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Parallel-plate RF resonator imaging of chemical shift resolved capillary flow. Magn Reson Imaging 2010; 28:826-33. [PMID: 20444567 DOI: 10.1016/j.mri.2010.03.033] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2009] [Revised: 02/25/2010] [Accepted: 03/05/2010] [Indexed: 11/21/2022]
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Meadowcroft MD, Zhang S, Liu W, Park BS, Connor JR, Collins CM, Smith MB, Yang QX. Direct magnetic resonance imaging of histological tissue samples at 3.0T. Magn Reson Med 2007; 57:835-41. [PMID: 17457873 PMCID: PMC4040526 DOI: 10.1002/mrm.21213] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Direct imaging of a histological slice is challenging. The vast difference in dimension between planar size and the thickness of histology slices would require an RF coil to produce a uniform RF magnetic (B1) field in a 2D plane with minimal thickness. In this work a novel RF coil designed specifically for imaging a histology slice was developed and tested. The experimental data demonstrate that the coil is highly sensitive and capable of producing a uniform B1 field distribution in a planar region of histological slides, allowing for the acquisition of high-resolution T2 images and T2 maps from a 60-microm-thick histological sample. The image intensity and T2 distributions were directly compared with histological staining of the relative iron concentration of the same slice. This work demonstrates the feasibility of using a microimaging histological coil to image thin slices of pathologically diseased tissue to obtain a precise one-to-one comparison between stained tissue and MR images.
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Affiliation(s)
- Mark D. Meadowcroft
- Center for NMR Research, Department of Radiology, Pennsylvania State University College of Medicine, Milton S. Hershey Medical Center, Hershey, Pennsylvania, USA
- Department of Neural Science and Behavioral Sciences, Pennsylvania State University College of Medicine, Milton S. Hershey Medical Center, Hershey, Pennsylvania, USA
- Department of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Shutong Zhang
- Department of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Wanzhan Liu
- Center for Magnetic Resonance Research, Department of Radiology, School of Medicine, University of Minnesota, Minneapolis, Minnesota, USA
| | - Bu Sik Park
- Center for NMR Research, Department of Radiology, Pennsylvania State University College of Medicine, Milton S. Hershey Medical Center, Hershey, Pennsylvania, USA
| | - James R. Connor
- Department of Neurosurgery, Pennsylvania State University College of Medicine, Milton S. Hershey Medical Center, Hershey, Pennsylvania, USA
| | - Christopher M. Collins
- Center for NMR Research, Department of Radiology, Pennsylvania State University College of Medicine, Milton S. Hershey Medical Center, Hershey, Pennsylvania, USA
| | - Michael B. Smith
- Center for NMR Research, Department of Radiology, Pennsylvania State University College of Medicine, Milton S. Hershey Medical Center, Hershey, Pennsylvania, USA
| | - Qing X. Yang
- Center for NMR Research, Department of Radiology, Pennsylvania State University College of Medicine, Milton S. Hershey Medical Center, Hershey, Pennsylvania, USA
- Department of Neurosurgery, Pennsylvania State University College of Medicine, Milton S. Hershey Medical Center, Hershey, Pennsylvania, USA
- Correspondence to: Qing X. Yang, Ph.D., Center for NMR Research, Department of Radiology, M.S. Hershey Medical Center, 500 University Drive, Hershey, PA 17033.
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Achiron A, Gicquel S, Miron S, Faibel M. Brain MRI lesion load quantification in multiple sclerosis: a comparison between automated multispectral and semi-automated thresholding computer-assisted techniques. Magn Reson Imaging 2002; 20:713-20. [PMID: 12591567 DOI: 10.1016/s0730-725x(02)00606-9] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
Brain magnetic resonance imaging (MRI) lesion volume measurement is an advantageous tool for assessing disease burden in multiple sclerosis (MS). We have evaluated two computer-assisted techniques: MSA multispectral automatic technique that is based on bayesian classification of brain tissue and NIH image analysis technique that is based on local (lesion by lesion) thresholding, to establish reliability and repeatability values for each technique. Brain MRIs were obtained for 30 clinically definite relapsing-remitting MS patients using a 2.0 Tesla MR scanner with contiguous, 3 mm thick axial, T1, T2 and PD weighted modalities. Digital (Dicom 3) images were analyzed independently by three observers; each analyzed the images twice, using the two different techniques (Total 360 analyses). Accuracy of lesion load measurements using phantom images of known volumes showed significantly better results for the MSA multispectral technique (p < 0.001). The mean intra-and inter-observer variances were, respectively, 0.04 +/- 0.4 (range 0.04-0.13), and 0.09 +/- 0.6 (range 0.01-0.26) for the multispectral MSA analysis technique, 0.24 +/- 2.27 (range 0.23-0.72) and 0.33 +/- 3.8 (range 0.47-1.36) for the NIH threshold technique. These data show that the MSA multispectral technique is significantly more accurate in lesion volume measurements, with better results of within and between observers' assessments, and the lesion load measurements are not influenced by increased disease burden. Measurements by the MSA multispectral technique were also faster and decreased analysis time by 43%. The MSA multispectral technique is a promising tool for evaluating MS patients. Non-biased recognition and delineation algorithms enable high accuracy, low intra-and inter-observer variances and fast assessment of MS related lesion load.
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Affiliation(s)
- Anat Achiron
- Multiple Sclerosis Center, Sheba Medical Center, Tel-Hashomer, Israel.
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Ibrahim TS, Lee R, Abduljalil AM, Baertlein BA, Robitaille PM. Dielectric resonances and B(1) field inhomogeneity in UHFMRI: computational analysis and experimental findings. Magn Reson Imaging 2001; 19:219-26. [PMID: 11358660 DOI: 10.1016/s0730-725x(01)00300-9] [Citation(s) in RCA: 102] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Abstract
B(1) Field inhomogeneity and the relative effects of dielectric resonances are analyzed within the context of ultra high field MRI. This is accomplished by calculating the electromagnetic fields inside spherical phantoms and within a human head model in the presence and absence of an RF coil. These calculations are then compared to gradient echo and RARE images, respectively. For the spherical phantoms, plane incident wave analyses are initially presented followed by full wave finite difference time domain (FDTD) calculations. The FDTD methods are then utilized to examine the electromagnetic interactions between the TEM resonator and an anatomically detailed human head model. The results at 340 MHz reveal that dielectric resonances are most strongly excited in objects similar in size to the human head when the conducting medium has a high dielectric constant and a low conductivity. It is concluded that in clinical UFHMRI, the most important determinants of B(1) field homogeneity consist of 1) the RF coil design, 2) the interaction between the RF coil, the excitation source and the sample, and finally 3) the geometry and electrical properties of the sample.
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Affiliation(s)
- T S Ibrahim
- Department of Electrical Engineering, The Ohio State University, Columbus, Ohio 43210, USA
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