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Lu P, Hong R, Tian G, Liu X, Sha Y, Zhang J, Wang X. Diffusional kurtosis imaging in differentiating nonarteritic anterior ischemic optic neuropathy from acute optic neuritis. Neuroradiology 2024; 66:797-807. [PMID: 38383677 DOI: 10.1007/s00234-024-03301-6] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Accepted: 01/27/2024] [Indexed: 02/23/2024]
Abstract
PURPOSE We aimed to determine the feasibility of using DKI to characterize pathological changes in nonarteritic anterior ischemic optic neuropathy (NAION) and to differentiate it from acute optic neuritis (ON). METHODS Orbital DKI was performed with a 3.0 T scanner on 75 patients (51 with NAION and 24 with acute ON) and 15 healthy controls. NAION patients were further divided into early and late groups. The mean kurtosis (MK), axial kurtosis (AK), radial kurtosis (RK), mean diffusivity (MD), fractional anisotropy (FA), radial diffusivity (RD), and axial diffusivity (AD) were calculated to perform quantitative analyses among groups; and receiver operating characteristic curve analyses were also performed to determine their effectiveness of differential diagnosis. In addition, correlation coefficients were calculated to explore the correlations of the DKI-derived data with duration of disease. RESULTS The MK, RK, and AK in the affected nerves with NAION were significantly higher than those in the controls, while the trend of FA, RD, and AD was a decline; in acute ON patients, except for RD, which increased, all DKI-derived kurtosis and diffusion parameters were significantly lower than controls (all P < 0.008). Only AK and MD had statistical differences between the early and late groups. Except for MD (early group) and FA, all other DKI-derived parameters were higher in NAION than in acute ON; and parameters in the early group showed better diagnostic efficacy in differentiating NAION from acute ON. Correlation analysis showed that time was negatively correlated with MK, RK, AK, and FA and positively correlated with MD, RD, and AD (all P < 0.05). CONCLUSION DKI is helpful for assessing the specific pathologic abnormalities resulting from ischemia in NAION by comparison with acute ON. Early DKI should be performed to aid in the diagnosis and evaluation of NAION.
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Affiliation(s)
- Ping Lu
- Department of Radiology, The First Affiliated Hospital of Soochow University, 188 Shizi Road, Suzhou, 215006, China
- Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, 26 Daoqian Street, Suzhou, 215002, China
| | - Rujian Hong
- Department of Radiology, Eye & ENT Hospital of Fudan University, 83 Fenyang Road, Shanghai, 200031, China
| | - Guohong Tian
- Department of Ophthalmology, Eye & ENT Hospital of Fudan University, 83 Fenyang Road, Shanghai, 200031, China
| | - Xilan Liu
- Department of Radiology, Eye & ENT Hospital of Fudan University, 83 Fenyang Road, Shanghai, 200031, China
| | - Yan Sha
- Department of Radiology, Eye & ENT Hospital of Fudan University, 83 Fenyang Road, Shanghai, 200031, China
| | - Jibin Zhang
- Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, 26 Daoqian Street, Suzhou, 215002, China
| | - Ximing Wang
- Department of Radiology, The First Affiliated Hospital of Soochow University, 188 Shizi Road, Suzhou, 215006, China.
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Guo Y, Guo T, Huang C, Sun P, Wu Z, Jin Z, Zheng C, Li X. Combining T1rho and advanced diffusion MRI for noninvasively staging liver fibrosis: an experimental study in rats. Abdom Radiol (NY) 2024:10.1007/s00261-024-04327-3. [PMID: 38607572 DOI: 10.1007/s00261-024-04327-3] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 03/30/2024] [Accepted: 04/02/2024] [Indexed: 04/13/2024]
Abstract
PURPOSE To investigate the value of imaging parameters derived from T1 relaxation times in the rotating frame (T1ρ or T1rho), diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) in assessment of liver fibrosis in rats and propose an optimal diagnostic model based on multiparametric MRI. METHODS Thirty rats were divided into one control group and four fibrosis experimental groups (n = 6 for each group). Liver fibrosis was induced by administering thioacetamide (TAA) for 2, 4, 6, and 8 weeks. T1ρ, mean kurtosis (MK), mean diffusivity (MD), perfusion fraction (f), true diffusion coefficient (D), and pseudo-diffusion coefficient (D*) were measured and compared among different fibrosis stages. An optimal diagnostic model was established and the diagnostic efficiency was evaluated by receiver operating characteristic (ROC) curve analysis. RESULTS The mean AUC values, sensitivity, and specificity of T1ρ and MD derived from DKI across all liver fibrosis stages were comparable but much higher than those of other imaging parameters (0.954, 92.46, 91.85 for T1ρ; 0.949, 92.52, 91.24 for MD). The model combining T1ρ and MD exhibited better diagnostic performance with higher AUC values than any individual method for staging liver fibrosis (≥ F1: 1.000 (0.884-1.000); ≥ F2: 0.935 (0.782-0.992); ≥ F3: 0.982 (0.852-1.000); F4: 0.986 (0.859-1.000)). CONCLUSION Among the evaluated imaging parameters, T1ρ and MD were superior for differentiating varying liver fibrosis stages. The model combining T1ρ and MD was promising to be a credible diagnostic biomarker to detect and accurately stage liver fibrosis.
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Affiliation(s)
- Yiwan Guo
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Tingting Guo
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Chen Huang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Peng Sun
- Clinical & Technical Support, Philips Healthcare, No. 1628, Zhongshan Road, Wuhan, China
| | - Zhigang Wu
- Clinical & Technical Support, Philips Healthcare, No. 1628, Zhongshan Road, Wuhan, China
| | - Ziwei Jin
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Chuansheng Zheng
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Xin Li
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China.
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Das CJ, Malagi AV, Sharma R, Mehndiratta A, Kumar V, Khan MA, Seth A, Kaushal S, Nayak B, Kumar R, Gupta AK. Intravoxel incoherent motion and diffusion kurtosis imaging and their machine-learning-based texture analysis for detection and assessment of prostate cancer severity at 3 T. NMR Biomed 2024:e5144. [PMID: 38556777 DOI: 10.1002/nbm.5144] [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] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 02/01/2024] [Accepted: 02/20/2024] [Indexed: 04/02/2024]
Abstract
OBJECTIVES To evaluate the role of combined intravoxel incoherent motion and diffusion kurtosis imaging (IVIM-DKI) and their machine-learning-based texture analysis for the detection and assessment of severity in prostate cancer (PCa). MATERIALS AND METHODS Eighty-eight patients underwent MRI on a 3 T scanner after giving informed consent. IVIM-DKI data were acquired using 13 b values (0-2000 s/mm2) and analyzed using the IVIM-DKI model with the total variation (TV) method. PCa patients were categorized into two groups: clinically insignificant prostate cancer (CISPCa) (Gleason grade ≤ 6) and clinically significant prostate cancer (CSPCa) (Gleason grade ≥ 7). One-way analysis-of-variance, t test, and receiver operating characteristic analysis was performed to measure the discriminative ability to detect PCa using IVIM-DKI parameters. A chi-square test was used to select important texture features of apparent diffusion coefficient (ADC) and IVIM-DKI parameters. These selected texture features were used in an artificial neural network for PCa detection. RESULTS ADC and diffusion coefficient (D) were significantly lower (p < 0.001), and kurtosis (k) was significantly higher (p < 0.001), in PCa as compared with benign prostatic hyperplasia (BPH) and normal peripheral zone (PZ). ADC, D, and k showed high areas under the curves (AUCs) of 0.92, 0.89, and 0.88, respectively, in PCa detection. ADC and D were significantly lower (p < 0.05) as compared with CISPCa versus CSPCa. D for detecting CSPCa was high, with an AUC of 0.63. A negative correlation of ADC and D with GS (ADC, ρ = -0.33; D, ρ = -0.35, p < 0.05) and a positive correlation of k with GS (ρ = 0.22, p < 0.05) were observed. Combined IVIM-DKI texture showed high AUC of 0.83 for classification of PCa, BPH, and normal PZ. CONCLUSION D, f, and k computed using the IVIM-DKI model with the TV method were able to differentiate PCa from BPH and normal PZ. Texture features of combined IVIM-DKI parameters showed high accuracy and AUC in PCa detection.
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Affiliation(s)
- Chandan J Das
- Department of Radio-Diagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi, India
| | - Archana Vadiraj Malagi
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Raju Sharma
- Department of Radio-Diagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi, India
| | - Amit Mehndiratta
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Virendra Kumar
- Department of Nuclear Magnetic Resonance, All India Institute of Medical Sciences, New Delhi, India
| | - Maroof A Khan
- Department of Biostatistics, All India Institute of Medical Sciences, New Delhi, India
| | - Amlesh Seth
- Department of Urology, All India Institute of Medical Sciences, New Delhi, India
| | - Seema Kaushal
- Department of Pathology, All India Institute of Medical Sciences, New Delhi, India
| | - Baibaswata Nayak
- Department of Gastroenterology (Molecular Biology Division), All India Institute of Medical Sciences, New Delhi, India
| | - Rakesh Kumar
- Department of Nuclear Medicine, All India Institute of Medical Sciences, New Delhi, India
| | - Arun Kumar Gupta
- Department of Radio-Diagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi, India
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Kopřivová T, Keřkovský M, Jůza T, Vybíhal V, Rohan T, Kozubek M, Dostál M. Possibilities of Using Multi-b-value Diffusion Magnetic Resonance Imaging for Classification of Brain Lesions. Acad Radiol 2024; 31:261-272. [PMID: 37932166 DOI: 10.1016/j.acra.2023.10.002] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 09/25/2023] [Accepted: 10/03/2023] [Indexed: 11/08/2023]
Abstract
In contrast to conventional diffusion magnetic resonance imaging (MRI), multi-b-value diffusion MRI methods are able to separate the signal from free water, pseudo-diffusion, and non-Gaussian components of water molecule diffusion. These approaches can then be utilised in so-called intravoxel incoherent motion imaging and diffusion kurtosis imaging. Various parameters provided by these methods can describe additional characteristics of the tissue microstructure and potentially help in the diagnosis and classification of various pathological processes. In this review, we present the basic principles and methods of analysing multi-b-value diffusion imaging data and specifically focus on the known possibilities for its use in the diagnosis of brain lesions. We also suggest possible directions for further research.
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Affiliation(s)
- Tereza Kopřivová
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Jihlavská 20, 625 00, Brno, Czech Republic (T.K., M.K., T.J., T.R., M.D.)
| | - Miloš Keřkovský
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Jihlavská 20, 625 00, Brno, Czech Republic (T.K., M.K., T.J., T.R., M.D.).
| | - Tomáš Jůza
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Jihlavská 20, 625 00, Brno, Czech Republic (T.K., M.K., T.J., T.R., M.D.); Department of Biophysics, Faculty of Medicine, Masaryk University, Brno, Czech Republic (T.J., M.D.)
| | - Václav Vybíhal
- Department of Neurosurgery, Faculty of Medicine, Masaryk University Brno and University Hospital Brno, 625 00, Brno, Czech Republic (V.V.)
| | - Tomáš Rohan
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Jihlavská 20, 625 00, Brno, Czech Republic (T.K., M.K., T.J., T.R., M.D.)
| | - Michal Kozubek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Šumavská, Brno, Czech Republic (M.K.)
| | - Marek Dostál
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Jihlavská 20, 625 00, Brno, Czech Republic (T.K., M.K., T.J., T.R., M.D.); Department of Biophysics, Faculty of Medicine, Masaryk University, Brno, Czech Republic (T.J., M.D.)
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Lindhardt TB, Skoven CS, Bordoni L, Østergaard L, Liang Z, Hansen B. Anesthesia-related brain microstructure modulations detected by diffusion magnetic resonance imaging. NMR Biomed 2023:e5033. [PMID: 37712335 DOI: 10.1002/nbm.5033] [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: 10/05/2022] [Revised: 07/06/2023] [Accepted: 08/09/2023] [Indexed: 09/16/2023]
Abstract
Recent studies have shown significant changes to brain microstructure during sleep and anesthesia. In vivo optical microscopy and magnetic resonance imaging (MRI) studies have attributed these changes to anesthesia and sleep-related modulation of the brain's extracellular space (ECS). Isoflurane anesthesia is widely used in preclinical diffusion MRI (dMRI) and it is therefore important to investigate if the brain's microstructure is affected by anesthesia to an extent detectable with dMRI. Here, we employ diffusion kurtosis imaging (DKI) to assess brain microstructure in the awake and anesthetized mouse brain (n = 22). We find both mean diffusivity (MD) and mean kurtosis (MK) to be significantly decreased in the anesthetized mouse brain compared with the awake state (p < 0.001 for both). This effect is observed in both gray matter and white matter. To further investigate the time course of these changes we introduce a method for time-resolved fast DKI. With this, we show the time course of the microstructural alterations in mice (n = 5) as they transition between states in an awake-anesthesia-awake paradigm. We find that the decrease in MD and MK occurs rapidly after delivery of gas isoflurane anesthesia and that values normalize only slowly when the animals return to the awake state. Finally, time-resolved fast DKI is employed in an experimental mouse model of brain edema (n = 4), where cell swelling causes the ECS volume to decrease. Our results show that isoflurane affects DKI parameters and metrics of brain microstructure and point to isoflurane causing a reduction in the ECS volume. The demonstrated DKI methods are suitable for in-bore perturbation studies, for example, for investigating microstructural modulations related to sleep/wake-dependent functions of the glymphatic system. Importantly, our study shows an effect of isoflurane anesthesia on rodent brain microstructure that has broad relevance to preclinical dMRI.
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Affiliation(s)
- Thomas Beck Lindhardt
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Sino-Danish Center for Education and Research, Aarhus, Denmark
- University of the Chinese Academy of Sciences, Beijing, China
| | - Christian Stald Skoven
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Luca Bordoni
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Letten Center, University of Oslo, Oslo, Norway
| | - Leif Østergaard
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Radiology, Neuroradiology Research Unit, Aarhus University Hospital, Aarhus, Denmark
| | - Zhifeng Liang
- CAS Center for Excellence in Brain Sciences and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, China
| | - Brian Hansen
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
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Kruper J, Benson NC, Caffarra S, Owen J, Wu Y, Lee AY, Lee CS, Yeatman JD, Rokem A. Optic radiations representing different eccentricities age differently. Hum Brain Mapp 2023; 44:3123-3135. [PMID: 36896869 PMCID: PMC10171550 DOI: 10.1002/hbm.26267] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 01/10/2023] [Accepted: 02/16/2023] [Indexed: 03/11/2023] Open
Abstract
The neural pathways that carry information from the foveal, macular, and peripheral visual fields have distinct biological properties. The optic radiations (OR) carry foveal and peripheral information from the thalamus to the primary visual cortex (V1) through adjacent but separate pathways in the white matter. Here, we perform white matter tractometry using pyAFQ on a large sample of diffusion MRI (dMRI) data from subjects with healthy vision in the U.K. Biobank dataset (UKBB; N = 5382; age 45-81). We use pyAFQ to characterize white matter tissue properties in parts of the OR that transmit information about the foveal, macular, and peripheral visual fields, and to characterize the changes in these tissue properties with age. We find that (1) independent of age there is higher fractional anisotropy, lower mean diffusivity, and higher mean kurtosis in the foveal and macular OR than in peripheral OR, consistent with denser, more organized nerve fiber populations in foveal/parafoveal pathways, and (2) age is associated with increased diffusivity and decreased anisotropy and kurtosis, consistent with decreased density and tissue organization with aging. However, anisotropy in foveal OR decreases faster with age than in peripheral OR, while diffusivity increases faster in peripheral OR, suggesting foveal/peri-foveal OR and peripheral OR differ in how they age.
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Affiliation(s)
- John Kruper
- Department of PsychologyUniversity of WashingtonSeattleWashingtonUSA
- eScience InstituteUniversity of WashingtonSeattleWashingtonUSA
| | - Noah C. Benson
- eScience InstituteUniversity of WashingtonSeattleWashingtonUSA
| | - Sendy Caffarra
- Graduate School of Education, Stanford University and Division of Developmental‐Behavioral Pediatrics, Stanford University School of MedicineStanford UniversityStanfordCaliforniaUSA
- Department of Biomedical, Metabolic and Neural SciencesUniversity of Modena and Reggio EmiliaModenaItaly
| | - Julia Owen
- Department of OphthalmologyUniversity of WashingtonSeattleWashingtonUSA
- Roger and Angie Karalis Johnson Retina CenterUniversity of WashingtonSeattleWashingtonUSA
| | - Yue Wu
- Department of OphthalmologyUniversity of WashingtonSeattleWashingtonUSA
- Roger and Angie Karalis Johnson Retina CenterUniversity of WashingtonSeattleWashingtonUSA
| | - Aaron Y. Lee
- Department of OphthalmologyUniversity of WashingtonSeattleWashingtonUSA
- Roger and Angie Karalis Johnson Retina CenterUniversity of WashingtonSeattleWashingtonUSA
| | - Cecilia S. Lee
- Department of OphthalmologyUniversity of WashingtonSeattleWashingtonUSA
- Roger and Angie Karalis Johnson Retina CenterUniversity of WashingtonSeattleWashingtonUSA
| | - Jason D. Yeatman
- Graduate School of Education, Stanford University and Division of Developmental‐Behavioral Pediatrics, Stanford University School of MedicineStanford UniversityStanfordCaliforniaUSA
| | - Ariel Rokem
- Department of PsychologyUniversity of WashingtonSeattleWashingtonUSA
- eScience InstituteUniversity of WashingtonSeattleWashingtonUSA
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Singhal S, Saran S, Saxena S, Bhadoria AS, Grimm R. Role of diffusion kurtosis imaging in evaluating microstructural changes in spinal cord of patients with cervical spondylosis. Eur Spine J 2023; 32:986-993. [PMID: 36738338 DOI: 10.1007/s00586-023-07559-x] [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] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 12/29/2022] [Accepted: 01/22/2023] [Indexed: 02/05/2023]
Abstract
STUDY DESIGN Analytical cross-sectional study. PURPOSE To study the role of diffusion kurtosis imaging (DKI) in evaluating microstructural changes in patients with cervical spondylosis. OVERVIEW OF LITERATURE Cervical spondylosis is a common progressive degenerative disorder of the spine. Conventional magnetic resonance imaging (MRI) can only detect the changes in the spinal cord once there are visual signal changes; hence, it underestimates the extent of the injury. Newer imaging techniques like Diffusion Tensor and Kurtosis Imaging can evaluate the microstructural changes in cervical spinal cord before the obvious signal changes appear. METHODS Conventional MRI, diffusion tensor imaging (DTI), and DKI scans were performed for 90 cervical spondylosis patients on 1.5-T MR Siemens Magnetom aera after obtaining informed consent. Eight patients were excluded due to poor image quality. Fractional anisotropy (FA) colour maps and diffusion kurtosis (DK) maps corresponding to spinal cord cross sections at C2-C3 intervertebral disc level (control) and at the most stenotic levels were obtained. Modified Japanese Orthopaedic Association (mJOA) scoring was used for clinical assessment of the spinal cord function. The changes in DTI and DKI parameters and their correlation with mJOA scores were analysed by SPSS 23 software. RESULTS In our study, mean FA and mean kurtosis (MK) values at the stenotic level (0.54, 1.02) were significantly lower than values at the non-stenotic segment (0.70, 1.27). The mean diffusivity (MD) value at the stenotic segment (1.25) was significantly higher than in the non-stenotic segment (1.09). We also observed a strong positive correlation between mJOA score and FA and MK values and a negative correlation between mJOA score and MD values, suggesting a correlation of FA, MK, and MD with the clinical severity of the disease. CONCLUSION Addition of DTI and DKI sequences helps in early identification of the disease without any additional cost incurred by the patient.
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Affiliation(s)
- Shailvi Singhal
- Department of Radiology, All India Institute of Medical Sciences, Rishikesh, 249203, India
| | - Sonal Saran
- Department of Radiology, All India Institute of Medical Sciences, Rishikesh, 249203, India.
| | - Sudhir Saxena
- Department of Radiology, All India Institute of Medical Sciences, Rishikesh, 249203, India
| | - Ajeet Singh Bhadoria
- Department of Community and Family Medicine, All India Institute of Medical Sciences, Rishikesh, India
| | - Robert Grimm
- MR Application predevelopment, Siemens Healthcare, Erlangen, Germany
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DiPiero M, Rodrigues PG, Gromala A, Dean DC. Applications of advanced diffusion MRI in early brain development: a comprehensive review. Brain Struct Funct 2023; 228:367-392. [PMID: 36585970 PMCID: PMC9974794 DOI: 10.1007/s00429-022-02605-8] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 12/21/2022] [Indexed: 01/01/2023]
Abstract
Brain development follows a protracted developmental timeline with foundational processes of neurodevelopment occurring from the third trimester of gestation into the first decade of life. Defining structural maturational patterns of early brain development is a critical step in detecting divergent developmental trajectories associated with neurodevelopmental and psychiatric disorders that arise later in life. While considerable advancements have already been made in diffusion magnetic resonance imaging (dMRI) for pediatric research over the past three decades, the field of neurodevelopment is still in its infancy with remarkable scientific and clinical potential. This comprehensive review evaluates the application, findings, and limitations of advanced dMRI methods beyond diffusion tensor imaging, including diffusion kurtosis imaging (DKI), constrained spherical deconvolution (CSD), neurite orientation dispersion and density imaging (NODDI) and composite hindered and restricted model of diffusion (CHARMED) to quantify the rapid and dynamic changes supporting the underlying microstructural architectural foundations of the brain in early life.
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Affiliation(s)
- Marissa DiPiero
- Department of Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | | | - Alyssa Gromala
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Douglas C Dean
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA.
- Department of Pediatrics, University of Wisconsin-Madison, Madison, WI, 53705, USA.
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, 53705, USA.
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Xie SH, Lang R, Li B, Zhao H, Wang P, He JL, Ma XY, Wu Q, Wang SY, Zhang HP, Gao Y, Wu JL. Evaluation of diffuse glioma grade and proliferation activity by different diffusion-weighted-imaging models including diffusion kurtosis imaging ( DKI) and mean apparent propagator (MAP) MRI. Neuroradiology 2023; 65:55-64. [PMID: 35835879 DOI: 10.1007/s00234-022-03000-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 06/20/2022] [Indexed: 01/10/2023]
Abstract
PURPOSE To evaluate two advanced diffusion models, diffusion kurtosis imaging and the newly proposed mean apparent propagation factor-magnetic resonance imaging, in the grading of gliomas and the assessing of their proliferative activity. METHODS Fifty-nine patients with clinically diagnosed and pathologically proven gliomas were enrolled in this retrospective study. All patients underwent DKI and MAP-MRI scans. Manually outline the ROI of the tumour parenchyma. After delineation, the imaging parameters were extracted using only the data from within the ROI including mean diffusion kurtosis (MK), return-to-origin probability (RTOP), Q-space inverse variance (QIV) and non-Gaussian index (NG), and the differences in each parameter in the classification of glioma were compared. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of these parameters. RESULTS MK, NG, RTOP and QIV were significantly different amongst the different grades of glioma. MK, NG and RTOP had excellent diagnostic value in differentiating high-grade from low-grade glioma, with largest areas under the curve (AUCs; 0.929, 0.933 and 0.819, respectively; P < 0.01). MK and NG had the largest AUCs (0.912 and 0.904) when differentiating grade II tumours from III tumours (P < 0.01) and large AUCs (0.791 and 0.786) when differentiating grade III from grade IV tumours. Correlation analysis of tumour proliferation activity showed that MK, NG and QIV were strongly correlated with the Ki-67 LI (P < 0.001). CONCLUSION MK, RTOP and NG can effectively represent the microstructure of these altered tumours. Multimodal diffusion-weighted imaging is valuable for the preoperative evaluation of glioma grade and tumour proliferative activity.
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Abstract
Bipolar disorder (BD) is a severe mental illness associated with alterations in brain organization. Neuroimaging studies have generated a large body of knowledge regarding brain morphological and functional abnormalities in BD. Current advances in the field have focussed on the need for more precise neuroimaging biomarkers. Here we present a selective overview of precision neuroimaging biomarkers for BD, focussing on personalized metrics and novel neuroimaging methods aiming to provide mechanistic insights into the brain alterations associated with BD. The evidence presented covers (a) machine learning techniques applied to neuroimaging data to differentiate patients with BD from healthy individuals or other clinical groups; (b) the 'brain-age-gap-estimation (brainAGE), which is an individualized measure of brain health; (c) diffusional kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI) and Positron Emission Tomography (PET) techniques that open new opportunities to measure microstructural changes in neurite/synaptic integrity and function.
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Affiliation(s)
- Delfina Janiri
- Department of Psychiatry, Fondazione Policlinico Universitario 'Agostino Gemelli' IRCCS, Roma, Italy.,Department of Psychiatry and Neurology, Sapienza University of Rome, Roma, Italy
| | - Sophia Frangou
- Department of Psychiatry, Icahn School of Medicine, Mount Sinai, NY, USA.,Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver British Columbia, Canada
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11
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Granata V, Fusco R, Belli A, Danti G, Bicci E, Cutolo C, Petrillo A, Izzo F. Diffusion weighted imaging and diffusion kurtosis imaging in abdominal oncological setting: why and when. Infect Agent Cancer 2022; 17:25. [PMID: 35681237 PMCID: PMC9185934 DOI: 10.1186/s13027-022-00441-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [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: 04/30/2022] [Accepted: 05/30/2022] [Indexed: 12/13/2022] Open
Abstract
This article provides an overview of diffusion kurtosis (DKI) imaging in abdominal oncology. DKI allows for more data on tissue structures than the conventional diffusion model (DWI). However, DKI requires high quality images at b-values greater than 1000 s/mm2 and high signal-to-noise ratio (SNR) that traditionally MRI systems are not able to acquire and therefore there are generally amplified anatomical distortions on the images due to less homogeneity of the field. Advances in both hardware and software on modern MRI scanners have currently enabled ultra-high b-value imaging and offered the ability to apply DKI to multiple extracranial sites. Previous studies have evaluated the ability of DKI to characterize and discriminate tumor grade compared to conventional DWI. Additionally, in several studies the DKI sequences used were based on planar echo (EPI) acquisition, which is susceptible to motion, metal and air artefacts and prone to low SNRs and distortions, leading to low quality images for some small lesions, which may affect the accuracy of the results. Another problem is the optimal b-value of DKI, which remains to be explored and not yet standardized, as well as the manual selection of the ROI, which could affect the accuracy of some parameters.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", I-80131, Naples, Italy.
| | | | - Andrea Belli
- Division of Hepatobiliary Surgical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", I-80131, Naples, Italy
| | - Ginevra Danti
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology, SIRM Foundation, Milan, Italy
| | - Eleonora Bicci
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Salerno, Italy
| | - Antonella Petrillo
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", I-80131, Naples, Italy
| | - Francesco Izzo
- Division of Hepatobiliary Surgical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", I-80131, Naples, Italy
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12
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Kornaropoulos EN, Winzeck S, Rumetshofer T, Wikstrom A, Knutsson L, Correia MM, Sundgren PC, Nilsson M. Sensitivity of Diffusion MRI to White Matter Pathology: Influence of Diffusion Protocol, Magnetic Field Strength, and Processing Pipeline in Systemic Lupus Erythematosus. Front Neurol 2022; 13:837385. [PMID: 35557624 PMCID: PMC9087851 DOI: 10.3389/fneur.2022.837385] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 03/16/2022] [Indexed: 11/13/2022] Open
Abstract
There are many ways to acquire and process diffusion MRI (dMRI) data for group studies, but it is unknown which maximizes the sensitivity to white matter (WM) pathology. Inspired by this question, we analyzed data acquired for diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) at 3T (3T-DTI and 3T-DKI) and DTI at 7T in patients with systemic lupus erythematosus (SLE) and healthy controls (HC). Parameter estimates in 72 WM tracts were obtained using TractSeg. The impact on the sensitivity to WM pathology was evaluated for the diffusion protocol, the magnetic field strength, and the processing pipeline. Sensitivity was quantified in terms of Cohen's d for group comparison. Results showed that the choice of diffusion protocol had the largest impact on the effect size. The effect size in fractional anisotropy (FA) across all WM tracts was 0.26 higher when derived by DTI than by DKI and 0.20 higher in 3T compared with 7T. The difference due to the diffusion protocol was larger than the difference due to magnetic field strength for the majority of diffusion parameters. In contrast, the difference between including or excluding different processing steps was near negligible, except for the correction of distortions from eddy currents and motion which had a clearly positive impact. For example, effect sizes increased on average by 0.07 by including motion and eddy correction for FA derived from 3T-DTI. Effect sizes were slightly reduced by the incorporation of denoising and Gibbs-ringing removal (on average by 0.011 and 0.005, respectively). Smoothing prior to diffusion model fitting generally reduced effect sizes. In summary, 3T-DTI in combination with eddy current and motion correction yielded the highest sensitivity to WM pathology in patients with SLE. However, our results also indicated that the 3T-DKI and 7T-DTI protocols used here may be adjusted to increase effect sizes.
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Affiliation(s)
- Evgenios N. Kornaropoulos
- Clinical Sciences, Diagnostic Radiology, Lund University, Lund, Sweden
- Division of Anaesthesia, University of Cambridge, Cambridge, United Kingdom
| | - Stefan Winzeck
- Division of Anaesthesia, University of Cambridge, Cambridge, United Kingdom
- BioMedIA Group, Department of Computing, Imperial College London, London, United Kingdom
| | | | - Anna Wikstrom
- Clinical Sciences, Diagnostic Radiology, Lund University, Lund, Sweden
| | - Linda Knutsson
- Department of Medical Radiation Physics, Lund University, Lund, Sweden
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Marta M. Correia
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
| | - Pia C. Sundgren
- Clinical Sciences, Diagnostic Radiology, Lund University, Lund, Sweden
- Lund University BioImaging Center, Lund University, Lund, Sweden
- Department of Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden
| | - Markus Nilsson
- Clinical Sciences, Diagnostic Radiology, Lund University, Lund, Sweden
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13
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Anania V, Collier Q, Veraart J, Buikema AE, Vanhevel F, Billiet T, Jeurissen B, den Dekker AJ, Sijbers J. Improved diffusion parameter estimation by incorporating T 2 relaxation properties into the DKI-FWE model. Neuroimage 2022;:119219. [PMID: 35447354 DOI: 10.1016/j.neuroimage.2022.119219] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 04/08/2022] [Accepted: 04/14/2022] [Indexed: 11/23/2022] Open
Abstract
The free water elimination (FWE) model and its kurtosis variant (DKI-FWE) can separate tissue and free water signal contributions, thus providing tissue-specific diffusional information. However, a downside of these models is that the associated parameter estimation problem is ill-conditioned, necessitating the use of advanced estimation techniques that can potentially bias the parameter estimates. In this work, we propose the T2-DKI-FWE model that exploits the T2 relaxation properties of both compartments, thereby better conditioning the parameter estimation problem and providing, at the same time, an additional potential biomarker (the T2 of tissue). In our approach, the T2 of tissue is estimated as an unknown parameter, whereas the T2 of free water is assumed known a priori and fixed to a literature value (1573 ms). First, the error propagation of an erroneous assumption on the T2 of free water is studied. Next, the improved conditioning of T2-DKI-FWE compared to DKI-FWE is illustrated using the Cramér-Rao lower bound matrix. Finally, the performance of the T2-DKI-FWE model is compared to that of the DKI-FWE and T2-DKI models on both simulated and real datasets. The error due to a biased approximation of the T2 of free water was found to be relatively small in various diffusion metrics and for a broad range of erroneous assumptions on its underlying ground truth value. Compared to DKI-FWE, using the T2-DKI-FWE model is beneficial for the identifiability of the model parameters. Our results suggest that the T2-DKI-FWE model can achieve precise and accurate diffusion parameter estimates, through effective reduction of free water partial volume effects and by using a standard nonlinear least squares approach. In conclusion, incorporating T2 relaxation properties into the DKI-FWE model improves the conditioning of the model fitting, while only requiring an acquisition scheme with at least two different echo times.
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14
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Hu R, Kim H, Kim J, Allen JW, Sun PZ. Fast diffusion kurtosis imaging in acute ischemic stroke shows mean kurtosis-diffusivity mismatch. J Neuroimaging 2022; 32:941-946. [PMID: 35436024 DOI: 10.1111/jon.13000] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 03/23/2022] [Accepted: 03/24/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND AND PURPOSE Diffusion kurtosis imaging (DKI) is an advanced technique more specific to irreversible ischemic injury than conventional diffusion-weighted imaging (DWI). However, its clinical translation has been limited by a long acquisition time and complex postprocessing. METHODS A fast DKI sequence (3 minutes) was implemented on a 3T MRI (Siemens Trio) and piloted as part of an inpatient brain MRI protocol. Mean kurtosis (MK) and mean diffusivity (MD) maps were postprocessed automatically at the scanner console and sent to the Picture Archiving and Communications System. We retrospectively reviewed consecutive patients in a 5-month period with acute ischemic stroke due to large vessel occlusion. MK and MD of the ischemic infarcts and contralateral normal brain were measured, and lesion volumes were measured in large infarcts using semiautomated segmentation. RESULTS Twenty-two patients were included in the study (median age 66). The median time from last known well to MRI was 37 hours. MD and MK maps were successfully processed and demonstrated acute infarction in concordance with DWI in all cases. Infarcted regions had higher MK and lower MD compared to contralateral normal-appearing regions. MK lesion volume was significantly smaller than MD volume. CONCLUSION In this pilot study, we demonstrated the feasibility of incorporating a fast DKI sequence into a clinical MRI protocol. Acute infarcts were depicted on kurtosis maps, and MK lesion volumes were smaller than MD, in accordance with prior works. Future studies are needed to determine the role of DKI in acute stroke treatment selection and prognostication.
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Affiliation(s)
- Ranliang Hu
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Hahnsung Kim
- Yerkes Imaging Center, Yerkes National Primate Research Center, Emory University, Atlanta, Georgia, USA
| | - Jinsuh Kim
- Deapartment of Radiology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Jason W Allen
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Phillip Zhe Sun
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia, USA.,Yerkes Imaging Center, Yerkes National Primate Research Center, Emory University, Atlanta, Georgia, USA
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Raj S, Vyas S, Modi M, Garg G, Singh P, Kumar A, Kamal Ahuja C, Goyal MK, Govind V. Comparative Evaluation of Diffusion Kurtosis Imaging and Diffusion Tensor Imaging in Detecting Cerebral Microstructural Changes in Alzheimer Disease. Acad Radiol 2022; 29 Suppl 3:S63-S70. [PMID: 33612351 DOI: 10.1016/j.acra.2021.01.018] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 01/15/2021] [Accepted: 01/16/2021] [Indexed: 12/11/2022]
Abstract
OBJECTIVE Comparative evaluation of diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) using a whole-brain atlas to comprehensively evaluate microstructural changes in the brain of Alzheimer disease (AzD) patients. METHODS Twenty-seven AzD patients and 25 age-matched controls were included. MRI data was analyzed using a whole-brain atlas with inclusion of 98 region of interests. White matter (WM) microstructural changes were assessed by Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), Kurtosis fractional anisotropy (KFA), mean kurtosis (MK), axial kurtosis (AK) and radial kurtosis (RK). Gray matter (GM) integrity was evaluated using KFA, MK, RK, AK and MD. Comparison of the DKI and DTI metrics were done using student t-test (p ≤ 0.001). RESULTS In AzD patients widespread increase in MD, AD and RD were found in various WM and GM region of interests. The extent of abnormality for DKI parameters was more limited in both GM and WM regions and revealed reduced kurtosis values except in lentiform nuclei. Both DKI and DTI parameters were sensitive to detect abnormality in WM areas with coherent and complex fiber arrangement. Receiver operating characteristic curve analysis for hippocampal values revealed the highest specificity of 88% for AK <0.6965 and highest sensitivity of 95.2% for MD >1.2659. CONCLUSION AzD patients have microstructural changes in both WM and GM and are well-depicted by both DKI and DTI. The alterations in kurtosis parameters, however, are more limited and correlate with areas in the brain primarily involved in cognition.
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Mohtasib R, Alghamdi J, Jobeir A, Masawi A, Pedrosa de Barros N, Billiet T, Struyfs H, Phan TV, Van Hecke W, Ribbens A. MRI biomarkers for Alzheimer's disease: the impact of functional connectivity in the default mode network and structural connectivity between lobes on diagnostic accuracy. Heliyon 2022; 8:e08901. [PMID: 35198768 DOI: 10.1016/j.heliyon.2022.e08901] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/09/2021] [Accepted: 01/31/2022] [Indexed: 11/23/2022] Open
Abstract
Background At present, clinical use of MRI in Alzheimer's disease (AD) is mostly focused on the assessment of brain atrophy, namely in the hippocampal region. Despite this, multiple biomarkers reflecting structural and functional brain connectivity changes have shown promising results in the assessment of AD. To help identify the most relevant ones that may stand a chance of being used in clinical practice, we compared multiple biomarker in terms of their value to discriminate AD from healthy controls and analyzed their age dependency. Methods 20 AD patients and 20 matched controls underwent MRI-scanning (3T GE), including T1-weighted, diffusion-MRI, and resting-state-fMRI (rsfMRI). Whole brain, white matter, gray matter, cortical gray matter and hippocampi volumes were measured using icobrain. rsfMRI between regions of the default-mode-network (DMN) was assessed using group independent-component-analysis. Median diffusivity and kurtosis were determined in gray and white-matter. DTI data was used to evaluate pairwise structural connectivity between lobar regions and the hippocampi. Logistic-Regression and Random-Forest models were trained to classify AD-status based on, respectively different isolated features and age, and feature-groups combined with age. Results Hippocampal features, features reflecting the functional connectivity between the medial-Pre-Frontal-Cortex (mPFC) and the posterior regions of the DMN, and structural interhemispheric frontal connectivity showed the strongest differences between AD-patients and controls. Structural interhemispheric parietal connectivity, structural connectivity between the parietal lobe and hippocampus in the right hemisphere, and mPFC-DMN-features, showed only an association with AD-status (p < 0.05) but not with age. Hippocampi volumes showed an association both with age and AD-status (p < 0.05). Smallest-hippocampus-volume was the most discriminative feature. The best performance (accuracy:0.74, sensitivity:0.74, specificity:0.74) was obtained with an RF-model combining the best feature from each feature-group (smallest hippocampus volume, WM volume, median GM MD, lTPJ-mPFC connectivity and structural interhemispheric frontal connectivity) and age. Conclusions Brain connectivity changes caused by AD are reflected in multiple MRI-biomarkers. Decline in both the functional DMN-connectivity and the parietal interhemispheric structural connectivity may assist sepparating healthy-aging driven changes from AD, complementing hippocampal volumes which are affected by both aging and AD.
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17
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Zhang H, Li Q, Liu L, Qu X, Wang Q, Yang B, Xian J. Altered Microstructure of Cerebral Gray Matter in Neuromyelitis Optica Spectrum Disorder-Optic Neuritis: A DKI Study. Front Neurosci 2022; 15:738913. [PMID: 34987355 PMCID: PMC8720872 DOI: 10.3389/fnins.2021.738913] [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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 11/26/2021] [Indexed: 11/13/2022] Open
Abstract
The purpose of this study was to analyze microstructural alterations in cerebral gray matter using non-Gaussian diffusion kurtosis imaging (DKI) in neuromyelitis optica spectrum disorder (NMOSD) patients with optic neuritis (NMOSD-ON). DKI was performed in 14 NMOSD-ON patients and 22 normal controls (NCs). DKI-derived metrics, including mean kurtosis (MK), radial kurtosis (RK), axial kurtosis (AK), fractional anisotropy (FA), and mean diffusivity (MD), were voxel-wisely compared by two-sample t-tests with gaussian random field (GRF) correction between the two groups. The correlations between altered DKI metrics and clinical features were analyzed. Compared with NCs, NMOSD-ON patients showed significantly decreased MK and RK both in the left inferior temporal gyrus (ITG), and decreased AK in the bilateral calcarine (CAL). While increased MD in the left fusiform gyrus (FFG), right CAL, and right hippocampus (HIP)/parahippocampal gyrus (PHG) were found. Furthermore, correlation analysis showed that mean deviation was negatively correlated with AK values of bilateral CAL and positively correlated with MD values of right CAL (q < 0.05, false discovery rate (FDR) corrected). For NMOSD-ON patients, microstructural abnormalities in the occipital visual cortex are correlated with clinical disability. These findings may provide complementary information to understand the neuropathological mechanisms underlying the impairments of cerebral gray matter in NMOSD-ON.
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Affiliation(s)
- Hanjuan Zhang
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Qing Li
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Lei Liu
- Department of Neurology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Xiaoxia Qu
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Qian Wang
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Bingbing Yang
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Junfang Xian
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
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Kang L, Chen J, Huang J, Zhang T, Xu J. Identifying epilepsy based on machine-learning technique with diffusion kurtosis tensor. CNS Neurosci Ther 2021; 28:354-363. [PMID: 34939745 PMCID: PMC8841295 DOI: 10.1111/cns.13773] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [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: 09/04/2021] [Revised: 11/11/2021] [Accepted: 11/14/2021] [Indexed: 12/20/2022] Open
Abstract
Introduction Epilepsy is a serious hazard to human health. Minimally invasive surgery is an extremely effective treatment to refractory epilepsy currently if the location of epileptic foci is given. However, it is challenging to locate the epileptic foci since a multitude of patients are MRI‐negative. It is well known that DKI (diffusion kurtosis imaging) can analyze the pathological changes of local tissues and other regions of epileptic foci at the molecular level. In this article, we propose a new localization way for epileptic foci based on machine‐learning method with kurtosis tensor in DKI. Methods We recruited 59 children with hippocampus epilepsy and 70 age‐ and sex‐matched normal controls; their T1‐weighted images and DKI were collected simultaneously. Then, the hippocampus in DKI is segmented based on a mask as a local brain region, and DKE is utilized to estimate the kurtosis tensor of each subject's hippocampus. Finally, the kurtosis tensor is fed into SVM (support vector machine) to identify epilepsy. Results The classifier produced 95.24% accuracy for patient versus normal controls, which is higher than that obtained with FA (fractional anisotropy) and MK (mean kurtosis). Experimental results show that the kurtosis tensor is a kind of remarkable feature to identify epilepsy, which indicates that DKI images can act as an important biomarker for epilepsy from the view of clinical diagnosis. Conclusion Although the classification task for epileptic patients and normal controls discussed in this article did not directly achieve the location of epileptic foci and only identified epilepsy on certain brain region, the epileptic foci can be located with the results of identifying results on other brain regions.
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Affiliation(s)
- Li Kang
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China.,The Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen, China
| | - Jin Chen
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China.,The Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen, China
| | - Jianjun Huang
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China.,The Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen, China
| | - Tijiang Zhang
- The Affiliate Hospital of Zunyi Medical University, Zunyi, China
| | - Jiahui Xu
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China.,The Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen, China
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Qiu J, Deng K, Wang P, Chen C, Luo Y, Yuan S, Wen J. Application of diffusion kurtosis imaging to the study of edema in solid and peritumoral areas of glioma. Magn Reson Imaging 2021; 86:10-16. [PMID: 34793876 DOI: 10.1016/j.mri.2021.11.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 11/08/2021] [Accepted: 11/10/2021] [Indexed: 12/14/2022]
Abstract
OBJECTIVE When gliomas grow in an infiltrative form, high-grade malignant glioma tissue extends beyond the contrast-enhancing tumor boundary, and this diffuse non-enhancing tumor infiltration is not visible on conventional MRI. The purpose of this study was to evaluate the of diffusion kurtosis imaging (DKI)-derived parameters in a group of patients with pre-operative gliomas, evaluating changes in the solid tumor and peritumoral edema area, and investigating their use for evaluating the recurrence and prognosis of gliomas. METHODS In this retrospective study, 51 patients with gliomas who underwent biopsy or surgery underwent DKI scans before surgery. DKI scans were performed to generate DKI parameter maps of the solid tumor and peritumoral edema areas. In the solid tumor area, the kurtosis parameters showed the highest area under the curve (AUC), sensitivity, and specificity for distinguishing high- and low-grade gliomas (all P < 0.01). RESULTS In the peritumoral edema area, significant differences were found between groups with grade III and IV gliomas (P < 0.05). DKI parameters were found to correlate with clinical Ki-67 scores within the solid tumor area (MK: R2 = 0.288, P < 0.001; Kr: R2 = 0.270, P < 0.001; Ka: R2 = 0.274, P < 0.001; MD: R2 = 0.223, P < 0.001; FA: R2 = 0.098, P < 0.01). No significant correlations were found between Ki-67 and kurtosis parameters of peritumoral edema. CONCLUSIONS In this study, DKI showed potential utility for studying solid tumor and peritumoral edema of high grade gliomas.
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Affiliation(s)
- Jun Qiu
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Kexue Deng
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Peng Wang
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Chuanyu Chen
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Yi Luo
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Shuya Yuan
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Jie Wen
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China.
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Zheng H, Lin J, Lin Q, Zheng W. Magnetic Resonance Image of Neonatal Acute Bilirubin Encephalopathy: A Diffusion Kurtosis Imaging Study. Front Neurol 2021; 12:645534. [PMID: 34512498 PMCID: PMC8425508 DOI: 10.3389/fneur.2021.645534] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [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: 01/06/2021] [Accepted: 07/16/2021] [Indexed: 01/31/2023] Open
Abstract
Background and Objective: The abnormal T1-weighted imaging of MRI can be used to characterize neonatal acute bilirubin encephalopathy (ABE) in newborns, but has limited use in evaluating the severity and prognosis of ABE. This study aims to assess the value of diffusion kurtosis imaging (DKI) in detecting ABE and understanding its pathogenesis. Method: Seventy-six newborns with hyperbilirubinemia were grouped into three groups (mild group, moderate group, and severe group) based on serum bilirubin levels. All the patients underwent conventional MRI and DKI serial, as well as 40 healthy full-term infants (control group). The regions of interest (ROIs) were the bilateral globus pallidus, dorsal thalamus, frontal lobe, auditory radiation, superior temporal gyrus, substantia nigra, hippocampus, putamen, and inferior olivary nucleus. The values of mean diffusivity (MD), axial kurtosis (AK), radial kurtosis (RK), and mean kurtosis (MK), and fractional anisotropy (FA), radial diffusivity (RD), and axis diffusivity (AD) of the ROIs were evaluated. All newborns were followed up and evaluated using the Denver Development Screening Test (DDST). According to the follow-up results, the patients were divided into the normal group, the suspicious abnormal group, and the abnormal group. Result: Compared with the control group, significant differences were observed with the increased MK of dorsal thalamus, AD of globus pallidus in the moderate group, and increased RD, MK, AK, and RK value of globus pallidus, dorsal thalamus, auditory radiation, superior temporal gyrus, and hippocampus in the severe group. The peak value of total serum bilirubin was moderately correlated with the MK of globus pallidus, dorsal thalamus, and auditory radiation and was positively correlated with the other kurtosis value. Out of 76 patients, 40 finished the DDST, and only 9 patients showed an abnormality. Compared with the normal group, the AK value of inferior olivary nucleus showed significant differences (p < 0.05) in the suspicious abnormal group, and the MK of globus pallidus, temporal gyrus, and auditory radiation; RK of globus pallidus, dorsal thalamus, and auditory radiation; and MD of globus pallidus showed significant differences (p < 0.05) in the abnormal group. Conclusion: DKI can reflect the subtle structural changes of neonatal ABE, and MK is a sensitive indicator to indicate the severity of brain damage.
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Affiliation(s)
- Hongyi Zheng
- Department of Radiology, The Second Affiliated Hospital, Medical College of Shantou University, Shantou, China
| | - Jiefen Lin
- Department of Radiology, The Second Affiliated Hospital, Medical College of Shantou University, Shantou, China
| | - Qihuan Lin
- Department of Radiology, The Second Affiliated Hospital, Medical College of Shantou University, Shantou, China
| | - Wenbin Zheng
- Department of Radiology, The Second Affiliated Hospital, Medical College of Shantou University, Shantou, China
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Henriques RN, Jespersen SN, Jones DK, Veraart J. Toward more robust and reproducible diffusion kurtosis imaging. Magn Reson Med 2021; 86:1600-1613. [PMID: 33829542 PMCID: PMC8199974 DOI: 10.1002/mrm.28730] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 01/20/2021] [Accepted: 01/24/2021] [Indexed: 12/21/2022]
Abstract
PURPOSE The general utility of diffusion kurtosis imaging (DKI) is challenged by its poor robustness to imaging artifacts and thermal noise that often lead to implausible kurtosis values. THEORY AND METHODS A robust scalar kurtosis index can be estimated from powder-averaged diffusion-weighted data. We introduce a novel DKI estimator that uses this scalar kurtosis index as a proxy for the mean kurtosis to regularize the fit. RESULTS The regularized DKI estimator improves the robustness and reproducibility of the kurtosis metrics and results in parameter maps with enhanced quality and contrast. CONCLUSION Our novel DKI estimator promotes the wider use of DKI in clinical research and potentially diagnostics by improving the reproducibility and precision of DKI fitting and, as such, enabling enhanced visual, quantitative, and statistical analyses of DKI parameters.
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Affiliation(s)
| | - Sune N. Jespersen
- Center of Functionally Integrative Neuroscience (CFIN) and MINDLabDepartment of Clinical MedicineAarhus UniversityAarhusDenmark
- Department of Physics and AstronomyAarhus UniversityAarhusDenmark
| | - Derek K. Jones
- CUBRICSchool of PsychologyCardiff UniversityCardiffUK
- Mary MacKillop Institute for Health ResearchAustralian Catholic UniversityMelbourneVictoriaAustralia
| | - Jelle Veraart
- Center for Biomedical ImagingNew York University Grossman School of MedicineNew YorkNYUSA
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22
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Nuessle NC, Behling F, Tabatabai G, Castaneda Vega S, Schittenhelm J, Ernemann U, Klose U, Hempel JM. ADC-Based Stratification of Molecular Glioma Subtypes Using High b-Value Diffusion-Weighted Imaging. J Clin Med 2021; 10:jcm10163451. [PMID: 34441747 PMCID: PMC8397197 DOI: 10.3390/jcm10163451] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [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: 06/08/2021] [Accepted: 08/02/2021] [Indexed: 11/16/2022] Open
Abstract
PURPOSE To investigate the diagnostic performance of in vivo ADC-based stratification of integrated molecular glioma grades. MATERIALS AND METHODS Ninety-seven patients with histopathologically confirmed glioma were evaluated retrospectively. All patients underwent pre-interventional MRI-examination including diffusion-weighted imaging (DWI) with implemented b-values of 500, 1000, 1500, 2000, and 2500 s/mm2. Apparent Diffusion Coefficient (ADC), Mean Kurtosis (MK), and Mean Diffusivity (MD) maps were generated. The average values were compared among the molecular glioma subgroups of IDH-mutant and IDH-wildtype astrocytoma, and 1p/19q-codeleted oligodendroglioma. One-way ANOVA with post-hoc Games-Howell correction compared average ADC, MD, and MK values between molecular glioma groups. A Receiver Operating Characteristic (ROC) analysis determined the area under the curve (AUC). RESULTS Two b-value-dependent ADC-based evaluations presented statistically significant differences between the three molecular glioma sub-groups (p < 0.001, respectively). CONCLUSIONS High-b-value ADC from preoperative DWI may be used to stratify integrated molecular glioma subgroups and save time compared to diffusion kurtosis imaging. Higher b-values of up to 2500 s/mm2 may present an important step towards increasing diagnostic accuracy compared to standard DWI protocol.
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Affiliation(s)
- Nils C. Nuessle
- Department of Neuroradiology, University Hospital Tübingen, Eberhard Karls University, 72076 Tübingen, Germany; (U.E.); (U.K.); (J.-M.H.)
- Correspondence:
| | - Felix Behling
- Department of Neurosurgery, University Hospital Tübingen, Eberhard Karls University, 72076 Tübingen, Germany;
- Departments of Neurology and Interdisciplinary Neuro-Oncology, University Hospital Tübingen, Hertie Institute for Clinical Brain Research, Eberhard Karls University, 72076 Tübingen, Germany;
| | - Ghazaleh Tabatabai
- Departments of Neurology and Interdisciplinary Neuro-Oncology, University Hospital Tübingen, Hertie Institute for Clinical Brain Research, Eberhard Karls University, 72076 Tübingen, Germany;
| | - Salvador Castaneda Vega
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, University Hospital Tübingen, Eberhard Karls University, 72076 Tübingen, Germany;
| | - Jens Schittenhelm
- Department of Pathology and Neuropathology, University Hospital Tübingen, Institute of Neuropathology, Eberhard Karls University, 72076 Tübingen, Germany;
| | - Ulrike Ernemann
- Department of Neuroradiology, University Hospital Tübingen, Eberhard Karls University, 72076 Tübingen, Germany; (U.E.); (U.K.); (J.-M.H.)
| | - Uwe Klose
- Department of Neuroradiology, University Hospital Tübingen, Eberhard Karls University, 72076 Tübingen, Germany; (U.E.); (U.K.); (J.-M.H.)
| | - Johann-Martin Hempel
- Department of Neuroradiology, University Hospital Tübingen, Eberhard Karls University, 72076 Tübingen, Germany; (U.E.); (U.K.); (J.-M.H.)
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23
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Oliviero S, Del Gratta C. Impact of the acquisition protocol on the sensitivity to demyelination and axonal loss of clinically feasible DWI techniques: a simulation study. MAGMA 2021; 34:523-543. [PMID: 33417079 DOI: 10.1007/s10334-020-00899-5] [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] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 11/19/2020] [Accepted: 11/22/2020] [Indexed: 06/12/2023]
Abstract
OBJECTIVE To evaluate: (a) the specific effect that the demyelination and axonal loss have on the DW signal, and (b) the impact of the sequence parameters on the sensitivity to damage of two clinically feasible DWI techniques, i.e. DKI and NODDI. METHODS We performed a Monte Carlo simulation of water diffusion inside a novel synthetic model of white matter in the presence of axonal loss and demyelination, with three compartments with permeable boundaries between them. We compared DKI and NODDI in their ability to detect and assess the damage, using several acquisition protocols. We used the F test statistic as an index of the sensitivity for each DWI parameter to axonal loss and demyelination, respectively. RESULTS DKI parameters significantly changed with increasing axonal loss, but, in most cases, not with demyelination; all the NODDI parameters showed sensitivity to both the damage processes (at p < 0.01). However, the acquisition protocol strongly affected the sensitivity to damage of both the DKI and NODDI parameters and, especially for NODDI, the parameter absolute values also. DISCUSSION This work is expected to impact future choices for investigating white matter microstructure in focusing on specific stages of the disease, and for selecting the appropriate experimental framework to obtain optimal data quality given the purpose of the experiment.
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Affiliation(s)
- Stefania Oliviero
- Department Neurosciences, Imaging, and Clinical Sciences, Institute for Advanced Biomedical Technologies, ITAB, Gabriele D'Annunzio University, Chieti, Italy.
| | - Cosimo Del Gratta
- Department Neurosciences, Imaging, and Clinical Sciences, Institute for Advanced Biomedical Technologies, ITAB, Gabriele D'Annunzio University, Chieti, Italy
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24
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Henriques RN, Correia MM, Marrale M, Huber E, Kruper J, Koudoro S, Yeatman JD, Garyfallidis E, Rokem A. Diffusional Kurtosis Imaging in the Diffusion Imaging in Python Project. Front Hum Neurosci 2021; 15:675433. [PMID: 34349631 PMCID: PMC8327208 DOI: 10.3389/fnhum.2021.675433] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 06/17/2021] [Indexed: 12/28/2022] Open
Abstract
Diffusion-weighted magnetic resonance imaging (dMRI) measurements and models provide information about brain connectivity and are sensitive to the physical properties of tissue microstructure. Diffusional Kurtosis Imaging (DKI) quantifies the degree of non-Gaussian diffusion in biological tissue from dMRI. These estimates are of interest because they were shown to be more sensitive to microstructural alterations in health and diseases than measures based on the total anisotropy of diffusion which are highly confounded by tissue dispersion and fiber crossings. In this work, we implemented DKI in the Diffusion in Python (DIPY) project-a large collaborative open-source project which aims to provide well-tested, well-documented and comprehensive implementation of different dMRI techniques. We demonstrate the functionality of our methods in numerical simulations with known ground truth parameters and in openly available datasets. A particular strength of our DKI implementations is that it pursues several extensions of the model that connect it explicitly with microstructural models and the reconstruction of 3D white matter fiber bundles (tractography). For instance, our implementations include DKI-based microstructural models that allow the estimation of biophysical parameters, such as axonal water fraction. Moreover, we illustrate how DKI provides more general characterization of non-Gaussian diffusion compatible with complex white matter fiber architectures and gray matter, and we include a novel mean kurtosis index that is invariant to the confounding effects due to tissue dispersion. In summary, DKI in DIPY provides a well-tested, well-documented and comprehensive reference implementation for DKI. It provides a platform for wider use of DKI in research on brain disorders and in cognitive neuroscience.
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Affiliation(s)
| | - Marta M. Correia
- Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
| | - Maurizio Marrale
- Department of Physics and Chemistry “Emilio Segrè”, University of Palermo, Palermo, Italy
- National Institute for Nuclear Physics (INFN), Catania Division, Catania, Italy
| | - Elizabeth Huber
- Department of Speech and Hearing, Institute for Learning and Brain Science, University of Washington, Seattle, WA, United States
| | - John Kruper
- Department of Psychology and eScience Institute, The University of Washington, Seattle, WA, United States
| | - Serge Koudoro
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computer Science and Engineering, Indiana University, Bloomington, IN, United States
| | - Jason D. Yeatman
- Department of Speech and Hearing, Institute for Learning and Brain Science, University of Washington, Seattle, WA, United States
- Department of Pediatrics, Graduate School of Education, Stanford University, Stanford, CA, United States
| | - Eleftherios Garyfallidis
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computer Science and Engineering, Indiana University, Bloomington, IN, United States
| | - Ariel Rokem
- Department of Psychology and eScience Institute, The University of Washington, Seattle, WA, United States
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25
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Hasan KM, Yamada K. Overview of Diffusion Tensor, Diffusion Kurtosis, and Q-space Imaging and Software Tools. Magn Reson Imaging Clin N Am 2021; 29:263-268. [PMID: 33902908 DOI: 10.1016/j.mric.2021.02.003] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
This work offers a short up-to-date review of diffusion-weighted MR imaging (dMRI) and software tools that are used widely to process and analyze clinical dMRI. A consolidated dMRI protocol for clinical applications that enables the mapping of tissue microstructural attributes is presented.
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Affiliation(s)
- Khader M Hasan
- Department of Diagnostic and Interventional Radiology, The University of Texas Health Science Center, McGovern Medical School, Houston, Texas, USA.
| | - Kei Yamada
- Department of Radiology, Kyoto Prefectural University of Medicine, 465 Kajii-cho, Kamigyo-ku, Kyoto 6028566, Japan
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26
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Zheng X, Li M, Wang P, Li X, Zhang Q, Zeng S, Jiang T, Hu X. Assessment of chronic allograft injury in renal transplantation using diffusional kurtosis imaging. BMC Med Imaging 2021; 21:63. [PMID: 33827457 PMCID: PMC8028790 DOI: 10.1186/s12880-021-00595-3] [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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Accepted: 03/28/2021] [Indexed: 11/12/2022] Open
Abstract
Background Chronic allograft injury (CAI) is a significant reason for which many grafts were lost. The study was conducted to assess the usefulness of diffusional kurtosis imaging (DKI) technology in the non-invasive assessment of CAI. Methods Between February 2019 and October 2019, 110 renal allograft recipients were included to analyze relevant DKI parameters. According to estimated glomerular filtration rate (eGFR) (mL/min/ 1.73 m2) level, they were divided to 3 groups: group 1, eGFR ≥ 60 (n = 10); group 2, eGFR 30–60 (n = 69); group 3, eGFR < 30 (n = 31). We performed DKI on a clinical 3T magnetic resonance imaging system. We measured the area of interest to determine the mean kurtosis (MK), mean diffusivity (MD), and apparent diffusion coefficient (ADC) of the renal cortex and medulla. We performed a Pearson correlation analysis to determine the relationship between eGFR and the DKI parameters. We used the receiver operating characteristic curve to estimate the predicted values of DKI parameters in the CAI evaluation. We randomly selected five patients from group 2 for biopsy to confirm CAI. Results With the increase of creatinine, ADC, and MD of the cortex and medulla decrease, MK of the cortex and medulla gradually increase. Among the three different eGFR groups, significant differences were found in cortical and medullary MK (P = 0.039, P < 0.001, P < 0.001, respectively). Cortical and medullary ADC and MD are negatively correlated with eGFR (r = − 0.49, − 0.44, − 0.57, − 0.57, respectively; P < 0.001), while cortical and medullary MK are positively correlated with eGFR (r = 0.42, 0.38; P < 0.001). When 0.491 was set as the cutoff value, MK's CAI assessment showed 87% sensitivity and 100% specificity. All five patients randomly selected for biopsy from the second group confirmed glomerulosclerosis and tubular atrophy/interstitial fibrosis. Conclusion The DKI technique is related to eGFR as allograft injury progresses and is expected to become a potential non-invasive method for evaluating CAI. Supplementary Information The online version contains supplementary material available at 10.1186/s12880-021-00595-3.
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Affiliation(s)
- Xin Zheng
- Department of Urology, Beijing Youan Hospital, Capital Medical University, No. 8, Xi Tou Tiao, Youanmen Wai, Fengtai District, Beijing, 100069, People's Republic of China
| | - Min Li
- Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, 8 Gongren Tiyuchang Nanlu, Chaoyang District, Beijing, 200020, People's Republic of China
| | - Pan Wang
- Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, 8 Gongren Tiyuchang Nanlu, Chaoyang District, Beijing, 200020, People's Republic of China
| | - Xiangnan Li
- Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, 8 Gongren Tiyuchang Nanlu, Chaoyang District, Beijing, 200020, People's Republic of China
| | - Qiang Zhang
- Institute of Urology, Capital Medical University, 8 Gongren Tiyuchang Nanlu, Chaoyang District, Beijing, 200020, People's Republic of China.,Department of Urology, Beijing Chao-Yang Hospital, Capital Medical University, 8 Gongren Tiyuchang Nanlu, Chaoyang District, Beijing, 200020, People's Republic of China
| | - Song Zeng
- Institute of Urology, Capital Medical University, 8 Gongren Tiyuchang Nanlu, Chaoyang District, Beijing, 200020, People's Republic of China.,Department of Urology, Beijing Chao-Yang Hospital, Capital Medical University, 8 Gongren Tiyuchang Nanlu, Chaoyang District, Beijing, 200020, People's Republic of China
| | - Tao Jiang
- Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, 8 Gongren Tiyuchang Nanlu, Chaoyang District, Beijing, 200020, People's Republic of China.
| | - Xiaopeng Hu
- Institute of Urology, Capital Medical University, 8 Gongren Tiyuchang Nanlu, Chaoyang District, Beijing, 200020, People's Republic of China. .,Department of Urology, Beijing Chao-Yang Hospital, Capital Medical University, 8 Gongren Tiyuchang Nanlu, Chaoyang District, Beijing, 200020, People's Republic of China.
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27
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Maximov II, van der Meer D, de Lange AMG, Kaufmann T, Shadrin A, Frei O, Wolfers T, Westlye LT. Fast qualitY conTrol meThod foR derIved diffUsion Metrics (YTTRIUM) in big data analysis: U.K. Biobank 18,608 example. Hum Brain Mapp 2021; 42:3141-3155. [PMID: 33788350 PMCID: PMC8193531 DOI: 10.1002/hbm.25424] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [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: 11/01/2020] [Revised: 03/10/2021] [Accepted: 03/13/2021] [Indexed: 12/12/2022] Open
Abstract
Deriving reliable information about the structural and functional architecture of the brain in vivo is critical for the clinical and basic neurosciences. In the new era of large population‐based datasets, when multiple brain imaging modalities and contrasts are combined in order to reveal latent brain structural patterns and associations with genetic, demographic and clinical information, automated and stringent quality control (QC) procedures are important. Diffusion magnetic resonance imaging (dMRI) is a fertile imaging technique for probing and visualising brain tissue microstructure in vivo, and has been included in most standard imaging protocols in large‐scale studies. Due to its sensitivity to subject motion and technical artefacts, automated QC procedures prior to scalar diffusion metrics estimation are required in order to minimise the influence of noise and artefacts. However, the QC procedures performed on raw diffusion data cannot guarantee an absence of distorted maps among the derived diffusion metrics. Thus, robust and efficient QC methods for diffusion scalar metrics are needed. Here, we introduce Fast qualitY conTrol meThod foR derIved diffUsion Metrics (YTTRIUM), a computationally efficient QC method utilising structural similarity to evaluate diffusion map quality and mean diffusion metrics. As an example, we applied YTTRIUM in the context of tract‐based spatial statistics to assess associations between age and kurtosis imaging and white matter tract integrity maps in U.K. Biobank data (n = 18,608). To assess the influence of outliers on results obtained using machine learning (ML) approaches, we tested the effects of applying YTTRIUM on brain age prediction. We demonstrated that the proposed QC pipeline represents an efficient approach for identifying poor quality datasets and artefacts and increases the accuracy of ML based brain age prediction.
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Affiliation(s)
- Ivan I Maximov
- Department of Psychology, University of Oslo, Oslo, Norway.,Norwegian Centre for Mental Disorders Research (NORMENT), Department of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
| | - Dennis van der Meer
- Norwegian Centre for Mental Disorders Research (NORMENT), Department of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,School of Mental Health and Neuroscience, Faculty of Health Medicine and Life Sciences, Maastricht University, The Netherlands
| | - Ann-Marie G de Lange
- Department of Psychology, University of Oslo, Oslo, Norway.,Norwegian Centre for Mental Disorders Research (NORMENT), Department of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,LREN, Centre for Research in Neurosciences - Department of Clinical Neurosciences, CHUV and University of Lausanne, Lausanne, Switzerland.,Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK
| | - Tobias Kaufmann
- Norwegian Centre for Mental Disorders Research (NORMENT), Department of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Alexey Shadrin
- Norwegian Centre for Mental Disorders Research (NORMENT), Department of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Oleksandr Frei
- Norwegian Centre for Mental Disorders Research (NORMENT), Department of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Thomas Wolfers
- Department of Psychology, University of Oslo, Oslo, Norway.,Norwegian Centre for Mental Disorders Research (NORMENT), Department of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway.,Norwegian Centre for Mental Disorders Research (NORMENT), Department of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
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Sun C, Zou N, Chen H, Zhang A, Sun L, Liu Z, Bian J. The effect of magnetic guiding BMSCs on hypoxic-ischemic brain damage via magnetic resonance imaging evaluation. Magn Reson Imaging 2021; 79:59-65. [PMID: 33727146 DOI: 10.1016/j.mri.2021.03.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 09/16/2020] [Accepted: 03/10/2021] [Indexed: 11/20/2022]
Abstract
Hypoxic-ischemic brain damage (HIBD) is a critical disease in pediatric neurosurgery with high mortality rate and frequently leads to neurological sequelae. The role of bone marrow mesenchymal stem cells (BMSCs) in neuroprotection has been recognized. However, using the imaging methods to dynamically assess the neuroprotective effects of BMSCs is rarely reported. In this study, BMSCs were isolated, cultured and identified. Flow cytometry assay had shown the specific surface molecular markers of BMSCs, which indicated that the cultivated cells were purified BMSCs. The results demonstrated that CD29 and CD90 were highly expressed, whilst CD45 and CD11b were negatively expressed. Further, BMSCs were transplanted into Sprague Dawley (SD) rats established HIBD via three ways, including lateral ventricle (LV) injection, tail vein (TV) injection, and LV injection with magnetic guiding. Magnetic resonance imaging (MRI) was used to monitor and assess the treatment effect of super paramagnetic iron oxide (SPIO)-labeled BMSCs. The mean kurtosis (MK) values from diffusion kurtosis imaging (DKI) exhibited the significant differences. It was found that the MK value of HIBD group increased compared with that in Sham. At the meantime, the MK values of LV + HIBD, TV + HIBD and Magnetic+LV + HIBD groups decreased compared with that in HIBD group. Among these, the MK value reduced most significantly in Magnetic+LV + HIBD group. MRI illustrated that the treatment effect of Magnetic+LV + HIBD group was best. In addition, HE staining and TUNEL assay measured the pathological changes and apoptosis of brain tissues, which further verified the MRI results. All data suggest that magnetic guiding BMSCs, a targeted delivery way, is a new strategic theory for HIBD treatment. The DKI technology of MRI can dynamically evaluate the neuroprotective effects of transplanted BMSCs in HIBD.
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Hellewell SC, Welton T, Eisenhuth K, Tchan MC, Grieve SM. Diffusion kurtosis imaging detects subclinical white matter abnormalities in Phenylketonuria. Neuroimage Clin 2021; 29:102555. [PMID: 33461111 DOI: 10.1016/j.nicl.2020.102555] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 12/02/2020] [Accepted: 12/29/2020] [Indexed: 12/19/2022]
Abstract
OBJECTIVE Phenylketonuria (PKU) is an autosomal recessive disorder whereby deficiencies in phenylalanine metabolism cause progressive neurological dysfunction. Managing PKU is challenging, with disease monitoring focussed on short-term phenylalanine control rather than measures of neuronal damage. Conventional imaging lacks sensitivity, however diffusion kurtosis imaging (DKI), a new MRI method may reveal subclinical white matter structural changes in PKU. METHODS This cohort study involved adults with PKU recruited during routine clinical care. MRI, neurocognitive assessment and historical phenylalanine (Phe) levels were collected. A hypothesis-generating case study comparing diet-compliant and non-compliant siblings confirmed that DKI metrics are sensitive to dietary adherence and prompted a candidate metric (Krad/KFA ratio). We then tested this metric in a Replication cohort (PKU = 20; controls = 43). RESULTS Both siblings scored outside the range of controls for all DKI-based metrics, with severe changes in the periventricular white matter and a gradient of severity toward the cortex. Krad/KFA provided clear separation by diagnosis in the Replication cohort (p < 0.001 in periventricular, deep and pericortical compartments). The ratio also correlated negatively with attention (r = -0.51 & -0.50, p < 0.05) and positively with 3-year mean Phe (r = 0.45 & 0.58, p < 0.01). CONCLUSION DKI reveals regionally-specific, progressive abnormalities of brain diffusion characteristics in PKU, even in the absence of conspicuous clinical signs or abnormalities on conventional MRI. A DKI-based marker derived from these scores (Krad/KFA ratio) was sensitive to cognitive impairment and PKU control over the medium term and may provide a meaningful subclinical biomarker of end-organ damage.
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30
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Granata V, Fusco R, Risi C, Ottaiano A, Avallone A, De Stefano A, Grimm R, Grassi R, Brunese L, Izzo F, Petrillo A. Diffusion-Weighted MRI and Diffusion Kurtosis Imaging to Detect RAS Mutation in Colorectal Liver Metastasis. Cancers (Basel) 2020; 12:cancers12092420. [PMID: 32858990 PMCID: PMC7565693 DOI: 10.3390/cancers12092420] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [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: 08/02/2020] [Revised: 08/24/2020] [Accepted: 08/25/2020] [Indexed: 12/11/2022] Open
Abstract
Simple Summary Imaging derived parameters can provide data on tumor phenotype as well as cancer microenvironment. Radiomics has recently shown potential in realizing personalized medicine. The aim of the manuscript is to detect RAS mutation in colorectal liver metastasis by Diffusion-Weighted Magnetic Resonance Imaging (DWI-MRI) - and Diffusion Kurtosis imaging (DKI)-derived parameters. We demonstrated that DKI derived parameters allows to detect RAS mutation in liver metastasis. Abstract Objectives: To detect RAS mutation in colorectal liver metastasis by Diffusion-Weighted Magnetic Resonance Imaging (DWI-MRI) - and Diffusion Kurtosis imaging (DKI)-derived parameters. Methods: In total, 106 liver metastasis (60 metastases with RAS mutation) in 52 patients were included in this retrospective study. Diffusion and perfusion parameters were derived by DWI (apparent diffusion coefficient (ADC), basal signal (S0), pseudo-diffusion coefficient (DP), perfusion fraction (FP) and tissue diffusivity (DT)) and DKI data (mean of diffusion coefficient (MD) and mean of diffusional Kurtosis (MK)). Wilcoxon–Mann–Whitney U tests for non-parametric variables and receiver operating characteristic (ROC) analyses were calculated with area under ROC curve (AUC). Moreover, pattern recognition approaches (linear classifier, support vector machine, k-nearest neighbours, decision tree), with features selection methods and a leave-one-out cross validation approach, were considered. Results: A significant discrimination between the group with RAS mutation and the group without RAS mutation was obtained by the standard deviation value of MK (MK STD), by the mean value of MD, and by that of FP. The best results were reached by MK STD with an AUC of 0.80 (sensitivity of 72%, specificity of 85%, accuracy of 79%) using a cut-off of 203.90 × 10−3, and by the mean value of MD with AUC of 0.80 (sensitivity of 84%, specificity of 73%, accuracy of 77%) using a cut-off of 1694.30 mm2/s × 10−6. Considering all extracted features or the predictors obtained by the features selection method (the mean value of S0, the standard deviation value of MK, FP and of DT), the tested pattern recognition approaches did not determine an increase in diagnostic accuracy to detect RAS mutation (AUC of 0.73 and 0.69, respectively). Conclusions: Diffusion-Weighted imaging and Diffusion Kurtosis imaging could be used to detect the RAS mutation in liver metastasis. The standard deviation value of MK and the mean value of MD were the more accurate parameters in the RAS mutation detection, with an AUC of 0.80.
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Affiliation(s)
- Vincenza Granata
- Radiology Division, Istituto Nazionale Tumori–IRCCS-Fondazione G. Pascale, Napoli, Italia, Via Mariano Semmola, 80131 Naples, Italy; (V.G.); (A.P.)
| | - Roberta Fusco
- Radiology Division, Istituto Nazionale Tumori–IRCCS-Fondazione G. Pascale, Napoli, Italia, Via Mariano Semmola, 80131 Naples, Italy; (V.G.); (A.P.)
- Correspondence: ; Tel.: +39-0815-90714; Fax: 39-0815-903825
| | - Chiara Risi
- Radiology Division, Universita’ Degli Studi Di Napoli Federico II, 80131 Naples, Italy;
| | - Alessandro Ottaiano
- Abdominal Oncology Division, Istituto Nazionale Tumori–IRCCS-Fondazione G. Pascale, Napoli, Italia, Via Mariano Semmola, 80131 Naples, Italy; (A.O.); (A.A.); (A.D.S.)
| | - Antonio Avallone
- Abdominal Oncology Division, Istituto Nazionale Tumori–IRCCS-Fondazione G. Pascale, Napoli, Italia, Via Mariano Semmola, 80131 Naples, Italy; (A.O.); (A.A.); (A.D.S.)
| | - Alfonso De Stefano
- Abdominal Oncology Division, Istituto Nazionale Tumori–IRCCS-Fondazione G. Pascale, Napoli, Italia, Via Mariano Semmola, 80131 Naples, Italy; (A.O.); (A.A.); (A.D.S.)
| | - Robert Grimm
- Siemens Healthcare GmbH, 91052 Erlangen, Germany;
| | - Roberta Grassi
- Radiology Division, Universita’ Degli Studi Della Campania Luigi Vanvitelli, Piazza Miraglia, 80138 Naples, Italy;
| | - Luca Brunese
- Rector of the Universita’ Degli Studi Del Molise, 86100 Molise, Italy;
| | - Francesco Izzo
- Hepatobiliary Surgical Oncology Division, Istituto Nazionale Tumori–IRCCS-Fondazione G. Pascale, Napoli, Italia, Via Mariano Semmola, 80131 Naples, Italy;
| | - Antonella Petrillo
- Radiology Division, Istituto Nazionale Tumori–IRCCS-Fondazione G. Pascale, Napoli, Italia, Via Mariano Semmola, 80131 Naples, Italy; (V.G.); (A.P.)
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Muftuler LT, Meier TB, Keith M, Budde MD, Huber DL, McCrea MA. Serial Diffusion Kurtosis Magnetic Resonance Imaging Study during Acute, Subacute, and Recovery Periods after Sport-Related Concussion. J Neurotrauma 2020; 37:2081-2092. [PMID: 32253977 DOI: 10.1089/neu.2020.6993] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Sport-related concussion (SRC) is common in contact sports, but there remains a lack of reliable, unbiased biomarkers of brain injury and recovery. Although the symptoms of SRC generally resolve over a period of days to weeks, the lack of a biomarker impairs detection and return-to-play decisions. To this date, the pathophysiological recovery profile and relationships between brain changes and symptoms remained unclear. In the current study, diffusion kurtosis imaging (DKI) was used to monitor the effects of SRC on the brain and the trajectory of recovery in concussed American football players (n = 96) at <48 h, and 8, 15, and 45 days post-injury, who were compared with a matched group of uninjured players (n = 82). The concussed group reported significantly higher symptoms within 48 h after injury than controls, which resolved by the 8-day follow-up. The concussed group also demonstrated poorer performance on balance testing at <48 h and 8 days than controls. There were no significant differences between the groups in the Standardized Assessment of Concussion (SAC), a cognitive screening measure. DKI data were acquired with 3 mm isotropic resolution, and analyzed using tract-based spatial statistics (TBSS). Additionally, voxel- and region of interest-based analyses were also conducted. At <48 h, the concussed group showed significantly higher axial kurtosis than the control group. These differences increased in extent and magnitude at 8 days, then receded at 15 days, and returned to the normal levels by 45 days. Kurtosis fractional anisotropy (FA) exhibited a delayed response, with a consistent increase by days 15 and 45. The results indicate that changes detected in the acute period appear to be prolonged compared with clinical recovery, but additional brain changes not observable acutely appear to progress. Although further studies are needed to understand the pathological features of DKI changes after SRC, these findings highlight a potential disparity between clinical symptoms and pathophysiological recovery after SRC.
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Affiliation(s)
- L Tugan Muftuler
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.,Center for Neurotrauma Research, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Timothy B Meier
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.,Center for Neurotrauma Research, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Monica Keith
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Matthew D Budde
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.,Center for Neurotrauma Research, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Daniel L Huber
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.,Center for Neurotrauma Research, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Michael A McCrea
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.,Center for Neurotrauma Research, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
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Vidić I, Egnell L, Jerome NP, White NS, Karunamuni R, Rakow-Penner R, Dale AM, Bathen TF, Goa PE. Modeling the diffusion-weighted imaging signal for breast lesions in the b = 200 to 3000 s/mm 2 range: quality of fit and classification accuracy for different representations. Magn Reson Med 2020; 84:1011-1023. [PMID: 31975448 DOI: 10.1002/mrm.28161] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 12/11/2019] [Accepted: 12/12/2019] [Indexed: 01/19/2023]
Abstract
PURPOSE To evaluate different non-Gaussian representations for the diffusion-weighted imaging (DWI) signal in the b-value range 200 to 3000 s/mm2 in benign and malignant breast lesions. METHODS Forty-three patients diagnosed with benign (n = 18) or malignant (n = 25) tumors of the breast underwent DWI (b-values 200, 600, 1200, 1800, 2400, and 3000 s/mm2 ). Six different representations were fit to the average signal from regions of interest (ROIs) at different b-value ranges. Quality of fit was assessed by the corrected Akaike information criterion (AICc), and the Friedman test was used for assessing representation ranks. The area under the curve (AUC) of receiver operating characteristic curves were used to evaluate the power of derived parameters to differentiate between malignant and benign lesions. The lesion ROI was divided in central and peripheral parts to assess potential effect of heterogeneity. Sensitivity to noise-floor correction was also evaluated. RESULTS The Padé exponent was ranked as the best based on AICc, whereas 3 models (kurtosis, fractional, and biexponential) achieved the highest AUC = 0.99 for lesion differentiation. The monoexponential model at bmax = 600 s/mm2 already provides AUC = 0.96, with considerably shorter acquisition time and simpler analysis. Significant differences between central and peripheral parts of lesions were found in malignant lesions. The mono- and biexponential models were most stable against varying degrees of noise-floor correction. CONCLUSION Non-Gaussian representations are required for fitting of the DWI curve at high b-values in breast lesions. However, the added clinical value from the high b-value data for differentiation of benign and malignant lesions is not clear.
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Affiliation(s)
- Igor Vidić
- Department of Physics, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
| | - Liv Egnell
- Department of Physics, NTNU-Norwegian University of Science and Technology, Trondheim, Norway.,Clinic of Radiology and Nuclear Medicine, St. Olavs University Hospital, Trondheim, Norway
| | - Neil P Jerome
- Clinic of Radiology and Nuclear Medicine, St. Olavs University Hospital, Trondheim, Norway.,Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Nathan S White
- Department of Radiology, University of California San Diego, La Jolla, California.,Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, California.,HealthLytix Inc., San Diego, California
| | - Roshan Karunamuni
- Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, California
| | - Rebecca Rakow-Penner
- Department of Radiology, University of California San Diego, La Jolla, California
| | - Anders M Dale
- Department of Radiology, University of California San Diego, La Jolla, California.,Department of Neurosciences, University of California, San Diego, La Jolla, California
| | - Tone F Bathen
- Clinic of Radiology and Nuclear Medicine, St. Olavs University Hospital, Trondheim, Norway.,Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Pål Erik Goa
- Department of Physics, NTNU-Norwegian University of Science and Technology, Trondheim, Norway.,Clinic of Radiology and Nuclear Medicine, St. Olavs University Hospital, Trondheim, Norway
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Chuhutin A, Hansen B, Wlodarczyk A, Owens T, Shemesh N, Jespersen SN. Diffusion Kurtosis Imaging maps neural damage in the EAE model of multiple sclerosis. Neuroimage 2019; 208:116406. [PMID: 31830588 PMCID: PMC9358435 DOI: 10.1016/j.neuroimage.2019.116406] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [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: 04/03/2019] [Revised: 11/20/2019] [Accepted: 11/25/2019] [Indexed: 01/22/2023] Open
Abstract
Diffusion kurtosis imaging (DKI) is an imaging modality that yields novel
disease biomarkers and in combination with nervous tissue modeling, provides
access to microstructural parameters. Recently, DKI and subsequent estimation of
microstructural model parameters has been used for assessment of tissue changes
in neurodegenerative diseases and associated animal models. In this study, mouse
spinal cords from the experimental autoimmune encephalomyelitis (EAE) model of
multiple sclerosis (MS) were investigated for the first time using DKI in
combination with biophysical modeling to study the relationship between
microstructural metrics and degree of animal dysfunction. Thirteen spinal cords
were extracted from animals with varied grades of disability and scanned in a
high-field MRI scanner along with five control specimen. Diffusion weighted data
were acquired together with high resolution T2*
images. Diffusion data were fit to estimate diffusion and kurtosis tensors and
white matter modeling parameters, which were all used for subsequent statistical
analysis using a linear mixed effects model. T2*
images were used to delineate focal demyelination/inflammation. Our results
reveal a strong relationship between disability and measured microstructural
parameters in normal appearing white matter and gray matter. Relationships
between disability and mean of the kurtosis tensor, radial kurtosis, radial
diffusivity were similar to what has been found in other hypomyelinating MS
models, and in patients. However, the changes in biophysical modeling parameters
and in particular in extra-axonal axial diffusivity were clearly different from
previous studies employing other animal models of MS. In conclusion, our data
suggest that DKI and microstructural modeling can provide a unique contrast
capable of detecting EAE-specific changes correlating with clinical
disability.
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Affiliation(s)
| | | | - Agnieszka Wlodarczyk
- Department of Neurobiology Research, Institute for Molecular Medicine,University of South Denmark, Odense, Denmark
| | - Trevor Owens
- Department of Neurobiology Research, Institute for Molecular Medicine,University of South Denmark, Odense, Denmark
| | - Noam Shemesh
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Sune Nørhøj Jespersen
- CFIN, Aarhus University, Aarhus, Denmark; Department of Physics, Aarhus University, Aarhus, Denmark
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Xue Y, Zhang Z, Wen C, Liu H, Wang S, Li J, Zhuge Q, Chen W, Ye Q. Characterization of Alzheimer's Disease Using Ultra-high b-values Apparent Diffusion Coefficient and Diffusion Kurtosis Imaging. Aging Dis 2019; 10:1026-1036. [PMID: 31595200 PMCID: PMC6764724 DOI: 10.14336/ad.2018.1129] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [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: 09/09/2018] [Accepted: 11/29/2018] [Indexed: 12/13/2022] Open
Abstract
The aim of the study is to investigate the diffusion characteristics of Alzheimer’s disease (AD) patients using an ultra-high b-values apparent diffusion coefficient (ADC_uh) and diffusion kurtosis imaging (DKI). A total of 31 AD patients and 20 healthy controls (HC) who underwent both MRI examination and clinical assessment were included in this study. Diffusion weighted imaging (DWI) was acquired with 14 b-values in the range of 0 and 5000 s/mm2. Diffusivity was analyzed in selected regions, including the amygdala (AMY), hippocampus (HIP), thalamus (THA), caudate (CAU), globus pallidus (GPA), lateral ventricles (LVe), white matter (WM) of the frontal lobe (FL), WM of the temporal lobe (TL), WM of the parietal lobe (PL) and centrum semiovale (CS). The mean, median, skewness and kurtosis of the conventional apparent diffusion coefficient (ADC), DKI (including two variables, Dapp and Kapp) and ADC_uh values were calculated for these selected regions. Compared to the HC group, the ADC values of AD group were significantly higher in the right HIP and right PL (WM), while the ADC_uh values of the AD group increased significantly in the WM of the bilateral TL and right CS. In the AD group, the Kapp values in the bilateral LVe, bilateral PL/left TL (WM) and right CS were lower than those in the HC group, while the Dapp value of the right PL (WM) increased. The ADC_uh value of the right TL was negatively correlated with MMSE (mean, r=-0.420, p=0.019). The ADC value and Dapp value have the same regions correlated with MMSE. Compared with the ADC_uh, combining ADC_uh and ADC parameters will result in a higher AUC (0.894, 95%CI=0.803-0.984, p=0.022). Comparing to ADC or DKI, ADC_uh has no significant difference in the detectability of AD, but ADC_uh can better reflect characteristic alternation in unconventional brain regions of AD patients.
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Affiliation(s)
- Yingnan Xue
- 1Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhenhua Zhang
- 1Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Caiyun Wen
- 1Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Huiru Liu
- 1Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Suyuan Wang
- 1Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jiance Li
- 1Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qichuan Zhuge
- 2Zhejiang Provincial Key Laboratory of Aging and Neurological Disorder Research, Department of Neurosurgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Weijian Chen
- 1Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qiong Ye
- 1Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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Maehara M. Development of voxel-based optimization diffusion kurtosis imaging ( DKI) and comparison with conventional DKI. Radiol Phys Technol 2019; 12:290-298. [PMID: 31278593 DOI: 10.1007/s12194-019-00523-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 06/25/2019] [Accepted: 06/26/2019] [Indexed: 11/29/2022]
Abstract
The aims of this study were to implement voxel-based optimization diffusion kurtosis imaging (DKI) and evaluate the accuracy of the method for the analysis of diffusion imaging data in comparison with conventional DKI. Conventional DKI and voxel-based optimization DKI were tested on a phantom and a human in a 1.5 T whole-body scanner. The differences in the diffusion coefficient (D) and diffusion kurtosis (K) values were analyzed using the Mann-Whitney U test, and the Holm correction was applied to the statistical analyses. In the phantom study, the D value resulting from voxel-based optimization DKI was significantly lower than those from conventional DKI in water and agarose solutions at concentrations of 50 and 100 g/L (all p < 0.01). Moreover, the K value was significantly lower in the water and agarose solutions at concentrations of 50, 100, and 200 g/L (all p < 0.01). In the human study, the D value resulting from voxel-based optimization DKI was significantly lower than that of conventional DKI in both white matter (WM), and gray matter (GM) (all p < 0.01). Moreover, the K value was significantly lower in cerebrospinal fluid, WM, and GM (all p < 0.01). To correctly measure the DKI, the optimized b values for each voxel must be used. Voxel-based optimization DKI is a method that optimizes the b values for each voxel. It appears that voxel-based optimization DKI improves the accuracy of the K value for biological tissues.
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Affiliation(s)
- Masanori Maehara
- Department of Radiology, Nihon University School of Dentistry at Matsudo, 2-870-1, Sakae-nishi, Matsudo, 271-8587, Chiba, Japan.
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36
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Mastropietro A, Rizzo G, Fontana L, Figini M, Bernardini B, Straffi L, Marcheselli S, Ghirmai S, Nuzzi NP, Malosio ML, Grimaldi M. Microstructural characterization of corticospinal tract in subacute and chronic stroke patients with distal lesions by means of advanced diffusion MRI. Neuroradiology 2019; 61:1033-45. [PMID: 31263922 DOI: 10.1007/s00234-019-02249-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Accepted: 06/18/2019] [Indexed: 12/11/2022]
Abstract
PURPOSE The aim of the paper is to evaluate if advanced dMRI techniques, including diffusion kurtosis imaging (DKI) and neurite orientation dispersion and density imaging (NODDI), could provide novel insights into the subtle microarchitectural modifications occurring in the corticospinal tract (CST) of stroke patients in subacute and chronic phases. METHODS Seventeen subjects (age 68 ± 11 years) in the subacute phase (14 ± 3 days post-stroke), 10 of whom rescanned in the chronic phase (231 ± 36 days post-stroke), were enrolled. Images were acquired using a 3-T MRI scanner with a two-shell EPI protocol (20 gradient directions, b = 700 s/mm2, 3 b = 0; 64 gradient directions, b = 2000 s/mm2, 9 b = 0). DTI-, DKI-, and NODDI-derived parameters were calculated in the posterior limb of the internal capsule (PLIC) and in the cerebral peduncle (CP). RESULTS In the subacute phase, a reduction of FA, AD, and KA values was correlated with an increase of ODI, RD, and AK parameters, in both the ipsilesional PLIC and CP, suggesting that increased fiber dispersion can be the main structural factor. In the chronic phase, a reduction of FA and an increase of ODI persisted in the ipsilesional areas. This was associated with reduced Fic and increased MD, with a concomitant reduction of MK and increase of RD, suggesting that fiber reduction, possibly due to nerve degeneration, could play an important role. CONCLUSIONS This study shows that advanced dMRI approaches can help elucidate the underpinning architectural modifications occurring in the CST after stroke. Further follow-up studies on bigger cohorts are needed to evaluate if DKI- and NODDI-derived parameters might be proposed as complementary biomarkers of brain microstructural alterations.
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Kremneva EI, Legostaeva LA, Morozova SN, Sergeev DV, Sinitsyn DO, Iazeva EG, Suslin AS, Suponeva NA, Krotenkova MV, Piradov MA, Maximov II. Feasibility of Non-Gaussian Diffusion Metrics in Chronic Disorders of Consciousness. Brain Sci 2019; 9:brainsci9050123. [PMID: 31137909 PMCID: PMC6562474 DOI: 10.3390/brainsci9050123] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [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: 04/23/2019] [Revised: 05/23/2019] [Accepted: 05/23/2019] [Indexed: 01/06/2023] Open
Abstract
Diagnostic accuracy of different chronic disorders of consciousness (DOC) can be affected by the false negative errors in up to 40% cases. In the present study, we aimed to investigate the feasibility of a non-Gaussian diffusion approach in chronic DOC and to estimate a sensitivity of diffusion kurtosis imaging (DKI) metrics for the differentiation of vegetative state/unresponsive wakefulness syndrome (VS/UWS) and minimally conscious state (MCS) from a healthy brain state. We acquired diffusion MRI data from 18 patients in chronic DOC (11 VS/UWS, 7 MCS) and 14 healthy controls. A quantitative comparison of the diffusion metrics for grey (GM) and white (WM) matter between the controls and patient group showed a significant (p < 0.05) difference in supratentorial WM and GM for all evaluated diffusion metrics, as well as for brainstem, corpus callosum, and thalamus. An intra-subject VS/UWS and MCS group comparison showed only kurtosis metrics and fractional anisotropy differences using tract-based spatial statistics, owing mainly to macrostructural differences on most severely lesioned hemispheres. As a result, we demonstrated an ability of DKI metrics to localise and detect changes in both WM and GM and showed their capability in order to distinguish patients with a different level of consciousness.
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Affiliation(s)
- Elena I Kremneva
- Research Center of Neurology, 80 Volokolamskoe shosse, 125367 Moscow, Russia.
| | | | - Sofya N Morozova
- Research Center of Neurology, 80 Volokolamskoe shosse, 125367 Moscow, Russia.
| | - Dmitry V Sergeev
- Research Center of Neurology, 80 Volokolamskoe shosse, 125367 Moscow, Russia.
| | - Dmitry O Sinitsyn
- Research Center of Neurology, 80 Volokolamskoe shosse, 125367 Moscow, Russia.
| | - Elizaveta G Iazeva
- Research Center of Neurology, 80 Volokolamskoe shosse, 125367 Moscow, Russia.
| | - Aleksandr S Suslin
- Research Center of Neurology, 80 Volokolamskoe shosse, 125367 Moscow, Russia.
| | - Natalia A Suponeva
- Research Center of Neurology, 80 Volokolamskoe shosse, 125367 Moscow, Russia.
| | - Marina V Krotenkova
- Research Center of Neurology, 80 Volokolamskoe shosse, 125367 Moscow, Russia.
| | - Michael A Piradov
- Research Center of Neurology, 80 Volokolamskoe shosse, 125367 Moscow, Russia.
| | - Ivan I Maximov
- Department of Psychology, University of Oslo, Forskningsveien 3A, 0373 Oslo, Norway.
- Norwegian Centre for Mental Disorders Research (NORMENT), Norway and Institute of Clinical Medicine, University of Oslo, Oslo Universitetssykehus Bygg 48 Ullevål, 0317 Oslo, Norway.
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Sato T, Endo K, Kakisaka K, Suzuki Y, Kooka Y, Sawara K, Ito K, Sasaki M, Takikawa Y. Decreased Mean Kurtosis in the Putamen is a Diagnostic Feature of Minimal Hepatic Encephalopathy in Patients with Cirrhosis. Intern Med 2019; 58:1217-1224. [PMID: 30626839 PMCID: PMC6543222 DOI: 10.2169/internalmedicine.2116-18] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Objective To prevent the development of overt hepatic encephalopathy, the early intervention for minimal hepatic encephalopathy (MHE) based on an accurate diagnosis is essential. This study investigated whether or not magnetic resonance diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) could detect brain microstructure abnormalities in MHE. The aim was to confirm whether or not brain microstructure abnormalities detected by magnetic resonance (MR) imaging could be used for the diagnosis of MHE. Methods Thirty-two subjects were prospectively examined with a 3-T MR scanner. Tract-based spatial statistics and region of interest analyses of diffusion imaging were performed to compare the mean kurtosis (MK), fractional anisotropy (FA), and mean diffusivity (MD) values between patients with and without minimal hepatic encephalopathy. The diagnostic performance for the detection of MHE was assessed with a receiver operating characteristic analysis. Results Ten subjects were diagnosed with MHE by neuropsychological testing. After the exclusion of unsuitable subjects, we analyzed 9 subjects with MHE and 14 without MHE. The patients with MHE had a reduced MK in the widespread white matter. We also found significant decreases in the MK in the caudate nucleus, putamen, globus pallidus, and/or thalamus in the subjects with MHE. The MK in the putamen showed the best diagnostic performance for differentiating the subjects with MHE from those without MHE (cut-off value, 0.74; sensitivity, 0.89; specificity, 0.86). Conclusion DKI detects changes in the cerebral white matter and basal ganglia regions of patients with MHE more sensitively than DTI. The MK values in the putamen can be a useful marker for diagnosing MHE from cirrhotic patients without MHE.
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Affiliation(s)
- Takuro Sato
- Division of Hepatology, Department of Internal Medicine, Iwate Medical University, Japan
| | - Kei Endo
- Division of Hepatology, Department of Internal Medicine, Iwate Medical University, Japan
| | - Keisuke Kakisaka
- Division of Hepatology, Department of Internal Medicine, Iwate Medical University, Japan
| | - Yuji Suzuki
- Division of Hepatology, Department of Internal Medicine, Iwate Medical University, Japan
| | - Yohei Kooka
- Division of Hepatology, Department of Internal Medicine, Iwate Medical University, Japan
| | - Kei Sawara
- Division of Hepatology, Department of Internal Medicine, Iwate Medical University, Japan
| | - Kenji Ito
- Division of Ultrahigh Field MRI, Institute for Biomedical Sciences, Iwate Medical University, Japan
| | - Makoto Sasaki
- Division of Ultrahigh Field MRI, Institute for Biomedical Sciences, Iwate Medical University, Japan
| | - Yasuhiro Takikawa
- Division of Hepatology, Department of Internal Medicine, Iwate Medical University, Japan
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Li L, Chopp M, Ding G, Davoodi-Bojd E, Li Q, Mahmood A, Xiong Y, Jiang Q. Diffuse white matter response in trauma-injured brain to bone marrow stromal cell treatment detected by diffusional kurtosis imaging. Brain Res 2019; 1717:127-135. [PMID: 31009610 DOI: 10.1016/j.brainres.2019.04.020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Revised: 03/25/2019] [Accepted: 04/18/2019] [Indexed: 12/14/2022]
Abstract
Diffuse white matter (WM) response to traumatic brain injury (TBI) and transplantation of human bone marrow stromal cells (hMSCs) after the injury were non-invasively and dynamically investigated. Male Wistar rats (300-350 g) subjected to TBI were intravenously injected with 1 ml of saline (n = 10) or with hMSCs in suspension (∼3 × 106 hMSCs, n = 10) 1-week post-TBI. MRI measurements of T2-weighted imaging and diffusional kurtosis imaging (DKI) were acquired on all animals at multiple time points up to 3-months post-injury. Functional outcome was assessed using the Morris water maze test. DKI-derived metrics of fractional anisotropy (FA), axonal water fraction (AWF) and radial kurtosis (RK) longitudinally reveal an evolving pattern of structural alteration post-TBI occurring in the brain region remote from primary impact site. The progressive structural change is characterized by gradual disruption of WM integrity at an early stage (weeks post-TBI), followed by spontaneous recovery at a later stage (months post-TBI). Transplantation of hMSCs post-TBI promotes this structural plasticity as indicated by significantly increased FA and AWF in conjunction with substantially elevated RK at the later stage. Our long-term imaging data demonstrate that hMSC therapy leads to modified temporal profiles of these metrics, inducing an earlier presence of enhanced structural remodeling, which may contribute to improved functional recovery.
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Affiliation(s)
- Lian Li
- Department of Neurology, Henry Ford Health System, Detroit, MI 48202, USA.
| | - Michael Chopp
- Department of Neurology, Henry Ford Health System, Detroit, MI 48202, USA; Department of Physics, Oakland University, Rochester, MI 48309, USA.
| | - Guangliang Ding
- Department of Neurology, Henry Ford Health System, Detroit, MI 48202, USA.
| | | | - Qingjiang Li
- Department of Neurology, Henry Ford Health System, Detroit, MI 48202, USA.
| | - Asim Mahmood
- Department of Neurosurgery, Henry Ford Health System, Detroit, MI 48208, USA.
| | - Ye Xiong
- Department of Neurosurgery, Henry Ford Health System, Detroit, MI 48208, USA.
| | - Quan Jiang
- Department of Neurology, Henry Ford Health System, Detroit, MI 48202, USA.
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Ouyang M, Jeon T, Sotiras A, Peng Q, Mishra V, Halovanic C, Chen M, Chalak L, Rollins N, Roberts TPL, Davatzikos C, Huang H. Differential cortical microstructural maturation in the preterm human brain with diffusion kurtosis and tensor imaging. Proc Natl Acad Sci U S A 2019; 116:4681-8. [PMID: 30782802 DOI: 10.1073/pnas.1812156116] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Delineating cortical microstructure differentiation is important for understanding complicated yet precisely organized patterns in early developing brain. Knowledge of cortical differentiation predominantly from histological studies is limited in localized and discrete cortical regions. We quantified the preterm brain cerebral cortical profile with microstructural complexity [indexed by mean kurtosis (MK)] and microstructural organization [indexed by fractional anisotropy (FA)] from advanced diffusion MRI. Cortical MK and FA maps revealed a heterogeneous maturation signature. Spatiotemporally distinctive disruption of radial glia and decrease of neuronal density among cortical regions were inferred by FA and MK decreases, respectively. These findings suggest that diffusion kurtosis metrics are significant imaging markers for microstructural differentiation of the developmental brain in health and disease. During the third trimester, the human brain undergoes rapid cellular and molecular processes that reshape the structural architecture of the cerebral cortex. Knowledge of cortical differentiation obtained predominantly from histological studies is limited in localized and small cortical regions. How cortical microstructure is differentiated across cortical regions in this critical period is unknown. In this study, the cortical microstructural architecture across the entire cortex was delineated with non-Gaussian diffusion kurtosis imaging as well as conventional diffusion tensor imaging of 89 preterm neonates aged 31–42 postmenstrual weeks. The temporal changes of cortical mean kurtosis (MK) or fractional anisotropy (FA) were heterogeneous across the cortical regions. Cortical MK decreases were observed throughout the studied age period, while cortical FA decrease reached its plateau around 37 weeks. More rapid decreases in MK were found in the primary visual region, while faster FA declines were observed in the prefrontal cortex. We found that distinctive cortical microstructural changes were coupled with microstructural maturation of associated white matter tracts. Both cortical MK and FA measurements predicted the postmenstrual age of preterm infants accurately. This study revealed a differential 4D spatiotemporal cytoarchitectural signature inferred by non-Gaussian diffusion barriers inside the cortical plate during the third trimester. The cytoarchitectural processes, including dendritic arborization and neuronal density decreases, were inferred by regional cortical FA and MK measurements. The presented findings suggest that cortical MK and FA measurements could be used as effective imaging markers for cortical microstructural changes in typical and potentially atypical brain development.
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Braeckman K, Descamps B, Pieters L, Vral A, Caeyenberghs K, Vanhove C. Dynamic changes in hippocampal diffusion and kurtosis metrics following experimental mTBI correlate with glial reactivity. Neuroimage Clin 2019; 21:101669. [PMID: 30658945 PMCID: PMC6412089 DOI: 10.1016/j.nicl.2019.101669] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Revised: 01/04/2019] [Accepted: 01/05/2019] [Indexed: 01/05/2023]
Abstract
Diffusion magnetic resonance imaging biomarkers can provide quantifiable information of the brain tissue after a mild traumatic brain injury (mTBI). However, the commonly applied diffusion tensor imaging (DTI) model is not very specific to changes in the underlying cellular structures. To overcome these limitations, other diffusion models have recently emerged to provide a more complete view on the damage profile following TBI. In this study, we investigated longitudinal changes in advanced diffusion metrics following experimental mTBI, utilising three different diffusion models in a rat model of mTBI, including DTI, diffusion kurtosis imaging and a white matter model. Moreover, we investigated the association between the diffusion metrics with histological markers, including glial fibrillary acidic protein (GFAP), neurofilaments and synaptophysin in order to investigate specificity. Our results revealed significant decreases in mean diffusivity in the hippocampus and radial diffusivity and radial extra axonal diffusivity (RadEAD) in the cingulum one week post injury. Furthermore, correlation analysis showed that increased values of fractional anisotropy one day post injury in the hippocampus was highly correlated with GFAP reactivity three months post injury. Additionally, we observed a positive correlation between GFAP on one hand and the kurtosis parameters in the hippocampus on the other hand three months post injury. This result indicated that prolonged glial activation three months post injury is related to higher kurtosis values at later time points. In conclusion, our findings point out to the possible role of kurtosis metrics as well as metrics from the white matter model as prognostic biomarker to monitor prolonged glial reactivity and inflammatory responses after a mTBI not only at early timepoints but also several months after injury. Advanced diffusion metrics show longitudinal changes following mTBI Radial diffusivity (RD) and radial extra-axonal diffusivity ↓ in the cingulum Mean diffusivity ↓ in the hippocampus In the cingulum RD is continuously decreased until three months post injury Glial activity correlates with fractional anisotropy in hippocampus
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Affiliation(s)
- Kim Braeckman
- Infinity Lab, Medical Imaging and Signal Processing Group, UGent, Ghent, Belgium.
| | - Benedicte Descamps
- Infinity Lab, Medical Imaging and Signal Processing Group, UGent, Ghent, Belgium.
| | - Leen Pieters
- Department of Human Structure and Repair, UGent, Ghent, Belgium.
| | - Anne Vral
- Department of Human Structure and Repair, UGent, Ghent, Belgium.
| | - Karen Caeyenberghs
- Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, VIC, Australia.
| | - Christian Vanhove
- Infinity Lab, Medical Imaging and Signal Processing Group, UGent, Ghent, Belgium.
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Guerreri M, Palombo M, Caporale A, Fasano F, Macaluso E, Bozzali M, Capuani S. Age-related microstructural and physiological changes in normal brain measured by MRI γ-metrics derived from anomalous diffusion signal representation. Neuroimage 2018; 188:654-667. [PMID: 30583064 DOI: 10.1016/j.neuroimage.2018.12.044] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2018] [Revised: 12/11/2018] [Accepted: 12/20/2018] [Indexed: 12/29/2022] Open
Abstract
Nowadays, increasing longevity associated with declining cerebral nervous system functions, suggests the need for continued development of new imaging contrast mechanisms to support the differential diagnosis of age-related decline. In our previous papers, we developed a new imaging contrast metrics derived from anomalous diffusion signal representation and obtained from diffusion-weighted (DW) data collected by varying diffusion gradient strengths. Recently, we highlighted that the new metrics, named γ-metrics, depended on the local inhomogeneity due to differences in magnetic susceptibility between tissues and diffusion compartments in young healthy subjects, thus providing information about myelin orientation and iron content within cerebral regions. The major structural modifications occurring in brain aging are myelinated fibers damage in nerve fibers and iron accumulation in gray matter nuclei. Therefore, we investigated the potential of γ-metrics in relation to other conventional diffusion metrics such as DTI, DKI and NODDI in detecting age-related structural changes in white matter (WM) and subcortical gray matter (scGM). DW-images were acquired in 32 healthy subjects, adults and elderly (age range 20-77 years) using 3.0T and 12 b-values up to 5000 s/mm2. Association between diffusion metrics and subjects' age was assessed using linear regression. A decline in mean γ (Mγ) in the scGM and a complementary increase in radial γ (γ⊥) in frontal WM, genu of corpus callosum and anterior corona radiata with advancing age were found. We suggested that the increase in γ⊥ might reflect declined myelin density, and Mγ decrease might mirror iron accumulation. An increase in D// and a decrease in the orientation dispersion index (ODI) were associated with axonal loss in the pyramidal tracts, while their inverted trends within the thalamus were thought to be linked to reduced architectural complexity of nerve fibers. γ-metrics together with conventional diffusion-metrics can more comprehensively characterize the complex mechanisms underlining age-related changes than conventional diffusion techniques alone.
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Affiliation(s)
- Michele Guerreri
- SAIMLAL Department, Sapienza, Piazzale Aldo Moro, 5, 00185, Roma, RM, Italy; Institute for Complex Systems, CNR, Rome, Italy.
| | - Marco Palombo
- Institute for Complex Systems, CNR, Rome, Italy; Department of Computer Science & Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Alessandra Caporale
- Institute for Complex Systems, CNR, Rome, Italy; Laboratory for Structural, Physiologic and Functional Imaging, Perelman School of Medicine University of Pennsylvania, Philadelphia, PA, USA
| | | | | | - Marco Bozzali
- Neuroimaging Laboratory, Santa Lucia Foundation, Rome, Italy
| | - Silvia Capuani
- Institute for Complex Systems, CNR, Rome, Italy; Neuroimaging Laboratory, Santa Lucia Foundation, Rome, Italy
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Allen JW, Yazdani M, Kang J, Magnussen MJ, Qiu D, Hu W. Patients with Mild Cognitive Impairment May be Stratified by Advanced Diffusion Metrics and Neurocognitive Testing. J Neuroimaging 2018; 29:79-84. [PMID: 30548151 DOI: 10.1111/jon.12588] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Revised: 11/21/2018] [Accepted: 11/23/2018] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND AND PURPOSE Mild cognitive impairment (MCI) is a prevalent disorder, with a subset of patients progressing to dementia each year. Although MCI may be subdivided into amnestic or vascular types as well as into single or multiple cognitive domain involvement, most prior studies using advanced diffusion imaging have not accounted for these categories. The purpose of the current study was to determine if the pattern of diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) metrics in patients with amnestic MCI (aMCI) correlate to specific cognitive domain impairments. METHODS Nineteen consecutive patients with aMCI referred for brain magnetic resonance imaging (MRI) were included. All subjects underwent neurocognitive testing. A z-score was calculated for each domain and a composite of all four domains. Brain MRI included standard structural imaging and diffusion imaging. Volumetric, DTI, and DKI metrics were calculated and statistical analysis was performed with adjustments for multiple measures and comparisons. RESULTS Statistically significant correlations between diffusion metrics and cognitive z-scores were detected: visuospatial-visuoconstructional z-scores only correlated with alterations in the corpus callosum splenium, executive functioning z-scores with the corpus callosum genu, memory testing z-scores with the left hippocampus, and composite z-scores with the anterior centrum semiovale. CONCLUSION Neuroimaging studies of patients with aMCI to date have assumed a population with homogeneous cognitive impairment. Our results demonstrate selective patterns of regional diffusion metric alterations correlate to specific cognitive domain impairments. Future studies should account for this heterogeneity, and this may also be useful for prognostication.
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Affiliation(s)
- Jason W Allen
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA.,Department of Neurology, Emory University, Atlanta, GA
| | - Milad Yazdani
- Department of Radiology and Radiological Sciences, Medical University of South Carolina, Charleston, SC
| | - Jian Kang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI
| | | | - Deqiang Qiu
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA
| | - William Hu
- Department of Neurology, Emory University, Atlanta, GA
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De Luca A, Leemans A, Bertoldo A, Arrigoni F, Froeling M. A robust deconvolution method to disentangle multiple water pools in diffusion MRI. NMR Biomed 2018; 31:e3965. [PMID: 30052293 PMCID: PMC6221109 DOI: 10.1002/nbm.3965] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2018] [Revised: 05/30/2018] [Accepted: 05/31/2018] [Indexed: 05/06/2023]
Abstract
The diffusion-weighted magnetic resonance imaging (dMRI) signal measured in vivo arises from multiple diffusion domains, including hindered and restricted water pools, free water and blood pseudo-diffusion. Not accounting for the correct number of components can bias metrics obtained from model fitting because of partial volume effects that are present in, for instance, diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI). Approaches that aim to overcome this shortcoming generally make assumptions about the number of considered components, which are not likely to hold for all voxels. The spectral analysis of the dMRI signal has been proposed to relax assumptions on the number of components. However, it currently requires a clinically challenging signal-to-noise ratio (SNR) and accounts only for two diffusion processes defined by hard thresholds. In this work, we developed a method to automatically identify the number of components in the spectral analysis, and enforced its robustness to noise, including outlier rejection and a data-driven regularization term. Furthermore, we showed how this method can be used to take into account partial volume effects in DTI and DKI fitting. The proof of concept and performance of the method were evaluated through numerical simulations and in vivo MRI data acquired at 3 T. With simulations our method reliably decomposed three diffusion components from SNR = 30. Biases in metrics derived from DTI and DKI were considerably reduced when components beyond hindered diffusion were taken into account. With the in vivo data our method determined three macro-compartments, which were consistent with hindered diffusion, free water and pseudo-diffusion. Taking free water and pseudo-diffusion into account in DKI resulted in lower mean diffusivity and higher fractional anisotropy values in both gray and white matter. In conclusion, the proposed method allows one to determine co-existing diffusion compartments without prior assumptions on their number, and to account for undesired signal contaminations within clinically achievable SNR levels.
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Affiliation(s)
- Alberto De Luca
- PROVIDI Lab, Image Sciences InstituteUMC Utrecht and Utrecht Universitythe Netherlands
| | - Alexander Leemans
- PROVIDI Lab, Image Sciences InstituteUMC Utrecht and Utrecht Universitythe Netherlands
| | | | - Filippo Arrigoni
- Neuroimaging LabScientific Institute, IRCCS Eugenio MedeaBosisio PariniItaly
| | - Martijn Froeling
- Radiology DepartmentUMC Utrecht and Utrecht Universitythe Netherlands
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Shi B, Yuan F, Yan F, Zhang H, Pan Z, Chen W, Wang G, Tan J, Zhang Y, Ren Y, Du L. Evaluation of Effects of TGF-β1 Inhibition on Gastric Cancer in Nude Mice by Diffusion Kurtosis Imaging and In-Line X-ray Phase Contrast Imaging With Sequential Histology. J Magn Reson Imaging 2018; 49:1553-1564. [PMID: 30291648 PMCID: PMC6585615 DOI: 10.1002/jmri.26523] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [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: 06/29/2018] [Revised: 09/07/2018] [Accepted: 09/07/2018] [Indexed: 12/28/2022] Open
Abstract
Background Accurate and complete response evaluation after treatment is important to implement individualized therapy for gastric cancer. Purpose To investigate the effectiveness of diffusion kurtosis imaging (DKI) and in‐line X‐ray phase contrast imaging (ILXPCI) in the assessment of the therapeutic efficacy by transforming growth factor beta 1 (TGF‐β1) inhibition. Study Type Prospective animal study. Animal Model Thirty nude mice subcutaneous xenotransplantation tumor model of gastric cancer for DKI and 10 peritoneal metastasis nude mice model for ILXPCI. Field Strength/Sequence Examinations before and serially at 7, 14, 21, and 28 days after TGF‐β1 inhibition treatment were performed at 3T MRI including T2‐weighted imaging (T2WI) and DKI with five b values of 0, 500, 1000, 1500, 2000 s/mm2; ILXPCI examinations were performed at 14 days after treatment. Assessment DKI parameters (apparent diffusion coefficient [ADC], diffusivity [D] and kurtosis [K]) were calculated by two experienced radiologists after postprocessing. Statistical Tests For the differences in all the parameters between the baseline and each timepoint for both the treated and the control mice, the Mann–Whitney test was used. The Spearman correlation test was used to evaluate correlations among the DKI parameters and corresponding pathologic necrosis fraction (NF). Results ADC, D, and K values were significantly different between the two groups after treatment (P < 0.05). Serial measurements in the treated group showed that the ADC, D, and K values were significantly different at 7, 14, 21, and 28 days compared with baseline (P < 0.05). There were significant correlations between DKI parameters and NF (ADC, r = 0.865, P < 0.001; D, r = 0.802, P < 0.001; K, r = –0.944, P < 0.001). The ILXPCI results in the treated group showed a stronger absorption area than the control group. Data Conclusion DKI may be used to evaluate the complete course therapeutic effects of gastric cancer induced by TGF‐β1 inhibition, and the ILXPCI technique will improve the tumor microstructure resolution. Level of Evidence: 1 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2019;49:1553–1564.
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Affiliation(s)
- Bowen Shi
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
| | - Fei Yuan
- Department of Pathology, RuiJin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
| | - Zilai Pan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
| | - Weibo Chen
- Philips Healthcare, Shanghai, P.R. China
| | | | - Jingwen Tan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
| | - Yang Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
| | - Yuqi Ren
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, P.R. China
| | - Lianjun Du
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
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Hu L, Xu Q, Li J, Wang F, Xu X, Sun Z, Ma X, Liu Y, Wang Q, Wang D. No differences in brain microstructure between young KIBRA-C carriers and non-carriers. Oncotarget 2018; 9:1200-1209. [PMID: 29416687 PMCID: PMC5787430 DOI: 10.18632/oncotarget.23348] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [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: 10/27/2017] [Accepted: 12/04/2017] [Indexed: 11/25/2022] Open
Abstract
KIBRA rs17070145 polymorphism is associated with variations in memory function and the microstructure of related brain areas. Diffusion kurtosis imaging (DKI) as an extension of diffusion tensor imaging that can provide more information about changes in microstructure, based on the idea that water diffusion in biological tissues is heterogeneous due to structural hindrance and restriction. We used DKI to explore the relationship between KIBRA gene polymorphism and brain microstructure in young adults. We recruited 100 healthy young volunteers, including 53 TT carriers and 47 C allele carriers. No differences were detected between the TT homozygotes and C-allele carriers for any diffusion and kurtosis parameter. These results indicate KIBRA rs17070145 polymorphism likely has little or no effect on brain microstructure in young adults.
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Affiliation(s)
- Li Hu
- Department of Radiology, Qilu Hospital of Shandong University, Jinan 250012, China
| | - Qunxing Xu
- Medical Examination Center, Qilu Hospital of Shandong University, Jinan 250012, China
| | - Jizhen Li
- Mental Health Center of Shandong Province, Jinan 250012, China
| | - Feifei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Jinan 250012, China
| | - Xinghua Xu
- Department of Radiology, Qilu Hospital of Shandong University, Jinan 250012, China
| | - Zhiyuan Sun
- Department of Radiology, Qilu Hospital of Shandong University, Jinan 250012, China
| | - Xiangxing Ma
- Department of Radiology, Qilu Hospital of Shandong University, Jinan 250012, China
| | - Yong Liu
- Brainnetome Center, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Qing Wang
- Department of Radiology, Qilu Hospital of Shandong University, Jinan 250012, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Jinan 250012, China
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Maximov II, Tonoyan AS, Pronin IN. Differentiation of glioma malignancy grade using diffusion MRI. Phys Med 2017; 40:24-32. [PMID: 28712716 DOI: 10.1016/j.ejmp.2017.07.002] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2017] [Revised: 06/26/2017] [Accepted: 07/04/2017] [Indexed: 12/31/2022] Open
Abstract
Modern diffusion MR protocols allow one to acquire the multi-shell diffusion data with high diffusion weightings in a clinically feasible time. In the present work we assessed three diffusion approaches based on diffusion and kurtosis tensor imaging (DTI, DKI), and neurite orientation dispersion and density imaging (NODDI) as possible biomarkers for human brain glioma grade differentiation based on the one diffusion protocol. We used three diffusion weightings (so called b-values) equal to 0, 1000, and 2500s/mm2 with 60 non-coplanar diffusion directions in the case of non-zero b-values. The patient groups of the glioma grades II, III, and IV consist of 8 subjects per group. We found that DKI, and NODDI scalar metrics can be effectively used as glioma grade biomarkers with a significant difference (p<0.05) for grading between low- and high-grade gliomas, in particular, for glioma II versus glioma III grades, and glioma III versus glioma IV grades. The use of mean/axial kurtosis and intra-axonal fraction/orientation dispersion index metrics allowed us to obtain the most feasible and reliable differentiation criteria. For example, in the case of glioma grades II, III, and IV the mean kurtosis is equal to 0.31, 0.51, and 0.90, and the orientation dispersion index is equal to 0.14, 0.30, and 0.59, respectively. The limitations and perspectives of the biophysical diffusion models based on intra-/extra-axonal compartmentalisation for glioma differentiation are discussed.
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Affiliation(s)
- Ivan I Maximov
- Experimental Physics III, TU Dortmund University, 44221, Germany.
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Shaw CB, Hui ES, Helpern JA, Jensen JH. Tensor estimation for double-pulsed diffusional kurtosis imaging. NMR Biomed 2017; 30:e3722. [PMID: 28328072 DOI: 10.1002/nbm.3722] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2016] [Revised: 02/08/2017] [Accepted: 02/09/2017] [Indexed: 06/06/2023]
Abstract
Double-pulsed diffusional kurtosis imaging (DP-DKI) represents the double diffusion encoding (DDE) MRI signal in terms of six-dimensional (6D) diffusion and kurtosis tensors. Here a method for estimating these tensors from experimental data is described. A standard numerical algorithm for tensor estimation from conventional (i.e. single diffusion encoding) diffusional kurtosis imaging (DKI) data is generalized to DP-DKI. This algorithm is based on a weighted least squares (WLS) fit of the signal model to the data combined with constraints designed to minimize unphysical parameter estimates. The numerical algorithm then takes the form of a quadratic programming problem. The principal change required to adapt the conventional DKI fitting algorithm to DP-DKI is replacing the three-dimensional diffusion and kurtosis tensors with the 6D tensors needed for DP-DKI. In this way, the 6D diffusion and kurtosis tensors for DP-DKI can be conveniently estimated from DDE data by using constrained WLS, providing a practical means for condensing DDE measurements into well-defined mathematical constructs that may be useful for interpreting and applying DDE MRI. Data from healthy volunteers for brain are used to demonstrate the DP-DKI tensor estimation algorithm. In particular, representative parametric maps of selected tensor-derived rotational invariants are presented.
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Affiliation(s)
- Calvin B Shaw
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina, USA
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Edward S Hui
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong, SAR, China
| | - Joseph A Helpern
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina, USA
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, USA
- Department of Neuroscience, Medical University of South Carolina, Charleston, South Carolina, USA
- Department of Neurology, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Jens H Jensen
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina, USA
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, USA
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De Luca A, Bertoldo A, Froeling M. Effects of perfusion on DTI and DKI estimates in the skeletal muscle. Magn Reson Med 2016; 78:233-246. [PMID: 27538923 DOI: 10.1002/mrm.26373] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2016] [Revised: 06/28/2016] [Accepted: 07/18/2016] [Indexed: 12/20/2022]
Abstract
PURPOSE In this study, we evaluated the effects of perfusion of the skeletal muscle on diffusion tensor imaging (DTI) and diffusional kurtosis imaging (DKI) parameters and their reproducibility. METHODS Diffusion tensor imaging and DKI models, with and without intravoxel incoherent motion (IVIM) correction, were applied to simulated data at different physiological conditions and signal-to-noise ratio levels. Next, the same models were applied to data of the right calf of five healthy volunteers, acquired twice at 3 telsa. For six muscles, we evaluated the correlation of the perfusion signal fraction, with parameters derived from DTI and DKI, and performed repeatability analysis with and without IVIM correction. Additionally, the IVIM correction was compared to a multishell acquisition approach that minimizes perfusion effects on DTI estimates. RESULTS Simulations and acquired data showed that DTI and DKI estimates were biased proportionally to the perfusion signal fraction, and that IVIM correction was needed for accurate estimation of the DTI and DKI parameters. However, taking perfusion into account did not improve repeatability. CONCLUSION Blood perfusion has an effect on DTI and DKI estimations, but it can be minimized with IVIM correction or multishell acquisition strategies. Magn Reson Med 78:233-246, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Alberto De Luca
- Department of Information Engineering, University of Padova, Padova, Italy.,Department of Radiology, University Medical Center, Utrecht, The Netherlands.,Neuroimaging Lab, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, LC, Italy
| | | | - Martijn Froeling
- Department of Radiology, University Medical Center, Utrecht, The Netherlands
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50
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Shaw CB, Jensen JH, Deardorff RL, Spampinato MV, Helpern JA. Comparison of Diffusion Metrics Obtained at 1.5T and 3T in Human Brain With Diffusional Kurtosis Imaging. J Magn Reson Imaging 2016; 45:673-680. [PMID: 27402163 DOI: 10.1002/jmri.25380] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2016] [Accepted: 06/21/2016] [Indexed: 12/23/2022] Open
Abstract
PURPOSE To quantitatively compare diffusion metrics for human brain estimated with diffusional kurtosis imaging (DKI) at applied field strengths of 1.5 and 3T. MATERIALS AND METHODS DKI data for brain were acquired at both 1.5 and 3T from each of six healthy volunteers using a twice-refocused diffusion-weighted imaging sequence. From these data, parametric maps of mean diffusivity (MD), axial diffusivity (D‖ ), radial diffusivity (D⊥ ), fractional anisotropy (FA), mean diffusional kurtosis (MK), axial kurtosis (K‖ ), radial kurtosis (K⊥ ), and kurtosis fractional anisotropy (KFA) were estimated. Comparisons of the results from the two field strengths were made for each metric using both Bland-Altman plots and linear regression to calculate coefficients of determination (R2 ) and best fit lines. RESULTS Diffusion metrics measured at 1.5 and 3T were observed to be similar. Linear regression of the full datasets had coefficients of determination varying from a low of R2 = 0.86 for KFA to a high of R2 = 0.97 for FA. The slopes of the 3T vs. 1.5T best linear fits varied from 0.881 ± 0.009 for KFA to 1.038 ± 0.010 for D‖ . From a Bland-Altman analysis of selected regions of interest, the mean differences of the metrics for the two field strengths were all found to be less than 6%, except for KFA, which showed the largest relative discrepancy of 10%. CONCLUSION Diffusion metrics measured with DKI at 1.5 and 3T are strongly correlated and typically differ by only a few percent. The somewhat higher discrepancy for the KFA is argued to mainly reflect the effects of signal noise. This supports the robustness DKI results with respect to field strength. LEVEL OF EVIDENCE 3 J. Magn. Reson. Imaging 2017;45:673-680.
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Affiliation(s)
- Calvin B Shaw
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina, USA.,Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Jens H Jensen
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina, USA.,Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Rachael L Deardorff
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina, USA.,Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Maria Vittoria Spampinato
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Joseph A Helpern
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina, USA.,Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, USA.,Department of Neurosciences, Medical University of South Carolina, Charleston, South Carolina, USA.,Department of Neurology, Medical University of South Carolina, Charleston, South Carolina, USA
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