Spatial Resolution Of Magnetic Resonance Imaging Research Articles

3D‐SRDM: 3D super‐resolution of MRI volumes based on diffusion model

AbstractBrain magnetic resonance imaging (MRI) is crucial for diagnosing and understanding neurological disorders, but inherent limitations hinder the visualization of fine details in brain structures. The emergence of super‐resolution techniques, especially deep‐learning methods, has improved imaging quality of MRI, by increasing MRI spatial resolution. At present, deep‐learning algorithms mostly performed super‐resolution on 2D MRI images. However, considering 3D nature of MRI, 3D models are more suitable for brain MRI super‐resolution. To achieve finer brain structural details, this study proposes a 3D brain MRI super‐resolution method based on diffusion model (3D‐SRDM), which is a fast and easily trainable neural network for the generation of high‐resolution brain MRI images. In our 3D‐SRDM model, the self‐attention module in U‐Net is replaced with 3D spatial attention mechanism. The network structure of 3D‐SRDM is optimized to reduce training parameters. Moreover, accelerated sampling from denoising diffusion implicit model is also incorporated to reduce time consumption. By these optimizations, compared with original diffusion model, the proposed model can achieve about 10‐ and 5‐fold speed increase at 4× and 8× super‐resolution of 3D brain MRI volumes, respectively, almost without affecting image quality. Thus, 3D‐SRDM has potential application value in efficiently generating high‐resolution 3D brain MRI images, thus facilitating the doctors' diagnosis.

The impact of 4D-Flow MRI spatial resolution on patient-specific CFD simulations of the thoracic aorta

Magnetic Resonance Imaging (MRI) is considered the gold standard of medical imaging technologies as it allows for accurate imaging of blood vessels. 4-Dimensional Flow Magnetic Resonance Imaging (4D-Flow MRI) is built on conventional MRI, and provides flow data in the three vector directions and a time resolved magnitude data set. As such it can be used to retrospectively calculate haemodynamic parameters of interest, such as Wall Shear Stress (WSS). However, multiple studies have indicated that a significant limitation of the imaging technique is the spatiotemporal resolution that is currently available. Recent advances have proposed and successfully integrated 4D-Flow MRI imaging techniques with Computational Fluid Dynamics (CFD) to produce patient-specific simulations that have the potential to aid in treatments,surgical decision making, and risk stratification. However, the consequences of using insufficient 4D-Flow MRI spatial resolutions on any patient-specific CFD simulations is currently unclear, despite being a recognised limitation. The research presented in this study aims to quantify the inaccuracies in patient-specific 4D-Flow MRI based CFD simulations that can be attributed to insufficient spatial resolutions when acquiring 4D-Flow MRI data. For this research, a patient has undergone four 4D-Flow MRI scans acquired at various isotropic spatial resolutions and patient-specific CFD simulations have subsequently been run using geometry and velocity data produced from each scan. It was found that compared to CFD simulations based on a 1.5,{text {mm}} times 1.5,{text {mm}} times 1.5,{text {mm}}, using a spatial resolution of 4,{text {mm}} times 4,{text {mm}} times 4,{text {mm}} substantially underestimated the maximum velocity magnitude at peak systole by 110.55%. The impacts of 4D-Flow MRI spatial resolution on WSS calculated from CFD simulations have been investigated and it has been shown that WSS is underestimated in CFD simulations that are based on a coarse 4D-Flow MRI spatial resolution. The authors have concluded that a minimum 4D-Flow MRI spatial resolution of 1.5,{text {mm}} times 1.5,{text {mm}} times 1.5,{text {mm}} must be used when acquiring 4D-Flow MRI data to perform patient-specific CFD simulations. A coarser spatial resolution will produce substantial differences within the flow field and geometry.

Scan-Specific Generative Neural Network for MRI Super-Resolution Reconstruction.

The interpretation and analysis of Magnetic resonance imaging (MRI) benefit from high spatial resolution. Unfortunately, direct acquisition of high spatial resolution MRI is time-consuming and costly, which increases the potential for motion artifact, and suffers from reduced signal-to-noise ratio (SNR). Super-resolution reconstruction (SRR) is one of the most widely used methods in MRI since it allows for the trade-off between high spatial resolution, high SNR, and reduced scan times. Deep learning has emerged for improved SRR as compared to conventional methods. However, current deep learning-based SRR methods require large-scale training datasets of high-resolution images, which are practically difficult to obtain at a suitable SNR. We sought to develop a methodology that allows for dataset-free deep learning-based SRR, through which to construct images with higher spatial resolution and of higher SNR than can be practically obtained by direct Fourier encoding. We developed a dataset-free learning method that leverages a generative neural network trained for each specific scan or set of scans, which in turn, allows for SRR tailored to the individual patient. With the SRR from three short duration scans, we achieved high quality brain MRI at an isotropic spatial resolution of 0.125 cubic mm with six minutes of imaging time for T2 contrast and an average increase of 7.2 dB (34.2%) in SNR to these short duration scans. Motion compensation was achieved by aligning the three short duration scans together. We assessed our technique on simulated MRI data and clinical data acquired from 15 subjects. Extensive experimental results demonstrate that our approach achieved superior results to state-of-the-art methods, while in parallel, performed at reduced cost as scans delivered with direct high-resolution acquisition.

Quantitative Magnetic Resonance Imaging of the Pancreas of Individuals With Diabetes.

Magnetic resonance imaging (MRI) has the potential to improve our understanding of diabetes and improve both diagnosis and monitoring of the disease. Although the spatial resolution of MRI is insufficient to directly image the endocrine pancreas in people, the increasing awareness that the exocrine pancreas is also involved in diabetes pathogenesis has spurred new MRI applications. These techniques build upon studies of exocrine pancreatic diseases, for which MRI has already developed into a routine clinical tool for diagnosis and monitoring of pancreatic cancer and pancreatitis. By adjusting the imaging contrast and carefully controlling image acquisition and processing, MRI can quantify a variety of tissue pathologies. This review introduces a number of quantitative MRI techniques that have been applied to study the diabetic pancreas, summarizes progress in validating and standardizing each technique, and discusses the need for image analyses that account for spatial heterogeneity in the pancreas.

Vessel Wall Magnetic Resonance Imaging Biomarkers of Symptomatic Intracranial Atherosclerosis: A Meta-Analysis.

Intracranial atherosclerotic disease is a common cause of stroke worldwide. Intracranial vessel wall magnetic resonance imaging may be able to identify imaging biomarkers of symptomatic plaque. We performed a meta-analysis to evaluate the strength of association of imaging features of symptomatic plaque leading to downstream ischemic events. Effects on the strength of association were also assessed accounting for possible sources of bias and variability related to study design and magnetic resonance parameters. PubMed, Scopus, Web of Science, EMBASE, and Cochrane databases were searched up to October 2019. Two independent reviewers extracted data on study design, vessel wall magnetic resonance imaging techniques, and imaging end points. Per-lesion odds ratios (OR) were calculated and pooled using a bivariate random-effects model. Subgroup analyses, sensitivity analysis, and evaluation of publication bias were also performed. Twenty-one articles met inclusion criteria (1750 lesions; 1542 subjects). Plaque enhancement (OR, 7.42 [95% CI, 3.35-16.43]), positive remodeling (OR, 5.60 [95% CI, 2.23-14.03]), T1 hyperintensity (OR, 2.05 [95% CI, 1.27-3.32]), and surface irregularity (OR, 4.50 [95% CI, 1.39-8.57]) were significantly associated with downstream ischemic events. T2 signal intensity was not significant (P=0.59). Plaque enhancement was significantly associated with downstream ischemic events in all subgroup analyses and showed stronger associations when measured in retrospectively designed studies (P=0.02), by a radiologist as a rater (P<0.001), and on lower vessel wall magnetic resonance imaging spatial resolution sequences (P=0.02). Plaque enhancement, positive remodeling, T1 hyperintensity, and surface irregularity emerged as strong imaging biomarkers of symptomatic plaque in patients with ischemic events. Plaque enhancement remained significant accounting for sources of bias and variability in both study design and instrument. Future studies evaluating plaque enhancement as a predictive marker for stroke recurrence with larger sample sizes would be valuable.

CerebrA, registration and manual label correction of Mindboggle-101 atlas for MNI-ICBM152 template

Accurate anatomical atlases are recognized as important tools in brain-imaging research. They are widely used to estimate disease-specific changes and therefore, are of great relevance in extracting regional information on volumetric variations in clinical cohorts in comparison to healthy populations. The use of high spatial resolution magnetic resonance imaging and the improvement in data preprocessing methods have enabled the study of structural volume changes on a wide range of disorders, particularly in neurodegenerative diseases where different brain morphometry analyses are being broadly used in an effort to improve diagnostic biomarkers. In the present dataset, we introduce the Cerebrum Atlas (CerebrA) along with the MNI-ICBM2009c average template. MNI-ICBM2009c is the most recent version of the MNI-ICBM152 brain average, providing a higher level of anatomical details. Cerebra is based on an accurate non-linear registration of cortical and subcortical labelling from Mindboggle 101 to the symmetric MNI-ICBM2009c atlas, followed by manual editing.

Role of Breast MRI in the Evaluation and Detection of DCIS: Opportunities and Challenges.

Historically, breast magnetic resonance imaging (MRI) was not considered an effective modality in the evaluation of ductal carcinoma in situ (DCIS). Over the past decade this has changed, with studies demonstrating that MRI is the most sensitive imaging tool for detection of all grades of DCIS. It has been suggested that not only is breast MRI the most sensitive imaging tool for detection but it may also detect the most clinically relevant DCIS lesions. The role and outcomes of MRI in the preoperative setting for patients with DCIS remains controversial; however, several studies have shown benefit in the preoperative evaluation of extent of disease as well as predicting an underlying invasive component. The most common presentation of DCIS on MRI is nonmass enhancement (NME) in a linear or segmental distribution pattern. Maximizing breast MRI spatial resolution is therefore beneficial, given the frequent presentation of DCIS as NME on MRI. Emerging MRI techniques, such as diffusion-weighted imaging (DWI), have shown promising potential to discriminate DCIS from benign and invasive lesions. Future opportunities including advanced imaging visual techniques, radiomics/radiogenomics, and machine learning / artificial intelligence may also be applicable to the detection and treatment of DCIS. Level of Evidence: 3 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2020;52:697-709.

Identification of sampling patterns for high-resolution compressed sensing MRI of porous materials: 'learning' from X-ray microcomputed tomography data.

There exists a strong motivation to increase the spatial resolution of magnetic resonance imaging (MRI) acquisitions so that MRI can be used as a microscopy technique in the study of porous materials. This work introduces a method for identifying novel data sampling patterns to achieve undersampling schemes for compressed sensing MRI (CS-MRI) acquisitions, enabling 3D spatial resolutions of 17.6 µm to be achieved. A data-driven learning approach is used to derive k-space undersampling schemes for 3D MRI acquisitions from 3D X-ray microcomputed tomography (µCT) datasets acquired at a higher spatial resolution than can be acquired using MRI. The performance of the new sampling approach was compared to other, well-established sampling strategies using simulated MRI data obtained from high-resolution µCT images of rock core plugs. These simulations were performed for a range of different k-space sampling fractions (0.125-0.375) using images of Ketton limestone. The method was then extended to consideration of imaging Estaillades limestone and Fontainebleau sandstone. The results show that the new sampling approach performs as well as or better than conventional variable density sampling and without need for time-consuming parameter optimisation. Further, a bespoke sampling pattern is produced for each rock type. The novel undersampling strategy was employed to acquire 3D magnetic resonance images of a Ketton limestone rock at spatial resolutions of 35 and 17.6 µm. The ability of the k-space sampling scheme produced using the new approach in enabling reconstruction of the pore space characteristics of the rock was then demonstrated by benchmarking against the pore space statistics obtained from high-resolution µCT data. The MRI data acquired at 17.6 µm resolution gave excellent agreement with the pore size distribution obtained from the X-ray microcomputed tomography dataset, while the pore coordination number distribution obtained from the MRI data was slightly skewed to lower coordination numbers. This approach provides a method of producing a k-space undersampling pattern for MRI acquisition at a spatial resolution for which a fully sampled acquisition at that spatial resolution would be impractically long. The approach can be easily extended to other CS-MRI techniques, such as spatially resolved flow and relaxation time mapping. LAY DESCRIPTION: Magnetic resonance imaging (MRI) is widely used to study the microstructure of, and fluid transport phenomena in porous media relevant for engineering applications. A major application is the study of water and hydrocarbon transport in porous sedimentary rocks, which typically have pore sizes smaller than 100 µm. The spatial resolution of routine MRI acquisitions, however, is limited to several hundred µm due to the relatively low sensitivity of the magnetic resonance method. Therefore, there exists a strong motivation to increase the spatial resolution of MRI by one to two orders of magnitude to be able to study these rocks at a pore scale. This work reports the initial step towards achieving this. Three-dimensional images of rock pore structure are acquired at both 35 and 17.6 µm spatial resolution. In ongoing work, these methods are now being incorporated into magnetic resonance velocity imaging methods, thereby enabling imaging of both pore structure and hydrodynamics at these much higher spatial resolutions than were hitherto possible. Although X-ray microcomputed tomography (µCT) produces high spatial resolution images, it is far more limited in being able to spatially map transport processes (i.e. flow) in porous media. This work reports a strategy for accelerating the image acquisition time such that sufficient signal-to-noise ratio (SNR) is achieved to increase the spatial resolution, that is, the voxel size within which there is sufficient SNR within the resulting image. To achieve this, a technique known as compressed sensing is used which exploits undersampling of the acquired data relative to the standard fully sampled image. In MRI, data are acquired in so-called k-space and Fourier transformed to yield the real space image. The challenge, when undersampling, is to optimise the specific points in k-space that are acquired because these will influence the quality of the resulting image. This work reports a straightforward, robust strategy for identifying the optimal sets of k-space points to acquire. The method introduced uses simulated MRI images calculated from high-resolution µCT images of the rocks of interest, from which optimised MRI sampling patterns are obtained. The method does not require any optimisation of parameters for its implementation, which is a significant advantage compared to other strategies. Moreover, we show that the pore space characteristics of the acquired MRI images are in excellent agreement with the same characteristics obtained from a high-resolution µCT image.

Safety Aspects, Tolerability and Modeling of Retinofugal Alternating Current Stimulation.

BackgroundWhile alternating current stimulation (ACS) is gaining relevance as a tool in research and approaching clinical applications, its mechanisms of action remain unclear. A review by Schutter and colleagues argues for a retinal origin of transcranial ACS’ neuromodulatory effects. Interestingly, there is an alternative application form of ACS specifically targeting α-oscillations in the visual cortex via periorbital electrodes (retinofugal alternating current stimulation, rACS). To further compare these two methods and investigate retinal effects of ACS, we first aim to establish the safety and tolerability of rACS.ObjectiveThe goal of our research was to evaluate the safety of rACS via finite-element modeling, theoretical safety limits and subjective report.Methods20 healthy subjects were stimulated with rACS as well as photic stimulation and reported adverse events following stimulation. We analyzed stimulation parameters at electrode level as well as distributed metric estimates from an ultra-high spatial resolution magnetic resonance imaging (MRI)-derived finite element human head model and compared them to existing safety limits.ResultsTopographical modeling revealed the highest current densities in the anterior visual pathway, particularly retina and optic nerve. Stimulation parameters and finite element modeling estimates of rACS were found to be well below existing safety limits. No serious adverse events occurred.ConclusionOur findings are in line with existing safety guidelines for retinal and neural damage and establish the tolerability and feasibility of rACS. In comparison to tACS, retinofugal stimulation of the visual cortex provides an anatomically circumscribed model to systematically study the mechanisms of action of ACS.

Identification of Alzheimer’s Disease on the Basis of a Voxel-Wise Approach

Robust prediction of Alzheimer’s disease (AD) helps in the early diagnosis of AD and may support the treatment of AD patients. In this study, for early detection of AD and prediction of mild cognitive impairment (MCI) conversion, we develop an automatic computer-aided diagnosis (CAD) framework based on a merit-based feature selection method through a whole-brain voxel-wise analysis using baseline magnetic resonance imaging (MRI) data. We also explore the impact of different MRI spatial resolution on the voxel-wise metric AD classification and MCI conversion prediction. We assessed the proposed CAD framework using the whole-brain voxel-wise MRI features of 507 J-ADNI participants (146 healthy controls [HCs], 102 individuals with stable MCI [sMCI], 112 with progressive MCI [pMCI], and 147 with AD) among four clinically relevant pairs of diagnostic groups at different imaging resolutions (i.e., 2, 4, 8, and 16 mm). Using a support vector machine classifier through a 10-fold cross-validation strategy at a spatial resolution of 2 mm, the proposed CAD framework yielded classification accuracies of 91.13%, 74.77%, 81.12%, and 81.78% in identifying AD/healthy control, sMCI/pMCI, sMCI/AD, and pMCI/HC, respectively. The experimental results show that a lower spatial resolution (i.e., 2 mm) may provide more robust information to trace the neuronal loss-related brain atrophy in AD.

Channel Splitting Network for Single MR Image Super-Resolution.

High resolution magnetic resonance (MR) imaging is desirable in many clinical applications due to its contribution to more accurate subsequent analyses and early clinical diagnoses. Single image super-resolution (SISR) is an effective and cost efficient alternative technique to improve the spatial resolution of MR images. In the past few years, SISR methods based on deep learning techniques, especially convolutional neural networks (CNNs), have achieved the state-of-the-art performance on natural images. However, the information is gradually weakened and training becomes increasingly difficult as the network deepens. The problem is more serious for medical images because lacking high quality and effective training samples makes deep models prone to underfitting or overfitting. Nevertheless, many current models treat the hierarchical features on different channels equivalently, which is not helpful for the models to deal with the hierarchical features discriminatively and targetedly. To this end, we present a novel channel splitting network (CSN) to ease the representational burden of deep models. The proposed CSN model divides the hierarchical features into two branches, i.e., residual branch and dense branch, with different information transmissions. The residual branch is able to promote feature reuse, while the dense branch is beneficial to the exploration of new features. Besides, we also adopt the merge-and-run mapping to facilitate information integration between different branches. The extensive experiments on various MR images, including proton density (PD), T1, and T2 images, show that the proposed CSN model achieves superior performance over other state-of-the-art SISR methods.

Whole-body diffusion-weighted MRI for operability assessment in patients with colorectal cancer and peritoneal metastases

BackgroundCorrect staging of patients with colorectal cancer is of utmost importance for the prediction of operability. Although computed tomography (CT) has a good overall performance, estimation of peritoneal cancer spread is a known weakness, a problem that cannot always be overcome by Fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG-PET/CT); especially in infiltrative and miliary disease spread. Due to its high spatial and contrast resolution magnetic resonance imaging (MRI) with diffusion-weighted imaging (DWI) might have a better performance. Our aim was to evaluate the added value of whole-body diffusion-weighted MRI (WB-DWI/MRI) to CT for prediction of peritoneal cancer spread and operability assessment in colorectal cancer patients with clinically suspected peritoneal carcinomatosis (PC).MethodsThis institutional review board approved retrospective study included sixty colorectal cancer patients who underwent WB-DWI/MRI in addition to CT for clinically suspected peritoneal metastases. WB-DWI/MRI and CT were assessed for detecting PC following the peritoneal cancer index (PCI), determination of PCI-score categorized as PC < 12, PCI = 12–15 and PCI > 15, detection of nodal and distant metastases and estimation of overall operability. Histopathology after surgery and biopsy and/or 6 months follow-up were used as reference standard.ResultsFor detection of PC, CT had 43.2% sensitivity, 95.6% specificity, 84.5% positive predictive value (PPV) and 75.2% negative predictive value (NPV). WB-DWI/MRI had 97.8% sensitivity, 93.2% specificity, 88.9% PPV and 98.7% NPV. WB-DWI/MRI enabled better detection of inoperable distant metastases (all 12 patients) than CT (2/12 patients) and significantly improved prediction of PCI category [WB-DWI/MRI PCI < 12: 37/39 patients (94.9%); PCI = 12–15: 4/4 patients (100%); PCI > 15: 16/17 patients (94.1%) versus CT PCI < 12: 38/39 patients (97.4%); PCI = 12–15: 0/4 patients (0%); PCI > 15: 2/17 patients (11.8%); p < 0.0001)]. WB-DWI/MRI improved prediction of inoperability over CT with 90.6% sensitivity compared to 25% (p < 0.0001).ConclusionsWB-DWI/MRI significantly outperformed CT for estimation of spread of PC, overall staging and prediction of operability. Pending validation in larger prospective trials, WB-DWI/MRI could be used to guide surgical planning and minimize unnecessary exploratory laparotomies.

High spatial resolution magnetic resonance imaging of insects covered with a hard exoskeleton

AbstractThe morphological investigation of insects is usually performed using histologic serial sections and subsequent reconstruction of the structures from these sections. The achievement of cross sections for microtomy is time‐consuming and the risk of damaging sections is inevitable. Recently, X‐ray computed tomography (micro‐CT) was used to provide adequate spatial resolution without destroying the specimens. Micro‐CT is limited by the low x‐ray contrast of the insect soft tissues and image quality is relatively poor. Magnetic resonance imaging (MRI) allows the study of morphologic classification of the insects with sufficient spatial resolution and provides a noninvasive mean to determine disease abnormalities and progression in vivo and longitudinally. The morphologic classification of the insects with sufficient spatial resolution analyzed the potential of MR imaging. However, a stag beetle has a particularly hard exoskeleton protecting internal organs and nerves. It is challenge to obtain high spatial resolution images using MRI. The aim of this study was to characterize optimal MRI protocols for the investigation of stag beetles and to evaluate the morphologic characterization of the stag beetles by a 9.4 T MRI scanner. In this study, MR imaging provided the spatial resolution necessary for the examination of morphologic structures of the insects on our hardware‐software platform. This study plays a significant role in providing the high spatial resolution, ideally required for routine application to the study of internal morphology of insects, arachnids and crustaceans whose organs, nerves and muscles are protected by the hard exoskeleton.

Spectral characterization of tissues in high spectral and spatial resolution MR images: Implications for a classification-based synthetic CT algorithm.

To characterize the spectral parameters of tissues with high spectral and spatial resolution magnetic resonance images to be used as a foundation for a classification-based synthetic CT algorithm. A phantom was constructed consisting of a section of fresh beef leg with bone embedded in 1% agarose gel. The high spectral and spatial (HiSS) resolution MR imaging sequence used had 1.0 mm in-plane resolution and 11.1 Hz spectral resolution. This sequence was used to image the phantom and one patient. Post-processing was performed off-line with IDL and included Fourier transformation of the time-domain data, labeling of fat and water peaks, and fitting the magnitude spectra with Lorentzian functions. Images of the peak height and peak integral of both the water and fat resonances were generated and analyzed. Several regions-of-interest (ROIs) were identified in phantom: bone marrow, cortical bone, adipose tissue, muscle, agar gel, and air; in the patient, no agar gel was present but an ROI of saline in the bladder was analyzed. All spectra were normalized by the noise within each voxel; thus, all parameters are reported in terms of signal-to-noise (SNR). The distributions of tissue spectral parameters were analyzed and scatterplots generated. Water peak height in cortical bone was compared to air using a nonparametric t-test. Composition of the various ROIs in terms of water, fat, or fat and water was also reported. In phantom, the scatterplot of peak height (water versus fat) showed good separation of bone marrow and adipose tissue. Water versus fat integral scatterplot showed better separation of muscle and cortical bone than the peak height scatterplot. In the patient data, the distributions of water and fat peak heights were similar to that in phantom, with more overlap of bone marrow and cortical bone than observed in phantom. The relationship between bone marrow and cortical bone for peak integral was better separated than those of peak heights in the patient data. For both the phantom and patient, there was a significant amount of overlap in spectral parameters of cortical bone versus air. These results show promising results for utilizing HiSS imaging in a classification-based synthetic CT algorithm. Cortical bone and air overlap was expected due to the short T2* of bone; reducing early echo times would improve the SNR in bone and image data from these early echoes could help differentiate these tissue types. Further studies need to be done with the goal of better separation of air and bone, and to extend the concept to volumetric imaging before it can be clinically applied.

Fast bilateral breast coverage with high spectral and spatial resolution (HiSS) MRI at 3T.

To develop and assess a full-coverage, sensitivity encoding (SENSE)-accelerated breast high spatial and spectral resolution (HiSS) magnetic resonance imaging (MRI) within clinically reasonable times as a potential nonenhanced MRI protocol for breast density measurement or breast cancer screening. Sixteen women with biopsy-proven cancer or suspicious lesions, and 13 women who were healthy volunteers or were screened for breast cancer, received 3T breast MRI exams, including SENSE-accelerated HiSS MRI, which was implemented as a submillimeter spatial resolution echo-planar spectroscopic imaging (EPSI) sequence. In postprocessing, fat and water resonance peak height and integral images were generated from EPSI data. The postprocessing software was custom-designed, and new algorithms were developed to enable processing of whole-coverage axial HiSS datasets. Water peak height HiSS images were compared to pre- and postcontrast T1 -weighted images. Fat suppression was quantified as parenchymal-to-suppressed-fat signal ratio in HiSS water peak height and nonenhanced T1 -weighted images, and artifact levels were scored. Approximately a 4-fold decrease in acquisition speed, with a concurrent 2.5-fold decrease in voxel size, was achieved, with low artifact levels, and with spectral signal-to-noise ratio (SNR) of 45:1. Fat suppression was 1.9 times more effective (P < 0.001) in HiSS images than in T1 -weighted images (SPAIR), and HiSS images showed higher SNR in the axilla. HiSS MRI visualized 10 of 13 malignant lesions identified on dynamic contrast-enhanced (DCE)-MRI, and did not require skin removal in postprocessing to generate maximum intensity projection images. We demonstrate full-coverage, SENSE-accelerated breast HiSS MRI within clinically reasonable times, as a potential protocol for breast density measurement or breast cancer screening. 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1341-1348.

Six-Fold Acceleration of High-Spatial Resolution 3D SPACE MRI of the Knee Through Incoherent k-Space Undersampling and Iterative Reconstruction-First Experience.

The aim of this study was to prospectively test the hypothesis that 6-fold acceleration of a 3-dimensional (3D) turbo spin echo (TSE) magnetic resonance imaging (MRI) pulse sequence with k-space undersampling and iterative reconstruction is feasible for fast high spatial resolution MRI of the knee, while yielding similar image quality and diagnostic performance when compared with a conventional 2-dimensional (2D) TSE MRI standard. The study was approved by the institutional review board. A 10-minute isotropic 3D TSE knee protocol consisting of accelerated intermediate-weighted (repetition time, 900 milliseconds; echo time, 29 milliseconds; voxel size, 0.5 × 0.5 × 0.5 mm; acquisition time, 4:45 minutes) and fat-saturated T2-weighted (repetition time, 900 milliseconds; echo time, 92 milliseconds; voxel size, 0.5 × 0.5 × 0.5 mm; acquisition time, 5:10 minutes) SPACE (sampling perfection with application optimized contrast using different flip angle evolutions) sequence prototypes was compared against a 20-minute 2D TSE standard protocol. The accelerated SPACE sequences were equipped with an optional variable-density poisson-disc pattern as an undersampling mask. An undersampling factor of 0.17 was chosen (6-fold acceleration compared with an acquisition with full sampling). An iterative, sensitivity encoding-type reconstruction with L1 norm-based regularization term was used. The study was performed on a 3 T MRI system using a 15-channel transmit/receive knee coil. The study groups included 15 asymptomatic volunteers and 15 patient volunteers. Quantitative and qualitative assessments were performed by 2 observers. Outcome variables included signal and contrast-to-noise ratio, image quality, and diagnostic accuracy. Qualitative and quantitative measurements were statistically analyzed using nonparametric tests. P values of less than 0.01 were considered significant. The signal-to-noise ratios of 2D and 3D MRI were similar with the exception of fluid, which was brighter on 2D MRI. Relevant contrast-to-noise ratios of 2D MRI were higher than 3D MRI; however, observer ratings for satisfaction, image quality, and visibility of anatomic structures were similar for 2D and 3D MRI. There was moderate to excellent interobserver (κ = 0.54-1.00) and intermethod (κ = 0.54-1.00) agreement for assessing menisci, cartilage, ligaments, cartilage, and bone. Two-dimensional and 3D MRI had similar sensitivity (100%/100%, respectively) and specificity (87%/75%, respectively) for detecting 9 meniscal tears (P = 1.00). We demonstrate the successful clinical implementation of 3D TSE MRI with incoherent k-space undersampling and iterative reconstruction for 6-fold accelerated high spatial resolution isotropic 3D MRI data acquisition. Our preliminary assessments suggest similar image quality and diagnostic performance of a comprehensive 10-minute 3D TSE MRI prototype protocol and 20-minute TSE MRI standard protocol.

Dental MRI using wireless intraoral coils.

Currently, the gold standard for dental imaging is projection radiography or cone-beam computed tomography (CBCT). These methods are fast and cost-efficient, but exhibit poor soft tissue contrast and expose the patient to ionizing radiation (X-rays). The need for an alternative imaging modality e.g. for soft tissue management has stimulated a rising interest in dental magnetic resonance imaging (MRI) which provides superior soft tissue contrast. Compared to X-ray imaging, however, so far the spatial resolution of MRI is lower and the scan time is longer. In this contribution, we describe wireless, inductively-coupled intraoral coils whose local sensitivity enables high resolution MRI of dental soft tissue. In comparison to CBCT, a similar image quality with complementary contrast was obtained ex vivo. In-vivo, a voxel size of the order of 250∙250∙500 μm3 was achieved in 4 min only. Compared to dental MRI acquired with clinical equipment, the quality of the images was superior in the sensitive volume of the coils and is expected to improve the planning of interventions and monitoring thereafter. This method may enable a more accurate dental diagnosis and avoid unnecessary interventions, improving patient welfare and bringing MRI a step closer to becoming a radiation-free alternative for dental imaging.

Magnetic resonance spectroscopy and imaging on fresh human brain tumor biopsies at microscopic resolution.

The metabolic composition and concentration knowledge provided by magnetic resonance spectroscopy (MRS) liquid and high-resolution magic angle spinning spectroscopy (HR-MAS) has a relevant impact in clinical practice during magnetic resonance imaging (MRI) monitoring of human tumors. In addition, the combination of morphological and chemical information by MRI and MRS has been particularly useful for diagnosis and prognosis of tumor evolution. MRI spatial resolution reachable in human beings is limited for safety reasons and the demanding necessary conditions are only applicable on experimental model animals. Nevertheless, MRS and MRI can be performed on human biopsies at high spatial resolution, enough to allow a direct correlation between the chemical information and the histological features observed in such biopsies. Although HR-MAS is nowadays a well-established technique for spectroscopic analysis of tumor biopsies, with this approach just a mean metabolic profile of the whole sample can be obtained and thus the high histological heterogeneity of some important tumors is mostly neglected. The value of metabolic HR-MAS data strongly depends on a wide statistical analysis and usually the microanatomical rationale for the correlation between histology and spectroscopy is lost. We present here a different approach for the combined use of MRI and MRS on fresh human brain tumor biopsies with native contrast. This approach has been designed to achieve high spatial (18 × 18 × 50μm) and spectral (0.031μL) resolution in order to obtain as much spatially detailed morphological and metabolical informationas possible without any previous treatment that can alter the sample. The preservation of native tissue conditions can provide information that can be translated to in vivo studies and additionally opens the possibility of performing other techniques to obtain complementary information from the same sample.

New insights into lung diseases using hyperpolarized gas MRI

Hyperpolarized (HP) gases are a new class of contrast agents that permit to obtain high temporal and spatial resolution magnetic resonance images (MRIs) of the lung airspaces. HP gas MRI has become an important research tool not only for morphological and functional evaluation of normal pulmonary physiology but also for regional quantification of pathologic changes occurring in several lung diseases. The purpose of this work is to provide an introduction to MRI using HP noble gases, describing both the basic principles of the technique and the new information about lung disease provided by clinical studies with this method. The applications of the technique in normal subjects, smoking related lung disease, asthma, and cystic fibrosis are reviewed.

Preliminary assessment of dispersion versus absorption analysis of high spectral and spatial resolution magnetic resonance images in the diagnosis of breast cancer.

Water resonance lineshapes observed in breast lesions imaged with high spectral and spatial resolution (HiSS) magnetic resonance imaging have been shown to contain diagnostically useful non-Lorentzian components. The purpose of this work is to update a previous method of breast lesion diagnosis by including phase-corrected absorption and dispersion spectra. This update includes information about the shape of the complex water resonance, which could improve the performance of a computer-aided diagnosis breast lesion classification scheme. The non-Lorentzian characteristics observed in complex breast lesion water resonance spectra are characterized by comparing a plot of the real versus imaginary components of the spectrum to that of a perfect complex Lorentzian spectrum, a "dispersion versus absorption" (DISPA) analysis technique. Distortion in the shape of the observed spectra indicates underlying physiologic changes, which have been shown to be correlated with malignancy. These spectral shape distortions in each lesion voxel are quantified by summing the deviations in DISPA radius from an ideal complex Lorentzian spectrum over all Fourier components, yielding a "total radial difference" (TRD). We limited our analysis to those voxels in each lesion with the largest TRD. The number of voxels considered was dependent on the lesion size. The TRD was used to classify voxels from 15 malignant and 8 benign lesions ([Formula: see text] voxels after voxel elimination). Lesion discrimination performance was evaluated for both the average and variance of the TRD within each lesion. Area under the receiver operating characteristic curve (ROC AUC) was used to assess both the voxel- and lesion-based discrimination methods in the task of distinguishing between malignant and benign. In the task of distinguishing voxels from malignant and benign lesions, TRD yielded an AUC of 0.89 (95% confidence interval [0.84, 0.91]). In the task of distinguishing malignant from benign lesions, the average radial difference yielded an AUC of 0.90 (95% confidence interval [0.71, 1.00]) and the variance in the radial difference yielded an AUC of 0.84 (95% confidence interval [0.61, 0.99]). We have applied the DISPA spectroscopic analysis method to HiSS data in order to identify and quantify voxels in breast lesions displaying non-Lorentzian characteristics. We have shown that a breast lesion classification scheme based on the absorption and dispersion spectral data obtained from HiSS acquisitions may outperform a similar classifier based on single off-peak component analysis, as it uses shape details of the entire spectrum instead of the magnitude at a single spectral location.