Workshop on reconstruction schemes for magnetic resonance data: summary of findings and recommendations

[EN] The high fidelity reconstruction of compressed and low-resolution magnetic resonance (MR) data is essential for simultaneously improving patient care, accuracy in diagnosis and quality in clinical research. Sponsored by the Royal Society through the Newton Mobility Grant Scheme, we held a half-day workshop on reconstruction schemes for MR data on the 17 of August 2016 to discuss new ideas from related research fields that could be useful to overcome the shortcomings of the conventional reconstruction methods that have been evaluated up to date. Participants were 21 university students, computer scientists, image analysts, engineers and physicists from institutions from 6 different countries. The discussion evolved around exploring new avenues to achieve high resolution, high quality and fast acquisition of MR imaging. In this article, we summarise the topics covered throughout the workshop and make recommendations for ongoing and future works.The workshop was sponsored by the Royal Society through the Newton Mobility Grant NI150340 to E.O.-I. and M.C.V.H. M.C.V.H. is funded by Row Fogo Charitable Trust; R.O.R. is funded by the Ministry of Education, Research, Culture and Sports of Valencia (Spain) under the programme VALi+d 2015; E.O.-I. is funded by Bogazici University, and the research presented at the workshop was supported by TUBITAK Career Development Grant 112E036, EU Marie Curie IRG Grant FP7-PEOPLE-RG-2009 256528, Tubitak 1001 Research Grant 115S219, and Bogazici University BAP Grant 10844SUP; I.M. is funded by core funds from the University of Edinburgh, including the Scottish Funding Council; A.J.V.B. is funded by the Marie Sklodowska Curie scholarship which is part of the European Union's H2020 Framework Programme (H2020-MSCA-ITN-2014) under the grant agreement number 642685 MacSeNet; and V.G.O. and P.F. are privately funded.Ozturk-Isik, E.; Marshall, I.; Filipiak, P.; Benjamin, AJV.; Ones, VG.; Ortiz-Ramón, R.; Valdes Hernandez, MDC. (2017). Workshop on reconstruction schemes for magnetic resonance data: summary of findings and recommendations. Royal Society Open Science. 4(2):1-4. https://doi.org/10.1098/rsos.160731144

Implementation of 3 T Lactate-Edited 3D 1H MR Spectroscopic Imaging with Flyback Echo-Planar Readout for Gliomas Patients

The purpose of this study was to implement a new lactate-edited 3D 1H magnetic resonance spectroscopic imaging (MRSI) sequence at 3 T and demonstrate the feasibility of using this sequence for measuring lactate in patients with gliomas. A 3D PRESS MRSI sequence incorporating shortened, high bandwidth 180° pulses, new dual BASING lactate-editing pulses, high bandwidth very selective suppression (VSS) pulses and a flyback echo-planar readout was implemented at 3 T. Over-prescription factor of PRESS voxels was optimized using phantom to minimize chemical shift artifacts. The lactate-edited flyback sequence was compared with lactate-edited MRSI using conventional elliptical k-space sampling in a phantom and volunteers, and then applied to patients with gliomas. The results demonstrated the feasibility of detecting lactate within a short scan time of 9.5 min in both phantoms and patients. Over-prescription of voxels gave less chemical shift artifacts allowing detection of lactate on the majority of the selected volume. The normalized SNR of brain metabolites using the flyback encoding were comparable to the SNR of brain metabolites using conventional phase encoding MRSI. The specialized lactate-edited 3D MRSI sequence was able to detect lactate in brain tumor patients at 3 T. The implementation of this technique means that brain lactate can be evaluated in a routine clinical setting to study its potential as a marker for prognosis and response to therapy

The Open Brain Consent: Informing research participants and obtaining consent to share brain imaging data

Having the means to share research data openly is essential to modern science. For human research, a key aspect in this endeavor is obtaining consent from participants, not just to take part in a study, which is a basic ethical principle, but also to share their data with the scientific community. To ensure that the participants' privacy is respected, national and/or supranational regulations and laws are in place. It is, however, not always clear to researchers what the implications of those are, nor how to comply with them. The Open Brain Consent (https://open-brain-consent.readthedocs.io) is an international initiative that aims to provide researchers in the brain imaging community with information about data sharing options and tools. We present here a short history of this project and its latest developments, and share pointers to consent forms, including a template consent form that is compliant with the EU general data protection regulation. We also share pointers to an associated data user agreement that is not only useful in the EU context, but also for any researchers dealing with personal (clinical) data elsewhere

Implementation of Parallel Imaging Techniques for Lipid Unaliasing and Faster Acquisition for Improving Spatial Characterization of Magnetic Resonance Spectroscopic Imaging of Gliomas

Magnetic resonance imaging (MRI) tools have been commonly utilized in the management of patients diagnosed with gliomas. Functional and metabolic MRI techniques have been proposed to add information regarding the tissue characteristics and biochemistry for better tumor localization, treatment planning and follow up of the disease. Magnetic resonance spectroscopic imaging (MRSI) is a metabolic imaging technique used to analyze brain tissue chemistry. MR spectroscopic and diffusion imaging were utilized to study the spatial characteristics of grade 3 gliomas. The contrast enhancing regions appeared to be the most malignant tumor area in contrast enhancing patients, and choline to NAA index (CNI) greater than four regions appeared to be the most malignant focus in the non-enhancing patients. The presence of lipids within MR spectra in sub-regions of tumor may indicate cellular membrane breakdown due to cell death. Another potential source of lipids is from subcutaneous fat that may be aliased into the spectral field of view due to chemical shift artifact and low bandwidth of the selection pulses. It is therefore important to identify the origins of lipid resonances, either artifact or from pathologic state, for accurate assessment of the disease state. A post-processing method based on the sensitivity encoding (SENSE) technique was developed to reduce lipid contamination in the spectra to increase the spectral quantification accuracy. The major limitation of acquiring spectral data for gliomas is the long data acquisition time. The possibility of combining ellipsoidal sampling with two parallel imaging techniques, SENSE and generalized autocalibrating partially parallel acquisitions (GRAPPA), was investigated using simulations. Two fast data acquisition schemes, SENSE and ellipsoidal SENSE, were developed to scan patients at 3T within 9 and 4.5 minutes, respectively. Both SENSE and ellipsoidal SENSE sampling resulted in clinically interpretable spectra with high correlation to the ellipsoidal sampling. The results of this dissertation suggest that magnetic resonance spectroscopic imaging is an important technique for spatially characterizing brain tumors that can be acquired in a shorter time to obtain equivalent disease related information. It is expected that shorter scan times will result in less patient discomfort and motion artifacts, and will increase the scanner throughput

T1 and T2 Metabolite Relaxation Times in Normal Brain at 3T and 7T

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Final Gleason Score Prediction Using Discriminant Analysis and Support Vector Machine Based on Preoperative Multiparametric MR Imaging of Prostate Cancer at 3T

Objective. This study aimed at evaluating linear discriminant analysis (LDA) and support vector machine (SVM) classifiers for estimating final Gleason score preoperatively using multiparametric magnetic resonance imaging (mp-MRI) and clinical parameters. Materials and Methods. Thirty-three patients who underwent mp-MRI on a 3T clinical MR scanner and radical prostatectomy were enrolled in this study. The input features for classifiers were age, the presence of a palpable prostate abnormality, prostate specific antigen (PSA) level, index lesion size, and Likert scales of T2 weighted MRI (T2w-MRI), diffusion weighted MRI (DW-MRI), and dynamic contrast enhanced MRI (DCE-MRI) estimated by an experienced radiologist. SVM based recursive feature elimination (SVM-RFE) was used for eliminating features. Principal component analysis (PCA) was applied for data uncorrelation. Results. Using a standard PCA before final Gleason score classification resulted in mean sensitivities of 51.19% and 64.37% and mean specificities of 72.71% and 39.90% for LDA and SVM, respectively. Using a Gaussian kernel PCA resulted in mean sensitivities of 86.51% and 87.88% and mean specificities of 63.99% and 56.83% for LDA and SVM, respectively. Conclusion. SVM classifier resulted in a slightly higher sensitivity but a lower specificity than LDA method for final Gleason score prediction for prostate cancer for this limited patient population

Spatial Characteristics of Newly Diagnosed Grade 3 Glioma Assessed by Magnetic Resonance Metabolic and Diffusion Tensor Imaging1

The spatial heterogeneity in magnetic resonance (MR) metabolic and diffusion parameters and their relationship were studied for patients with treatment-naive grade 3 gliomas. MR data were evaluated from 51 patients with newly diagnosed grade 3 gliomas. Anatomic, diffusion, and metabolic imaging data were considered. Variations in metabolite levels, apparent diffusion coefficient (ADC), and fractional anisotropy (FA) were evaluated in regions of gadolinium enhancement and T2 hyperintensity as well as regions with abnormal metabolic signatures. Contrast enhancement was present in only 21 of the 51 patients. When present, the enhancing component of the lesion had higher choline-to-N-acetylaspartate index (CNI), higher choline, lower N-acetylaspartate, similar creatine, similar ADC and FA, and higher lactate/lipid than the nonenhancing lesion. Regions with CNI ≥ 4 had higher choline, lower N-acetylaspartate, higher lactate/lipid, higher ADC, and lower FA than normal-appearing white matter and regions with intermediate CNI values. For lesions that exhibited gadolinium enhancement, the metabolite levels and diffusion parameters in the region of enhancement were consistent with it corresponding to the most abnormal portion of the tumor. For nonenhancing lesions, areas with CNI ≥ 4 were the most abnormal in metabolic and diffusion parameters. This suggests that the region with the highest CNI might provide a good target for biopsies for nonenhancing lesions to obtain a representative histologic diagnosis of its degree of malignancy. Metabolic and diffusion parameter levels may be of interest not only for directing tissue sampling but also for defining the targets for focal therapy and assessing response to therapy

The cerebral blood flow deficits in Parkinson's disease with mild cognitive impairment using arterial spin labeling MRI

Yildirim, Zerrin/0000-0002-5128-1784; Eryurek, Kardelen/0000-0003-0482-3963; Gurvit, Hakan/0000-0003-2908-8475; Arslan, Dilek Betul/0000-0002-1124-3695WOS: 000545915200001PubMed: 32632889Parkinson's disease (PD) with mild cognitive impairment (PD-MCI) is currently diagnosed based on an arbitrarily predefined standard deviation of neuropsychological test scores, and more objective biomarkers for PD-MCI diagnosis are needed. the purpose of this study was to define possible brain perfusion-based biomarkers of not only mild cognitive impairment, but also risky gene carriers in PD using arterial spin labeling magnetic resonance imaging (ASL-MRI). Fifteen healthy controls (HC), 26 cognitively normal PD (PD-CN), and 27 PD-MCI subjects participated in this study. ASL-MRI data were acquired by signal targeting with alternating radio-frequency labeling with Look-Locker sequence at 3 T. Single nucleotide polymorphism genotyping for rs9468 [microtubule-associated protein tau (MAPT) H1/H1 versus H1/H2 haplotype] was performed using a Stratagene Mx3005p real-time polymerase chain-reaction system (Agilent Technologies, USA). There were 15 subjects withMAPTH1/H1 and 11 subjects withMAPTH1/H2 within PD-MCI, and 33 subjects withMAPTH1/H1 and 19 subjects withMAPTH1/H2 within all PD. Voxel-wise differences of cerebral blood flow (CBF) values between HC, PD-CN and PD-MCI were assessed by one-way analysis of variance followed by pairwise post hoc comparisons. Further, the subgroup of PD patients carrying the riskyMAPTH1/H1 haplotype was compared with noncarriers (MAPTH1/H2 haplotype) in terms of CBF by a two-samplettest. A pattern that could be summarized as "posterior hypoperfusion" (PH) differentiated the PD-MCI group from the HC group with an accuracy of 92.6% (sensitivity = 93%, specificity = 93%). Additionally, the PD patients withMAPTH1/H1 haplotype had decreased perfusion than the ones with H1/H2 haplotype at the posterior areas of the visual network (VN), default mode network (DMN), and dorsal attention network (DAN). the PH-type pattern in ASL-MRI could be employed as a biomarker of both current cognitive impairment and future cognitive decline in PD.Scientific and Technological Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [1001, 115S219]; Istanbul University Scientific Research Projects Unit Project [1567/42362]This study was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) 1001 Grant #115S219 and Istanbul University Scientific Research Projects Unit Project #1567/42362

Data from: Workshop on reconstruction schemes for magnetic resonance data: summary of findings and recommendations

The high-fidelity reconstruction of compressed and low-resolution magnetic resonance (MR) data is essential for simultaneously improving patient care, accuracy in diagnosis and quality in clinical research. Sponsored by the Royal Society through the Newton Mobility Grant Scheme, we held a half-day workshop on reconstruction schemes for MR data on 17 August 2016 to discuss new ideas from related research fields that could be useful to overcome the shortcomings of the conventional reconstruction methods that have been evaluated to date. Participants were 21 university students, computer scientists, image analysts, engineers and physicists from institutions from six different countries. The discussion evolved around exploring new avenues to achieve high resolution, high quality and fast acquisition of MR imaging. In this article, we summarize the topics covered throughout the workshop and make recommendations for ongoing and future works