In vivo magnetic resonance microscopy of Drosophilae at 9.4 T

In preclinical research, genetic studies have made considerable progress as a result of the development of transgenic animal models of human diseases. Consequently, there is now a need for higher resolution MRI to provide finer details for studies of small animals (rats, mice) or very small animals (insects). One way to address this issue is to work with high-magnetic-field spectrometers (dedicated to small animal imaging) with strong magnetic field gradients. It is also necessary to develop a complete methodology (transmit/receive coil, pulse sequence, fixing system, air supply, anesthesia capabilities, etc.). In this study, we developed noninvasive protocols, both in vitro and in vivo (from coil construction to image generation), for drosophila MRI at 9.4 T. The 10*10*80-μm resolution makes it possible to visualize whole drosophila (head, thorax, abdomen) and internal organs (ovaries, longitudinal and transverse muscles, bowel, proboscis, antennae and optical lobes). We also provide some results obtained with a Drosophila model of muscle degeneration. This opens the way for new applications of structural genetic modification studies using MRI of drosophila.     

Mechanism of disease in early osteoarthritis: application of modern MR imaging techniques — a technical report

The application of biomolecular magnetic resonance imaging becomes increasingly important in the context of early cartilage changes in degenerative and inflammatory joint disease before gross morphological changes become apparent. In this limited technical report, we investigate the correlation of MRI T1, T2 and T1ρ relaxation times with quantitative biochemical measurements of proteoglycan and collagen contents of cartilage in close synopsis with histologic morphology. A recently developed MRI sequence, T1ρ, was able to detect early intracartilaginous degeneration quantitatively and also qualitatively by color mapping demonstrating a higher sensitivity than standard T2-weighted sequences. The results correlated highly with reduced proteoglycan content and disrupted collagen architecture as measured by biochemistry and histology. The findings lend support to a clinical implementation that allows rapid visual capturing of pathology on a local, millimeter level. Further information about articular cartilage quality otherwise not detectable in vivo, via normal inspection, is needed for orthopedic treatment decisions in the present and future.     

7 T versus 3 T contrast-enhanced breast Magnetic Resonance Imaging of invasive ductulolobular carcinoma: First clinical experience

Purpose

Here we describe our first experience with contrast-enhanced (CE) MRI of breast cancer at 7 tesla (T), compared to 3 T and histopathology.

Materials and Methods

A 52 year old female patient with a mammographically suspicious breast mass (BI-RADS V) underwent 7 T CE-MRI. Results were described according to the BI-RADS-MRI criteria and compared to 3 T and histopathology.

Results

After contrast administration, a homogeneously enhancing, irregular spiculated mass was depicted at both 3 T and 7 T; sizes were identical. The most malignant kinetic curve was characterized by a rapid initial rise followed by a wash-out pattern in the delayed phase, i.e. a type 3 curve, at both field strengths. Even though T1-effects of contrast agents are suggested to be reduced at higher fields, quantification of contrast enhancement-to-noise ratio showed a ratio of 4.6 at 7 T and 2.8 at 3 T when comparing contrast-to-noise of the mass before and after contrast administration. Both examinations, using a single dose of gadolinium-based contrast agent, achieved good image quality. Final histopathological evaluation showed an invasive ductulolobular carcinoma with an intraductal component.

Conclusion

This initial experience suggests that clinical contrast-enhanced 7 T MRI of the breast is technically feasible and may allow BI-RADS-conform analysis.     

Evaluation of ADC measurements among solid pancreatic masses by respiratory-triggered diffusion-weighted MR imaging with inversion-recovery fat-suppression technique at 3.0 T

Purpose

The objective of this paper was to investigate the value of apparent diffusion coefficients (ADCs) for differential diagnosis among solid pancreatic masses using respiratory triggered diffusion-weighted MR imaging with inversion-recovery fat-suppression technique (RT-IR-DWI) at 3.0 T.

Materials and Methods

20 normal volunteers and 72 patients (Pancreatic ductal adenocarcinoma [PDCA, n = 30], mass-forming pancreatitis [MFP, n = 15], solid pseudopapillary neoplasm [SPN, n = 12], and pancreatic neuroendocrine tumor[PNET, n = 15]) underwent RT-IR-DWI (b values: 0 and 600 s/mm²) at 3.0 T. Results were correlated with histopathologic data and follow-up imaging. ADC values among different types of pancreatic tissue were statistically analyzed and compared.

Results

Statistical difference was noticed in ADC values among normal pancreas, MFP, PDCA, SPN and PNET by ANOVA (p < .001). Normal pancreas had the highest ADC value, then followed by PNET, PDCA, MFP and SPN. There was noticeable statistical difference in ADC values among PDCA, MFP and normal pancreas by Least Significant Difference (LSD) (p < .001). ADC of SPN was statistically lower than that of PNET (p = 0.1800 × 10^− 4), PDCA (p = 0.0300 × 10^− 4) and normal pancreas (p = 0.0007 × 10^− 4). ADC of PNET was statistically lower than that of normal pancreas (p = 0.0360) and higher than that of MFP (p = 9.3000 × 10^− 4).

Conclusions

ADC measurements using RT-IR-DWI at 3.0 T may aid to disclose the histopathological pattern of normal pancreas and solid pancreatic masses, which may be helpful in characterizing solid pancreatic lesions.     

A linear algorithm of the reference region model for DCE-MRI is robust and relaxes requirements for temporal resolution

Dynamic contrast enhanced MRI (DCE-MRI) has utility for improving clinical diagnoses of solid tumors, and for evaluating the early responses of anti-angiogenic chemotherapies. The Reference Region Model (RRM) can improve the clinical implementation of DCE-MRI by substituting the contrast enhancement of muscle for the Arterial Input Function that is used in traditional DCE-MRI methodologies. The RRM is typically fitted to experimental results with a non-linear least squares algorithm. This report demonstrates that this algorithm produces inaccurate and imprecise results when DCE-MRI results have low SNR or slow temporal resolution. To overcome this limitation, a linear least-squares algorithm has been derived for the Reference Region Model. This new algorithm improves accuracy and precision of fitting the Reference Region Model to DCE-MRI results, especially for voxel-wise analyses. This linear algorithm is insensitive to injection speeds, and has 300- to 8000-fold faster calculation speed relative to the non-linear algorithm. The linear algorithm produces more accurate results for over a wider range of permeabilities and blood volumes of tumor vasculature. This new algorithm, termed the Linear Reference Region Model, has strong potential to improve clinical DCE-MRI evaluations.     

High resolution dynamic cardiac MRI using partial separability of spatiotemporal signals with a novel sampling scheme

The partial separability (PS) of spatiotemporal signals has been exploited to accelerate dynamic cardiac MRI by sampling two datasets (training and imaging datasets) without breath-holding or ECG triggering. According to the theory of partially separable functions, the wider the range of spatial frequency components covered by the training dataset, the more accurate the temporal constraint imposed by the PS model. Therefore, it is necessary to develop a new sampling scheme for the PS model in order to cover a wider range of spatial frequency components. In this paper, we propose the use of radial sampling trajectories for collecting the training dataset and Cartesian sampling trajectories for collecting the imaging dataset. In vivo high resolution cardiac MRI experiments demonstrate that the proposed data sampling scheme can significantly improve the image quality. The image quality using the PS model with the proposed sampling scheme is comparable to that of a commercial method using retrospective cardiac gating and breath-holding. The success of this study demonstrates great potential for high-quality, high resolution dynamic cardiac MRI without ECG gating or breath-holding through use of the PS model and the novel data sampling scheme.     

Response of bilateral breasts to the endogenous hormonal fluctuation in a menstrual cycle evaluated using 3D MRI

The normal breast tissue responds to the fluctuation of endogenous hormones during a menstrual cycle (MC) and shows changes in breast density. The changes between left and right breasts of the same women were compared to evaluate the symmetrical response. Twenty-four healthy women were recruited in this study. Four weekly magnetic resonance imaging (MRI) studies were performed during one MC. A computer algorithm was used to segment the breast and the fibroglandular tissue to measure the fibroglandular tissue volume (FV) and three morphological parameters: circularity, convexity and irregularity. The coefficient of variation (CV) for each parameter measured among four MRI studies was calculated; also, the maximal percent change between two MRI studies that show the highest and the lowest FV was calculated. These parameters measured from left and right breasts were compared using Pearson correlation.     

Improved dynamic MRI reconstruction by exploiting sparsity and rank-deficiency

In this paper we address the problem of dynamic MRI reconstruction from partially sampled K-space data. Our work is motivated by previous studies in this area that proposed exploiting the spatiotemporal correlation of the dynamic MRI sequence by posing the reconstruction problem as a least squares minimization regularized by sparsity and low-rank penalties. Ideally the sparsity and low-rank penalties should be represented by the l₀-norm and the rank of a matrix; however both are NP hard penalties. The previous studies used the convex l₁-norm as a surrogate for the l₀-norm and the non-convex Schatten-q norm (0 < q ≤ 1) as a surrogate for the rank of matrix. Following past research in sparse recovery, we know that non-convex l_p-norm (0 < p ≤ 1) is a better substitute for the NP hard l₀-norm than the convex l₁-norm. Motivated by these studies, we propose improvements over the previous studies by replacing the l₁-norm sparsity penalty by the l_p-norm. Thus, we reconstruct the dynamic MRI sequence by solving a least squares minimization problem regularized by l_p-norm as the sparsity penalty and Schatten-q norm as the low-rank penalty. There are no efficient algorithms to solve the said problems. In this paper, we derive efficient algorithms to solve them. The experiments have been carried out on Dynamic Contrast Enhanced (DCE) MRI datasets. Both quantitative and qualitative analysis indicates the superiority of our proposed improvement over the existing methods.     

Magnetic resonance imaging in laboratory petrophysical core analysis

Magnetic resonance imaging (MRI) is a well-known technique in medical diagnosis and materials science. In the more specialized arena of laboratory-scale petrophysical rock core analysis, the role of MRI has undergone a substantial change in focus over the last three decades. Initially, alongside the continual drive to exploit higher magnetic field strengths in MRI applications for medicine and chemistry, the same trend was followed in core analysis. However, the spatial resolution achievable in heterogeneous porous media is inherently limited due to the magnetic susceptibility contrast between solid and fluid. As a result, imaging resolution at the length-scale of typical pore diameters is not practical and so MRI of core-plugs has often been viewed as an inappropriate use of expensive magnetic resonance facilities. Recently, there has been a paradigm shift in the use of MRI in laboratory-scale core analysis. The focus is now on acquiring data in the laboratory that are directly comparable to data obtained from magnetic resonance well-logging tools (i.e., a common physics of measurement). To maintain consistency with well-logging instrumentation, it is desirable to measure distributions of transverse (_T2$T_2$) relaxation time–the industry-standard metric in well-logging–at the laboratory-scale. These _T2$T_2$ distributions can be spatially resolved over the length of a core-plug. The use of low-field magnets in the laboratory environment is optimal for core analysis not only because the magnetic field strength is closer to that of well-logging tools, but also because the magnetic susceptibility contrast is minimized, allowing the acquisition of quantitative image voxel (or pixel) intensities that are directly scalable to liquid volume. Beyond simple determination of macroscopic rock heterogeneity, it is possible to utilize the spatial resolution for monitoring forced displacement of oil by water or chemical agents, determining capillary pressure curves, and estimating wettability. The history of MRI in petrophysics is reviewed and future directions considered, including advanced data processing techniques such as compressed sensing reconstruction and Bayesian inference analysis of under-sampled data. Although this review focuses on rock core analysis, the techniques described are applicable in a wider context to porous media in general, such as cements, soils, ceramics, and catalytic materials.     

Supervised Domain Adaptation for Automated Semantic Segmentation of the Atrial Cavity

Atrial fibrillation (AF) is the most common cardiac arrhythmia. At present, cardiac ablation is the main treatment procedure for AF. To guide and plan this procedure, it is essential for clinicians to obtain patient-specific 3D geometrical models of the atria. For this, there is an interest in automatic image segmentation algorithms, such as deep learning (DL) methods, as opposed to manual segmentation, an error-prone and time-consuming method. However, to optimize DL algorithms, many annotated examples are required, increasing acquisition costs. The aim of this work is to develop automatic and high-performance computational models for left and right atrium (LA and RA) segmentation from a few labelled MRI volumetric images with a 3D Dual U-Net algorithm. For this, a supervised domain adaptation (SDA) method is introduced to infer knowledge from late gadolinium enhanced (LGE) MRI volumetric training samples (80 LA annotated samples) to a network trained with balanced steady-state free precession (bSSFP) MR images of limited number of annotations (19 RA and LA annotated samples). The resulting knowledge-transferred model SDA outperformed the same network trained from scratch in both RA (Dice equals 0.9160) and LA (Dice equals 0.8813) segmentation tasks.