Noise in MRI

Any-nucleus distributed active programmable transmit coil

PURPOSE

There are 118 known elements. Nearly all of them have NMR active isotopes and at least 39 different nuclei have biological relevance. Despite this, most of today's MRI is based on only one nucleus-1H. To facilitate imaging all potential nuclei, we present a single transmit coil able to excite arbitrary nuclei in human-scale MRI.

THEORY AND METHODS

We present a completely new type of RF coil, the Any-nucleus Distributed Active Programmable Transmit Coil (ADAPT Coil), with fast switches integrated into the structure of the coil to allow it to operate at any relevant frequency. This coil eliminates the need for the expensive traditional RF amplifier by directly converting direct current (DC) power into RF magnetic fields with frequencies chosen by digital control signals sent to the switches. Semiconductor switch imperfections are overcome by segmenting the coil.

RESULTS

Circuit simulations demonstrated the effectiveness of the ADAPT Coil approach, and a 9 cm diameter surface ADAPT Coil was implemented. Using the ADAPT Coil, 1H, 23Na, 2H, and 13C phantom images were acquired, and 1H and 23Na ex vivo images were acquired. To excite different nuclei, only digital control signals were changed, which can be programmed in real time.

CONCLUSION

The ADAPT Coil presents a low-cost, scalable, and efficient method for exciting arbitrary nuclei in human-scale MRI. This coil concept provides further opportunities for scaling, programmability, lowering coil costs, lowering dead-time, streamlining multinuclear MRI workflows, and enabling the study of dozens of biologically relevant nuclei.

AcquisitionFocus: Joint Optimization of Acquisition Orientation and Cardiac Volume Reconstruction Using Deep Learning

In cardiac cine imaging, acquiring high-quality data is challenging and time-consuming due to the artifacts generated by the heart's continuous movement. Volumetric, fully isotropic data acquisition with high temporal resolution is, to date, intractable due to MR physics constraints. To assess whole-heart movement under minimal acquisition time, we propose a deep learning model that reconstructs the volumetric shape of multiple cardiac chambers from a limited number of input slices while simultaneously optimizing the slice acquisition orientation for this task. We mimic the current clinical protocols for cardiac imaging and compare the shape reconstruction quality of standard clinical views and optimized views. In our experiments, we show that the jointly trained model achieves accurate high-resolution multi-chamber shape reconstruction with errors of <13 mm HD95 and Dice scores of >80%, indicating its effectiveness in both simulated cardiac cine MRI and clinical cardiac MRI with a wide range of pathological shape variations.

Combining variational mode decomposition with regularisation techniques to denoise MRI data

In this paper, we propose a novel method for removing noise from MRI data by exploiting regularisation techniques combined with variational mode decomposition. Variational mode decomposition is a new decomposition technique for sparse decomposition of a 1D or 2D signal into a set of modes. In turn, regularisation is a method that can translate the ill-posed problem (e.g., image denoising) into a well-posed problem. The proposed method aims to remove the noise from the image in two steps. In the first step, the MR imaging data are decomposed by the 2D variational mode decomposition algorithm. In the second step, for effective suppression of Rician noise from MRI data, we used the fused lasso signal approximator with all modes acquired from the medical scan. The performance of the proposed approach was compared with state-of-the-art reference methods based on different metrics, that is, the peak signal-to-noise ratio, the structural similarity index metrics, the high-frequency error norm, the quality index based on local variance, and the sharpness index. The experiments were performed on the basis of both simulated and real images. The presented results prove the high denoising performance of the proposed algorithm; particularly under heavy noise conditions.

Survey of Denoising, Segmentation and Classification of Pancreatic Cancer Imaging

BACKGROUND

Pancreatic cancer is one of the most serious problems that has taken many lives worldwide. The diagnostic procedure using the traditional approaches was manual by visually analyzing the large volumes of the dataset, making it time-consuming and prone to subjective errors. Hence the need for the computer-aided diagnosis system (CADs) emerged that comprises the machine and deep learning approaches for denoising, segmentation and classification of pancreatic cancer.

INTRODUCTION

There are different modalities used for the diagnosis of pancreatic cancer, such as Positron Emission Tomography/Computed Tomography (PET/CT), Magnetic Resonance Imaging (MRI), Multiparametric-MRI (Mp-MRI), Radiomics and Radio-genomics. Although these modalities gave remarkable results in diagnosis on the basis of different criteria. CT is the most commonly used modality that produces detailed and fine contrast images of internal organs of the body. However, it may also contain a certain amount of gaussian and rician noise that is necessary to be preprocessed before segmentation of the required region of interest (ROI) from the images and classification of cancer.

METHOD

This paper analyzes different methodologies used for the complete diagnosis of pancreatic cancer, including the denoising, segmentation and classification, along with the challenges and future scope for the diagnosis of pancreatic cancer.

RESULT

Various filters are used for denoising and image smoothening and filters as gaussian scale mixture process, non-local means, median filter, adaptive filter and average filter have been used more for better results.

CONCLUSION

In terms of segmentation, atlas based region-growing method proved to give better results as compared to the state of the art whereas, for the classification, deep learning approaches outperformed other methodologies to classify the images as cancerous and non- cancerous. These methodologies have proved that CAD systems have become a better solution to the ongoing research proposals for the detection of pancreatic cancer worldwide.

Noise2Recon: Enabling SNR-robust MRI reconstruction with semi-supervised and self-supervised learning

PURPOSE

To develop a method for building MRI reconstruction neural networks robust to changes in signal-to-noise ratio (SNR) and trainable with a limited number of fully sampled scans.

METHODS

We propose Noise2Recon, a consistency training method for SNR-robust accelerated MRI reconstruction that can use both fully sampled (labeled) and undersampled (unlabeled) scans. Noise2Recon uses unlabeled data by enforcing consistency between model reconstructions of undersampled scans and their noise-augmented counterparts. Noise2Recon was compared to compressed sensing and both supervised and self-supervised deep learning baselines. Experiments were conducted using retrospectively accelerated data from the mridata three-dimensional fast-spin-echo knee and two-dimensional fastMRI brain datasets. All methods were evaluated in label-limited settings and among out-of-distribution (OOD) shifts, including changes in SNR, acceleration factors, and datasets. An extensive ablation study was conducted to characterize the sensitivity of Noise2Recon to hyperparameter choices.

RESULTS

In label-limited settings, Noise2Recon achieved better structural similarity, peak signal-to-noise ratio, and normalized-RMS error than all baselines and matched performance of supervised models, which were trained with14 × $$ 14\times $$ more fully sampled scans. Noise2Recon outperformed all baselines, including state-of-the-art fine-tuning and augmentation techniques, among low-SNR scans and when generalizing to OOD acceleration factors. Augmentation extent and loss weighting hyperparameters had negligible impact on Noise2Recon compared to supervised methods, which may indicate increased training stability.

CONCLUSION

Noise2Recon is a label-efficient reconstruction method that is robust to distribution shifts, such as changes in SNR, acceleration factors, and others, with limited or no fully sampled training data.

Magnetic resonance imaging in the diagnosis of periodontal and periapical disease

OBJECTIVES

Early pre-clinical inflammatory changes in periodontal and/or periapical lesions, which typically precede bone loss, are challenging to diagnose using ionizing-radiation-based imaging modalities. MRI provides relevant additional diagnostic information of inflammatory processes in soft and hard tissues. The aim of the present study is to undertake a systematic review of the literature on MRI in the diagnosis of periodontal and/or periapical disease.

METHODS AND MATERIALS

The PubMed/MEDLINE and Scopus bibliographic databases were searched (2000-2021) using the search string: ("MRI" or "magnetic resonance imaging") and ("periodontitis" or "periodontal" or "apical pathology" or "endodontic pathology" or "periapical" or "furcation" or "intrabony"). The search was limited to studies published in English. The studies were assessed independently by three reviewers, focusing on the MRI sequences, imaging modalities (radiographs, cone beam CT (CBCT), and MRI), disease definition, assessed parameters, and outcome measurements.

RESULTS

The search strategy yielded 34 studies, from which 13 were included. Overall, the findings of MRI were in agreement with CBCT. The studies showed that MRI provided diagnostic information of the hard and soft tissue components affected by periodontal and/or periapical disease with a fairly high sensitivity and specificity. However, the assessed parameters (e.g. MRI acquisition protocols, and disease definition) differed substantially.

CONCLUSIONS

The included studies indicate that the use of MRI in the diagnosis of periodontal and/or periapical disease is feasible and promising. More studies are needed to define the accuracy of this non-ionizing-radiation-based diagnostic modality, in the assessment of periodontal and/or periapical lesions.

Efficient imaging using spiral acquisitions on a portable 50-mT MR head scanner

Ultralow-field (ULF) magnetic resonance imaging (MRI) can suffer from inferior image quality because of low signal-to-noise ratio (SNR). As an efficient way to cover the k-space, the spiral acquisition technique has shown great potential in improving imaging SNR efficiency at ULF. The current study aimed to address the problems of noise and blurring cancelation in the ULF case with spiral trajectory, and we proposed a spiral-out sequence for brain imaging using a portable 50-mT MRI system. The proposed sequence consisted of three modules: noise calibration, field map acquisition, and imaging. In the calibration step, transfer coefficients were obtained between signals from primary and noise-pick-up coils to perform electromagnetic interference (EMI) cancelation. Embedded field map acquisition was performed to correct accumulated phase error due to main field inhomogeneity. Considering imaging SNR, a lower bandwidth for data sampling was adopted in the sequence design because the 50-mT scanner is in a low SNR regime. Image reconstruction proceeded with sampled data by leveraging system imperfections, such as gradient delays and concomitant fields. The proposed method can provide images with higher SNR efficiency compared with its Cartesian counterparts. An improvement in temporal SNR of approximately 23%-44% was measured via phantom and in vivo experiments. Distortion-free images with a noise suppression rate of nearly 80% were obtained by the proposed technique. A comparison was also made with a state-of-the-art EMI cancelation algorithm used in the ULF-MRI system. SNR efficiency-enhanced spiral acquisitions were investigated for ULF-MR scanners and future studies could focus on various image contrasts based on our proposed approach to widen ULF applications.

Experimental demonstration of diffusion limitations on resolution and SNR in MR microscopy

PURPOSE

MR microscopy is in principle capable of producing images at cellular resolution (<10 µm), but various factors limit the quality achieved in practice. A recognized limit on the signal to noise ratio and spatial resolution is the dephasing of transverse magnetization caused by diffusion of spins in strong gradients. Such effects may be reduced by using phase encoding instead of frequency encoding read-out gradients. However, experimental demonstration of the quantitative benefits of phase encoding are lacking, and the exact conditions in which it is preferred are not clearly established. We quantify the conditions where phase encoding outperforms a readout gradient with emphasis on the detrimental effects of diffusion on SNR and resolution.

METHODS

A 15.2 T Bruker MRI scanner, with 1 T/m gradients, and micro solenoid RF coils < 1 mm in diameter, were used to quantify diffusion effects on resolution and the signal to noise ratio of frequency and phase encoded acquisitions. Frequency and phase encoding's spatial resolution and SNR per square root time were calculated and measured for images at the diffusion limited resolution. The point spread function was calculated and measured for phase and frequency encoding using additional constant time phase gradients with voxels 3-15 µm in dimension.

RESULTS

The effect of diffusion during the readout gradient on SNR was experimentally demonstrated. The achieved resolutions of frequency and phase encoded acquisitions were measured via the point-spread-function and shown to be lower than the nominal resolution. SNR per square root time and actual resolution were calculated for a wide range of maximum gradient amplitudes, diffusion coefficients, and relaxation properties. The results provide a practical guide on how to choose between phase encoding and a conventional readout. Images of excised rat spinal cord at 10 µm × 10 µm in-plane resolution demonstrate phase encoding's benefits in the form of higher measured resolution and higher SNR than the same image acquired with a conventional readout.

CONCLUSION

We provide guidelines to determine the extent to which phase encoding outperforms frequency encoding in SNR and resolution given a wide range of voxel sizes, sample, and hardware properties.

Off-resonance artifact correction for MRI: A review

In magnetic resonance imaging (MRI), inhomogeneity in the main magnetic field used for imaging, referred to as off-resonance, can lead to image artifacts ranging from mild to severe depending on the application. Off-resonance artifacts, such as signal loss, geometric distortions, and blurring, can compromise the clinical and scientific utility of MR images. In this review, we describe sources of off-resonance in MRI, how off-resonance affects images, and strategies to prevent and correct for off-resonance. Given recent advances and the great potential of low-field and/or portable MRI, we also highlight the advantages and challenges of imaging at low field with respect to off-resonance.

Comparison of liver iron concentration calculated from R2* at 1.5 T and 3 T

PURPOSE

R2*, a measurement obtained using magnetic resonance imaging (MRI) can be used to estimate liver iron concentration (LIC). 3 T and 1.5 T scanners can be used but conversion of 3 T R2* to LIC is less well validated. In this study the aim was to compare 3 T-R2* LIC and 1.5 T-R2* LIC estimations to assess if they can be used interchangeably.

METHODS

Thirty participants were scanned at both 1.5 T and 3 T. R2* was measured at both field strengths. 3 T R2* and 1.5 R2* were compared using linear regression and were converted to LIC using different calibration curves. Pearson's rho and Intraclass Correlation Coefficients (ICCs) were used to assess correlation and agreement between 1.5 and 3 T LIC. Bland Altman plots were used to assess bias and limits of agreement.

RESULTS

All 1.5 T and 3 T LIC comparisons gave Pearson's rho of 0.99 (p < 0.001). ICC ranged from 0.83 (p = 0.005) to 0.96 (p < 0.001). Biases had magnitude of less than 0.2 mg/g dry weight.

CONCLUSION

Agreement and bias between 3 and 1.5 T-R2* LIC depended on the method used for conversion. There were instances when the agreement was excellent and bias was small, indicating that potentially 3 T-R2* LIC can be used alongside or instead of 1.5 T-R2* LIC but care needs to be taken over the conversion methods selected.

TRIAL REGISTRATION NUMBER

Clinicaltrials.gov NCT03743272, 16 November 2018.

Tumor pH Imaging Using Chemical Exchange Saturation Transfer (CEST)-MRI

Magnetic resonance imaging (MRI) is a noninvasive imaging technique that allows for physiological and functional studies of the tumor microenvironment. Within MRI, the emerging field of chemical exchange saturation transfer (CEST) has been largely exploited for assessing a salient feature of all solid tumors, extracellular acidosis. Iopamidol-based tumor pH imaging has been demonstrated to provide accurate and high spatial resolution extracellular tumor pH maps to elucidate tumor aggressiveness and for assessing response to therapy, with a high potential for clinical translation. Here, we describe the overall setup and steps for measuring tumor extracellular pH of tumor models in mice by exploiting MRI-CEST pH imaging with a preclinical MRI scanner following the administration of iopamidol. We address issues of pH calibration curve setup, animal handling, pH-responsive contrast agent injection, acquisition protocol, and image processing for accurate quantification and visualization of tumor acidosis.

AutoComBat: a generic method for harmonizing MRI-based radiomic features

The use of multicentric data is becoming essential for developing generalizable radiomic signatures. In particular, Magnetic Resonance Imaging (MRI) data used in brain oncology are often heterogeneous in terms of scanners and acquisitions, which significantly impact quantitative radiomic features. Various methods have been proposed to decrease dependency, including methods acting directly on MR images, i.e., based on the application of several preprocessing steps before feature extraction or the ComBat method, which harmonizes radiomic features themselves. The ComBat method used for radiomics may be misleading and presents some limitations, such as the need to know the labels associated with the "batch effect". In addition, a statistically representative sample is required and the applicability of a signature whose batch label is not present in the train set is not possible. This work aimed to compare a priori and a posteriori radiomic harmonization methods and propose a code adaptation to be machine learning compatible. Furthermore, we have developed AutoComBat, which aims to automatically determine the batch labels, using either MRI metadata or quality metrics as inputs of the proposed constrained clustering. A heterogeneous dataset consisting of high and low-grade gliomas coming from eight different centers was considered. The different methods were compared based on their ability to decrease relative standard deviation of radiomic features extracted from white matter and on their performance on a classification task using different machine learning models. ComBat and AutoComBat using image-derived quality metrics as inputs for batch assignment and preprocessing methods presented promising results on white matter harmonization, but with no clear consensus for all MR images. Preprocessing showed the best results on the T1w-gd images for the grading task. For T2w-flair, AutoComBat, using either metadata plus quality metrics or metadata alone as inputs, performs better than the conventional ComBat, highlighting its potential for data harmonization. Our results are MRI weighting, feature class and task dependent and require further investigations on other datasets.

Toward Nanomolar Multi-Component Analysis by 19F NMR

The widespread application of nuclear magnetic resonance (NMR) spectroscopy in detection is currently hampered by its inherently low sensitivity and complications resulting from the undesired signal overlap. Here, we report a detection scheme to address these challenges, where analytes are recognized by ¹⁹F-labeled probes to induce characteristic shifts of ¹⁹F resonances that can be used as "chromatographic" signatures to pin down each low-concentration analyte in complex mixtures. This unique signal transduction mechanism allows detection sensitivity to be enhanced by using massive chemically equivalent ¹⁹F atoms, which was achieved through the proper installation of nonafluoro-tert-butoxy groups on probes of high structural symmetry. It is revealed that the binding of an analyte to the probe can be sensed by as many as 72 chemically equivalent ¹⁹F atoms, allowing the quantification of analytes at nanomolar concentrations to be routinely performed by NMR. Applications on the detection of trace amounts of prohibited drug molecules and water contaminants were demonstrated. The high sensitivity and robust resolving ability of this approach represent a first step toward extending the application of NMR to scenarios that are now governed by chromatographic and mass spectrometry techniques. The detection scheme also makes possible the highly sensitive non-invasive multi-component analysis that is difficult to achieve by other analytical methods.

Arterial spin labeling MR image denoising and reconstruction using unsupervised deep learning

Arterial spin labeling (ASL) imaging is a powerful magnetic resonance imaging technique that allows to quantitatively measure blood perfusion non-invasively, which has great potential for assessing tissue viability in various clinical settings. However, the clinical applications of ASL are currently limited by its low signal-to-noise ratio (SNR), limited spatial resolution, and long imaging time. In this work, we propose an unsupervised deep learning-based image denoising and reconstruction framework to improve the SNR and accelerate the imaging speed of high resolution ASL imaging. The unique feature of the proposed framework is that it does not require any prior training pairs but only the subject's own anatomical prior, such as T1-weighted images, as network input. The neural network was trained from scratch in the denoising or reconstruction process, with noisy images or sparely sampled k-space data as training labels. Performance of the proposed method was evaluated using in vivo experiment data obtained from 3 healthy subjects on a 3T MR scanner, using ASL images acquired with 44-min acquisition time as the ground truth. Both qualitative and quantitative analyses demonstrate the superior performance of the proposed txtc framework over the reference methods. In summary, our proposed unsupervised deep learning-based denoising and reconstruction framework can improve the image quality and accelerate the imaging speed of ASL imaging.

Artifact- and content-specific quality assessment for MRI with image rulers

In clinical practice MR images are often first seen by radiologists long after the scan. If image quality is inadequate either patients have to return for an additional scan, or a suboptimal interpretation is rendered. An automatic image quality assessment (IQA) would enable real-time remediation. Existing IQA works for MRI give only a general quality score, agnostic to the cause of and solution to low-quality scans. Furthermore, radiologists' image quality requirements vary with the scan type and diagnostic task. Therefore, the same score may have different implications for different scans. We propose a framework with multi-task CNN model trained with calibrated labels and inferenced with image rulers. Labels calibrated by human inputs follow a well-defined and efficient labeling task. Image rulers address varying quality standards and provide a concrete way of interpreting raw scores from the CNN. The model supports assessments of two of the most common artifacts in MRI: noise and motion. It achieves accuracies of around 90%, 6% better than the best previous method examined, and 3% better than human experts on noise assessment. Our experiments show that label calibration, image rulers, and multi-task training improve the model's performance and generalizability.

Real-Time Magnetic Resonance Imaging

Real-time magnetic resonance imaging (RT-MRI) allows for imaging dynamic processes as they occur, without relying on any repetition or synchronization. This is made possible by modern MRI technology such as fast-switching gradients and parallel imaging. It is compatible with many (but not all) MRI sequences, including spoiled gradient echo, balanced steady-state free precession, and single-shot rapid acquisition with relaxation enhancement. RT-MRI has earned an important role in both diagnostic imaging and image guidance of invasive procedures. Its unique diagnostic value is prominent in areas of the body that undergo substantial and often irregular motion, such as the heart, gastrointestinal system, upper airway vocal tract, and joints. Its value in interventional procedure guidance is prominent for procedures that require multiple forms of soft-tissue contrast, as well as flow information. In this review, we discuss the history of RT-MRI, fundamental tradeoffs, enabling technology, established applications, and current trends. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 1.

The Impact of Resampling and Denoising Deep Learning Algorithms on Radiomics in Brain Metastases MRI

Simple Summary

Due to the central role of magnetic resonance Imaging (MRI) in the management of patients with cancer, waiting lists exceed clinically relevant delays. For this reason, many research groups and MRI manufacturers develop algorithms as resampling and denoising models to allow faster acquisition time without deterioration in image quality. Whereas these algorithms are available in all new MRI, it is not clear how they will impact image features as well as the validity of statistical model of radiomics which use deep images characteristics to predict treatment outcome. The aim of this study was to develop resampling and denoising deep learning (DL) models and evaluate their impact on radiomics from post-Gd-T1w-MRI brain images with brain metastases. We show that resampling and denoising DL models reconstruct low resolution and noised MRI images acquired quickly into high quality images. While fast acquisition loses most of the radiomic-features and invalidates predictive radiomic models, DL models restore these parameters.

Abstract

Background: Magnetic resonance imaging (MRI) is predominant in the therapeutic management of cancer patients, unfortunately, patients have to wait a long time to get an appointment for examination. Therefore, new MRI devices include deep-learning (DL) solutions to save acquisition time. However, the impact of these algorithms on intensity and texture parameters has been poorly studied. The aim of this study was to evaluate the impact of resampling and denoising DL models on radiomics. Methods: Resampling and denoising DL model was developed on 14,243 T1 brain images from 1.5T-MRI. Radiomics were extracted from 40 brain metastases from 11 patients (2049 images). A total of 104 texture features of DL images were compared to original images with paired t-test, Pearson correlation and concordance-correlation-coefficient (CCC). Results: When two times shorter image acquisition shows strong disparities with the originals concerning the radiomics, with significant differences and loss of correlation of 79.81% and 48.08%, respectively. Interestingly, DL models restore textures with 46.15% of unstable parameters and 25.96% of low CCC and without difference for the first-order intensity parameters. Conclusions: Resampling and denoising DL models reconstruct low resolution and noised MRI images acquired quickly into high quality images. While fast MRI acquisition loses most of the radiomic features, DL models restore these parameters.

Anisotropic neural deblurring for MRI acceleration

PURPOSE

MRI has become the tool of choice for brain imaging, providing unrivalled contrast between soft tissues, as well as a wealth of information about anatomy, function, and neurochemistry. Image quality, in terms of spatial resolution and noise, is strongly dependent on acquisition duration. A typical brain MRI scan may last several minutes, with total protocol duration often exceeding 30 minutes. Long scan duration leads to poor patient experience, long waiting time for appointments, and high costs. Therefore, shortening MRI scans is crucial. In this paper, we investigate the enhancement of low-resolution (LR) brain MRI scanning, to enable shorter acquisition times without compromising the diagnostic value of the images.

METHODS

We propose a novel fully convolutional neural enhancement approach. It is optimized for accelerated LR MRI acquisitions obtained by reducing the acquisition matrix size only along phase encoding direction. The network is trained to transform the LR acquisitions into corresponding high-resolution (HR) counterparts in an end-to-end manner. In contrast to previous neural-based MRI enhancement algorithms, such as DAGAN, the LR images used for training are real acquisitions rather than smoothed, downsampled versions of the HR images.

RESULTS

The proposed method is validated qualitatively and quantitatively for an acceleration factor of 4. Favourable comparison is demonstrated against the state-of-the-art DeblurGAN and DAGAN algorithms in terms of PSNR and SSIM scores. The result was further confirmed by an image quality rating experiment performed by four senior neuroradiologists.

CONCLUSIONS

The proposed method may become a valuable tool for scan time reduction in brain MRI. In continuation of this research, the validation should be extended to larger datasets acquired for different imaging protocols, and considering several MRI machines produced by different vendors.

Acquisition and quantification pipeline for in vivo hyperpolarized 13 C MR spectroscopy

PURPOSE

The goal of this study was to combine a specialized acquisition method with a new quantification pipeline to accurately and efficiently probe the metabolism of hyperpolarized ¹³ C-labeled compounds in vivo. In this study, we tested our approach on [2-¹³ C]pyruvate and [1-¹³ C]α-ketoglutarate data in rat orthotopic brain tumor models at 3T.

METHODS

We used a multiband metabolite-specific radiofrequency (RF) excitation in combination with a variable flip angle scheme to minimize substrate polarization loss and measure fast metabolic processes. We then applied spectral-temporal denoising using singular value decomposition to enhance spectral quality. This was combined with LCModel-based automatic ¹³ C spectral fitting and flip angle correction to separate overlapping signals and rapidly quantify the different metabolites.

RESULTS

Denoising improved the metabolite signal-to-noise ratio (SNR) by approximately 5. It also improved the accuracy of metabolite quantification as evidenced by a significant reduction of the Cramer Rao lower bounds. Furthermore, the use of the automated and user-independent LCModel-based quantification approach could be performed rapidly, with the kinetic quantification of eight metabolite peaks in a 12-spectrum array achieved in less than 1 minute.

CONCLUSION

The specialized acquisition method combined with denoising and a new quantification pipeline using LCModel for the first time for hyperpolarized ¹³ C data enhanced our ability to monitor the metabolism of [2-¹³ C]pyruvate and [1-¹³ C]α-ketoglutarate in rat orthotopic brain tumor models in vivo. This approach could be broadly applicable to other hyperpolarized agents both preclinically and in the clinical setting.

Responsive Nanoparticles to Enable a Focused Ultrasound-Stimulated Magnetic Resonance Imaging Spotlight

Magnetic resonance imaging (MRI)-guided high-intensity focused ultrasound (HIFU) has been applied as a therapeutic tool in the clinic, and enhanced MRI contrast for depiction of target tissues will improve the precision and applicability of HIFU therapy. This work presents a "spotlight MRI" contrast enhancement technique, which combines four essential components: periodic HIFU stimulation, strong modulation of T₁ caused by HIFU, rapid MRI signal collection, and spotlight MRI spectral signal processing. The T₁ modulation is enabled by a HIFU-responsive nanomaterial based on mesoporous silica nanoparticles with Pluronic polymers (Poloxamers) and MRI contrast agents attached. With periodic HIFU stimulation in a precisely defined region containing the nanomaterial, strong periodic MRI T₁-weighted signal changes are generated. Rapid MRI signal collection of the periodic signal changes is realized by a rapid dynamic 3D MRI technique, and spotlight MRI spectral signal processing creates modulation enhancement maps (MEM) that suppress background signal and spotlight the spatial location with nanomaterials experiencing HIFU stimulation. In particular, a framework is presented to analyze the trade-offs between different parameter choices for the signal processing method. The optimal parameter choices under a specific experimental setting achieved MRI contrast enhancement of more than 2 orders of magnitude at the HIFU focal point, compared to controls.

An assessment of noise variance estimations in Bayes threshold denoising under stationary wavelet domain on brain lesions and tumor MRIs

Purpose

A major key success factor regarding proficient Bayes threshold denoising refers to noise variance estimation. This paper focuses on assessing different noise variance estimations in three Bayes threshold models on two different characteristic brain lesions/tumor magnetic resonance imaging (MRIs).

Design/methodology/approach

Here, three Bayes threshold denoising models based on different noise variance estimations under the stationary wavelet transforms (SWT) domain are mainly assessed, compared to state-of-the-art non-local means (NLMs). Each of those three models, namely D1, GB and DR models, respectively, depends on the most detail wavelet subband at the first resolution level, on the entirely global detail subbands and on the detail subband in each direction/resolution. Explicit and implicit denoising performance are consecutively assessed by threshold denoising and segmentation identification results.

Findings

Implicit performance assessment points the first–second best accuracy, 0.9181 and 0.9048 Dice similarity coefficient (Dice), sequentially yielded by GB and DR; reliability is indicated by 45.66% Dice dropping of DR, compared against 53.38, 61.03 and 35.48% of D1 GB and NLMs, when increasing 0.2 to 0.9 noise level on brain lesions MRI. For brain tumor MRI under 0.2 noise level, it denotes the best accuracy of 0.9592 Dice, resulted by DR; however, 8.09% Dice dropping of DR, relative to 6.72%, 8.85 and 39.36% of D1, GB and NLMs is denoted. The lowest explicit and implicit denoising performances of NLMs are obviously pointed.

Research limitations/implications

A future improvement of denoising performance possibly refers to creating a semi-supervised denoising conjunction model. Such model utilizes the denoised MRIs, resulted by DR and D1 thresholding model as uncorrupted image version along with the noisy MRIs, representing corrupted version ones during autoencoder training phase, to reconstruct the original clean image.

Practical implications

This paper should be of interest to readers in the areas of technologies of computing and information science, including data science and applications, computational health informatics, especially applied as a decision support tool for medical image processing.

Originality/value

In most cases, DR and D1 provide the first–second best implicit performances in terms of accuracy and reliability on both simulated, low-detail small-size region-of-interest (ROI) brain lesions and realistic, high-detail large-size ROI brain tumor MRIs.

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.

SNR-enhanced diffusion MRI with structure-preserving low-rank denoising in reproducing kernel Hilbert spaces

PURPOSE

To introduce, develop, and evaluate a novel denoising technique for diffusion MRI that leverages nonlinear redundancy in the data to boost the SNR while preserving signal information.

METHODS

We exploit nonlinear redundancy of the dMRI data by means of kernel principal component analysis (KPCA), a nonlinear generalization of PCA to reproducing kernel Hilbert spaces. By mapping the signal to a high-dimensional space, a higher level of redundant information is exploited, thereby enabling better denoising than linear PCA. We implement KPCA with a Gaussian kernel, with parameters automatically selected from knowledge of the noise statistics, and validate it on realistic Monte Carlo simulations as well as with in vivo human brain submillimeter and low-resolution dMRI data. We also demonstrate KPCA denoising on multi-coil dMRI data.

RESULTS

SNR improvements up to 2.7 × were obtained in real in vivo datasets denoised with KPCA, in comparison to SNR gains of up to 1.8 × using a linear PCA denoising technique called Marchenko-Pastur PCA (MPPCA). Compared to gold-standard dataset references created from averaged data, we showed that lower normalized root mean squared error was achieved with KPCA compared to MPPCA. Statistical analysis of residuals shows that anatomical information is preserved and only noise is removed. Improvements in the estimation of diffusion model parameters such as fractional anisotropy, mean diffusivity, and fiber orientation distribution functions were also demonstrated.

CONCLUSION

Nonlinear redundancy of the dMRI signal can be exploited with KPCA, which allows superior noise reduction/SNR improvements than the MPPCA method, without loss of signal information.

Joint low-rank prior and difference of Gaussian filter for magnetic resonance image denoising

The low-rank matrix approximation (LRMA) is an efficient image denoising method to reduce additive Gaussian noise. However, the existing low-rank matrix approximation does not perform well in terms of Rician noise removal for magnetic resonance imaging (MRI). To this end, we propose a novel MR image denoising approach based on the extended difference of Gaussian (DoG) filter and nonlocal low-rank regularization. In the proposed method, a novel nonlocal self-similarity evaluation with the tight frame is exploited to improve the patch matching. To remove the Rician noise and preserve the edge details, the extended DoG filter is exploited to the nonlocal low-rank regularization model. The experimental results demonstrate that the proposed method can preserve more edge and fine structures while removing noise in MR image as compared with some of the existing methods.

Design of an Intraoral Dipole Antenna for Dental Applications

OBJECTIVE

In dental MRI, intraoral coils provide higher signal-to-noise ratio (SNR) than coils placed outside the mouth. This study aims to design an intraoral dipole antenna and demonstrates the feasibility of combining it with an extraoral coil.

METHODS

Dipole antenna design was chosen over loop design, as it is open toward the distal; therefore, it does not restrain tongue movement. The dipole design offers also an increased depth-of-sensitivity that allows for MRI of dental roots. Different dipole antenna designs were simulated using a finite-difference-time-domain approach. Ribbon, wire, and multi-wire arms were compared. The best design was improved further by covering the ends of the dipole arms with a high-permittivity material. Phantom and in vivo measurements were conducted on a 3T clinical MRI system.

RESULTS

The best transmit efficiency and homogeneity was achieved with a multi-wire curved dipole antenna with 7 wires for each arm. With an additional high-permittivity cap the transmit field inhomogeneity was further reduced from 20% to 5% along the dipole arm. When combined with extraoral flexible surface-coil, the coupling between the coils was less than -32dB and SNR was increased.

CONCLUSION

Using intraoral dipole design instead of loop improves patient comfort. We demonstrated feasibility of the intraoral dipole combined with an extraoral flexible coil-array for dental MRI. Dipole antenna enabled decreasing imaging field-of-view, and reduced the prevalent signal from tongue.

SIGNIFICANCE

This study highlights the advantages and the main challenges of the intraoral RF coils and describes a novel RF coil that addresses those challenges.

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.

Contemporary approaches to high-field magnetic resonance imaging with large field inhomogeneity

Despite its importance as a clinical imaging modality, magnetic resonance imaging remains inaccessible to most of the world's population due to its high cost and infrastructure requirements. Substantial effort is underway to develop portable, low-cost systems able to address MRI access inequality and to enable new uses of MRI such as bedside imaging. A key barrier to development of portable MRI systems is increased magnetic field inhomogeneity when using small polarizing magnets, which degrades image quality through distortions and signal dropout. Many approaches address field inhomogeneity by using a low polarizing field, approximately ten to hundreds of milli-Tesla. At low-field, even a large relative field inhomogeneity of several thousand parts-per-million (ppm) results in resonance frequency dispersion of only 1-2 kHz. Under these conditions, with necessarily wide pulse bandwidths, fast spin-echo sequences may be used at low field with negligible subject heating, and a broad range of other available imaging sequences can be implemented. However, high-field MRI, 1.5 T or greater, can provide substantially improved signal-to-noise ratio and image contrast, so that higher spatial resolution, clinical quality images may be acquired in significantly less time than is necessary at low-field. The challenge posed by small, high-field systems is that the relative field inhomogeneity, still thousands of ppm, becomes tens of kilohertz over the imaging volume. This article describes the physical consequences of field inhomogeneity on established gradient- and spin-echo MRI sequences, and suggests ways to reduce signal dropout and image distortion from field inhomogeneity. Finally, the practicality of currently available image contrasts is reviewed when imaging with a high magnetic field with large inhomogeneity.

Towards quantitative evaluation of wall shear stress from 4D flow imaging

Wall shear stress (WSS) is a relevant hemodynamic indicator of the local stress applied on the endothelium surface. More specifically, its spatiotemporal distribution reveals crucial in the evolution of many pathologies such as aneurysm, stenosis, and atherosclerosis. This paper introduces a new solution, called PaLMA, to quantify the WSS from 4D Flow MRI data. It relies on a two-step local parametric model, to accurately describe the vessel wall and the velocity-vector field in the neighborhood of a given point of interest. Extensive validations have been performed on synthetic 4D Flow MRI data, including four datasets generated from patient specific computational fluid dynamics simulations on carotids. The validation tests are focused on the impact of the noise component, of the resolution level, and of the segmentation accuracy concerning the vessel position in the context of complex flow patterns. In simulated cases aimed to reproduce clinical acquisition conditions, the WSS quantification performance reached by PaLMA is significantly higher (with a gain in RMSE of 12 to 27%) than the reference one obtained using the smoothing B-spline method proposed by Potters et al. (2015) method, while the computation time is equivalent for both WSS quantification methods.

Efficient spiral in-out and EPI balanced steady-state free precession cine imaging using a high-performance 0.55T MRI

PURPOSE

Low-field MRI offers favorable physical properties for SNR-efficient long readout acquisitions such as spiral and EPI. We used a 0.55 tesla (T) MRI system equipped with high-performance hardware to increase the sampling duty cycle and extend the TR of balanced steady-state free precession (bSSFP) cardiac cine acquisitions, which typically are limited by banding artifacts.

METHODS

We developed a high-efficiency spiral in-out bSSFP acquisition, with zeroth- and first-gradient moment nulling, and an EPI bSSFP acquisition for cardiac cine imaging using a contemporary MRI system modified to operate at 0.55T. Spiral in-out and EPI bSSFP cine protocols, with TR = 8 ms, were designed to maintain both spatiotemporal resolution and breath-hold length. Simulations, phantom imaging, and healthy volunteer imaging studies (n = 12) were performed to assess SNR and image quality using these high sampling duty-cycle bSSFP sequences.

RESULTS

Spiral in-out bSSFP performed favorably at 0.55T and generated good image quality, whereas EPI bSSFP suffered motion and flow artifacts. There was no difference in ejection fraction comparing spiral in-out with standard Cartesian imaging. Moreover, human images demonstrated a 79% ± 21% increase in myocardial SNR using spiral in-out bSSFP and 50% ± 14% increase in SNR using EPI bSSFP as compared with the reference Cartesian acquisition. Spiral in-out acquisitions at 0.55T recovered 69% ± 14% of the myocardial SNR at 1.5T.

CONCLUSION

Efficient bSSFP spiral in-out provided high-quality cardiac cine imaging and SNR recovery on a high-performance 0.55T MRI system.

Number of subjects required in common study designs for functional GABA magnetic resonance spectroscopy in the human brain at 3 Tesla

Magnetic resonance spectroscopy (MRS) is a research tool for measuring the concentration of metabolites such as γ-aminobutyric acid (GABA) and glutamate in the brain. MEGA-PRESS has been the preferred pulse sequence for GABA measurements due to low physiological GABA concentrations, hence low signal. To compensate, researchers incorporate long acquisition durations (7-10 min) making functional measurements of this metabolite challenging. Here, the acquisition duration and sample sizes required to detect specific concentration changes in GABA using MEGA-PRESS at 3 T are presented for both between-groups and within-session study designs. 75 spectra were acquired during rest using MEGA-PRESS from 41 healthy volunteers in 6 different brain regions at 3 T with voxel sizes between 13 and 22 cm3 . Between-group and within-session variance was calculated for different acquisition durations and power calculations were performed to determine the number of subjects required to detect a given percentage change in GABA/NAA signal ratio. Within-subject variability was assessed by sampling different segments of a single acquisition. Power calculations suggest that detecting a 15% change in GABA using a 2 min acquisition and a 27 cm3 voxel size, depending on the region, requires between 8 and 93 subjects using a within-session design. A between-group design typically requires more participants to detect the same difference. In brain regions with suboptimal shimming, the subject numbers can be up to 4-fold more. Collecting data for longer than 4 min in brain regions examined in this study is deemed unnecessary, as variance in the signal did not reduce further for longer durations.

Fast submillimeter diffusion MRI using gSlider-SMS and SNR-enhancing joint reconstruction

PURPOSE

We evaluate a new approach for achieving diffusion MRI data with high spatial resolution, large volume coverage, and fast acquisition speed.

THEORY AND METHODS

A recent method called gSlider-SMS enables whole-brain submillimeter diffusion MRI with high signal-to-noise ratio (SNR) efficiency. However, despite the efficient acquisition, the resulting images can still suffer from low SNR due to the small size of the imaging voxels. This work proposes to mitigate the SNR problem by combining gSlider-SMS with a regularized SNR-enhancing reconstruction approach.

RESULTS

Illustrative results show that, from gSlider-SMS data acquired over a span of only 25 minutes on a 3T scanner, the proposed method is able to produce 71 MRI images (64 diffusion encoding orientations with b = 1500 s/ mm 2 , and 7 images without diffusion weighting) of the entire in vivo human brain with nominal 0.66 mm spatial resolution. Using data acquired from 75 minutes of acquisition as a gold standard reference, we demonstrate that the proposed SNR-enhancement procedure leads to substantial improvements in estimated diffusion parameters compared to conventional gSlider reconstruction. Results also demonstrate that the proposed method has advantages relative to denoising methods based on low-rank matrix modeling. A theoretical analysis of the trade-off between spatial resolution and SNR suggests that the proposed approach has high efficiency.

CONCLUSIONS

The combination of gSlider-SMS with advanced regularized reconstruction enables high-resolution quantitative diffusion MRI from a relatively fast acquisition.

Opportunities in Interventional and Diagnostic Imaging by Using High-Performance Low-Field-Strength MRI

Background Commercial low-field-strength MRI systems are generally not equipped with state-of-the-art MRI hardware, and are not suitable for demanding imaging techniques. An MRI system was developed that combines low field strength (0.55 T) with high-performance imaging technology. Purpose To evaluate applications of a high-performance low-field-strength MRI system, specifically MRI-guided cardiovascular catheterizations with metallic devices, diagnostic imaging in high-susceptibility regions, and efficient image acquisition strategies. Materials and Methods A commercial 1.5-T MRI system was modified to operate at 0.55 T while maintaining high-performance hardware, shielded gradients (45 mT/m; 200 T/m/sec), and advanced imaging methods. MRI was performed between January 2018 and April 2019. T1, T2, and T2* were measured at 0.55 T; relaxivity of exogenous contrast agents was measured; and clinical applications advantageous at low field were evaluated. Results There were 83 0.55-T MRI examinations performed in study participants (45 women; mean age, 34 years ± 13). On average, T1 was 32% shorter, T2 was 26% longer, and T2* was 40% longer at 0.55 T compared with 1.5 T. Nine metallic interventional devices were found to be intrinsically safe at 0.55 T (<1°C heating) and MRI-guided right heart catheterization was performed in seven study participants with commercial metallic guidewires. Compared with 1.5 T, reduced image distortion was shown in lungs, upper airway, cranial sinuses, and intestines because of improved field homogeneity. Oxygen inhalation generated lung signal enhancement of 19% ± 11 (standard deviation) at 0.55 T compared with 7.6% ± 6.3 at 1.5 T (P = .02; five participants) because of the increased T1 relaxivity of oxygen (4.7e-4 mmHg-1sec-1). Efficient spiral image acquisitions were amenable to low field strength and generated increased signal-to-noise ratio compared with Cartesian acquisitions (P < .02). Representative imaging of the brain, spine, abdomen, and heart generated good image quality with this system. Conclusion This initial study suggests that high-performance low-field-strength MRI offers advantages for MRI-guided catheterizations with metal devices, MRI in high-susceptibility regions, and efficient imaging. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Grist in this issue.

Low-field MRI: An MR physics perspective

Historically, clinical MRI started with main magnetic field strengths in the ∼0.05-0.35T range. In the past 40 years there have been considerable developments in MRI hardware, with one of the primary ones being the trend to higher magnetic fields. While resulting in large improvements in data quality and diagnostic value, such developments have meant that conventional systems at 1.5 and 3T remain relatively expensive pieces of medical imaging equipment, and are out of the financial reach for much of the world. In this review we describe the current state-of-the-art of low-field systems (defined as 0.25-1T), both with respect to its low cost, low foot-print, and subject accessibility. Furthermore, we discuss how low field could potentially benefit from many of the developments that have occurred in higher-field MRI. In the first section, the signal-to-noise ratio (SNR) dependence on the static magnetic field and its impact on the achievable contrast, resolution, and acquisition times are discussed from a theoretical perspective. In the second section, developments in hardware (eg, magnet, gradient, and RF coils) used both in experimental low-field scanners and also those that are currently in the market are reviewed. In the final section the potential roles of new acquisition readouts, motion tracking, and image reconstruction strategies, currently being developed primarily at higher fields, are presented. Level of Evidence: 5 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019.

On the analysis of rapidly sampled fMRI data

Recent advances in parallel imaging and simultaneous multi-slice techniques have permitted whole-brain fMRI acquisitions at sub-second sampling intervals, without significantly sacrificing the spatial coverage and resolution. Apart from probing brain function at finer temporal scales, faster sampling rates may potentially lead to enhanced functional sensitivity, owing possibly to both cleaner neural representations (due to less aliased physiological noise) and additional statistical benefits (due to more degrees of freedom for a fixed scan duration). Accompanying these intriguing aspects of fast acquisitions, however, confusion has also arisen regarding (1) how to preprocess/analyze these fast fMRI data, and (2) what exactly is the extent of benefits with fast acquisitions, i.e., how fast is fast enough for a specific research aim? The first question is motivated by the altered spectral distribution and noise characteristics at short sampling intervals, while the second question seeks to reconcile the complicated trade-offs between the functional contrast-to-noise ratio and the effective degrees of freedom. Although there have been recent efforts to empirically approach different aspects of these two questions, in this work we discuss, from a theoretical perspective accompanied by some illustrative, proof-of-concept experimental in vivo human fMRI data, a few considerations that are rarely mentioned, yet are important for both preprocessing and optimizing statistical inferences for studies that employ acquisitions with sub-second sampling intervals. Several summary recommendations include concerns regarding advisability of relying on low-pass filtering to de-noise physiological contributions, employment of statistical models with sufficient complexity to account for the substantially increased serial correlation, and cautions regarding using rapid sampling to enhance functional sensitivity given that different analysis models may associate with distinct trade-offs between contrast-to-noise ratios and the effective degrees of freedom. As an example, we demonstrate that as TR shortens, the intrinsic differences in how noise is accommodated in general linear models and Pearson correlation analyses (assuming Gaussian distributed stochastic signals and noise) can result in quite different outcomes, either gaining or losing statistical power.

An Automatic Parameter Decision System of Bilateral Filtering with GPU-Based Acceleration for Brain MR Images

Bilateral filters have been extensively utilized in a number of image denoising applications such as segmentation, registration, and tissue classification. However, it requires burdensome adjustments of the filter parameters to achieve the best performance for each individual image. To address this problem, this paper proposes a computer-aided parameter decision system based on image texture features associated with neural networks. In our approach, parallel computing with the GPU architecture is first developed to accelerate the computation of the conventional bilateral filter. Subsequently, a back propagation network (BPN) scheme using significant image texture features as the input is established to estimate the GPU-based bilateral filter parameters and its denoising process. The k-fold cross validation method is exploited to evaluate the performance of the proposed automatic restoration framework. A wide variety of T1-weighted brain MR images were employed to train and evaluate this parameter-free decision system with GPU-based bilateral filtering, which resulted in a speed-up factor of 208 comparing to the CPU-based computation. The proposed filter parameter prediction system achieved a mean absolute percentage error (MAPE) of 6% and was classified as "high accuracy". Our automatic denoising framework dramatically removed noise in numerous brain MR images and outperformed several state-of-the-art methods based on the peak signal-to-noise ratio (PSNR). The usage of image texture features associated with the BPN to estimate the GPU-based bilateral filter parameters and to automate the denoising process is feasible and validated. It is suggested that this automatic restoration system is advantageous to various brain MR image-processing applications.

An automatic restoration framework based on GPU-accelerated collateral filtering in brain MR images

BACKGROUND

Image restoration is one of the fundamental and essential tasks within image processing. In medical imaging, developing an effective algorithm that can automatically remove random noise in brain magnetic resonance (MR) images is challenging. The collateral filter has been shown a more powerful algorithm than many existing methods. However, the computation of the collateral filter is more time-consuming and the selection of the filter parameters is also laborious. This paper proposes an automatic noise removal system based on the accelerated collateral filter for brain MR images.

METHODS

To solve these problems, we first accelerated the collateral filter with parallel computing using the graphics processing unit (GPU) architecture. We adopted the compute unified device architecture (CUDA), an application programming interface for the GPU by NVIDIA, to hasten the computation. Subsequently, the optimal filter parameters were selected and the automation was achieved by artificial neural networks. Specifically, an artificial neural network system associated with image feature analysis was adopted to establish the automatic image restoration framework. The best feature combination was selected by the paired t-test and the sequential forward floating selection (SFFS) methods.

RESULTS

Experimental results indicated that not only did the proposed automatic image restoration algorithm perform dramatically faster than the traditional collateral filter, but it also effectively removed the noise in a wide variety of brain MR images. A speed up gain of 34 was attained to process an MR image, which completed within 0.1 s. Representative illustrations of brain tumor images demonstrated the capability of identifying lesion boundaries, which outperformed many existing methods.

CONCLUSIONS

We believe that our accelerated and automated restoration framework is promising for achieving robust filtering in many brain MR image restoration applications.

Optimization of magnetization-prepared rapid gradient echo (MP-RAGE) sequence for neonatal brain MRI

BACKGROUND

Sequence optimization in neonates might improve detection sensitivity of abnormalities for a variety of conditions. However this has been historically challenging because tissue properties such as the longitudinal relaxation time and proton density differ significantly between neonates and adults.

OBJECTIVE

To optimize the magnetization-prepared rapid gradient echo (MP-RAGE) sequence to enhance both signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) efficiencies.

MATERIALS AND METHODS

We optimized neonatal MP-RAGE sequence through (1) reducing receive bandwidth to decrease noise, (2) shortening acquisition train length (acquisition number per repetition time or total number of read-out radiofrequency rephrasing pulses) using slice partial Fourier acquisition and (3) simulating the solution of Bloch's equation under optimal receive bandwidth and acquisition train length. Using the optimized sequence parameters, we scanned 12 healthy full-term infants within 2 weeks of birth and four preterm infants at 40 weeks' corrected age.

RESULTS

Compared with a previously published neonatal protocol, we were able to reduce the total scan time by reduce the total scan time by 60% and increase the average SNR efficiency by 160% (P<0.001) and the average CNR efficiency by 26% (P=0.029).

CONCLUSION

Our in vivo neonatal brain imaging experiments confirmed that both SNR and CNR efficiencies significantly increased with our proposed protocol. Our proposed optimization methodology could be readily extended to other populations (e.g., older children, adults), as well as different organ systems, field strengths and MR sequences.

A perspective on a rapid and radiation-free tracer imaging modality, magnetic particle imaging, with promise for clinical translation

Magnetic particle imaging (MPI), introduced at the beginning of the twenty-first century, is emerging as a promising diagnostic tool in addition to the current repertoire of medical imaging modalities. Using superparamagnetic iron oxide nanoparticles (SPIOs), that are available for clinical use, MPI produces high contrast and highly sensitive tomographic images with absolute quantitation, no tissue attenuation at-depth, and there are no view limitations. The MPI signal is governed by the Brownian and Néel relaxation behavior of the particles. The relaxation time constants of these particles can be utilized to map information relating to the local microenvironment, such as viscosity and temperature. Proof-of-concept pre-clinical studies have shown favourable applications of MPI for better understanding the pathophysiology associated with vascular defects, tracking cell-based therapies and nanotheranostics. Functional imaging techniques using MPI will be useful for studying the pathology related to viscosity changes such as in vascular plaques and in determining cell viability of superparamagnetic iron oxide nanoparticle labeled cells. In this review article, an overview of MPI is provided with discussions mainly focusing on MPI tracers, applications of translational capabilities ranging from diagnostics to theranostics and finally outline a promising path towards clinical translation.

A Bayesian Multiresolution Approach for Noise Removal in Medical Magnetic Resonance Images

A Bayesian approach using wavelet coefficient modeling is proposed for de-noising additive white Gaussian noise in medical magnetic resonance imaging (MRI). In a parallel acquisition process, the magnetic resonance image is affected by white Gaussian noise, which is additive in nature. A normal inverse Gaussian probability distribution function is taken for modeling the wavelet coefficients. A Bayesian approach is implemented for filtering the noisy wavelet coefficients. The maximum likelihood estimator and median absolute deviation estimator are used to find the signal parameters, signal variances, and noise variances of the distribution. The minimum mean square error estimator is used for estimating the true wavelet coefficients. The proposed method is simulated on MRI. Performance and image quality parameters show that the proposed method has the capability to reduce the noise more effectively than other state-of-the-art methods. The proposed method provides 8.83%, 2.02%, 6.61%, and 30.74% improvement in peak signal-to-noise ratio, structure similarity index, Pratt’s figure of merit, and Bhattacharyya coefficient, respectively, over existing well-accepted methods. The effectiveness of the proposed method is evaluated by using the mean squared difference (MSD) parameter. MSD shows the degree of dissimilarity and is 0.000324 for the proposed method, which is less than that of the other existing methods and proves the effectiveness of the proposed method. Experimental results show that the proposed method is capable of achieving better signal-to-noise ratio performance than other tested de-noising methods.

BLIPPED (BLIpped Pure Phase EncoDing) high resolution MRI with low amplitude gradients

MRI image resolution is proportional to the maximum k-space value, i.e. the temporal integral of the magnetic field gradient. High resolution imaging usually requires high gradient amplitudes and/or long spatial encoding times. Special gradient hardware is often required for high amplitudes and fast switching. We propose a high resolution imaging sequence that employs low amplitude gradients. This method was inspired by the previously proposed PEPI (π Echo Planar Imaging) sequence, which replaced EPI gradient reversals with multiple RF refocusing pulses. It has been shown that when the refocusing RF pulse is of high quality, i.e. sufficiently close to 180°, the magnetization phase introduced by the spatial encoding magnetic field gradient can be preserved and transferred to the following echo signal without phase rewinding. This phase encoding scheme requires blipped gradients that are identical for each echo, with low and constant amplitude, providing opportunities for high resolution imaging. We now extend the sequence to 3D pure phase encoding with low amplitude gradients. The method is compared with the Hybrid-SESPI (Spin Echo Single Point Imaging) technique to demonstrate the advantages in terms of low gradient duty cycle, compensation of concomitant magnetic field effects and minimal echo spacing, which lead to superior image quality and high resolution. The 3D imaging method was then applied with a parallel plate resonator RF probe, achieving a nominal spatial resolution of 17 μm in one dimension in the 3D image, requiring a maximum gradient amplitude of only 5.8 Gauss/cm.

The Empirical Effect of Gaussian Noise in Undersampled MRI Reconstruction

In Fourier-based medical imaging, sampling below the Nyquist rate results in an underdetermined system, in which a linear reconstruction will exhibit artifacts. Another consequence is lower signal-to-noise ratio (SNR) because of fewer acquired measurements. Even if one could obtain information to perfectly disambiguate the underdetermined system, the reconstructed image could still have lower image quality than a corresponding fully sampled acquisition because of reduced measurement time. The coupled effects of low SNR and underdetermined system during reconstruction makes it difficult to isolate the impact of low SNR on image quality. To this end, we present an image quality prediction process that reconstructs fully sampled, fully determined data with noise added to simulate the SNR loss induced by a given undersampling pattern. The resulting prediction image empirically shows the effects of noise in undersampled image reconstruction without any effect from an underdetermined system. We discuss how our image quality prediction process simulates the distribution of noise for a given undersampling pattern, including variable density sampling that produces colored noise in the measurement data. An interesting consequence of our prediction model is that recovery from an underdetermined nonuniform sampling is equivalent to a weighted least squares optimization that accounts for heterogeneous noise levels across measurements. Through experiments with synthetic and in vivo datasets, we demonstrate the efficacy of the image quality prediction process and show that it provides a better estimation of reconstruction image quality than the corresponding fully sampled reference image.

Optimal Broadband Noise Matching to Inductive Sensors: Application to Magnetic Particle Imaging

Inductive sensor-based measurement techniques are useful for a wide range of biomedical applications. However, optimizing the noise performance of these sensors is challenging at broadband frequencies, owing to the frequency-dependent reactance of the sensor. In this work, we describe the fundamental limits of noise performance and bandwidth for these sensors in combination with a low-noise amplifier. We also present three equivalent methods of noise matching to inductive sensors using transformer-like network topologies. Finally, we apply these techniques to improve the noise performance in magnetic particle imaging, a new molecular imaging modality with excellent detection sensitivity. Using a custom noise-matched amplifier, we experimentally demonstrate an 11-fold improvement in noise performance in a small animal magnetic particle imaging scanner.