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Mennecke A, Khakzar KM, German A, Herz K, Fabian MS, Liebert A, Blümcke I, Kasper BS, Nagel AM, Laun FB, Schmidt M, Winkler J, Dörfler A, Zaiss M. 7 tricks for 7 T CEST: Improving the reproducibility of multipool evaluation provides insights into the effects of age and the early stages of Parkinson's disease. NMR IN BIOMEDICINE 2023; 36:e4717. [PMID: 35194865 DOI: 10.1002/nbm.4717] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 02/10/2022] [Accepted: 02/21/2022] [Indexed: 05/23/2023]
Abstract
The objective of the current study was to optimize the postprocessing pipeline of 7 T chemical exchange saturation transfer (CEST) imaging for reproducibility and to prove this optimization for the detection of age differences and differences between patients with Parkinson's disease versus normal subjects. The following 7 T CEST MRI experiments were analyzed: repeated measurements of a healthy subject, subjects of two age cohorts (14 older, seven younger subjects), and measurements of 12 patients with Parkinson's disease. A slab-selective, B 1 + -homogeneous parallel transmit protocol was used. The postprocessing, consisting of motion correction, smoothing, B 0 -correction, normalization, denoising, B 1 + -correction and Lorentzian fitting, was optimized regarding the intrasubject and intersubject coefficient of variation (CoV) of the amplitudes of the amide pool and the aliphatic relayed nuclear Overhauser effect (rNOE) pool within the brain. Seven "tricks" for postprocessing accomplished an improvement of the mean voxel CoV of the amide pool and the aliphatic rNOE pool amplitudes of less than 5% and 3%, respectively. These postprocessing steps are: motion correction with interpolation of the motion of low-signal offsets (1) using the amide pool frequency offset image as reference (2), normalization of the Z-spectrum using the outermost saturated measurements (3), B 0 correction of the Z-spectrum with moderate spline smoothing (4), denoising using principal component analysis preserving the 11 highest intensity components (5), B 1 + correction using a linear fit (6) and Lorentzian fitting using the five-pool fit model (7). With the optimized postprocessing pipeline, a significant age effect in the amide pool can be detected. Additionally, for the first time, an aliphatic rNOE contrast between subjects with Parkinson's disease and age-matched healthy controls in the substantia nigra is detected. We propose an optimized postprocessing pipeline for CEST multipool evaluation. It is shown that by the use of these seven "tricks", the reproducibility and, thus, the statistical power of a CEST measurement, can be greatly improved and subtle changes can be detected.
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Affiliation(s)
- Angelika Mennecke
- Institute of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Katrin M Khakzar
- Institute of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Alexander German
- Institute of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Kai Herz
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Department of Biomedical Magnetic Resonance, University of Tuebingen, Tuebingen, Germany
| | - Moritz S Fabian
- Institute of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Andrzej Liebert
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Ingmar Blümcke
- Institute of Neuropathology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Burkhard S Kasper
- Department of Neurology, Epilepsy Centre, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Armin M Nagel
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Frederik B Laun
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Manuel Schmidt
- Institute of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Jürgen Winkler
- Department of Neurology, Epilepsy Centre, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Arnd Dörfler
- Institute of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Moritz Zaiss
- Institute of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
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Wiesmueller M, Wuest W, Mennecke A, May MS, Heiss R, Fuehres T, Janka R, Uder M, Doerfler A, Laun FB. Comparison of Diagnostic Performance and Image Quality between Topup-Corrected and Standard Readout-Segmented Echo-Planar Diffusion-Weighted Imaging for Cholesteatoma Diagnostics. Diagnostics (Basel) 2023; 13:diagnostics13071242. [PMID: 37046460 PMCID: PMC10093611 DOI: 10.3390/diagnostics13071242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/14/2023] [Accepted: 03/16/2023] [Indexed: 03/29/2023] Open
Abstract
This study compares the diagnostic performance and image quality of single-shot turbo spin-echo DWI (tseDWI), standard readout-segmented DWI (rsDWI), and a modified rsDWI version (topupDWI) for cholesteatoma diagnostics. Thirty-four patients with newly suspected unilateral cholesteatoma were examined on a 1.5 Tesla MRI scanner. Diagnostic performance was evaluated by calculating and comparing the sensitivity and specificity using histopathological results as the standard of reference. Image quality was independently reviewed by two readers using a 5-point Likert scale evaluating image distortions, susceptibility artifacts, image resolution, lesion conspicuity, and diagnostic confidence. Twenty-five cholesteatomas were histologically confirmed after surgery and originated in the study group. TseDWI showed the highest sensitivity with 96% (95% confidence interval (CI): 88–100%), followed by topupDWI with 92% (95% CI: 81–100%) for both readers. The sensitivity for rsDWI was 76% (95% CI: 59–93%) for reader 1 and 84% (95% CI: 70–98%) for reader 2, respectively. Both tseDWI and topupDWI revealed a specificity of 100% (95% CI: 66–100%) and rsDWI of 89% (95% CI: 52–100%). Both tseDWI and topupDWI showed fewer image distortions and susceptibility artifacts compared to rsDWI. Image resolution was consistently rated best for topupDWI, followed by rsDWI, which both outperformed tseDWI. TopupDWI and tseDWI showed comparable results for lesions’ conspicuity and diagnostic confidence, both outperforming rsDWI. Modified readout-segmented DWI using the topup-correction method is preferable to standard rsDWI and may be regarded as an accurate alternative to single-shot turbo spin-echo DWI in cholesteatoma diagnostics.
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Schneider TM, Ma J, Wagner P, Behl N, Nagel AM, Ladd ME, Heiland S, Bendszus M, Straub S. Multiparametric MRI for Characterization of the Basal Ganglia and the Midbrain. Front Neurosci 2021; 15:661504. [PMID: 34234639 PMCID: PMC8255625 DOI: 10.3389/fnins.2021.661504] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 05/17/2021] [Indexed: 12/02/2022] Open
Abstract
Objectives To characterize subcortical nuclei by multi-parametric quantitative magnetic resonance imaging. Materials and Methods: The following quantitative multiparametric MR data of five healthy volunteers were acquired on a 7T MRI system: 3D gradient echo (GRE) data for the calculation of quantitative susceptibility maps (QSM), GRE sequences with and without off-resonant magnetic transfer pulse for magnetization transfer ratio (MTR) calculation, a magnetization−prepared 2 rapid acquisition gradient echo sequence for T1 mapping, and (after a coil change) a density-adapted 3D radial pulse sequence for 23Na imaging. First, all data were co-registered to the GRE data, volumes of interest (VOIs) for 21 subcortical structures were drawn manually for each volunteer, and a combined voxel-wise analysis of the four MR contrasts (QSM, MTR, T1, 23Na) in each structure was conducted to assess the quantitative, MR value-based differentiability of structures. Second, a machine learning algorithm based on random forests was trained to automatically classify the groups of multi-parametric voxel values from each VOI according to their association to one of the 21 subcortical structures. Results The analysis of the integrated multimodal visualization of quantitative MR values in each structure yielded a successful classification among nuclei of the ascending reticular activation system (ARAS), the limbic system and the extrapyramidal system, while classification among (epi-)thalamic nuclei was less successful. The machine learning-based approach facilitated quantitative MR value-based structure classification especially in the group of extrapyramidal nuclei and reached an overall accuracy of 85% regarding all selected nuclei. Conclusion Multimodal quantitative MR enabled excellent differentiation of a wide spectrum of subcortical nuclei with reasonable accuracy and may thus enable sensitive detection of disease and nucleus-specific MR-based contrast alterations in the future.
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Affiliation(s)
- Till M Schneider
- Department of Neuroradiology, University of Heidelberg, Heidelberg, Germany
| | - Jackie Ma
- Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany
| | - Patrick Wagner
- Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany
| | - Nicolas Behl
- Division of Medical Physics in Radiology, German Cancer Research Center, Heidelberg, Germany
| | - Armin M Nagel
- Division of Medical Physics in Radiology, German Cancer Research Center, Heidelberg, Germany.,Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Mark E Ladd
- Division of Medical Physics in Radiology, German Cancer Research Center, Heidelberg, Germany.,Faculty of Physics and Astronomy and Faculty of Medicine, University of Heidelberg, Heidelberg, Germany
| | - Sabine Heiland
- Department of Neuroradiology, University of Heidelberg, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, University of Heidelberg, Heidelberg, Germany
| | - Sina Straub
- Division of Medical Physics in Radiology, German Cancer Research Center, Heidelberg, Germany
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