20
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Validation of T1 and T2 algorithms for quantitative MRI: performance by a vendor-independent software

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          Determination of the relaxation time constants T1 and T2 with quantitative magnetic resonance imaging is increasingly used for both research and clinical practice. Recently, groups have been formed within the Society of Cardiovascular Magnetic Resonance to address issues with relaxometry. However, so far they have avoided specific recommendations on methodology due to lack of consensus and current evolving research. Standardised widely available software may simplify this process.

          The purpose of the current study was to develop and validate vendor-independent T1 and T2 mapping modules and implement those in the versatile and widespread software Segment, freely available for research and FDA approved for clinical applications.

          Results

          The T1 and T2 mapping modules were developed and validated in phantoms at 1.5 T and 3 T with reference standard values calculated from reference pulse sequences using the Nelder-Mead Simplex optimisation method. The proposed modules support current commonly available MRI pulse sequences and both 2- and 3-parameter curve fitting. Images acquired in patients using three major vendors showed vendor-independence. Bias and variability showed high agreement with T1 and T2 reference standards for T1 (range 214–1752 ms) and T2 (range 45–338 ms), respectively.

          Conclusions

          The developed and validated T1 and T2 mapping and quantification modules generated relaxation maps from current commonly used MRI sequences and multiple signal models. Patient applications showed usability for three major vendors.

          Related collections

          Most cited references16

          • Record: found
          • Abstract: not found
          • Article: not found

          Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions

            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Standardized image interpretation and post processing in cardiovascular magnetic resonance: Society for Cardiovascular Magnetic Resonance (SCMR) Board of Trustees Task Force on Standardized Post Processing

            With mounting data on its accuracy and prognostic value, cardiovascular magnetic resonance (CMR) is becoming an increasingly important diagnostic tool with growing utility in clinical routine. Given its versatility and wide range of quantitative parameters, however, agreement on specific standards for the interpretation and post-processing of CMR studies is required to ensure consistent quality and reproducibility of CMR reports. This document addresses this need by providing consensus recommendations developed by the Task Force for Post Processing of the Society for Cardiovascular MR (SCMR). The aim of the task force is to recommend requirements and standards for image interpretation and post processing enabling qualitative and quantitative evaluation of CMR images. Furthermore, pitfalls of CMR image analysis are discussed where appropriate.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Extracellular volume imaging by magnetic resonance imaging provides insights into overt and sub-clinical myocardial pathology.

              Conventional late gadolinium enhancement (LGE) cardiac magnetic resonance can detect myocardial infarction and some forms of non-ischaemic myocardial fibrosis. However, quantitative imaging of extracellular volume fraction (ECV) may be able to detect subtle abnormalities such as diffuse fibrosis or post-infarct remodelling of remote myocardium. The aims were (1) to measure ECV in myocardial infarction and non-ischaemic myocardial fibrosis, (2) to determine whether ECV varies with age, and (3) to detect sub-clinical abnormalities in 'normal appearing' myocardium remote from regions of infarction. Cardiac magnetic resonance ECV imaging was performed in 126 patients with T1 mapping before and after injection of gadolinium contrast. Conventional LGE images were acquired for the left ventricle. In patients with a prior myocardial infarction, the infarct region had an ECV of 51 ± 8% which did not overlap with the remote 'normal appearing' myocardium that had an ECV of 27 ± 3% (P < 0.001, n = 36). In patients with non-ischaemic cardiomyopathy, the ECV of atypical LGE was 37 ± 6%, whereas the 'normal appearing' myocardium had an ECV of 26 ± 3% (P < 0.001, n = 30). The ECV of 'normal appearing' myocardium increased with age (r = 0.28, P = 0.01, n = 60). The ECV of 'normal appearing' myocardium remote from myocardial infarctions increased as left ventricular ejection fraction decreased (r = -0.50, P = 0.02). Extracellular volume fraction imaging can quantitatively characterize myocardial infarction, atypical diffuse fibrosis, and subtle myocardial abnormalities not clinically apparent on LGE images. Taken within the context of prior literature, these subtle ECV abnormalities are consistent with diffuse fibrosis related to age and changes remote from infarction.
                Bookmark

                Author and article information

                Contributors
                sebastian.bidhult@bme.lth.se
                kantasisg@gmail.com
                aletras@hotmail.com
                hakan.arheden@med.lu.se
                einar.heiberg@med.lu.se
                +46 46 17 10 00 , erik.hedstrom@med.lu.se
                Journal
                BMC Med Imaging
                BMC Med Imaging
                BMC Medical Imaging
                BioMed Central (London )
                1471-2342
                8 August 2016
                8 August 2016
                2016
                : 16
                : 46
                Affiliations
                [1 ]Department of Clinical Sciences Lund, Clinical Physiology, Lund University, Skane University Hospital, Lund, Sweden
                [2 ]Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden
                [3 ]Laboratory of Medical Informatics, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
                [4 ]Department of Clinical Sciences Lund, Diagnostic Radiology, Lund University, Skane University Hospital, Lund, Sweden
                Author information
                http://orcid.org/0000-0003-0041-9357
                Article
                148
                10.1186/s12880-016-0148-6
                4977731
                27501697
                baac6bd6-2ddc-4c98-95f1-1b5b1decd3a5
                © The Author(s). 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 16 December 2015
                : 28 July 2016
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100004359, Vetenskapsrådet;
                Award ID: 621-2012-4944
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/http://dx.doi.org/10.13039/501100003793, Hjärt-Lungfonden;
                Funded by: Region Skåne
                Funded by: Skåne University Hospital in Lund
                Funded by: Excellence Grant from the Greek General Secretariat for Research and Technology
                Categories
                Software
                Custom metadata
                © The Author(s) 2016

                Radiology & Imaging
                t1,t2,mapping,quantitative magnetic resonance imaging
                Radiology & Imaging
                t1, t2, mapping, quantitative magnetic resonance imaging

                Comments

                Comment on this article