluated in a Bayesian framework. The maximum a posteriori estimator, which introduces regularization by incorporating prior knowledge in a statistically well-defined way, is put forward as the estimator of choice and its accuracy, precision, and Bayesian mean squared error (BMSE) are used as performance criteria. Analytic calculations as well as Monte Carlo simulation experiments show that the rotated scheme outperforms the shifted scheme in terms of precision, accuracy, and BMSE. Furthermore, the superior performance of the rotated scheme is confirmed in real data experiments and in retrospective simulation experiments with and without inter-image motion. Results show that the rotated scheme allows regularized MS-SRR with a higher accuracy and precision than the shifted scheme, besides being more resilient to motion.

Pediatric magnetic resonance imaging: faster is better

Magnetic resonance imaging (MRI) has emerged as the preferred imaging modality for evaluating a wide range of pediatric medical conditions. Nevertheless, the long acquisition times associated with this technique can limit its widespread use in young children, resulting in motion-degraded or non-diagnostic studies. As a result, sedation or general anesthesia is often necessary to obtain diagnostic images, which has implications for the safety profile of MRI, the cost of the exam and the radiology department's clinical workflow. Over the last decade, several techniques have been developed to increase the speed of MRI, including parallel imaging, single-shot acquisition, controlled aliasing techniques, compressed sensing and artificial-intelligence-based reconstructions. These are advantageous because shorter examinations decrease the need for sedation and the severity of motion artifacts, increase scanner throughput, and improve system efficiency. In this review we discuss a framework for image acceleration in children that includes the synergistic use of state-of-the-art MRI hardware and optimized pulse sequences. The discussion is framed within the context of pediatric radiology and incorporates the authors' experience in deploying these techniques in routine clinical practice.

A novel algorithm for comprehensive quality assessment of clinical magnetic resonance images based on natural scene statistics in spatial domain

BACKGROUND

A magnetic resonance imaging (MRI)-specific objective image quality assessment (IQA) algorithm, the quality evaluation using multidirectional filters for MRI (QEMDIM), was previously reported. QEMDIM requires a set of reference images to calculate the quality score (S_Q) for an assessed image. S_Q may be affected by the quality of the reference set owing to the calculation procedure.

PURPOSE

To propose a modified version of the IQA algorithm and compare the IQA performance of the original and modified algorithms.

ASSESSMENT

Brain axial T₁- and T₂-weighted spin-echo images of varying quality levels (noise and blurring) were acquired from seven healthy men. Subjective IQA (paired comparisons) was conducted on the images, and subjective quality scores were obtained. With reference sets of various quality levels, QEMDIM and modified IQA were applied to the same images that underwent the subjective IQA. The correlation of each S_Q and modified score (S_mod) with the subjective scores was evaluated for content-related subsets of assessed images and for each reference set. The effect of the reference-set quality on the distribution of the correlation coefficients (CCs) was statistically evaluated for S_Q and S_mod using a one-way analysis of variance test with a significance level of 0.05. We also evaluated the variation in S_mod for images with almost the same qualities using the standard deviation (SD).

RESULTS

The CCs of S_Q varied significantly with the quality of the reference set, whereas that of S_mod did not. The SD of S_mod for almost-same-quality images was less than that corresponding to the confidence interval of the subjective scores.

CONCLUSION

Our modified algorithm was superior to QEMDIM in terms of IQA performance in clinical practice, especially in terms of accuracy, robustness, and reproducibility.

Practical Aspects of novel MRI Techniques in Neuroradiology: Part 2 - Acceleration Methods and Implications for Individual Regions

BACKGROUND

 Recently introduced MRI techniques facilitate accelerated examinations or increased resolution with the same duration. Further techniques offer homogeneous image quality in regions with anatomical transitions. The question arises whether and how these techniques can be adopted for routine diagnostic imaging.

METHODS

 Narrative review with an educational focus based on current literature research and practical experiences of different professions involved (physicians, MRI technologists/radiographers, physics/biomedical engineering). Different hardware manufacturers are considered.

RESULTS AND CONCLUSIONS

 Compressed sensing and simultaneous multi-slice imaging are novel acceleration techniques with different yet complimentary applications. They do not suffer from classical signal-to-noise-ratio penalties. Combining 3 D and acceleration techniques facilitates new broader examination protocols, particularly for clinical brain imaging. In further regions of the nervous systems mainly specific applications appear to benefit from recent technological improvements.

KEY POINTS

  · New acceleration techniques allow for faster or higher resolution examinations.. · New brain imaging approaches have evolved, including more universal examination protocols.. · Other regions of the nervous system are dominated by targeted applications of recently introduced MRI techniques..

CITATION FORMAT

· Sundermann B, Billebaut B, Bauer J et al. Practical Aspects of novel MRI Techniques in Neuroradiology: Part 2 - Acceleration Methods and Implications for Individual Regions. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1800-8789.

Use of real-time phase-contrast MRI to quantify the effect of spontaneous breathing on the cerebral arteries

Quantification of the effect of breathing on the cerebral circulation provides a better mechanistic understanding of the brain's circulatory system and is important in the early diagnosis of certain neurological diseases. However, conventional cine phase-contrast (CINE-PC) MRI cannot be used in this field of study because it only provides an average cardiac cycle flow curve reconstructed from multiple cardiac cycles. Unlike CINE-PC, phase-contrast echo-planar imaging (EPI-PC) can be used to quantify the blood flow rate in "real-time" and thus assess the effect of breathing on blood flow. Here, we first used post-processing software (developed in-house) to determine the feasibility of quantifying cerebral arterial blood flow with EPI-PC (relative to CINE-PC) in 16 participants. In a second step, we developed a new time-domain method for quantifying the intensity and the phase shift of the effects of breathing on the mean flow rate, stroke volume, cardiac period and amplitude of cerebral blood flow (in 10 participants). Our results showed that EPI-PC can quantify cerebral arterial blood flow rate with much the same degree of accuracy as CINE-PC but is more strongly influenced by differences in magnetic susceptibility. We found that breathing affected the mean flow rate, stroke volume and cardiac period of cerebral arterial blood flow.

Residual RAKI: A hybrid linear and non-linear approach for scan-specific k-space deep learning

Parallel imaging is the most clinically used acceleration technique for magnetic resonance imaging (MRI) in part due to its easy inclusion into routine acquisitions. In k-space based parallel imaging reconstruction, sub-sampled k-space data are interpolated using linear convolutions. At high acceleration rates these methods have inherent noise amplification and reduced image quality. On the other hand, non-linear deep learning methods provide improved image quality at high acceleration, but the availability of training databases for different scans, as well as their interpretability hinder their adaptation. In this work, we present an extension of Robust Artificial-neural-networks for k-space Interpolation (RAKI), called residual-RAKI (rRAKI), which achieves scan-specific machine learning reconstruction using a hybrid linear and non-linear methodology. In rRAKI, non-linear CNNs are trained jointly with a linear convolution implemented via a skip connection. In effect, the linear part provides a baseline reconstruction, while the non-linear CNN that runs in parallel provides further reduction of artifacts and noise arising from the linear part. The explicit split between the linear and non-linear aspects of the reconstruction also help improve interpretability compared to purely non-linear methods. Experiments were conducted on the publicly available fastMRI datasets, as well as high-resolution anatomical imaging, comparing GRAPPA and its variants, compressed sensing, RAKI, Scan Specific Artifact Reduction in K-space (SPARK) and the proposed rRAKI. Additionally, highly-accelerated simultaneous multi-slice (SMS) functional MRI reconstructions were also performed, where the proposed rRAKI was compred to Read-out SENSE-GRAPPA and RAKI. Our results show that the proposed rRAKI method substantially improves the image quality compared to conventional parallel imaging, and offers sharper images compared to SPARK and ℓ₁-SPIRiT. Furthermore, rRAKI shows improved preservation of time-varying dynamics compared to both parallel imaging and RAKI in highly-accelerated SMS fMRI.

Fast, Accurate, and Robust T2 Mapping of Articular Cartilage by Neural Networks

For T2 mapping, the underlying mono-exponential signal decay is traditionally quantified by non-linear Least-Squares Estimation (LSE) curve fitting, which is prone to outliers and computationally expensive. This study aimed to validate a fully connected neural network (NN) to estimate T2 relaxation times and to assess its performance versus LSE fitting methods. To this end, the NN was trained and tested in silico on a synthetic dataset of 75 million signal decays. Its quantification error was comparatively evaluated against three LSE methods, i.e., traditional methods without any modification, with an offset, and one with noise correction. Following in-situ acquisition of T2 maps in seven human cadaveric knee joint specimens at high and low signal-to-noise ratios, the NN and LSE methods were used to estimate the T2 relaxation times of the manually segmented patellofemoral cartilage. In-silico modeling at low signal-to-noise ratio indicated significantly lower quantification error for the NN (by medians of 6−33%) than for the LSE methods (p < 0.001). These results were confirmed by the in-situ measurements (medians of 10−35%). T2 quantification by the NN took only 4 s, which was faster than the LSE methods (28−43 s). In conclusion, NNs provide fast, accurate, and robust quantification of T2 relaxation times.

Ferumoxytol-enhanced ultrashort TE MRA and quantitative morphometry of the human kidney vasculature

PURPOSE

To evaluate the feasibility of Quantitative Ultrashort-Time-to-Echo Contrast-Enhanced (QUTE-CE) MRA using ferumoxytol as a contrast agent for abdominal angiography in the kidney.

METHODS

Four subjects underwent ferumoxytol-enhanced MRA with the 3D UTE Spiral VIBE WIP sequence at 3 T. Image quality metrics were quantified, specifically the blood Signal-to-Noise Ratio (SNR), blood-tissue Contrast-to-Noise Ratio (CNR) and Intraluminal Signal Heterogeneity (ISH) from both the aorta and inferior vena cava (IVC). Morphometric analysis of the vessels was performed using manual approach and semi-automatic approach using Vascular Modeling ToolKit (VMTK). Image quality and branching order were compared between QUTE-CE MRA and the Gadolinium (Gd) CEMRA reference image.

RESULTS

QUTE-CE MRA provides a bright blood snapshot that delineates arteries and veins equally in the same scan. The maximum SNR and CNR values were 3,282 ± 1,218 and 1,295 ± 580, respectively - significantly higher than available literature values using other CEMRA techniques. QUTE-CE MRA had lower ISH and depicted higher vessel branching order (7th vs 3rd) within the kidney compared to a standard dynamic clinical Gd CEMRA scan. Morphometric analysis yielded quantitative results for the total kidney volume, total cyst volume and for diameters of the branching arterial network down to the 7^th branch. Vessel curvature was significantly increased (p < 0.001) in the presence of a renal cyst compared to equivalent vessels in normal kidney regions.

CONCLUSION

QUTE-CE MRA is feasible for kidney angiography, providing greater detail of kidney vasculature, enabling quantitative morphometric analysis of the abdominal and intra-renal vessels and yielding metrics relevant to vascular diseases while using a contrast agent ferumoxytol that is safe for CKD patients.

RESUMEN: A flexible class of multi-parameter qMRI protocols

PURPOSE

To introduce a class of fast 3D quantitative MRI (qMRI) schemes (RESUME^N, for N=1,…,4) that allow for a thorough characterization of microstructural properties of brain tissues.

METHODS

An arbitrary multi-echo GRE acquisition optimized for quantitative susceptibility mapping (QSM) is complemented with an appropriate low flip-angle GRE sequence drawn from four possible choices. The acquired signals are processed to analytically derive the longitudinal relaxation (R₁) and free induction decay (R₂^∗) rates, as well as the proton density (PD) and QSM. A comprehensive modeling of the excitation and B₁^- profiles and of the RF-spoiling is included in the acquisition and processing pipeline.

RESULTS

The RESUME^N maps appear homogeneous throughout the field-of-view and exhibit comparable values and high SNR across the considered range of N values.

CONCLUSIONS

The introduced schemes represent a class of robust and flexible strategies to derive a thorough and fast qMRI study, suitable for a whole-brain acquisition with isotropic voxel resolution of 700 μm in less than 15 min.

OPERA: a novel method to reduce ghost and aliasing artifacts

OBJECTIVE

A method for Orthogonal Phase Encoding Reduction of Artifact (OPERA) was developed and tested.

MATERIALS AND METHODS

Because the position of ghosts and aliasing artifacts is predictable along columns or rows, OPERA combines the intensity values of two images acquired using the same parameters, but with swapped phase-encoding directions, to correct the artifacts. Simulations and phantom experiments were conducted to define the efficacy, robustness, and reproducibility. Clinical validation was performed on a total of 1003 images by comparing the OPERA-corrected images and the corresponding image standard in terms of Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR). The method efficacy was also rated using a Likert-type scale response by two experienced independent radiologists using a single-blinded procedure.

RESULTS

Simulations and phantom experiments demonstrated the robustness and effectiveness of OPERA in reducing artifacts strength. OPERA application did not significantly change the SNR [+ 4.16%; inter-quartile range (IQR): 2.72-5.01%] and CNR (+ 4.30%; IQR: 2.86-6.04%) values. The two radiologists observed a total of 893 original images with artifacts (89.03% of the total images), a reduction in the perceived artifacts of 82.0% and 83.9% (p < 0.0001), and an improvement in the perceived SNR (82.8% and 88.5%; K = 0.714) and perceived CNR (86.9-88.9%; K = 0.722).

DISCUSSION

The study demonstrated that OPERA reduces MR artifacts and improves the perceived image quality.

Image Findings of Acute to Subacute Craniocervical Arterial Dissection on Magnetic Resonance Vessel Wall Imaging: A Systematic Review and Proportion Meta-Analysis

Background and Purpose: This systematic review and meta-analysis aimed to evaluate the pooled proportion of image findings of acute to subacute craniocervical arterial dissection (AD) direct signs on magnetic resonance vessel wall imaging (MR-VWI) and to identify factors responsible for the heterogeneity across the included studies.

Methods: A systematic literature search in the Ovid-MEDLINE and EMBASE databases was performed for studies published on the relevant topic before April 14, 2020. Pooled sensitivity and specificity values and their 95% confidence intervals (CIs) were calculated using bivariate random-effects modeling. Meta-regression analyses were also performed to determine factors influencing heterogeneity.

Results: Eleven articles with data for 209 patients with acute to subacute craniocervical AD who underwent MR-VWI were included in this systematic review and meta-analysis. The most common findings on MR-VWI were wall hematoma (84%; 95% CI, 71%−92%), abnormal enhancement (72%; 95% CI, 49%−88%), aneurysmal dilatation (71%, 95% CI, 53%−84%), and intimal flap or double lumen signs (49%; 95% CI, 29%−71%). Among the potential covariates of heterogeneity, the presence of contrast-enhanced T1-weighted imaging (CE-T1WI) within the MR-VWI sequence combination significantly affected the pooled proportion of the intimal flap or double lumen signs.

Conclusion: Wall hematoma and intimal flap or double lumen signs were the most common and least common direct sign image findings, respectively, on MR-VWI in patients with acute to subacute craniocervical AD. Furthermore, the absence of CE-T1WI in MR-VWI protocol was the cause of heterogeneity for the detection of the intimal flap or double lumen signs. This data may help improve MR-VWI interpretation and enhance the understanding of the radiologic diagnosis of craniocervical AD.

A self-decoupled 32-channel receive array for human-brain MRI at 10.5 T

PURPOSE

Receive array layout, noise mitigation, and B₀ field strength are crucial contributors to SNR and parallel-imaging performance. Here, we investigate SNR and parallel-imaging gains at 10.5 T compared with 7 T using 32-channel receive arrays at both fields.

METHODS

A self-decoupled 32-channel receive array for human brain imaging at 10.5 T (10.5T-32Rx), consisting of 31 loops and one cloverleaf element, was co-designed and built in tandem with a 16-channel dual-row loop transmitter. Novel receive array design and self-decoupling techniques were implemented. Parallel imaging performance, in terms of SNR and noise amplification (g-factor), of the 10.5T-32Rx was compared with the performance of an industry-standard 32-channel receiver at 7 T (7T-32Rx) through experimental phantom measurements.

RESULTS

Compared with the 7T-32Rx, the 10.5T-32Rx provided 1.46 times the central SNR and 2.08 times the peripheral SNR. Minimum inverse g-factor value of the 10.5T-32Rx (min[1/g] = 0.56) was 51% higher than that of the 7T-32Rx (min[1/g] = 0.37) with R = 4 × 4 2D acceleration, resulting in significantly enhanced parallel-imaging performance at 10.5 T compared with 7 T. The g-factor values of 10.5 T-32 Rx were on par with those of a 64-channel receiver at 7 T (eg, 1.8 vs 1.9, respectively, with R = 4 × 4 axial acceleration).

CONCLUSION

Experimental measurements demonstrated effective self-decoupling of the receive array as well as substantial gains in SNR and parallel-imaging performance at 10.5 T compared with 7 T.

Optimal acceleration factor for image acquisition in turbo spin echo: diffusion-weighted imaging with compressed sensing

In this study, the change in the image quality and apparent diffusion coefficient (ADC) with increase in the acceleration factor (AF) was analyzed and the most optimal AF was determined to reduce the scan time while preserving the image quality. The AF was changed from 2 to 20 in the MR acquisitions. The similarities between the accelerated and reference images were determined based on the structural similarity (SSIM) index for DWI image and coefficient of variation (%CV) for ADC. The SSIM index decreased significantly when the AF ≥ 8 compared with when the AF = 2 (p < 0.05). In the reference image, the %CV of the ADC increased significantly when the AF ≥ 10 (p < 0.01). In conclusion, a remarkable decrease in the image quality and ADC was observed when the AF was > 8. Thus, an AF < 8 would be optimal for reducing the scan time while preserving the image quality.

The sensitivity of diffusion MRI to microstructural properties and experimental factors

Diffusion MRI is a non-invasive technique to study brain microstructure. Differences in the microstructural properties of tissue, including size and anisotropy, can be represented in the signal if the appropriate method of acquisition is used. However, to depict the underlying properties, special care must be taken when designing the acquisition protocol as any changes in the procedure might impact on quantitative measurements. This work reviews state-of-the-art methods for studying brain microstructure using diffusion MRI and their sensitivity to microstructural differences and various experimental factors. Microstructural properties of the tissue at a micrometer scale can be linked to the diffusion signal at a millimeter-scale using modeling. In this paper, we first give an introduction to diffusion MRI and different encoding schemes. Then, signal representation-based methods and multi-compartment models are explained briefly. The sensitivity of the diffusion MRI signal to the microstructural components and the effects of curvedness of axonal trajectories on the diffusion signal are reviewed. Factors that impact on the quality (accuracy and precision) of derived metrics are then reviewed, including the impact of random noise, and variations in the acquisition parameters (i.e., number of sampled signals, b-value and number of acquisition shells). Finally, yet importantly, typical approaches to deal with experimental factors are depicted, including unbiased measures and harmonization. We conclude the review with some future directions and recommendations on this topic.

The costs and benefits of estimating T1 of tissue alongside cerebral blood flow and arterial transit time in pseudo-continuous arterial spin labeling

Multi-post-labeling-delay pseudo-continuous arterial spin labeling (multi-PLD PCASL) allows for absolute quantification of the cerebral blood flow (CBF) as well as the arterial transit time (ATT). Estimating these perfusion parameters from multi-PLD PCASL data is a non-linear inverse problem, which is commonly tackled by fitting the single-compartment model (SCM) for PCASL, with CBF and ATT as free parameters. The longitudinal relaxation time of tissue T_1t is an important parameter in this model, as it governs the decay of the perfusion signal entirely upon entry in the imaging voxel. Conventionally, T_1t is fixed to a population average. This approach can cause CBF quantification errors, as T_1t can vary significantly inter- and intra-subject. This study compares the impact on CBF quantification, in terms of accuracy and precision, of either fixing T_1t , the conventional approach, or estimating it alongside CBF and ATT. It is shown that the conventional approach can cause a significant bias in CBF. Indeed, simulation experiments reveal that if T_1t is fixed to a value that is 10% off its true value, this may already result in a bias of 15% in CBF. On the other hand, as is shown by both simulation and real data experiments, estimating T_1t along with CBF and ATT results in a loss of CBF precision of the same order, even if the experiment design is optimized for the latter estimation problem. Simulation experiments suggest that an optimal balance between accuracy and precision of CBF estimation from multi-PLD PCASL data can be expected when using the two-parameter estimator with a fixed T_1t value between population averages of T_1t and the longitudinal relaxation time of blood T_1b .

Estimation error bound for GRAPPA diffusion-weighted MRI

In recent years, diffusion weight magnetic resonance imaging (DW-MRI) has become one of the most important MRI imaging modalities. The importance of the DW-MRI grew thanks to the combination of parallel magnetic resonance imaging (pMRI) techniques with the echo-planar imaging (EPI), which minimize scan time and lead to reduced distortion, allowing the DW-MRI to become a routine clinical exam. Additionally, this has brought various new parameters that influence image quality and biomarkers used in DW-MRI. This work aims to investigate the effects of these parameters on the estimation quality, by using the Cramér-Rao bound tool, which gives analytical expressions of the lower limit on the estimation error variance of different DW-MRI variables when using the pMRI technique. In particular, these bounds will be used to study and optimize the impact of different factors of generalized autocalibrating partially parallel acquisition (GRAPPA) technique and system parameters on the estimation quality of the desired clinical metrics. Moreover, the obtained results of this study can be exploited and adapted in all human body DW-MRI clinical routines, further improving disease diagnosis, and tractography studies.

Deep-learning-based image quality enhancement of compressed sensing magnetic resonance imaging of vessel wall: comparison of self-supervised and unsupervised approaches

While high-resolution proton density-weighted magnetic resonance imaging (MRI) of intracranial vessel walls is significant for a precise diagnosis of intracranial artery disease, its long acquisition time is a clinical burden. Compressed sensing MRI is a prospective technology with acceleration factors that could potentially reduce the scan time. However, high acceleration factors result in degraded image quality. Although recent advances in deep-learning-based image restoration algorithms can alleviate this problem, clinical image pairs used in deep learning training typically do not align pixel-wise. Therefore, in this study, two different deep-learning-based denoising algorithms-self-supervised learning and unsupervised learning-are proposed; these algorithms are applicable to clinical datasets that are not aligned pixel-wise. The two approaches are compared quantitatively and qualitatively. Both methods produced promising results in terms of image denoising and visual grading. While the image noise and signal-to-noise ratio of self-supervised learning were superior to those of unsupervised learning, unsupervised learning was preferable over self-supervised learning in terms of radiomic feature reproducibility.

A preliminary study of deep learning-based reconstruction specialized for denoising in high-frequency domain: usefulness in high-resolution three-dimensional magnetic resonance cisternography of the cerebellopontine angle

PURPOSE

Deep learning-based reconstruction (DLR) has been developed to reduce image noise and increase the signal-to-noise ratio (SNR). We aimed to evaluate the efficacy of DLR for high spatial resolution (HR)-MR cisternography.

METHODS

This retrospective study included 35 patients who underwent HR-MR cisternography. The images were reconstructed with or without DLR. The SNRs of the CSF and pons, contrast of the CSF and pons, and sharpness of the normal-side trigeminal nerve using full width at half maximum (FWHM) were compared between the two image types. Noise quality, sharpness, artifacts, and overall image quality of these two types of images were qualitatively scored.

RESULTS

The SNRs of the CSF and pons were significantly higher with DLR than without DLR (CSF 21.81 ± 7.60 vs. 15.33 ± 4.03, p < 0.001; pons 5.96 ± 1.38 vs. 3.99 ± 0.48, p < 0.001). There were no significant differences in the contrast of the CSF and pons (p = 0.225) and sharpness of the normal-side trigeminal nerve using FWHM (p = 0.185) without and with DLR, respectively. Noise quality and the overall image quality were significantly higher with DLR than without DLR (noise quality 3.95 ± 0.19 vs. 2.53 ± 0.44, p < 0.001; overall image quality 3.97 ± 0.17 vs. 2.97 ± 0.12, p < 0.001). There were no significant differences in sharpness (p = 0.371) and artifacts (p = 1) without and with DLR.

CONCLUSION

DLR can improve the image quality of HR-MR cisternography by reducing image noise without sacrificing contrast or sharpness.

Automated characterization of noise distributions in diffusion MRI data

Knowledge of the noise distribution in magnitude diffusion MRI images is the centerpiece to quantify uncertainties arising from the acquisition process. The use of parallel imaging methods, the number of receiver coils and imaging filters applied by the scanner, amongst other factors, dictate the resulting signal distribution. Accurate estimation beyond textbook Rician or noncentral chi distributions often requires information about the acquisition process (e.g., coils sensitivity maps or reconstruction coefficients), which is usually not available. We introduce two new automated methods using the moments and maximum likelihood equations of the Gamma distribution to estimate noise distributions as they explicitly depend on the number of coils, making it possible to estimate all unknown parameters using only the magnitude data. A rejection step is used to make the framework automatic and robust to artifacts. Simulations using stationary and spatially varying noncentral chi noise distributions were created for two diffusion weightings with SENSE or GRAPPA reconstruction and 8, 12 or 32 receiver coils. Furthermore, MRI data of a water phantom with different combinations of parallel imaging were acquired on a 3T Philips scanner along with noise-only measurements. Finally, experiments on freely available datasets from a single subject acquired on a 3T GE scanner are used to assess reproducibility when limited information about the acquisition protocol is available. Additionally, we demonstrated the applicability of the proposed methods for a bias correction and denoising task on an in vivo dataset acquired on a 3T Siemens scanner. A generalized version of the bias correction framework for non integer degrees of freedom is also introduced. The proposed framework is compared with three other algorithms with datasets from three vendors, employing different reconstruction methods. Simulations showed that assuming a Rician distribution can lead to misestimation of the noise distribution in parallel imaging. Results on the acquired datasets showed that signal leakage in multiband can also lead to a misestimation of the noise distribution. Repeated acquisitions of in vivo datasets show that the estimated parameters are stable and have lower variability than compared methods. Results for the bias correction and denoising task show that the proposed methods reduce the appearance of noise at high b-value. The proposed algorithms herein can estimate both parameters of the noise distribution automatically, are robust to signal leakage artifacts and perform best when used on acquired noise maps.

Magnetic resonance-guided radiation therapy: A review

Magnetic resonance-guided radiation therapy (MRgRT) is a promising approach to improving clinical outcomes for patients treated with radiation therapy. The roles of image guidance, adaptive planning and magnetic resonance imaging in radiation therapy have been increasing over the last two decades. Technical advances have led to the feasible combination of magnetic resonance imaging and radiation therapy technologies, leading to improved soft-tissue visualisation, assessment of inter- and intrafraction motion, motion management, online adaptive radiation therapy and the incorporation of functional information into treatment. MRgRT can potentially transform radiation oncology by improving tumour control and quality of life after radiation therapy and increasing convenience of treatment by shortening treatment courses for patients. Multiple groups have developed clinical implementations of MRgRT predominantly in the abdomen and pelvis, with patients having been treated since 2014. While studies of MRgRT have primarily been dosimetric so far, an increasing number of trials are underway examining the potential clinical benefits of MRgRT, with coordinated efforts to rigorously evaluate the benefits of the promising technology. This review discusses the current implementations, studies, potential benefits and challenges of MRgRT.

A Web-Based Educational Magnetic Resonance Simulator: Design, Implementation and Testing

A new web-based education-oriented magnetic resonance (MR) simulator is presented. We have identified the main requirements that this simulator should comply with, so that trainees can face useful practical tasks such as setting the exact slice position and its properties, selecting the correct protocol or fitting the parameters to acquire an image. The tool follows the client-server model. The client contains the interface that mimics the console of a real machine and several of its features. The server stores anatomical models and executes the bulk of the simulation. This cross-platform simulator has been used in two real educational scenarios. The acceptance of the tool has been measured using two criteria, namely, the System Usability Scale and the Likelihood to Recommend, both with satisfactory results. Therefore, we conclude that given the potential of the tool, it may play a relevant role for the training of MRI operators and other involved personnel.

Parallel magnetic resonance image reconstruction from a single-element parametric amplifier

In magnetic resonance imaging (MRI), acquisition speed is always an important issue. In this paper, we propose a promising technique to achieve parallel MRI (pMRI) on a single-channel spectrometer, using a novel Wireless Amplified Nuclear MR Detector (WAND) for spatial encoding in image reconstruction. For this, a planar structure double frequency WAND is designed and fabricated, where two of its frequencies - 'signal', ω1 and 'idler', ω2 are effectively utilized as two separate "channels" for accelerated acquisition. We provided a thorough background needed for the method and subsequently parallel imaging algorithms. Sum-of-Squares (SoS) reconstruction and GeneRalized Autocalibrating Partially Parallel Acquisition (GRAPPA) reconstruction are used to reconstruct as well as to analyze the SNR in the resulting images and validate our hypothesis. Experimental results using phantom datasets demonstrate that the proposed method of parallel imaging yield a better sensitivity for the combined images ('idler' + 'signal') than the sensitivity acquired for each individual image and thus significantly improving the reconstruction quality with optimal signal-to-noise ratio. We also demonstrated the achievable acceleration factor of this approach.

Towards an optimised processing pipeline for diffusion magnetic resonance imaging data: Effects of artefact corrections on diffusion metrics and their age associations in UK Biobank

Increasing interest in the structural and functional organisation of the human brain encourages the acquisition of big data sets comprising multiple neuroimaging modalities, often accompanied by additional information obtained from health records, cognitive tests, biomarkers and genotypes. Diffusion weighted magnetic resonance imaging data enables a range of promising imaging phenotypes probing structural connections as well as macroanatomical and microstructural properties of the brain. The reliability and biological sensitivity and specificity of diffusion data depend on processing pipeline. A state-of-the-art framework for data processing facilitates cross-study harmonisation and reduces pipeline-related variability. Using diffusion magnetic resonance imaging (MRI) data from 218 individuals in the UK Biobank, we evaluate the effects of different processing steps that have been suggested to reduce imaging artefacts and improve reliability of diffusion metrics. In lack of a ground truth, we compared diffusion metric sensitivity to age between pipelines. By comparing distributions and age sensitivity of the resulting diffusion metrics based on different approaches (diffusion tensor imaging, diffusion kurtosis imaging and white matter tract integrity), we evaluate a general pipeline comprising seven postprocessing blocks: noise correction; Gibbs ringing correction; evaluation of field distortions; susceptibility, eddy-current and motion-induced distortion corrections; bias field correction; spatial smoothing and final diffusion metric estimations. Based on this evaluation, we suggest an optimised processing pipeline for diffusion weighted MRI data.

Signal-to-noise ratio assessment of muscle diffusion tensor imaging using single image set and validation by the difference image method

OBJECTIVE

Signal-to-noise ratio (SNR) assessment is essential for accurate quantification of diffusion tensor imaging (DTI) metrics and usually requires the use of a difference image method using duplicate images. We aimed to estimate the SNR of DTI of thigh muscles using a single image set without duplicate images.

METHODS

DTI of one thigh were acquired on a 3 T scanner from 15 healthy adults, and scans with number of signal averages (NSA) = 4 and 8 were repeatedly acquired. SNR were evaluated for six thigh muscles. For SNR calculation from a single image set, diffusion-weighted images with similar diffusion encoding directions were grouped into pairs. The difference image of each pair was high-pass filtered in k-space to yield noise images. Noise images were also calculated with a difference method using two image sets as a reference. Subjects were divided into two groups for filter optimization and validation, respectively. The coefficient of repeatability (CR) of the SNR obtained from the two methods was also evaluated separately.

RESULTS

Bland-Altman analysis comparing the single image set method and the reference showed 95% limits of agreement of -9.2 to 9.2% for the optimization group and -12.5 to 12.6% for the validation group. The SNR measurement had a CR of 21.1% using the reference method, and 13.8% using the single image set method.

CONCLUSION

The single image method can be used for DTI SNR assessment and offers better repeatability.

ADVANCES IN KNOWLEDGE

SNR of skeletal muscle DTI can be assessed for any data set without duplicate images.

Learning-Based Quality Control for Cardiac MR Images

The effectiveness of a cardiovascular magnetic resonance (CMR) scan depends on the ability of the operator to correctly tune the acquisition parameters to the subject being scanned and on the potential occurrence of imaging artifacts, such as cardiac and respiratory motion. In the clinical practice, a quality control step is performed by visual assessment of the acquired images; however, this procedure is strongly operator-dependent, cumbersome, and sometimes incompatible with the time constraints in clinical settings and large-scale studies. We propose a fast, fully automated, and learning-based quality control pipeline for CMR images, specifically for short-axis image stacks. Our pipeline performs three important quality checks: 1) heart coverage estimation; 2) inter-slice motion detection; 3) image contrast estimation in the cardiac region. The pipeline uses a hybrid decision forest method-integrating both regression and structured classification models-to extract landmarks and probabilistic segmentation maps from both long- and short-axis images as a basis to perform the quality checks. The technique was tested on up to 3000 cases from the UK Biobank and on 100 cases from the UK Digital Heart Project and validated against manual annotations and visual inspections performed by expert interpreters. The results show the capability of the proposed pipeline to correctly detect incomplete or corrupted scans (e.g., on UK Biobank, sensitivity and specificity, respectively, 88% and 99% for heart coverage estimation and 85% and 95% for motion detection), allowing their exclusion from the analyzed dataset or the triggering of a new acquisition.

Noise reduction in diffusion MRI using non-local self-similar information in joint x-q space

Diffusion MRI affords valuable insights into white matter microstructures, but suffers from low signal-to-noise ratio (SNR), especially at high diffusion weighting (i.e., b-value). To avoid time-intensive repeated acquisition, post-processing algorithms are often used to reduce noise. Among existing methods, non-local means (NLM) has been shown to be particularly effective. However, most NLM algorithms for diffusion MRI focus on patch matching in the spatial domain (i.e., x-space) and disregard the fact that the data live in a combined 6D space covering both spatial domain and diffusion wavevector domain (i.e., q-space). This drawback leads to inaccurate patch matching in curved white matter structures and hence the inability to effectively use recurrent information for noise reduction. The goal of this paper is to overcome this limitation by extending NLM to the joint x-q space. Specifically, we define for each point in the x-q space a spherical patch from which we extract rotation-invariant features for patch matching. The ability to perform patch matching across q-samples allows patches from differentially orientated structures to be used for effective noise removal. Extensive experiments on synthetic, repeated-acquisition, and HCP data demonstrate that our method outperforms state-of-the-art methods, both qualitatively and quantitatively.

Local Indicators of Spatial Autocorrelation (LISA): Application to Blind Noise-Based Perceptual Quality Metric Index for Magnetic Resonance Images

Noise-based quality evaluation of MRI images is highly desired in noise-dominant environments. Current noise-based MRI quality evaluation methods have drawbacks which limit their effective performance. Traditional full-reference methods such as SNR and most of the model-based techniques cannot provide perceptual quality metrics required for accurate diagnosis, treatment and monitoring of diseases. Although techniques based on the Moran coefficients are perceptual quality metrics, they are full-reference methods and will be ineffective in applications where the reference image is not available. Furthermore, the predicted quality scores are difficult to interpret because their quality indices are not standardized. In this paper, we propose a new no-reference perceptual quality evaluation method for grayscale images such as MRI images. Our approach is formulated to mimic how humans perceive an image. It transforms noise level into a standardized perceptual quality score. Global Moran statistics is combined with local indicators of spatial autocorrelation in the form of local Moran statistics. Quality score is predicted from perceptually weighted combination of clustered and random pixels. Performance evaluation, comparative performance evaluation and validation by human observers, shows that the proposed method will be a useful tool in the evaluation of retrospectively acquired MRI images and the evaluation of noise reduction algorithms.