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PubMed: 0730-725X

Magic angle effect in magnetic resonance imaging of the Achilles tendon and enthesis.
Du J, Pak BC, Znamirowski R, Statum S, Takahashi A, Chung CB, Bydder GM Related Articles Magic angle effect in magnetic resonance imaging of the Achilles tendon and enthesis. Magn Reson Imaging. 2008 Nov 19; Authors: Du J, Pak BC, Znamirowski R, Statum S, Takahashi A, Chung CB, Bydder GM Collagen fibers in tendons and entheses are highly ordered. The protons within the bound water are subject to dipolar interactions whose strength depends on the orientation of the fibers to the static magnetic field B(0). Clinical pulse sequences have been employed to investigate this magic angle effect of the Achilles tendon, but only limited to imaging appearance with a signal void at many angular orientations due to its short T2. Here we investigated the magic angle effect of the Achilles tendons and entheses on a clinical 3-T scanner using clinical sequences as well as an ultrashort TE sequence with a minimal TE of 8 mus. Qualitative and quantitative investigation of the angular-dependent imaging appearance, T1 and T2* values were performed on five ankle specimens. There was a significant increase in signal intensity for all pulse sequences near the magic angle. Mean T2* for tendon increased from 1.94+/-0.28 ms at 0 degrees relative to the B(0) field to 15.25+/-2.13 ms at 55 degrees , and mean T1 increased from 598+/-37 ms at 0 degrees to 621+/-44 ms at 55 degrees . There was less magic angle effect for enthesis whose mean T2* increased from 4.12+/-0.37 ms at 0 degrees to 12.46+/-1.78 ms at 55 degrees , and mean T1 increased from 685+/-41 ms at 0 degrees to 718+/-56 ms at 55 degrees . PMID: 19022600 [PubMed - as supplied by publisher]

Magnetic Resonance in Medicine

Regression algorithm correcting for partial volume effects in arterial spin labeling MRI
Iris Asllani, Ajna Borogovac, Truman R. Brown Tue, 30 Sep 2008 09:20:00 -0000
Partial volume effects (PVE) are a consequence of limited spatial resolution in brain imaging. In arterial spin labeling (ASL) MRI, the problem is exacerbated by the nonlinear dependency of the ASL signal on magnetization contributions from each tissue within an imaged voxel. We have developed an algorithm that corrects for PVE in ASL imaging. The algorithm is based on a model that represents the voxel intensity as a weighted sum of pure tissue contribution, where the weighting coefficients are the tissue's fractional volume in the voxel. Using this algorithm, we were able to estimate cerebral blood flow (CBF) for gray matter (GM) and white matter (WM) independently. The average voxelwise ratio of GM to WM CBF was [sim]3.2, in good agreement with reports in the literature. As proof of concept, data from PVE-corrected method were compared with those from the conventional, PVE-uncorrected method. As hypothesized, the two yielded similar CBF values for voxels containing >95% GM and differed in proportion with the voxels' heterogeneity. More importantly, the GM CBF assessed with the PVE-corrected method was independent of the voxels' heterogeneity, implying that estimation of flow was unaffected by PVE. An example of application of this algorithm in motor-activation data is also given. Magn Reson Med, 2008. © 2008 Wiley-Liss, Inc.

Magnetic Resonance Materials in Physics, Biology and Medicine

Magnetic resonance imaging and spectroscopy of the prostate
Thu, 20 Nov 2008 07:49:49 -0000
Magnetic resonance imaging and spectroscopy of the prostate Content Type Journal ArticleCategory EditorialDOI 10.1007/s10334-008-0153-zAuthors Carolyn Mountford, Brigham and Women’s Hospital, Harvard Medical School Department of Radiology, Center for Clinical Spectroscopy 1640 Tremont St, BC-3-010-CC Boston MA 02120 USA Journal Magnetic Resonance Materials in Physics, Biology and MedicineOnline ISSN 1352-8661Print ISSN 0968-5243
Relevance of the skewness index in DTI exploration of multiple sclerosis
Sun, 09 Nov 2008 09:58:06 -0000
Abstract Object  To this day, no parameter can really monitor the progression of multiple sclerosis (MS). In this study, an index the skewness (S) derived from parameters calculated in diffusion tensor imaging (DTI) has been tested on MS patients for its ability to monitor the disease course. Materials and methods  Eighteen patients underwent two examinations within 3 months consisting of a clinical evaluation (EDSS) and DTI acquisitions on a 1.5 T imager. Tensor was calculated thanks to“home-made” software. Mean diffusivity (MD) and fractional anisotropy (FA) histograms were described for normal-appearing white matter (NAWM) and gray matter (GM) of patients with S and also with usually indices peak position (pp) and peak height (ph) for the whole group of patients and for two separate groups according to their clinical status (EDSS  ≤  3 and EDSS  > 3 at month 0). Results  Although no significant clinical evolution is observed over 3 months, S in GM showed a significant shift for both MD/FA histograms towards abnormal values for the whole group of patients (p = 0.02/p = 0.04) and for the group with EDSS  ≤  3 (p = 0.04/p = 0.007), while ph and pp do not. Conclusion  S in GM could be an alternative marker to monitor the disease course before the repercussion on the clinical score. Content Type Journal ArticleCategory Research ArticleDOI 10.1007/s10334-008-0149-8Authors Eliane Graulières, University Hospital of Rangueil Department of Biophysics 31059 Toulouse FranceJean-Albert Lotterie, University Hospital of Rangueil Department of Biophysics 31059 Toulouse FranceEmmanuelle Cassol, University Hospital of Rangueil Department of Biophysics 31059 Toulouse FranceAngélique Gerdelat, University Hospital of Purpan Department of Neurology 31059 Toulouse FranceMichel Clanet, University Hospital of Purpan Department of Neurology 31059 Toulouse FranceIsabelle Berry, University Hospital of Rangueil Department of Biophysics 31059 Toulouse France Journal Magnetic Resonance Materials in Physics, Biology and MedicineOnline ISSN 1352-8661Print ISSN 0968-5243
Multiproject–multicenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy
Fri, 07 Nov 2008 08:08:39 -0000
Abstract Justification  Automatic brain tumor classification by MRS has been under development for more than a decade. Nonetheless, to our knowledge, there are no published evaluations of predictive models with unseen cases that are subsequently acquired in different centers. The multicenter eTUMOUR project (2004–2009), which builds upon previous expertise from the INTERPRET project (2000–2002) has allowed such an evaluation to take place. Materials and Methods  A total of 253 pairwise classifiers for glioblastoma, meningioma, metastasis, and low-grade glial diagnosis were inferred based on 211 SV short TE INTERPRET MR spectra obtained at 1.5 T (PRESS or STEAM, 20–32 ms) and automatically pre-processed. Afterwards, the classifiers were tested with 97 spectra, which were subsequently compiled during eTUMOUR. Results  In our results based on subsequently acquired spectra, accuracies of around 90% were achieved for most of the pairwise discrimination problems. The exception was for the glioblastoma versus metastasis discrimination, which was below 78%. A more clear definition of metastases may be obtained by other approaches, such as MRSI + MRI. Conclusions  The prediction of the tumor type of in-vivo MRS is possible using classifiers developed from previously acquired data, in different hospitals with different instrumentation under the same acquisition protocols. This methodology may find application for assisting in the diagnosis of new brain tumor cases and for the quality control of multicenter MRS databases. Content Type Journal ArticleCategory Research ArticleDOI 10.1007/s10334-008-0146-yAuthors Juan M. García-Gómez, Universidad Politécnica de Valencia IBIME-Itaca Camino de Vera, s/n 46022 Valencia SpainJan Luts, Katholieke Universiteit Leuven Department of Electrical Engineering (ESAT), Research Division SCD Leuven BelgiumMargarida Julià-Sapé, CIBER de Bioingeniería, Biomateriales y Nanomedicina Barcelona SpainPatrick Krooshof, Radboud University Institute for Molecules and Materials Nijmegen (Gelderland) The NetherlandsSalvador Tortajada, Universidad Politécnica de Valencia IBIME-Itaca Camino de Vera, s/n 46022 Valencia SpainJavier Vicente Robledo, Universidad Politécnica de Valencia IBIME-Itaca Camino de Vera, s/n 46022 Valencia SpainWillem Melssen, Radboud University Institute for Molecules and Materials Nijmegen (Gelderland) The NetherlandsElies Fuster-García, Universidad Politécnica de Valencia IBIME-Itaca Camino de Vera, s/n 46022 Valencia SpainIván Olier, Universitat Autònoma de Barcelona Departament de Bioquímica i Biologia Molecular Cerdanyola del Vallès Barcelona SpainGeert Postma, Radboud University Institute for Molecules and Materials Nijmegen (Gelderland) The NetherlandsDaniel Monleón, CIBER de Bioingeniería, Biomateriales y Nanomedicina Barcelona SpainÀngel Moreno-Torres, Centre Diagnòstic Pedralbes, Esplugues de Llobregat Research Department Barcelona SpainJesús Pujol, Institut d’Alta Tecnologia-PRBB, CRC Corporació Sanitària Barcelona SpainAna-Paula Candiota, CIBER de Bioingeniería, Biomateriales y Nanomedicina Barcelona SpainM. Carmen Martínez-Bisbal, CIBER de Bioingeniería, Biomateriales y Nanomedicina Barcelona SpainJohan Suykens, Katholieke Universiteit Leuven Department of Electrical Engineering (ESAT), Research Division SCD Leuven BelgiumLutgarde Buydens, Radboud University Institute for Molecules and Materials Nijmegen (Gelderland) The NetherlandsBernardo Celda, CIBER de Bioingeniería, Biomateriales y Nanomedicina Barcelona SpainSabine Van Huffel, Katholieke Universiteit Leuven Department of Electrical Engineering (ESAT), Research Division SCD Leuven BelgiumCarles Arús, CIBER de Bioingeniería, Biomateriales y Nanomedicina Barcelona SpainMontserrat Robles, Universidad Politécnica de Valencia IBIME-Itaca Camino de Vera, s/n 46022 Valencia Spain Journal Magnetic Resonance Materials in Physics, Biology and MedicineOnline ISSN 1352-8661Print ISSN 0968-5243

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