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Fouto AR, Henriques RN, Golub M, Freitas AC, Ruiz-Tagle A, Esteves I, Gil-Gouveia R, Silva NA, Vilela P, Figueiredo P, Nunes RG. Impact of truncating diffusion MRI scans on diffusional kurtosis imaging. MAGMA 2024:10.1007/s10334-024-01153-y. [PMID: 38393541 DOI: 10.1007/s10334-024-01153-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 01/09/2024] [Accepted: 02/07/2024] [Indexed: 02/25/2024]
Abstract
OBJECTIVE Diffusional kurtosis imaging (DKI) extends diffusion tensor imaging (DTI), characterizing non-Gaussian diffusion effects but requires longer acquisition times. To ensure the robustness of DKI parameters, data acquisition ordering should be optimized allowing for scan interruptions or shortening. Three methodologies were used to examine how reduced diffusion MRI scans impact DKI histogram-metrics: 1) the electrostatic repulsion model (OptEEM); 2) spherical codes (OptSC); 3) random (RandomTRUNC). MATERIALS AND METHODS Pre-acquired diffusion multi-shell data from 14 female healthy volunteers (29±5 years) were used to generate reordered data. For each strategy, subsets containing different amounts of the full dataset were generated. The subsampling effects were assessed on histogram-based DKI metrics from tract-based spatial statistics (TBSS) skeletonized maps. To evaluate each subsampling method on simulated data at different SNRs and the influence of subsampling on in vivo data, we used a 3-way and 2-way repeated measures ANOVA, respectively. RESULTS Simulations showed that subsampling had different effects depending on DKI parameter, with fractional anisotropy the most stable (up to 5% error) and radial kurtosis the least stable (up to 26% error). RandomTRUNC performed the worst while the others showed comparable results. Furthermore, the impact of subsampling varied across distinct histogram characteristics, the peak value the least affected (OptEEM: up to 5% error; OptSC: up to 7% error) and peak height (OptEEM: up to 8% error; OptSC: up to 11% error) the most affected. CONCLUSION The impact of truncation depends on specific histogram-based DKI metrics. The use of a strategy for optimizing the acquisition order is advisable to improve DKI robustness to exam interruptions.
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Affiliation(s)
- Ana R Fouto
- Institute for Systems and Robotics-Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.
| | | | - Marc Golub
- Institute for Systems and Robotics-Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Andreia C Freitas
- Institute for Systems and Robotics-Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Amparo Ruiz-Tagle
- Institute for Systems and Robotics-Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Inês Esteves
- Institute for Systems and Robotics-Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Raquel Gil-Gouveia
- Neurology Department, Hospital da Luz, Lisbon, Portugal
- Center for Interdisciplinary Research in Health, Universidade Católica Portuguesa, Lisbon, Portugal
| | - Nuno A Silva
- Learning Health, Hospital da Luz, Lisbon, Portugal
| | - Pedro Vilela
- Imaging Department, Hospital da Luz, Lisbon, Portugal
| | - Patrícia Figueiredo
- Institute for Systems and Robotics-Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Rita G Nunes
- Institute for Systems and Robotics-Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
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Hu B, Wang X, He JB, Dai YJ, Zhang J, Yu Y, Sun Q, Lin-FengYan, Hu YC, Nan HY, Yang Y, Kaye AD, Cui GB, Wang W. Structural and functional brain changes in perimenopausal women who are susceptible to migraine: a study protocol of multi-modal MRI trial. BMC Med Imaging 2018; 18:26. [PMID: 30189858 PMCID: PMC6127929 DOI: 10.1186/s12880-018-0272-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Accepted: 08/29/2018] [Indexed: 01/01/2023] Open
Abstract
Background As a common clinical symptom that often bothers midlife females, migraine is closely associated with perimenopause. Previous studies suggest that one of the most prominent triggers is the sudden decline of estrogen during perimenopausal period. Hormone replacement therapy (HRT) is widely used to prevent this suffering in perimenopausal women, but effective diagnostic system is lacked for quantifying the severity of the diseaase. To avoid the abuse and overuse of HRT, we propose to conduct a diagnostic trial using multimodal MRI techniques to quantify the severity of these perimenopausal migraineurs who are susceptible to the decline of estrogen. Methods Perimenopausal women suffering from migraine will be recruited from the pain clinic of our hospital. Perimenopausal women not suffering from any kind of headache will be recruited from the local community. Clinical assessment and multi-modal MR imaging examination will be conducted. A follow up will be conducted once half year within 3 years. Pain behavior, neuropsychology scores, fMRI analysis combined with suitable statistical software will be used to reveal the potential association between these above traits and the susceptibility of migraine. Discussion Multi-modal imaging features of both healthy controls and perimenopausal women who are susceptible to estrogen decline will be acquired. Imaging features will include volumetric characteristics, white matter integrity, functional characteristics, topological properties, and perfusion properties. Clinical information, such as basic information, blood estrogen level, information of migraine, and a bunch of neurological scale will also be used for statistic assessment. This clinical trial would help to build an effective screen system for quantifying the severity of illness of those susceptible women during the perimenopausal period. Trial registration This study has already been registered at Clinical Trials. gov (ID: NCT02820974). Registration date: September 28th, 2014.
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Affiliation(s)
- Bo Hu
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), 569 Xinsi Road, Xi'an, 710038, Shaanxi, China
| | - Xu Wang
- Student Brigade, Fourth Military Medical University (Air Force Medical University), 169 West Changle Road, Xi'an, 710032, Shaanxi Province, China
| | - Jie-Bing He
- Student Brigade, Fourth Military Medical University (Air Force Medical University), 169 West Changle Road, Xi'an, 710032, Shaanxi Province, China
| | - Yu-Jie Dai
- Department of Clinical Nutrition, Xijing Hospital, The Fourth Military Medical University, Xi'an, China
| | - Jin Zhang
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), 569 Xinsi Road, Xi'an, 710038, Shaanxi, China
| | - Ying Yu
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), 569 Xinsi Road, Xi'an, 710038, Shaanxi, China
| | - Qian Sun
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), 569 Xinsi Road, Xi'an, 710038, Shaanxi, China
| | - Lin-FengYan
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), 569 Xinsi Road, Xi'an, 710038, Shaanxi, China
| | - Yu-Chuan Hu
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), 569 Xinsi Road, Xi'an, 710038, Shaanxi, China
| | - Hai-Yan Nan
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), 569 Xinsi Road, Xi'an, 710038, Shaanxi, China
| | - Yang Yang
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), 569 Xinsi Road, Xi'an, 710038, Shaanxi, China
| | - Alan D Kaye
- Departments of Anesthesiology and Pharmacology, Louisiana State University School of Medicine, New Orleans, Louisiana, USA
| | - Guang-Bin Cui
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), 569 Xinsi Road, Xi'an, 710038, Shaanxi, China.
| | - Wen Wang
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), 569 Xinsi Road, Xi'an, 710038, Shaanxi, China.
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Marrale M, Collura G, Brai M, Toschi N, Midiri F, La Tona G, Lo Casto A, Gagliardo C. Physics, Techniques and Review of Neuroradiological Applications of Diffusion Kurtosis Imaging (DKI). Clin Neuroradiol 2016; 26:391-403. [PMID: 26589207 DOI: 10.1007/s00062-015-0469-9] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2015] [Accepted: 09/22/2015] [Indexed: 01/23/2023]
Abstract
In recent years many papers about diagnostic applications of diffusion tensor imaging (DTI) have been published. This is because DTI allows to evaluate in vivo and in a non-invasive way the process of diffusion of water molecules in biological tissues. However, the simplified description of the diffusion process assumed in DTI does not permit to completely map the complex underlying cellular components and structures, which hinder and restrict the diffusion of water molecules. These limitations can be partially overcome by means of diffusion kurtosis imaging (DKI). The aim of this paper is the description of the theory of DKI, a new topic of growing interest in radiology. DKI is a higher order diffusion model that is a straightforward extension of the DTI model. Here, we analyze the physics underlying this method, we report our MRI acquisition protocol with the preprocessing pipeline used and the DKI parametric maps obtained on a 1.5 T scanner, and we review the most relevant clinical applications of this technique in various neurological diseases.
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