In JoVE (2)

Other Publications (40)

Articles by E. Brian Welch in JoVE

 JoVE Medicine

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

1Chemical and Physical Biology Program, Vanderbilt University, 2Department of Physical Medicine and Rehabilitation, Vanderbilt University School of Medicine, 3Radiology & Radiological Sciences, Vanderbilt University Medical Center, 4Department of Pharmacology, Vanderbilt University


JoVE 52415

 JoVE Medicine

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

1Institute of Imaging Science, Vanderbilt University, 2Department of Radiology and Radiological Sciences, Vanderbilt University, 3Department of Biomedical Engineering, Vanderbilt University, 4Department of Molecular Physiology and Biophysics, Vanderbilt University, 5Department of Physical Medicine and Rehabilitation, Vanderbilt University, 6Department of Physics and Astronomy, Vanderbilt University


JoVE 52352

Other articles by E. Brian Welch on PubMed

High-resolution 7T MRI of the Human Hippocampus in Vivo

Journal of Magnetic Resonance Imaging : JMRI. Nov, 2008  |  Pubmed ID: 18972336

To describe an initial experience imaging the human hippocampus in vivo using a 7T magnetic resonance (MR) scanner and a protocol developed for very high field neuroimaging.

Implementation of a Semi-automated Post-processing System for Parametric MRI Mapping of Human Breast Cancer

Journal of Digital Imaging. Aug, 2009  |  Pubmed ID: 18446412

Magnetic resonance imaging (MRI) investigations of breast cancer incorporate computationally intense techniques to develop parametric maps of pathophysiological tissue characteristics. Common approaches employ, for example, quantitative measurements of T (1), the apparent diffusion coefficient, and kinetic modeling based on dynamic contrast-enhanced MRI (DCE-MRI). In this paper, an integrated medical image post-processing and archive system (MIPAS) is presented. MIPAS demonstrates how image post-processing and user interface programs, written in the interactive data language (IDL) programming language with data storage provided by a Microsoft Access database, and the file system can reduce turnaround time for creating MRI parametric maps and provide additional organization for clinical trials. The results of developing the MIPAS are discussed including potential limitations of the use of IDL for the application framework and how the MIPAS design supports extension to other programming languages and imaging modalities. We also show that network storage of images and metadata has a significant (p < 0.05) increase in data retrieval time compared to collocated storage. The system shows promise for becoming both a robust research picture archival and communications system working with the standard hospital PACS and an image post-processing environment that extends to other medical image modalities.

A Nonrigid Registration Algorithm for Longitudinal Breast MR Images and the Analysis of Breast Tumor Response

Magnetic Resonance Imaging. Nov, 2009  |  Pubmed ID: 19525078

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can estimate parameters relating to blood flow and tissue volume fractions and therefore may be used to characterize the response of breast tumors to treatment. To assess treatment response, values of these DCE-MRI parameters are observed at different time points during the course of treatment. We propose a method whereby DCE-MRI data sets obtained in separate imaging sessions can be co-registered to a common image space, thereby retaining spatial information so that serial DCE-MRI parameter maps can be compared on a voxel-by-voxel basis. In performing inter-session breast registration, one must account for patient repositioning and breast deformation, as well as changes in tumor shape and volume relative to other imaging sessions. One challenge is to optimally register the normal tissues while simultaneously preventing tumor distortion. We accomplish this by extending the adaptive bases algorithm through adding a tumor-volume preserving constraint in the cost function. We also propose a novel method to generate the simulated breast magnetic resonance (MR) images, which can be used to evaluate the proposed registration algorithm quantitatively. The proposed nonrigid registration algorithm is applied to both simulated and real longitudinal 3D high resolution MR images and the obtained transformations are then applied to lower resolution physiological parameter maps obtained via DCE-MRI. The registration results demonstrate the proposed algorithm can successfully register breast MR images acquired at different time points and allow for analysis of the registered parameter maps.

Temporal Sampling Requirements for Reference Region Modeling of DCE-MRI Data in Human Breast Cancer

Journal of Magnetic Resonance Imaging : JMRI. Jul, 2009  |  Pubmed ID: 19557727

To assess the temporal sampling requirements needed for quantitative analysis of dynamic contrast-enhanced MRI (DCE-MRI) data with a reference region (RR) model in human breast cancer.

The Sign Convention for Phase Values on Different Vendor Systems: Definition and Implications for Susceptibility-weighted Imaging

Magnetic Resonance Imaging. Feb, 2010  |  Pubmed ID: 19699598

Practical Considerations for the Design of Sparse-spokes Pulses

Journal of Magnetic Resonance (San Diego, Calif. : 1997). Apr, 2010  |  Pubmed ID: 20172754

Sparse-spokes pulses are 2D slice-selective pulses that effectively mitigate inhomogeneities in the transmitted RF field and reduce unwanted RF artifacts in MR images. Here we consider the practical design of such pulses for high-field MRI and demonstrate limitations of the technique. We analyze the performance of pulses considering input noise as well as other effects such as saturation and T2( *) relaxation. We discuss in detail the correspondence between the reduction of RF inhomogeneities and the fidelity of the input parameters, such as the transmit B1+ field map and combined phase of the main B0 field and eddy-currents. Results include simulations, utilizing 7 T field maps acquired in phantoms and in-vivo, as well as in-vivo experiments. The necessary performance of system hardware components to achieve significant improvements is described.

Effect of MR Distortion on Targeting for Deep-brain Stimulation

IEEE Transactions on Bio-medical Engineering. Jul, 2010  |  Pubmed ID: 20388592

Deep-brain stimulation (DBS) surgery involves placing electrodes within specific deep-brain target nuclei. Surgeons employ MR imaging for preoperative selection of targets and computed tomography (CT) imaging for designing stereotactic frames used for intraoperative placement of electrodes at the targets. MR distortion may contribute to target-selection error in the MR scan and also to MR-CT registration error, each of which contributes to error in electrode placement. In this paper, we analyze the error contributed by the MR distortion to the total DBS targeting error. Distortion in conventional MR scans, both T1 and T2 weighted, were analyzed for six bilateral DBS patients in the typical areas of brain using typical scans on a 3-T clinical scanner. Mean targeting error due to MR distortion in T2 was found to be 0.07 +/- 0.025 mm with a maximum of 0.13 mm over 12 targets; error in the T1 images was smaller by 4%.

Validation of an Algorithm for the Nonrigid Registration of Longitudinal Breast MR Images Using Realistic Phantoms

Medical Physics. Jun, 2010  |  Pubmed ID: 20632566

The authors present a method to validate coregistration of breast magnetic resonance images obtained at multiple time points during the course of treatment. In performing sequential registration of breast images, the effects of patient repositioning, as well as possible changes in tumor shape and volume, must be considered. The authors accomplish this by extending the adaptive bases algorithm (ABA) to include a tumor-volume preserving constraint in the cost function. In this study, the authors evaluate this approach using a novel validation method that simulates not only the bulk deformation associated with breast MR images obtained at different time points, but also the reduction in tumor volume typically observed as a response to neoadjuvant chemotherapy.

Current and Future Trends in Magnetic Resonance Imaging Assessments of the Response of Breast Tumors to Neoadjuvant Chemotherapy

Journal of Oncology. 2010  |  Pubmed ID: 20953332

The current state-of-the-art assessment of treatment response in breast cancer is based on the response evaluation criteria in solid tumors (RECIST). RECIST reports on changes in gross morphology and divides response into one of four categories. In this paper we highlight how dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI) may be able to offer earlier, and more precise, information on treatment response in the neoadjuvant setting than RECIST. We then describe how longitudinal registration of breast images and the incorporation of intelligent bioinformatics approaches with imaging data have the potential to increase the sensitivity of assessing treatment response. We conclude with a discussion of the potential benefits of breast MRI at the higher field strength of 3T. For each of these areas, we provide a review, illustrative examples from clinical trials, and offer insights into future research directions.

Imaging Body Composition in Obesity and Weight Loss: Challenges and Opportunities

Diabetes, Metabolic Syndrome and Obesity : Targets and Therapy. 2010  |  Pubmed ID: 21437103

Obesity is a threat to public health worldwide primarily due to the comorbidities related to visceral adiposity, inflammation, and insulin resistance that increase risk for type 2 diabetes and cardiovascular disease. The translational research portfolio that originally described these risk factors was significantly enhanced by imaging techniques, such as dual-energy X-ray absorptiometry (DEXA), computed tomography (CT), and magnetic resonance imaging (MRI). In this article, we briefly review the important contributions of these techniques to understand the role of body composition in the pathogenesis of obesity-related complications. Notably, these imaging techniques have contributed greatly to recent findings identifying gender and racial differences in body composition and patterns of body composition change during weight loss. Although these techniques have the ability to generate good-quality body composition data, each possesses limitations. For example, DEXA is unable to differentiate type of fat, CT has better resolution but provides greater ionizing radiation exposure, and MRI tends to require longer imaging times and specialized equipment for acquisition and analysis. With the serious need for efficacious and cost-effective therapies to appropriately identify and treat at-risk obese individuals, there is greater need for translational tools that can further elucidate the interplay between body composition and the metabolic aberrations associated with obesity. In conclusion, we will offer our perspective on the evolution toward an ideal imaging method for body composition assessment in obesity and weight loss, and the challenges remaining to achieve this goal.

Dynamic B0 Shimming at 7 T

Magnetic Resonance Imaging. May, 2011  |  Pubmed ID: 21398062

Dynamic slice-wise shimming improves B0 field homogeneity by updating shim coil currents for every slice in a multislice acquisition, producing better field homogeneity over a volume than can be obtained by a single static global shim. The first aim of this work was to evaluate the performance of slice-wise field-map-based second-order dynamic shimming in a human high-field 7 T clinical scanner vis-à-vis image based second order static global shimming. Another goal was to characterize eddy currents induced by second and third order shim switching. A final aim was to compare global and dynamic shimming through shim orders to elucidate the relative benefits of going to higher orders and to dynamic shim updating from a static shimming regime. An external hardware module was used to store and dynamically update slice-optimized shim values during multislice data acquisition. High-bandwidth multislice gradient echo scans with B0 field mapping and low-bandwidth single-shot echo planar scans were performed on phantoms and humans using second-order dynamic and static global shims. For the measurement of second and third order shim induced eddy currents, step response temporal phase changes of individual shims were measured and fit to shim harmonics spatially and to multiexponential decay functions temporally. Finally, an order-wise field-map-based comparison was performed with first, second and third order global static shimming, first and second order dynamic shimming, as well as combined second or third order global and first order dynamic shim. Dynamic shimming considerably improved B0 homogeneity compared to static global shimming both in phantoms and in human subjects, reducing image distortion and signal dropout. The unshielded second and third order shims generated strong B0 and self and cross-term eddy fields, with multiple time constants ranging from milliseconds to seconds. Field homogeneity improved with increasing order of shim, with dynamic shimming performing better than global shimming. Hybrid global and dynamic shimming approach yielded field homogeneity better than global static shims but worse than dynamic shims.

Development of Chemical Exchange Saturation Transfer at 7 T

Magnetic Resonance in Medicine : Official Journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine. Sep, 2011  |  Pubmed ID: 21432902

Chemical exchange saturation transfer (CEST) MRI is a molecular imaging method that has previously been successful at reporting variations in tissue protein and glycogen contents and pH. We have implemented amide proton transfer (APT), a specific form of chemical exchange saturation transfer imaging, at high field (7 T) and used it to study healthy human subjects and patients with multiple sclerosis. The effects of static field inhomogeneities were mitigated using a water saturation shift referencing method to center each z-spectrum on a voxel-by-voxel basis. Contrary to results obtained at lower fields, APT imaging at 7 T revealed significant contrast between white and gray matters, with a higher APT signal apparent within the white matter. Preliminary studies of multiple sclerosis showed that the APT asymmetry varied with the type of lesion examined. An increase in APT asymmetry relative to healthy tissue was found in some lesions. These results indicate the potential utility of APT at high field as a noninvasive biomarker of white matter pathology, providing complementary information to other MRI methods in current clinical use.

Motion Correction in Diffusion-weighted MRI of the Breast at 3T

Journal of Magnetic Resonance Imaging : JMRI. May, 2011  |  Pubmed ID: 21509862

To provide a quantitative assessment of motion and distortion correction of diffusion-weighted images (DWIs) of the breast and to evaluate the effects of registration on the mean apparent diffusion coefficient (mADC).

On the Relationship Between the Apparent Diffusion Coefficient and Extravascular Extracellular Volume Fraction in Human Breast Cancer

Magnetic Resonance Imaging. Jun, 2011  |  Pubmed ID: 21531106

MRI techniques have been developed that can noninvasively probe the apparent diffusion coefficient (ADC) of water via diffusion-weighted MRI (DW-MRI). These methods have found much application in cancer where it is often found that the ADC within tumors is inversely correlated with tumor cell density, so that an increase in ADC in response to therapy can be interpreted as an imaging biomarker of positive treatment response. Dynamic contrast enhanced MRI (DCE-MRI) methods have also been developed and can noninvasively report on the extravascular extracellular volume fraction of tissues (denoted by v(e)). By conventional reasoning, the ADC should therefore also be directly proportional to v(e). Here we report measurements of both ADC and v(e) obtained from breast cancer patients at both 1.5 and 3.0 T. The 1.5-T data were acquired as part of normal standard of care, while the 3.0-T data were obtained from a dedicated research protocol. We found no statistically significant correlation between ADC and v(e) for the 1.5- or 3.0-T patient sets on either a voxel-by-voxel or a region-of-interest (ROI) basis. These data, combined with similar results from other disease sites in the literature, may indicate that the conventional interpretation of either ADC, v(e) or their relationship is not sufficient to explain experimental findings.

Quantitative Magnetization Transfer Imaging in Human Brain at 3 T Via Selective Inversion Recovery

Magnetic Resonance in Medicine : Official Journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine. Nov, 2011  |  Pubmed ID: 21608030

Quantitative magnetization transfer imaging yields indices describing the interactions between free water protons and immobile, macromolecular protons-including the macromolecular to free pool size ratio (PSR) and the rate of magnetization transfer between pools k(mf) . This study describes the first implementation of the selective inversion recovery quantitative magnetization transfer method on a clinical 3.0-T scanner in human brain in vivo. Selective inversion recovery data were acquired at 16 different inversion times in nine healthy subjects and two patients with relapsing remitting multiple sclerosis. Data were collected using a fast spin-echo readout and reduced repetition time, resulting in an acquisition time of 4 min for a single slice. In healthy subjects, excellent intersubject and intrasubject reproducibilities (assessed via repeated measures) were demonstrated. Furthermore, PSR values in white (mean ± SD = 11.4 ± 1.2%) and gray matter (7.5 ± 0.7%) were consistent with previously reported values, while k(mf) values were approximately 2-fold slower in both white (11 ± 2 s(-1) ) and gray matter (15 ± 6 s(-1) ). In relapsing remitting multiple sclerosis patients, quantitative magnetization transfer indices were sensitive to pathological changes in lesions and in normal appearing white matter.

Quantitative Effects of Using Compressed Sensing in Dynamic Contrast Enhanced MRI

Physics in Medicine and Biology. Aug, 2011  |  Pubmed ID: 21772079

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) involves the acquisition of images before, during and after the injection of a contrast agent. In order to perform quantitative modeling on the resulting signal intensity time course, data must be acquired rapidly, which compromises spatial resolution, signal to noise and/or field of view. One approach that may allow for gains in temporal or spatial resolution or signal to noise of an individual image is to use compressed sensing (CS) MRI. In this study, we demonstrate the accuracy of extracted pharmacokinetic parameters from DCE-MRI data obtained as part of pre-clinical and clinical studies in which fully sampled acquisitions have been retrospectively undersampled by factors of 2, 3 and 4 in Fourier space and then reconstructed with CS. The mean voxel-level concordance correlation coefficient for K(trans) (i.e. the volume transfer constant) obtained from the 2× accelerated and the fully sampled data is 0.92 and 0.90 for mouse and human data, respectively; for 3×, the results are 0.79 and 0.79, respectively; for 4×, the results are 0.64 and 0.70, respectively. The mean error in the tumor mean K(trans) for the mouse and human data at 2× acceleration is 1.8% and -4.2%, respectively; at 3×, 3.6% and -10%, respectively; at 4×, 7.8% and -12%, respectively. These results suggest that CS combined with appropriate reduced acquisitions may be an effective approach to improving image quality in DCE-MRI.

A Novel AIF Tracking Method and Comparison of DCE-MRI Parameters Using Individual and Population-based AIFs in Human Breast Cancer

Physics in Medicine and Biology. Sep, 2011  |  Pubmed ID: 21841212

Quantitative analysis of dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data requires the accurate determination of the arterial input function (AIF). A novel method for obtaining the AIF is presented here and pharmacokinetic parameters derived from individual and population-based AIFs are then compared. A Philips 3.0 T Achieva MR scanner was used to obtain 20 DCE-MRI data sets from ten breast cancer patients prior to and after one cycle of chemotherapy. Using a semi-automated method to estimate the AIF from the axillary artery, we obtain the AIF for each patient, AIF(ind), and compute a population-averaged AIF, AIF(pop). The extended standard model is used to estimate the physiological parameters using the two types of AIFs. The mean concordance correlation coefficient (CCC) for the AIFs segmented manually and by the proposed AIF tracking approach is 0.96, indicating accurate and automatic tracking of an AIF in DCE-MRI data of the breast is possible. Regarding the kinetic parameters, the CCC values for K(trans), v(p) and v(e) as estimated by AIF(ind) and AIF(pop) are 0.65, 0.74 and 0.31, respectively, based on the region of interest analysis. The average CCC values for the voxel-by-voxel analysis are 0.76, 0.84 and 0.68 for K(trans), v(p) and v(e), respectively. This work indicates that K(trans) and v(p) show good agreement between AIF(pop) and AIF(ind) while there is a weak agreement on v(e).

Integrating Medical Imaging Analyses Through a High-throughput Bundled Resource Imaging System

Proceedings - Society of Photo-Optical Instrumentation Engineers. 2011  |  Pubmed ID: 21841899

Exploitation of advanced, PACS-centric image analysis and interpretation pipelines provides well-developed storage, retrieval, and archival capabilities along with state-of-the-art data providence, visualization, and clinical collaboration technologies. However, pursuit of integrated medical imaging analysis through a PACS environment can be limiting in terms of the overhead required to validate, evaluate and integrate emerging research technologies. Herein, we address this challenge through presentation of a high-throughput bundled resource imaging system (HUBRIS) as an extension to the Philips Research Imaging Development Environment (PRIDE). HUBRIS enables PACS-connected medical imaging equipment to invoke tools provided by the Java Imaging Science Toolkit (JIST) so that a medical imaging platform (e.g., a magnetic resonance imaging scanner) can pass images and parameters to a server, which communicates with a grid computing facility to invoke the selected algorithms. Generated images are passed back to the server and subsequently to the imaging platform from which the images can be sent to a PACS. JIST makes use of an open application program interface layer so that research technologies can be implemented in any language capable of communicating through a system shell environment (e.g., Matlab, Java, C/C++, Perl, LISP, etc.). As demonstrated in this proof-of-concept approach, HUBRIS enables evaluation and analysis of emerging technologies within well-developed PACS systems with minimal adaptation of research software, which simplifies evaluation of new technologies in clinical research and provides a more convenient use of PACS technology by imaging scientists.

Integration of Diffusion-weighted MRI Data and a Simple Mathematical Model to Predict Breast Tumor Cellularity During Neoadjuvant Chemotherapy

Magnetic Resonance in Medicine : Official Journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine. Dec, 2011  |  Pubmed ID: 21956404

Diffusion-weighted magnetic resonance imaging data obtained early in the course of therapy can be used to estimate tumor proliferation rates, and the estimated rates can be used to predict tumor cellularity at the conclusion of therapy. Six patients underwent diffusion-weighted magnetic resonance imaging immediately before, after one cycle, and after all cycles of neoadjuvant chemotherapy. Apparent diffusion coefficient values were calculated for each voxel and for a whole tumor region of interest. Proliferation rates were estimated using the apparent diffusion coefficient data from the first two time points and then used with the logistic model of tumor growth to predict cellularity after therapy. The predicted number of tumor cells was then correlated to the corresponding experimental data. Pearson's correlation coefficient for the region of interest analysis yielded 0.95 (P = 0.004), and, after applying a 3 × 3 mean filter to the apparent diffusion coefficient data, the voxel-by-voxel analysis yielded a Pearson correlation coefficient of 0.70 ± 0.10 (P < 0.05).

Statistical Comparison of Dynamic Contrast-enhanced MRI Pharmacokinetic Models in Human Breast Cancer

Magnetic Resonance in Medicine : Official Journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine. Nov, 2011  |  Pubmed ID: 22127821

By fitting dynamic contrast-enhanced MRI data to an appropriate pharmacokinetic model, quantitative physiological parameters can be estimated. In this study, we compare four different models by applying four statistical measures to assess their ability to describe dynamic contrast-enhanced MRI data obtained in 28 human breast cancer patient sets: the chi-square test (χ(2) ), Durbin-Watson statistic, Akaike information criterion, and Bayesian information criterion. The pharmacokinetic models include the fast exchange limit model with (FXL_v(p) ) and without (FXL) a plasma component, and the fast and slow exchange regime models (FXR and SXR, respectively). The results show that the FXL_v(p) and FXR models yielded the smallest χ(2) in 45.64 and 47.53% of the voxels, respectively; they also had the smallest number of voxels showing serial correlation with 0.71 and 2.33%, respectively. The Akaike information criterion indicated that the FXL_v(p) and FXR models were preferred in 42.84 and 46.59% of the voxels, respectively. The Bayesian information criterion also indicated the FXL_v(p) and FXR models were preferred in 39.39 and 45.25% of the voxels, respectively. Thus, these four metrics indicate that the FXL_v(p) and the FXR models provide the most complete statistical description of dynamic contrast-enhanced MRI time courses for the patients selected in this study. Magn Reson Med, 2011. © 2011 Wiley Periodicals, Inc.

Early Prediction of the Response of Breast Tumors to Neoadjuvant Chemotherapy Using Quantitative MRI and Machine Learning

AMIA ... Annual Symposium Proceedings / AMIA Symposium. AMIA Symposium. 2011  |  Pubmed ID: 22195145

The ability to predict early in the course of treatment the response of breast tumors to neoadjuvant chemotherapy can stratify patients based on response for patient-specific treatment strategies. Currently response to neoadjuvant chemotherapy is evaluated based on physical exam or breast imaging (mammogram, ultrasound or conventional breast MRI). There is a poor correlation among these measurements and with the actual tumor size when measured by the pathologist during definitive surgery. We tested the feasibility of using quantitative MRI as a tool for early prediction of tumor response. Between 2007 and 2010 twenty consecutive patients diagnosed with Stage II/III breast cancer and receiving neoadjuvant chemotherapy were enrolled on a prospective imaging study. Our study showed that quantitative MRI parameters along with routine clinical measures can predict responders from non-responders to neoadjuvant chemotherapy. The best predictive model had an accuracy of 0.9, a positive predictive value of 0.91 and an AUC of 0.96.

Robustness of Quantitative Compressive Sensing MRI: the Effect of Random Undersampling Patterns on Derived Parameters for DCE- and DSC-MRI

IEEE Transactions on Medical Imaging. Feb, 2012  |  Pubmed ID: 22010146

Compressive sensing (CS) in Cartesian magnetic resonance imaging (MRI) involves random partial Fourier acquisitions. The random nature of these acquisitions can lead to variance in reconstruction errors. In quantitative MRI, variance in the reconstructed images translates to an uncertainty in the derived quantitative maps. We show that for a spatially regularized 2 ×-accelerated human breast CS DCE-MRI acquisition with a 192 (2) matrix size, the coefficients of variation (CoVs) in voxel-level parameters due to the random acquisition are 1.1%, 0.96%, and 1.5% for the tissue parameters K(trans), v(e), and v(p), with an average error in the mean of -2.5%, -2.0%, and -3.7%, respectively. Only 5% of the acquisition schemes had a systematic underestimation larger than than 4.2%, 3.7%, and 6.1%, respectively. For a 2 × -accelerated rat brain CS DSC-MRI study with a 64(2) matrix size, the CoVs due to the random acquisition were 19%, 9.5%, and 15% for the cerebral blood flow and blood volume and mean transit time, respectively, and the average errors in the tumor mean were 9.2%, 0.49%, and -7.0%, respectively. Across 11 000 different CS reconstructions, we saw no outliers in the distribution of parameters, suggesting that, despite the random undersampling schemes, CS accelerated quantitative MRI may have a predictable level of performance.

Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods

International Journal of Biomedical Imaging. 2012  |  Pubmed ID: 22481908

Compressive sensing (CS) has been shown to enable dramatic acceleration of MRI acquisition in some applications. Being an iterative reconstruction technique, CS MRI reconstructions can be more time-consuming than traditional inverse Fourier reconstruction. We have accelerated our CS MRI reconstruction by factors of up to 27 by using a split Bregman solver combined with a graphics processing unit (GPU) computing platform. The increases in speed we find are similar to those we measure for matrix multiplication on this platform, suggesting that the split Bregman methods parallelize efficiently. We demonstrate that the combination of the rapid convergence of the split Bregman algorithm and the massively parallel strategy of GPU computing can enable real-time CS reconstruction of even acquisition data matrices of dimension 4096(2) or more, depending on available GPU VRAM. Reconstruction of two-dimensional data matrices of dimension 1024(2) and smaller took ~0.3 s or less, showing that this platform also provides very fast iterative reconstruction for small-to-moderate size images.

Automating PACS Quality Control with the Vanderbilt Image Processing Enterprise Resource

Proceedings - Society of Photo-Optical Instrumentation Engineers. Feb, 2012  |  Pubmed ID: 24357910

Precise image acquisition is an integral part of modern patient care and medical imaging research. Periodic quality control using standardized protocols and phantoms ensures that scanners are operating according to specifications, yet such procedures do not ensure that individual datasets are free from corruption-for example due to patient motion, transient interference, or physiological variability. If unacceptable artifacts are noticed during scanning, a technologist can repeat a procedure. Yet, substantial delays may be incurred if a problematic scan is not noticed until a radiologist reads the scans or an automated algorithm fails. Given scores of slices in typical three-dimensional scans and wide-variety of potential use cases, a technologist cannot practically be expected inspect all images. In large-scale research, automated pipeline systems have had great success in achieving high throughput. However, clinical and institutional workflows are largely based on DICOM and PACS technologies; these systems are not readily compatible with research systems due to security and privacy restrictions. Hence, quantitative quality control has been relegated to individual investigators and too often neglected. Herein, we propose a scalable system, the Vanderbilt Image Processing Enterprise Resource-VIPER, to integrate modular quality control and image analysis routines with a standard PACS configuration. This server unifies image processing routines across an institutional level and provides a simple interface so that investigators can collaborate to deploy new analysis technologies. VIPER integrates with high performance computing environments has successfully analyzed all standard scans from our institutional research center over the course of the last 18 months.

Quantitative Effects of Inclusion of Fat on Muscle Diffusion Tensor MRI Measurements

Journal of Magnetic Resonance Imaging : JMRI. Nov, 2013  |  Pubmed ID: 23418124

To determine the minimum water percentage in a muscle region of interest that would allow diffusion tensor (DT-) MRI data to reflect the diffusion properties of pure muscle accurately.

Medical Imaging: Sleuthing Tissue Fingerprints

Nature. Mar, 2013  |  Pubmed ID: 23486057

Comparison of Gross Body Fat-water Magnetic Resonance Imaging at 3 Tesla to Dual-energy X-ray Absorptiometry in Obese Women

Obesity (Silver Spring, Md.). Apr, 2013  |  Pubmed ID: 23712980

Improved understanding of how depot-specific adipose tissue mass predisposes to obesity-related comorbidities could yield new insights into the pathogenesis and treatment of obesity as well as metabolic benefits of weight loss. We hypothesized that three-dimensional (3D) contiguous "fat-water" MR imaging (FWMRI) covering the majority of a whole-body field of view (FOV) acquired at 3 Tesla (3T) and coupled with automated segmentation and quantification of amount, type, and distribution of adipose and lean soft tissue would show great promise in body composition methodology.

Potential of Compressed Sensing in Quantitative MR Imaging of Cancer

Cancer Imaging : the Official Publication of the International Cancer Imaging Society. 2013  |  Pubmed ID: 24434808

Classic signal processing theory dictates that, in order to faithfully reconstruct a band-limited signal (e.g., an image), the sampling rate must be at least twice the maximum frequency contained within the signal, i.e., the Nyquist frequency. Recent developments in applied mathematics, however, have shown that it is often possible to reconstruct signals sampled below the Nyquist rate. This new method of compressed sensing (CS) requires that the signal have a concise and extremely dense representation in some mathematical basis. Magnetic resonance imaging (MRI) is particularly well suited for CS approaches, owing to the flexibility of data collection in the spatial frequency (Fourier) domain available in most MRI protocols. With custom CS acquisition and reconstruction strategies, one can quickly obtain a small subset of the full data and then iteratively reconstruct images that are consistent with the acquired data and sparse by some measure. Successful use of CS results in a substantial decrease in the time required to collect an individual image. This extra time can then be harnessed to increase spatial resolution, temporal resolution, signal-to-noise, or any combination of the three. In this article, we first review the salient features of CS theory and then discuss the specific barriers confronting CS before it can be readily incorporated into clinical quantitative MRI studies of cancer. We finally illustrate applications of the technique by describing examples of CS in dynamic contrast-enhanced MRI and dynamic susceptibility contrast MRI.

Canine Body Composition Quantification Using 3 Tesla Fat-water MRI

Journal of Magnetic Resonance Imaging : JMRI. Feb, 2014  |  Pubmed ID: 23596090

To test the hypothesis that a whole-body fat-water MRI (FWMRI) protocol acquired at 3 Tesla combined with semi-automated image analysis techniques enables precise volume and mass quantification of adipose, lean, and bone tissue depots that agree with static scale mass and scale mass changes in the context of a longitudinal study of large-breed dogs placed on an obesogenic high-fat, high-fructose diet.

Prospective Real-time Head Motion Correction Using Inductively Coupled Wireless NMR Probes

Magnetic Resonance in Medicine : Official Journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine. Oct, 2014  |  Pubmed ID: 24243810

Head motion continues to be a major source of artifacts and data quality degradation in MRI. The goal of this work was to develop and demonstrate a novel technique for prospective, 6 degrees of freedom (6DOF) rigid body motion estimation and real-time motion correction using inductively coupled wireless nuclear magnetic resonance (NMR) probe markers.

Multi-parametric MRI Characterization of Healthy Human Thigh Muscles at 3.0 T - Relaxation, Magnetization Transfer, Fat/water, and Diffusion Tensor Imaging

NMR in Biomedicine. Sep, 2014  |  Pubmed ID: 25066274

Muscle diseases commonly have clinical presentations of inflammation, fat infiltration, fibrosis, and atrophy. However, the results of existing laboratory tests and clinical presentations are not well correlated. Advanced quantitative MRI techniques may allow the assessment of myo-pathological changes in a sensitive and objective manner. To progress towards this goal, an array of quantitative MRI protocols was implemented for human thigh muscles; their reproducibility was assessed; and the statistical relationships among parameters were determined. These quantitative methods included fat/water imaging, multiple spin-echo T2 imaging (with and without fat signal suppression, FS), selective inversion recovery for T1 and quantitative magnetization transfer (qMT) imaging (with and without FS), and diffusion tensor imaging. Data were acquired at 3.0 T from nine healthy subjects. To assess the repeatability of each method, the subjects were re-imaged an average of 35 days later. Pre-testing lifestyle restrictions were applied to standardize physiological conditions across scans. Strong between-day intra-class correlations were observed in all quantitative indices except for the macromolecular-to-free water pool size ratio (PSR) with FS, a metric derived from qMT data. Two-way analysis of variance revealed no significant between-day differences in the mean values for any parameter estimate. The repeatability was further assessed with Bland-Altman plots, and low repeatability coefficients were obtained for all parameters. Among-muscle differences in the quantitative MRI indices and inter-class correlations among the parameters were identified. There were inverse relationships between fractional anisotropy (FA) and the second eigenvalue, the third eigenvalue, and the standard deviation of the first eigenvector. The FA was positively related to the PSR, while the other diffusion indices were inversely related to the PSR. These findings support the use of these T1 , T2 , fat/water, and DTI protocols for characterizing skeletal muscle using MRI. Moreover, the data support the existence of a common biophysical mechanism, water content, as a source of variation in these parameters.

Continuously Moving Table MRI with Golden Angle Radial Sampling

Magnetic Resonance in Medicine : Official Journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine. Dec, 2014  |  Pubmed ID: 25461600

Continuously moving table (CMT) MRI is a high throughput technique that has multiple applications in whole-body imaging. In this work, CMT MRI based on golden angle (GA, 111.246° azimuthal step) radial sampling is developed at 3 Tesla (T), with the goal of increased flexibility in image reconstruction using arbitrary profile groupings.

Detection of Microcalcifications by Characteristic Magnetic Susceptibility Effects Using MR Phase Image Cross-correlation Analysis

Medical Physics. Mar, 2015  |  Pubmed ID: 25735297

To develop and evaluate a new method for detecting calcium deposits using their characteristic magnetic susceptibility effects on magnetic resonance (MR) images at high fields and demonstrate its potential in practice for detecting breast microcalcifications.

DCEMRI.jl: a Fast, Validated, Open Source Toolkit for Dynamic Contrast Enhanced MRI Analysis

PeerJ. 2015  |  Pubmed ID: 25922795

We present a fast, validated, open-source toolkit for processing dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data. We validate it against the Quantitative Imaging Biomarkers Alliance (QIBA) Standard and Extended Tofts-Kety phantoms and find near perfect recovery in the absence of noise, with an estimated 10-20× speedup in run time compared to existing tools. To explain the observed trends in the fitting errors, we present an argument about the conditioning of the Jacobian in the limit of small and large parameter values. We also demonstrate its use on an in vivo data set to measure performance on a realistic application. For a 192 × 192 breast image, we achieved run times of <1 s. Finally, we analyze run times scaling with problem size and find that the run time per voxel scales as O(N (1.9)), where N is the number of time points in the tissue concentration curve. DCEMRI.jl was much faster than any other analysis package tested and produced comparable accuracy, even in the presence of noise.

Imaging Methods for Analyzing Body Composition in Human Obesity and Cardiometabolic Disease

Annals of the New York Academy of Sciences. Sep, 2015  |  Pubmed ID: 26250623

Advances in the technological qualities of imaging modalities for assessing human body composition have been stimulated by accumulating evidence that individual components of body composition have significant influences on chronic disease onset, disease progression, treatment response, and health outcomes. Importantly, imaging modalities have provided a systematic method for differentiating phenotypes of body composition that diverge from what is considered normal, that is, having low bone mass (osteopenia/osteoporosis), low muscle mass (sarcopenia), high fat mass (obesity), or high fat with low muscle mass (sarcopenic obesity). Moreover, advances over the past three decades in the sensitivity and quality of imaging not just to discern the amount and distribution of adipose and lean tissue but also to differentiate layers or depots within tissues and cells is enhancing our understanding of distinct mechanistic, metabolic, and functional roles of body composition within human phenotypes. In this review, we focus on advances in imaging technologies that show great promise for future investigation of human body composition and how they are being used to address the pandemic of obesity, metabolic syndrome, and diabetes.

Whole-body Continuously Moving Table Fat-water MRI with Dynamic B0 Shimming at 3 Tesla

Magnetic Resonance in Medicine. Jul, 2016  |  Pubmed ID: 26198380

The purpose of this work was to develop a rapid and robust whole-body fat-water MRI (FWMRI) method using a continuously moving table (CMT) with dynamic field corrections at 3 Tesla.

Incorporating Dixon Multi-echo Fat Water Separation for Novel Quantitative Magnetization Transfer of the Human Optic Nerve in Vivo

Magnetic Resonance in Medicine. Apr, 2016  |  Pubmed ID: 27037720

The optic nerve (ON) represents the sole pathway between the eyes and brain; consequently, diseases of the ON can have dramatic effects on vision. However, quantitative magnetization transfer (qMT) applications in the ON have been limited to ex vivo studies, in part because of the fatty connective tissue that surrounds the ON, confounding the magnetization transfer (MT) experiment. Therefore, the aim of this study was to implement a multi-echo Dixon fat-water separation approach to remove the fat component from MT images.

Multisite, Multivendor Validation of the Accuracy and Reproducibility of Proton-density Fat-fraction Quantification at 1.5T and 3T Using a Fat-water Phantom

Magnetic Resonance in Medicine. Apr, 2016  |  Pubmed ID: 27080068

To evaluate the accuracy and reproducibility of quantitative chemical shift-encoded (CSE) MRI to quantify proton-density fat-fraction (PDFF) in a fat-water phantom across sites, vendors, field strengths, and protocols.

Characterizing Active and Inactive Brown Adipose Tissue in Adult Humans Using PET-CT and MR Imaging

American Journal of Physiology. Endocrinology and Metabolism. Jul, 2016  |  Pubmed ID: 27166284

Activated brown adipose tissue (BAT) plays an important role in thermogenesis and whole body metabolism in mammals. Positron emission tomography (PET)-computed tomography (CT) imaging has identified depots of BAT in adult humans, igniting scientific interest. The purpose of this study is to characterize both active and inactive supraclavicular BAT in adults and compare the values to those of subcutaneous white adipose tissue (WAT). We obtained [(18)F]fluorodeoxyglucose ([(18)F]FDG) PET-CT and magnetic resonance imaging (MRI) scans of 25 healthy adults. Unlike [(18)F]FDG PET, which can detect only active BAT, MRI is capable of detecting both active and inactive BAT. The MRI-derived fat signal fraction (FSF) of active BAT was significantly lower than that of inactive BAT (means ± SD; 60.2 ± 7.6 vs. 62.4 ± 6.8%, respectively). This change in tissue morphology was also reflected as a significant increase in Hounsfield units (HU; -69.4 ± 11.5 vs. -74.5 ± 9.7 HU, respectively). Additionally, the CT HU, MRI FSF, and MRI R2* values are significantly different between BAT and WAT, regardless of the activation status of BAT. To the best of our knowledge, this is the first study to quantify PET-CT and MRI FSF measurements and utilize a semiautomated algorithm to identify inactive and active BAT in the same adult subjects. Our findings support the use of these metrics to characterize and distinguish between BAT and WAT and lay the foundation for future MRI analysis with the hope that some day MRI-based delineation of BAT can stand on its own.

Fat-water MRI of a Diet-induced Obesity Mouse Model at 15.2T

Journal of Medical Imaging (Bellingham, Wash.). Apr, 2016  |  Pubmed ID: 27226976

Quantitative fat-water MRI (FWMRI) methods provide valuable information about the distribution, volume, and composition of adipose tissue (AT). Ultra high field FWMRI of animal models may have the potential to provide insights into the progression of obesity and its comorbidities. Here, we present quantitative FWMRI with all known confounder corrections on a 15.2T preclinical scanner for noninvasive in vivo monitoring of an established diet-induced obesity mouse model. Male C57BL/6J mice were placed on a low-fat (LFD) or a high-fat diet (HFD). Three-dimensional (3-D) multiple gradient echo MRI at 15.2T was performed at baseline, 4, 8, 12, and 16 weeks after diet onset. A 3-D fat-water separation algorithm and additional processing were used to generate proton-density fat fraction (PDFF), local magnetic field offset, and [Formula: see text] maps. We examined these parameters in perirenal AT ROIs from LFD and HFD mice. The data suggest that PDFF, local field offset, and [Formula: see text] have different time course behaviors between LFD and HFD mice over 16 weeks. This work suggests FWMRI at 15.2T may be a useful tool for longitudinal studies of adiposity due to the advantages of ultra high field although further investigation is needed to understand the observed time course behavior.

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