10 results on '"Martin Wilson"'
Search Results
2. Adaptive baseline fitting for MR spectroscopy analysis
- Author
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Martin Wilson
- Subjects
03 medical and health sciences ,Spline (mathematics) ,0302 clinical medicine ,Fully automated ,WHITE MATTER TISSUE ,Fitting algorithm ,Radiology, Nuclear Medicine and imaging ,Overfitting ,Akaike information criterion ,Algorithm ,030217 neurology & neurosurgery ,030218 nuclear medicine & medical imaging ,Mathematics - Abstract
Purpose Accurate baseline modeling is essential for reliable MRS analysis and interpretation-particularly at short echo-times, where enhanced metabolite information coincides with elevated baseline interference. The degree of baseline smoothness is a key analysis parameter for metabolite estimation, and in this study, a new method is presented to estimate its optimal value. Methods An adaptive baseline fitting algorithm (ABfit) is described, incorporating a spline basis into a frequency-domain analysis model, with a penalty parameter to enforce baseline smoothness. A series of candidate analyses are performed over a range of smoothness penalties, as part of a 4-stage algorithm, and the Akaike information criterion is used to estimate the appropriate penalty. ABfit is applied to a set of simulated spectra with differing baseline features and experimentally acquired 2D MRSI-both at a field strength of 3 Tesla. Results Simulated analyses demonstrate metabolite errors result from 2 main sources: bias from an inflexible baseline (underfitting) and increased variance from an overly flexible baseline (overfitting). In the case of an ideal flat baseline, ABfit is shown to correctly estimate a highly rigid baseline, and for more realistic spectra a reasonable compromise between bias and variance is found. Analysis of experimentally acquired data demonstrates good agreement with known correlations between metabolite ratios and the contributing volumes of gray and white matter tissue. Conclusions ABfit has been shown to perform accurate baseline estimation and is suitable for fully automated routine MRS analysis.
- Published
- 2020
3. Methodological consensus on clinical proton MRS of the brain: Review and recommendations
- Author
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Carolyn E. Mountford, Arend Heerschap, Ramon Gonzalez, Dieter J. Meyerhoff, Rolf Gruetter, Martin O. Leach, Nouha Salibi, Peter B. Barker, Stephan Gruber, Cristina Cudalbu, In-Young Choi, Ivan Tkáč, Alberto Bizzi, Hoby P. Hetherington, Harish Poptani, Alexander P. Lin, Rakesh Gupta, Daniel B. Vigneron, Stefan Posse, Petra Susan Hüppi, Dennis W. J. Klomp, Małgorzata Marjańska, Kejal Kantarci, Risto A. Kauppinen, Ralph E. Hurd, Ovidiu C. Andronesi, Kevin M. Brindle, Tom W. J. Scheenen, Franklyn A. Howe, Ulrike Dydak, Martin Wilson, Patrick J. Bolan, Ralph Noeske, Brian J. Soher, Paul G. Mullins, Roland Kreis, Robert Bartha, Julie W Pan, Gülin Öz, Ian C.P. Smith, Andrew A. Maudsley, Eva-Maria Ratai, Andrew C. Peet, James B. Murdoch, Anke Henning, Marijn J. Kruiskamp, Sarah J. Nelson, Uzay E. Emir, and Peter R. Luijten
- Subjects
MRS ,Computer science ,brain ,Biomedical Engineering ,semi-LASER ,Brain and Behaviour ,Article ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Research community ,Urological cancers Radboud Institute for Molecular Life Sciences [Radboudumc 15] ,shimming ,Humans ,Radiology, Nuclear Medicine and imaging ,610 Medicine & health ,metabolites ,CIBM-AIT ,screening and diagnosis ,Brain Imaging ,ddc:618 ,Semi laser ,Magnetic Resonance Imaging ,4.1 Discovery and preclinical testing of markers and technologies ,Detection ,Nuclear Medicine & Medical Imaging ,Risk analysis (engineering) ,Radiology Nuclear Medicine and imaging ,consensus ,Urological cancers Radboud Institute for Health Sciences [Radboudumc 15] ,Medical Biophysics ,Biomedical Imaging ,Protons ,Proton mrs ,030217 neurology & neurosurgery - Abstract
Proton Magnetic Resonance Spectroscopy ((1)H MRS) provides non-invasive, quantitative metabolite profiles of tissue and has been shown to aid the clinical management of several brain diseases. Whilst most modern clinical MR scanners support MRS capabilities, routine use is largely restricted to specialized centers with good access to MR research support. Widespread adoption has been slow for several reasons, and technical challenges towards obtaining reliable good-quality results have been identified as a contributing factor. Considerable progress has been made by the research community to address many of these challenges, and in this paper a consensus is presented on deficiencies in widely available MRS methodology and validated improvements that are currently in routine use at several clinical research institutions. In particular, the localization error for the popular point resolved spectroscopy (PRESS) localization sequence was found to be unacceptably high at 3T, and the use of the semi-adiabatic localization by adiabatic selective refocusing (semi-LASER) sequence is a recommended solution. The incorporation of simulated metabolite basis-sets into analysis routines is recommended for reliably capturing the full spectral detail available from short echo time acquisitions. In addition, the importance of achieving a highly homogenous static magnetic field (B(0)) in the acquisition region is emphasized, and the limitations of current methods and hardware are discussed. Most recommendations require only software improvements, greatly enhancing the capabilities of clinical MRS on existing hardware. We anticipate the implementation of these recommendations will strengthen current clinical applications and advance progress towards developing and validating new MRS biomarkers for clinical use.
- Published
- 2019
4. Robust retrospective frequency and phase correction for single-voxel MR spectroscopy
- Author
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Martin Wilson
- Subjects
In vivo magnetic resonance spectroscopy ,Magnetic Resonance Spectroscopy ,Single voxel ,Signal-To-Noise Ratio ,Instability ,Spectral line ,030218 nuclear medicine & medical imaging ,Motion ,03 medical and health sciences ,0302 clinical medicine ,Robustness (computer science) ,Image Processing, Computer-Assisted ,Humans ,Frequency offset ,Computer Simulation ,Radiology, Nuclear Medicine and imaging ,Mathematics ,Subtraction ,Brain ,Water ,Glutathione ,Lipids ,Healthy Volunteers ,Data quality ,Artifacts ,Algorithm ,Algorithms ,030217 neurology & neurosurgery - Abstract
PURPOSE Subject motion and static field (B0 ) drift are known to reduce the quality of single voxel MR spectroscopy data due to incoherent averaging. Retrospective correction has previously been shown to improve data quality by adjusting the phase and frequency offset of each average to match a reference spectrum. In this work, a new method (RATS) is developed to be tolerant to large frequency shifts (>7 Hz) and baseline instability resulting from inconsistent water suppression. METHODS In contrast to previous approaches, the variable-projection method and baseline fitting is incorporated into the correction procedure to improve robustness to fluctuating baseline signals and optimization instability. RATS is compared to an alternative method, based on time-domain spectral registration (TDSR), using simulated data to model frequency, phase, and baseline instability. In addition, a J-difference edited glutathione in-vivo dataset is processed using both approaches and compared. RESULTS RATS offers improved accuracy and stability for large frequency shifts and unstable baselines. Reduced subtraction artifacts are demonstrated for glutathione edited MRS when using RATS, compared with uncorrected or TDSR corrected spectra. CONCLUSIONS The RATS algorithm has been shown to provide accurate retrospective correction of SVS MRS data in the presence of large frequency shifts and baseline instability. The method is rapid, generic and therefore readily incorporated into MRS processing pipelines to improve lineshape, SNR, and aid quality assessment.
- Published
- 2018
5. Classification of single-voxel1H spectra of childhood cerebellar tumors using lcmodel and whole tissue representations
- Author
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Franklyn A. Howe, Andrew C. Peet, Martin Wilson, Nigel P. Davies, and Felix Raschke
- Subjects
Male ,Ependymoma ,Pathology ,medicine.medical_specialty ,Magnetic Resonance Spectroscopy ,Single voxel ,Models, Neurological ,Childhood Cerebellar Tumors ,Sensitivity and Specificity ,User input ,Nuclear magnetic resonance ,medicine ,Humans ,Computer Simulation ,Radiology, Nuclear Medicine and imaging ,Diagnosis, Computer-Assisted ,Cerebellar Neoplasms ,Child ,neoplasms ,Mathematics ,Medulloblastoma ,Models, Statistical ,Pilocytic astrocytoma ,Infant ,Reproducibility of Results ,medicine.disease ,nervous system diseases ,Data set ,Child, Preschool ,Principal component analysis ,Female ,Protons ,Software - Abstract
In this study, mean tumor spectra are used as the basis functions in LCModel to create a direct classification tool for short echo time (1)H magnetic resonance spectroscopy of pediatric brain tumors. LCModel is a widely used analysis tool designed to fit a linear combination of individual metabolite spectra to in vivo spectra. Here, we have used LCModel to fit mean spectra and corresponding variability components of childhood cerebellar tumors, as calculated using principal component analysis, and assessed for classification accuracy. Classification was performed according to the highest estimated tumor proportion. This method was tested in a leave-one-out analysis discriminating between pediatric brain tumor spectra of medulloblastoma vs. pilocytic astrocytoma and medulloblastoma vs. pilocytic astrocytoma vs. ependymoma. Additionally, the effect of accepting different Cramér-Rao Lower Bound cut-off criteria on classification accuracy and estimated tissue proportions was investigated. The best classification results differentiating medulloblastoma vs. pilocytic astrocytoma and medulloblastoma vs. pilocytic astrocytoma vs. ependymoma were 100 and 87.7%, respectively. These results are comparable to a specialized pattern recognition analysis of this data set and give easy to interpret results in the form of estimated tissue proportions. The method requires minimal user input and is easily transferable across sites and to other magnetic resonance spectroscopy classification problems.
- Published
- 2012
6. A constrained least-squares approach to the automated quantitation of in vivo1H magnetic resonance spectroscopy data
- Author
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Risto A. Kauppinen, Greg Reynolds, Martin Wilson, Andrew C. Peet, and Theodoros N. Arvanitis
- Subjects
chemistry.chemical_compound ,Nuclear magnetic resonance ,chemistry ,In vivo ,Robustness (computer science) ,Metabolite ,Constrained least squares ,Hankel singular value ,Radiology, Nuclear Medicine and imaging ,Nuclear magnetic resonance spectroscopy ,Time domain ,Least squares ,Algorithm - Abstract
Totally Automatic Robust Quantitation in NMR (TARQUIN), a new method for the fully automatic analysis of short echo time in vivo (1)H Magnetic resonance spectroscopy is presented. Analysis is performed in the time domain using non-negative least squares, and a new method for applying soft constraints to signal amplitudes is used to improve fitting stability. Initial point truncation and Hankel singular value decomposition water removal are used to reduce baseline interference. Three methods were used to test performance. First, metabolite concentrations from six healthy volunteers at 3 T were compared with LCModel™. Second, a Monte-Carlo simulation was performed and results were compared with LCModel™ to test the accuracy of the new method. Finally, the new algorithm was applied to 1956 spectra, acquired clinically at 1.5 T, to test robustness to noisy, abnormal, artifactual, and poorly shimmed spectra. Discrepancies of less than approximately 20% were found between the main metabolite concentrations determined by TARQUIN and LCModel™ from healthy volunteer data. The Monte-Carlo simulation revealed that errors in metabolite concentration estimates were comparable with LCModel™. TARQUIN analyses were also found to be robust to clinical data of variable quality. In conclusion, TARQUIN has been shown to be an accurate and robust algorithm for the analysis of magnetic resonance spectroscopy data making it suitable for use in a clinical setting.
- Published
- 2010
7. An algorithm for the automated quantitation of metabolites in in vitro NMR signals
- Author
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Greg Reynolds, Martin Wilson, Andrew C. Peet, and Theodoros N. Arvanitis
- Subjects
Neurons ,Magnetic Resonance Spectroscopy ,Computer science ,Chemical shift ,Brain ,Basis function ,Nuclear magnetic resonance spectroscopy ,Signal ,Cell Line ,Biopolymers ,Robustness (computer science) ,Magic angle spinning ,Humans ,Radiology, Nuclear Medicine and imaging ,Time domain ,Sensitivity (control systems) ,Protons ,Algorithm ,Algorithms - Abstract
The quantitation of metabolite concentrations from in vitro NMR spectra is hampered by the sensitivity of peak positions to experimental conditions. The quantitation methods currently available are generally labor intensive and cannot readily be automated. Here, an algorithm is presented for the automatic time domain analysis of high-resolution NMR spectra. The TARQUIN algorithm uses a set of basis functions obtained by quantum mechanical simulation using predetermined parameters. Each basis function is optimized by subdividing it into a set of signals from magnetically equivalent spins and varying the simulated chemical shifts of each of these groups to match the signal undergoing analysis. A novel approach to the standard multidimensional minimization problem is introduced based on evaluating the fit resulting from different permutations of possible chemical shifts, obtained from one-dimensional searches. Results are presented from the analysis of 1H proton magic angle spinning spectra of cell lines illustrating the robustness of the method in a typical application. Simulation was used to investigate the biggest peak shifts that can be tolerated. Magn Reson Med, 2006. © 2006 Wiley-Liss, Inc.
- Published
- 2006
8. White matter mapping using diffusion tensor MRI
- Author
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Christopher R. Tench, L D Blumhardt, Paul S. Morgan, and Martin Wilson
- Subjects
Brain Mapping ,Multiple Sclerosis ,business.industry ,Pyramidal Tracts ,Partial volume ,Pattern recognition ,computer.software_genre ,Corpus callosum ,Magnetic Resonance Imaging ,Corpus Callosum ,White matter ,medicine.anatomical_structure ,Similarity (network science) ,Voxel ,medicine ,Humans ,Effective diffusion coefficient ,Radiology, Nuclear Medicine and imaging ,Artificial intelligence ,business ,computer ,Algorithms ,Diffusion MRI ,Tractography ,Mathematics - Abstract
Diffusion tensor MRI is used to define trajectories that reflect the long-range order of in vivo white matter (WM) fiber tracts. Fiber tracking is particularly prone to cumulative error from noise and partial volume along the length of the trajectory paths, but the overall shape of each path is anatomically meaningful. By considering only the long-range similarity of path shapes, a method of constructing 3D maps of specific WM structures has been developed. A trajectory is first computed from an operator-selected seed voxel, located within the anatomical structure of interest (SOI). Voxels from the same structure are then automatically identified based on the similarity of trajectory path shapes, assessed using Pearson's correlation coefficient. The corpus callosum and pyramidal tracts in 14 patients with multiple sclerosis, and in 10 healthy controls were mapped by this method, and the apparent diffusion coefficient (ADC) was measured. The ADC was significantly higher in patients than in controls, and higher in the corpus callosum than in the pyramidal tracts for both groups. Using this method the different functional structures in the WM may be identified and mapped. Within these maps, MRI parameters can be measured for subsequent comparison with relevant clinical data.
- Published
- 2002
9. Sensitivity encoding for fast (1) H MR spectroscopic imaging water reference acquisition
- Author
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Rebecca, Birch, Andrew C, Peet, Theodoros N, Arvanitis, and Martin, Wilson
- Subjects
Adult ,Brain Chemistry ,Aspartic Acid ,Brain Mapping ,Magnetic Resonance Spectroscopy ,Phantoms, Imaging ,Brain ,Glutamic Acid ,Reproducibility of Results ,Water ,Creatine ,Image Enhancement ,Healthy Volunteers ,Choline ,Image Processing, Computer-Assisted ,Humans - Abstract
Accurate and fast (1) H MR spectroscopic imaging (MRSI) water reference scans are important for absolute quantification of metabolites. However, the additional acquisition time required often precludes the water reference quantitation method for MRSI studies. Sensitivity encoding (SENSE) is a successful MR technique developed to reduce scan time. This study quantitatively assesses the accuracy of SENSE for water reference MRSI data acquisition, compared with the more commonly used reduced resolution technique.2D MRSI water reference data were collected from a phantom and three volunteers at 3 Tesla for full acquisition (306 s); 2× reduced resolution (64 s) and SENSE R = 3 (56 s) scans. Water amplitudes were extracted using MRS quantitation software (TARQUIN). Intensity maps and Bland-Altman statistics were generated to assess the accuracy of the fast-MRSI techniques.The average mean and standard deviation of differences from the full acquisition were 2.1 ± 3.2% for SENSE and 10.3 ± 10.7% for the reduced resolution technique, demonstrating that SENSE acquisition is approximately three times more accurate than the reduced resolution technique.SENSE was shown to accurately reconstruct water reference data for the purposes of in vivo absolute metabolite quantification, offering significant improvement over the more commonly used reduced resolution technique.
- Published
- 2014
10. A constrained least-squares approach to the automated quantitation of in vivo ¹H magnetic resonance spectroscopy data
- Author
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Martin, Wilson, Greg, Reynolds, Risto A, Kauppinen, Theodoros N, Arvanitis, and Andrew C, Peet
- Subjects
Adult ,Male ,Magnetic Resonance Spectroscopy ,Brain ,Reproducibility of Results ,Middle Aged ,Sensitivity and Specificity ,Young Adult ,Biopolymers ,Data Interpretation, Statistical ,Humans ,Least-Squares Analysis ,Protons ,Algorithms - Abstract
Totally Automatic Robust Quantitation in NMR (TARQUIN), a new method for the fully automatic analysis of short echo time in vivo (1)H Magnetic resonance spectroscopy is presented. Analysis is performed in the time domain using non-negative least squares, and a new method for applying soft constraints to signal amplitudes is used to improve fitting stability. Initial point truncation and Hankel singular value decomposition water removal are used to reduce baseline interference. Three methods were used to test performance. First, metabolite concentrations from six healthy volunteers at 3 T were compared with LCModel™. Second, a Monte-Carlo simulation was performed and results were compared with LCModel™ to test the accuracy of the new method. Finally, the new algorithm was applied to 1956 spectra, acquired clinically at 1.5 T, to test robustness to noisy, abnormal, artifactual, and poorly shimmed spectra. Discrepancies of less than approximately 20% were found between the main metabolite concentrations determined by TARQUIN and LCModel™ from healthy volunteer data. The Monte-Carlo simulation revealed that errors in metabolite concentration estimates were comparable with LCModel™. TARQUIN analyses were also found to be robust to clinical data of variable quality. In conclusion, TARQUIN has been shown to be an accurate and robust algorithm for the analysis of magnetic resonance spectroscopy data making it suitable for use in a clinical setting.
- Published
- 2010
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