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Santillo AF, Strandberg TO, Reislev NH, Nilsson M, Stomrud E, Spotorno N, van Westen D, Hansson O. Divergent functional connectivity changes associated with white matter hyperintensities. Neuroimage 2024; 296:120672. [PMID: 38851551 DOI: 10.1016/j.neuroimage.2024.120672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 05/17/2024] [Accepted: 06/06/2024] [Indexed: 06/10/2024] Open
Abstract
Age-related white matter hyperintensities are a common feature and are known to be negatively associated with structural integrity, functional connectivity, and cognitive performance. However, this has yet to be fully understood mechanistically. We analyzed multiple MRI modalities acquired in 465 non-demented individuals from the Swedish BioFINDER study including 334 cognitively normal and 131 participants with mild cognitive impairment. White matter hyperintensities were automatically quantified using fluid-attenuated inversion recovery MRI and parameters from diffusion tensor imaging were estimated in major white matter fibre tracts. We calculated fMRI resting state-derived functional connectivity within and between predefined cortical regions structurally linked by the white matter tracts. How change in functional connectivity is affected by white matter lesions and related to cognition (in the form of executive function and processing speed) was explored. We examined the functional changes using a measure of sample entropy. As expected hyperintensities were associated with disrupted structural white matter integrity and were linked to reduced functional interregional lobar connectivity, which was related to decreased processing speed and executive function. Simultaneously, hyperintensities were also associated with increased intraregional functional connectivity, but only within the frontal lobe. This phenomenon was also associated with reduced cognitive performance. The increased connectivity was linked to increased entropy (reduced predictability and increased complexity) of the involved voxels' blood oxygenation level-dependent signal. Our findings expand our previous understanding of the impact of white matter hyperintensities on cognition by indicating novel mechanisms that may be important beyond this particular type of brain lesions.
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Affiliation(s)
- Alexander F Santillo
- Department of Clinical Sciences, Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund/Malmö, Sweden. Postal address: Memory Clinic, Skåne University Hospital, SE-20502 Malmö, Sweden
| | - Tor O Strandberg
- Department of Clinical Sciences, Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund/Malmö, Sweden. Postal address: Memory Clinic, Skåne University Hospital, SE-20502 Malmö, Sweden
| | - Nina H Reislev
- Department of Clinical Sciences, Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund/Malmö, Sweden. Postal address: Memory Clinic, Skåne University Hospital, SE-20502 Malmö, Sweden; Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
| | - Markus Nilsson
- Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Diagnostic Radiology, Lund. Diagnostic Radiology, Lunds Universitet/SUS/Lund, 221 85 Lund, Sweden, Sweden
| | - Erik Stomrud
- Department of Clinical Sciences, Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund/Malmö, Sweden. Postal address: Memory Clinic, Skåne University Hospital, SE-20502 Malmö, Sweden; Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Nicola Spotorno
- Department of Clinical Sciences, Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund/Malmö, Sweden. Postal address: Memory Clinic, Skåne University Hospital, SE-20502 Malmö, Sweden
| | - Danielle van Westen
- Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Diagnostic Radiology, Lund. Diagnostic Radiology, Lunds Universitet/SUS/Lund, 221 85 Lund, Sweden, Sweden
| | - Oskar Hansson
- Department of Clinical Sciences, Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund/Malmö, Sweden. Postal address: Memory Clinic, Skåne University Hospital, SE-20502 Malmö, Sweden; Memory Clinic, Skåne University Hospital, Malmö, Sweden.
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2
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Kim JY, Partridge SC. Non-contrast Breast MR Imaging. Radiol Clin North Am 2024; 62:661-678. [PMID: 38777541 PMCID: PMC11116814 DOI: 10.1016/j.rcl.2023.12.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
Considering the high cost of dynamic contrast-enhanced MR imaging and various contraindications and health concerns related to administration of intravenous gadolinium-based contrast agents, there is emerging interest in non-contrast-enhanced breast MR imaging. Diffusion-weighted MR imaging (DWI) is a fast, unenhanced technique that has wide clinical applications in breast cancer detection, characterization, prognosis, and predicting treatment response. It also has the potential to serve as a non-contrast MR imaging screening method. Standardized protocols and interpretation strategies can help to enhance the clinical utility of breast DWI. A variety of other promising non-contrast MR imaging techniques are in development, but currently, DWI is closest to clinical integration, while others are still mostly used in the research setting.
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Affiliation(s)
- Jin You Kim
- Department of Radiology and Medical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan, Republic of Korea
| | - Savannah C Partridge
- Department of Radiology, University of Washington, Seattle, WA, USA; Fred Hutchinson Cancer Center, Seattle, WA, USA.
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3
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Uddin MN, Singh MV, Faiyaz A, Szczepankiewicz F, Nilsson M, Boodoo ZD, Sutton KR, Tivarus ME, Zhong J, Wang L, Qiu X, Weber MT, Schifitto G. Tensor-valued diffusion MRI detects brain microstructure changes in HIV infected individuals with cognitive impairment. RESEARCH SQUARE 2024:rs.3.rs-4482269. [PMID: 38946952 PMCID: PMC11213220 DOI: 10.21203/rs.3.rs-4482269/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Despite advancements, the prevalence of HIV-associated neurocognitive impairment remains at approximately 40%, attributed to factors like pre-cART (combination antiretroviral therapy) irreversible brain injury. People with HIV (PWH) treated with cART do not show significant neurocognitive changes over relatively short follow-up periods. However, quantitative neuroimaging may be able to detect ongoing subtle microstructural changes. This study aimed to investigate the sensitivity of tensor-valued diffusion encoding in detecting such changes in brain microstructural integrity in cART-treated PWH. Additionally, it explored relationships between these metrics, neurocognitive scores, and plasma levels of neurofilament light (NFL) chain and glial fibrillary acidic protein (GFAP). Using MRI at 3T, 24 PWH and 31 healthy controls underwent cross-sectional examination. The results revealed significant variations in b-tensor encoding metrics across white matter regions, with associations observed between these metrics, cognitive performance, and blood markers of neuronal and glial injury (NFL and GFAP). Moreover, a significant interaction between HIV status and imaging metrics was observed, particularly impacting total cognitive scores in both gray and white matter. These findings suggest that b-tensor encoding metrics offer heightened sensitivity in detecting subtle changes associated with axonal injury in HIV infection, underscoring their potential clinical relevance in understanding neurocognitive impairment in PWH.
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Zhou M, Stobbe R, Szczepankiewicz F, Budde M, Buck B, Kate M, Lloret M, Fairall P, Butcher K, Shuaib A, Emery D, Nilsson M, Westin CF, Beaulieu C. Tensor-valued diffusion MRI of human acute stroke. Magn Reson Med 2024; 91:2126-2141. [PMID: 38156813 DOI: 10.1002/mrm.29975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 11/18/2023] [Accepted: 11/27/2023] [Indexed: 01/03/2024]
Abstract
PURPOSE Tensor-valued diffusion encoding can disentangle orientation dispersion and subvoxel anisotropy, potentially offering insight into microstructural changes after cerebral ischemia. The purpose was to evaluate tensor-valued diffusion MRI in human acute ischemic stroke, assess potential confounders from diffusion time dependencies, and compare to Monte Carlo diffusion simulations of axon beading. METHODS Linear (LTE) and spherical (STE) b-tensor encoding with inherently different effective diffusion times were acquired in 21 acute ischemic stroke patients between 3 and 57 h post-onset at 3 T in 2.5 min. In an additional 10 patients, STE with 2 LTE yielding different effective diffusion times were acquired for comparison. Diffusional variance decomposition (DIVIDE) was used to estimate microscopic anisotropy (μFA), as well as anisotropic, isotropic, and total diffusional variance (MKA , MKI , MKT ). DIVIDE parameters, and diffusion tensor imaging (DTI)-derived mean diffusivity and fractional anisotropy (FA) were compared in lesion versus contralateral white matter. Monte Carlo diffusion simulations of various cylindrical geometries for all b-tensor protocols were used to interpret parameter measurements. RESULTS MD was ˜40% lower in lesions for all LTE/STE protocols. The DIVIDE parameters varied with effective diffusion time: higher μFA and MKA in lesion versus contralateral white matter for STE with longer effective diffusion time LTE, whereas the shorter effective diffusion time LTE protocol yielded lower μFA and MKA in lesions. Both protocols, regardless of diffusion time, were consistent with simulations of greater beading amplitude and intracellular volume fraction. CONCLUSION DIVIDE parameters depend on diffusion time in acute stroke but consistently indicate neurite beading and larger intracellular volume fraction.
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Affiliation(s)
- Mi Zhou
- Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Robert Stobbe
- Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
- Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada
| | | | - Matthew Budde
- Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Brian Buck
- Neurology, University of Alberta, Edmonton, Alberta, Canada
| | - Mahesh Kate
- Neurology, University of Alberta, Edmonton, Alberta, Canada
| | - Mar Lloret
- Neurology, University of Alberta, Edmonton, Alberta, Canada
| | - Paige Fairall
- Neurology, University of Alberta, Edmonton, Alberta, Canada
| | - Ken Butcher
- School of Clinical Medicine, University of New South Wales, Sydney, New South Wales, Australia
| | - Ashfaq Shuaib
- Neurology, University of Alberta, Edmonton, Alberta, Canada
| | - Derek Emery
- Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada
| | - Markus Nilsson
- Clinical Sciences Lund, Lund University, Lund, Scania, Sweden
| | - Carl-Fredrik Westin
- Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Christian Beaulieu
- Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
- Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada
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Yılmaz E, Güldoğan N, Ulus S, Türk EB, Mısır ME, Arslan A, Arıbal ME. Diagnostic value of synthetic diffusion-weighted imaging on breast magnetic resonance imaging assessment: comparison with conventional diffusion-weighted imaging. Diagn Interv Radiol 2024; 30:91-98. [PMID: 37888786 PMCID: PMC10916533 DOI: 10.4274/dir.2023.232466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 10/22/2023] [Indexed: 10/28/2023]
Abstract
PURPOSE To compare images generated by synthetic diffusion-weighted imaging (sDWI) with those from conventional DWI in terms of their diagnostic performance in detecting breast lesions when performing breast magnetic resonance imaging (MRI). METHODS A total of 128 consecutive patients with 135 enhanced lesions who underwent dynamic MRI between 2018 and 2021 were included. The sDWI and DWI signals were compared by three radiologists with at least 10 years of experience in breast radiology. RESULTS Of the 82 malignant lesions, 91.5% were hyperintense on sDWI and 73.2% were hyperintense on DWI. Of the 53 benign lesions, 71.7% were isointense on sDWI and 37.7% were isointense on DWI. sDWI provides accurate signal intensity data with statistical significance compared with DWI (P < 0.05). The diagnostic performance of DWI and sDWI to differentiate malignant breast masses from benign masses was as follows: sensitivity 73.1% [95% confidence interval (CI): 62-82], specificity 37.7% (95% CI: 24-52); sensitivity 91.5% (95% CI: 83-96), specificity 71.7% (95% CI: 57-83), respectively. The diagnostic accuracy of DWI and sDWI was 59.2% and 83.7%, respectively. However, when the DWI images were evaluated with apparent diffusion coefficient mapping and compared with the sDWI images, the sensitivity was 92.68% (95% CI: 84-97) and the specificity was 79.25% (95% CI: 65-89) with no statistically significant difference. The inter-reader agreement was almost perfect (P < 0.001). CONCLUSION Synthetic DWI is superior to DWI for lesion visibility with no additional acquisition time and should be taken into consideration when conducting breast MRI scans. The evaluation of sDWI in routine MRI reporting will increase diagnostic accuracy.
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Affiliation(s)
- Ebru Yılmaz
- Acıbadem Altunizade Hospital Breast Center, Department of Radiology, İstanbul, Türkiye
| | - Nilgün Güldoğan
- Acıbadem Altunizade Hospital Breast Center, Department of Radiology, İstanbul, Türkiye
| | - Sıla Ulus
- Acıbadem Ataşehir Hospital, Department of Radiology, İstanbul, Türkiye
| | - Ebru Banu Türk
- Acıbadem Altunizade Hospital Breast Center, Department of Radiology, İstanbul, Türkiye
| | - Mustafa Enes Mısır
- Acıbadem Mehmet Ali Aydınlar University, Department of Radiology, İstanbul, Türkiye
| | - Aydan Arslan
- University of Health Sciences Türkiye, Ümraniye Training and Research Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Mustafa Erkin Arıbal
- Acıbadem Mehmet Ali Aydınlar University, Department of Radiology, İstanbul, Türkiye
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Chakwizira A, Zhu A, Foo T, Westin CF, Szczepankiewicz F, Nilsson M. Diffusion MRI with free gradient waveforms on a high-performance gradient system: Probing restriction and exchange in the human brain. Neuroimage 2023; 283:120409. [PMID: 37839729 DOI: 10.1016/j.neuroimage.2023.120409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 09/29/2023] [Accepted: 10/12/2023] [Indexed: 10/17/2023] Open
Abstract
The dependence of the diffusion MRI signal on the diffusion time carries signatures of restricted diffusion and exchange. Here we seek to highlight these signatures in the human brain by performing experiments using free gradient waveforms designed to be selectively sensitive to the two effects. We examine six healthy volunteers using both strong and ultra-strong gradients (80, 200 and 300 mT/m). In an experiment featuring a large set of 150 gradient waveforms with different sensitivities to restricted diffusion and exchange, our results reveal unique and different time-dependence signatures in grey and white matter. Grey matter was characterised by both restricted diffusion and exchange and white matter predominantly by restricted diffusion. Exchange in grey matter was at least twice as fast as in white matter, across all subjects and all gradient strengths. The cerebellar cortex featured relatively short exchange times (115 ms). Furthermore, we show that gradient waveforms with tailored designs can be used to map exchange in the human brain. We also assessed the feasibility of clinical applications of the method used in this work and found that the exchange-related contrast obtained with a 25-minute protocol at 300 mT/m was preserved in a 4-minute protocol at 300 mT/m and a 10-minute protocol at 80 mT/m. Our work underlines the utility of free waveforms for detecting time dependence signatures due to restricted diffusion and exchange in vivo, which may potentially serve as a tool for studying diseased tissue.
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Affiliation(s)
- Arthur Chakwizira
- Department of Medical Radiation Physics, Clinical Sciences Lund, Lund University, Lund, Sweden.
| | - Ante Zhu
- GE Research, Niskayuna, New York, United States
| | - Thomas Foo
- GE Research, Niskayuna, New York, United States
| | - Carl-Fredrik Westin
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Filip Szczepankiewicz
- Department of Medical Radiation Physics, Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Markus Nilsson
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden; Department of Radiology, Skåne University Hospital, Lund, Sweden
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Jakabek D, Power BD, Spotorno N, Macfarlane MD, Walterfang M, Velakoulis D, Nilsson C, Waldö ML, Lätt J, Nilsson M, van Westen D, Lindberg O, Looi JCL, Santillo AF. Structural and microstructural thalamocortical network disruption in sporadic behavioural variant frontotemporal dementia. Neuroimage Clin 2023; 39:103471. [PMID: 37473493 PMCID: PMC10371821 DOI: 10.1016/j.nicl.2023.103471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 06/09/2023] [Accepted: 07/06/2023] [Indexed: 07/22/2023]
Abstract
BACKGROUND Using multi-block methods we combined multimodal neuroimaging metrics of thalamic morphology, thalamic white matter tract diffusion metrics, and cortical thickness to examine changes in behavioural variant frontotemporal dementia. (bvFTD). METHOD Twenty-three patients with sporadic bvFTD and 24 healthy controls underwent structural and diffusion MRI scans. Clinical severity was assessed using the Clinical Dementia Rating scale and behavioural severity using the Frontal Behaviour Inventory by patient caregivers. Thalamic volumes were manually segmented. Anterior and posterior thalamic radiation fractional anisotropy and mean diffusivity were extracted using Tract-Based Spatial Statistics. Finally, cortical thickness was assessed using Freesurfer. We used shape analyses, diffusion measures, and cortical thickness as features in sparse multi-block partial least squares (PLS) discriminatory analyses to classify participants within bvFTD or healthy control groups. Sparsity was tuned with five-fold cross-validation repeated 10 times. Final model fit was assessed using permutation testing. Additionally, sparse multi-block PLS was used to examine associations between imaging features and measures of dementia severity. RESULTS Bilateral anterior-dorsal thalamic atrophy, reduction in mean diffusivity of thalamic projections, and frontotemporal cortical thinning, were the main features predicting bvFTD group membership. The model had a sensitivity of 96%, specificity of 68%, and was statistically significant using permutation testing (p = 0.012). For measures of dementia severity, we found similar involvement of regional thalamic and cortical areas as in discrimination analyses, although more extensive thalamo-cortical white matter metric changes. CONCLUSIONS Using multimodal neuroimaging, we demonstrate combined structural network dysfunction of anterior cortical regions, cortical-thalamic projections, and anterior thalamic regions in sporadic bvFTD.
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Affiliation(s)
| | - Brian D Power
- School of Medicine, The University of Notre Dame Australia, Fremantle, Australia
| | - Nicola Spotorno
- Department of Clinical Sciences, Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund/Malmö, Sweden
| | | | - Mark Walterfang
- Neuropsychiatry Unit, Royal Melbourne Hospital, Melbourne, Australia; Department of Psychiatry, University of Melbourne, Melbourne, Australia
| | - Dennis Velakoulis
- Neuropsychiatry Unit, Royal Melbourne Hospital, Melbourne, Australia; Department of Psychiatry, University of Melbourne, Melbourne, Australia
| | - Christer Nilsson
- Department of Clinical Sciences, Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund/Malmö, Sweden
| | - Maria Landqvist Waldö
- Clinical Sciences Helsingborg, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Jimmy Lätt
- Diagnostic Radiology, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Markus Nilsson
- Diagnostic Radiology, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Danielle van Westen
- Imaging and Function, Skane University Hospital, Lund, Sweden; Diagnostic Radiology, Institution for Clinical Sciences, Lund University, Lund, Sweden
| | - Olof Lindberg
- Department of Clinical Sciences, Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund/Malmö, Sweden
| | - Jeffrey C L Looi
- Academic Unit of Psychiatry and Addiction Medicine, The Australian National University School of Medicine and Psychology, Canberra Hospital, Canberra, Australian Capital Territory, Australia
| | - Alexander F Santillo
- Department of Clinical Sciences, Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund/Malmö, Sweden.
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8
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Teh I, Shelley D, Boyle JH, Zhou F, Poenar A, Sharrack N, Foster RJ, Yuldasheva NY, Parker GJM, Dall'Armellina E, Plein S, Schneider JE, Szczepankiewicz F. Cardiac q-space trajectory imaging by motion-compensated tensor-valued diffusion encoding in human heart in vivo. Magn Reson Med 2023; 90:150-165. [PMID: 36941736 PMCID: PMC10952623 DOI: 10.1002/mrm.29637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 01/25/2023] [Accepted: 02/23/2023] [Indexed: 03/23/2023]
Abstract
PURPOSE Tensor-valued diffusion encoding can probe more specific features of tissue microstructure than what is available by conventional diffusion weighting. In this work, we investigate the technical feasibility of tensor-valued diffusion encoding at high b-values with q-space trajectory imaging (QTI) analysis, in the human heart in vivo. METHODS Ten healthy volunteers were scanned on a 3T scanner. We designed time-optimal gradient waveforms for tensor-valued diffusion encoding (linear and planar) with second-order motion compensation. Data were analyzed with QTI. Normal values and repeatability were investigated for the mean diffusivity (MD), fractional anisotropy (FA), microscopic FA (μFA), isotropic, anisotropic and total mean kurtosis (MKi, MKa, and MKt), and orientation coherence (Cc ). A phantom, consisting of two fiber blocks at adjustable angles, was used to evaluate sensitivity of parameters to orientation dispersion and diffusion time. RESULTS QTI data in the left ventricular myocardium were MD = 1.62 ± 0.07 μm2 /ms, FA = 0.31 ± 0.03, μFA = 0.43 ± 0.07, MKa = 0.20 ± 0.07, MKi = 0.13 ± 0.03, MKt = 0.33 ± 0.09, and Cc = 0.56 ± 0.22 (mean ± SD across subjects). Phantom experiments showed that FA depends on orientation dispersion, whereas μFA was insensitive to this effect. CONCLUSION We demonstrated the first tensor-valued diffusion encoding and QTI analysis in the heart in vivo, along with first measurements of myocardial μFA, MKi, MKa, and Cc . The methodology is technically feasible and provides promising novel biomarkers for myocardial tissue characterization.
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Affiliation(s)
- Irvin Teh
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
| | - David Shelley
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
- Leeds Teaching Hospitals TrustLeedsUK
| | - Jordan H. Boyle
- Faculty of Industrial Design EngineeringDelft University of TechnologyDelftNetherlands
| | - Fenglei Zhou
- Center for Medical Image Computing, Department of Medical Physics & Biomedical Engineering and Department of NeuroinflammationUniversity College LondonLondonUK
- Astrea BioseparationCombertonUK
| | - Ana‐Maria Poenar
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
| | - Noor Sharrack
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
| | - Richard J. Foster
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
| | - Nadira Y. Yuldasheva
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
| | - Geoff J. M. Parker
- Center for Medical Image Computing, Department of Medical Physics & Biomedical Engineering and Department of NeuroinflammationUniversity College LondonLondonUK
- Bioxydyn LimitedManchesterUK
| | - Erica Dall'Armellina
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
| | - Sven Plein
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
| | - Jürgen E. Schneider
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
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Brabec J, Englund E, Bengzon J, Szczepankiewicz F, van Westen D, Sundgren PC, Nilsson M. Coregistered histology sections with diffusion tensor imaging data at 200 µm resolution in meningioma tumors. Data Brief 2023; 48:109261. [PMID: 37383742 PMCID: PMC10294079 DOI: 10.1016/j.dib.2023.109261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 05/08/2023] [Accepted: 05/17/2023] [Indexed: 06/30/2023] Open
Abstract
A significant problem in diffusion MRI (dMRI) is the lack of understanding regarding which microstructural features account for the variability in the diffusion tensor imaging (DTI) parameters observed in meningioma tumors. A common assumption is that mean diffusivity (MD) and fractional anisotropy (FA) from DTI are inversely proportional to cell density and proportional to tissue anisotropy, respectively. Although these associations have been established across a wide range of tumors, they have been challenged for interpreting within-tumor variations where several additional microstructural features have been suggested as contributing to MD and FA. To facilitate the investigation of the biological underpinnings of DTI parameters, we performed ex-vivo DTI at 200 µm isotropic resolution on sixteen excised meningioma tumor samples. The samples exhibit a variety of microstructural features because the dataset includes meningiomas of six different meningioma types and two different grades. Diffusion-weighted signal (DWI) maps, DWI maps averaged over all directions for given b-value, signal intensities without diffusion encoding (S0) as well as DTI parameters: MD, FA, in-plane FA (FAIP), axial diffusivity (AD) and radial diffusivity (RD), were coregistered to Hematoxylin & Eosin- (H&E) and Elastica van Gieson-stained (EVG) histological sections by a non-linear landmark-based approach. Here, we provide DWI signal and DTI maps coregistered to histology sections and describe the pipeline for processing the raw DTI data and the coregistration. The raw, processed, and coregistered data are hosted by Analytic Imaging Diagnostics Arena (AIDA) data hub registry, and software tools for processing are provided via GitHub. We hope that data can be used in research and education concerning the link between the meningioma microstructure and parameters obtained by DTI.
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Affiliation(s)
- Jan Brabec
- Medical Radiation Physics, Clinical Sciences Lund, Lund University, Lund, Sweden
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | | | - Johan Bengzon
- Division of Neurosurgery, Skane University Hospital, Lund, Sweden
- Stem Cell Center, Department of Clinical Sciences, Lund, Sweden
| | | | | | - Pia C. Sundgren
- Diagnostic Radiology, Clinical Sciences Lund, Lund University, Lund, Sweden
- Lund University Bioimaging Centre, Lund University, Lund, Sweden
- Department of Medical Imaging and Physiology, Skåne University Hospital, Lund University, Lund, Sweden
| | - Markus Nilsson
- Medical Radiation Physics, Clinical Sciences Lund, Lund University, Lund, Sweden
- Diagnostic Radiology, Clinical Sciences Lund, Lund University, Lund, Sweden
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10
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Figini M, Castellano A, Bailo M, Callea M, Cadioli M, Bouyagoub S, Palombo M, Pieri V, Mortini P, Falini A, Alexander DC, Cercignani M, Panagiotaki E. Comprehensive Brain Tumour Characterisation with VERDICT-MRI: Evaluation of Cellular and Vascular Measures Validated by Histology. Cancers (Basel) 2023; 15:2490. [PMID: 37173965 PMCID: PMC10177485 DOI: 10.3390/cancers15092490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 04/14/2023] [Accepted: 04/17/2023] [Indexed: 05/15/2023] Open
Abstract
The aim of this work was to extend the VERDICT-MRI framework for modelling brain tumours, enabling comprehensive characterisation of both intra- and peritumoural areas with a particular focus on cellular and vascular features. Diffusion MRI data were acquired with multiple b-values (ranging from 50 to 3500 s/mm2), diffusion times, and echo times in 21 patients with brain tumours of different types and with a wide range of cellular and vascular features. We fitted a selection of diffusion models that resulted from the combination of different types of intracellular, extracellular, and vascular compartments to the signal. We compared the models using criteria for parsimony while aiming at good characterisation of all of the key histological brain tumour components. Finally, we evaluated the parameters of the best-performing model in the differentiation of tumour histotypes, using ADC (Apparent Diffusion Coefficient) as a clinical standard reference, and compared them to histopathology and relevant perfusion MRI metrics. The best-performing model for VERDICT in brain tumours was a three-compartment model accounting for anisotropically hindered and isotropically restricted diffusion and isotropic pseudo-diffusion. VERDICT metrics were compatible with the histological appearance of low-grade gliomas and metastases and reflected differences found by histopathology between multiple biopsy samples within tumours. The comparison between histotypes showed that both the intracellular and vascular fractions tended to be higher in tumours with high cellularity (glioblastoma and metastasis), and quantitative analysis showed a trend toward higher values of the intracellular fraction (fic) within the tumour core with increasing glioma grade. We also observed a trend towards a higher free water fraction in vasogenic oedemas around metastases compared to infiltrative oedemas around glioblastomas and WHO 3 gliomas as well as the periphery of low-grade gliomas. In conclusion, we developed and evaluated a multi-compartment diffusion MRI model for brain tumours based on the VERDICT framework, which showed agreement between non-invasive microstructural estimates and histology and encouraging trends for the differentiation of tumour types and sub-regions.
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Affiliation(s)
- Matteo Figini
- Centre for Medical Image Computing and Department of Computer Science, University College London, London WC1V 6LJ, UK
| | - Antonella Castellano
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Michele Bailo
- Department of Neurosurgery and Gamma Knife Radiosurgery, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Marcella Callea
- Pathology Unit, IRCCS Ospedale San Raffaele, 20132 Milan, Italy
| | | | - Samira Bouyagoub
- Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Brighton BN1 9RR, UK
| | - Marco Palombo
- Centre for Medical Image Computing and Department of Computer Science, University College London, London WC1V 6LJ, UK
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff CF24 4HQ, UK
| | - Valentina Pieri
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Pietro Mortini
- Department of Neurosurgery and Gamma Knife Radiosurgery, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Andrea Falini
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Daniel C. Alexander
- Centre for Medical Image Computing and Department of Computer Science, University College London, London WC1V 6LJ, UK
| | - Mara Cercignani
- Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Brighton BN1 9RR, UK
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff CF24 4HQ, UK
| | - Eleftheria Panagiotaki
- Centre for Medical Image Computing and Department of Computer Science, University College London, London WC1V 6LJ, UK
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11
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Chakwizira A, Zhu A, Foo T, Westin CF, Szczepankiewicz F, Nilsson M. Diffusion MRI with free gradient waveforms on a high-performance gradient system: Probing restriction and exchange in the human brain. ARXIV 2023:arXiv:2304.02764v1. [PMID: 37064535 PMCID: PMC10104199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
The dependence of the diffusion MRI signal on the diffusion time carries signatures of restricted diffusion and exchange. Here we seek to highlight these signatures in the human brain by performing experiments using free gradient waveforms that are selectively sensitive to the two effects. We examine six healthy volunteers using both strong and ultra-strong gradients (80, 200 and 300 mT/m). In an experiment featuring a large set of gradient waveforms with different sensitivities to restricted diffusion and exchange (150 samples), our results reveal unique time-dependence signatures in grey and white matter, where the former is characterised by both restricted diffusion and exchange and the latter predominantly exhibits restricted diffusion. Furthermore, we show that gradient waveforms with independently varying sensitivities to restricted diffusion and exchange can be used to map exchange in the human brain. We consistently find that exchange in grey matter is at least twice as fast as in white matter, across all subjects and all gradient strengths. The shortest exchange times observed in this study were in the cerebellar cortex (115 ms). We also assess the feasibility of future clinical applications of the method used in this work, where we find that the grey-white matter exchange contrast obtained with a 25-minute 300 mT/m protocol is preserved by a 4-minute 300 mT/m and a 10-minute 80 mT/m protocol. Our work underlines the utility of free waveforms for detecting time-dependence signatures due to restricted diffusion and exchange in vivo, which may potentially serve as a tool for studying diseased tissue.
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Affiliation(s)
- Arthur Chakwizira
- Medical Radiation Physics, Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Ante Zhu
- GE Research, Niskayuna, New York, USA
| | | | - Carl-Fredrik Westin
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | | | - Markus Nilsson
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
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Rashidi F, Khanmirzaei MH, Hosseinzadeh F, Kolahchi Z, Jafarimehrabady N, Moghisseh B, Aarabi MH. Cingulum and Uncinate Fasciculus Microstructural Abnormalities in Parkinson's Disease: A Systematic Review of Diffusion Tensor Imaging Studies. BIOLOGY 2023; 12:biology12030475. [PMID: 36979166 PMCID: PMC10045759 DOI: 10.3390/biology12030475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 03/12/2023] [Accepted: 03/15/2023] [Indexed: 03/30/2023]
Abstract
Diffusion tensor imaging (DTI) is gaining traction in neuroscience research as a tool for evaluating neural fibers. The technique can be used to assess white matter (WM) microstructure in neurodegenerative disorders, including Parkinson disease (PD). There is evidence that the uncinate fasciculus and the cingulum bundle are involved in the pathogenesis of PD. These fasciculus and bundle alterations correlate with the symptoms and stages of PD. PRISMA 2022 was used to search PubMed and Scopus for relevant articles. Our search revealed 759 articles. Following screening of titles and abstracts, a full-text review, and implementing the inclusion criteria, 62 papers were selected for synthesis. According to the review of selected studies, WM integrity in the uncinate fasciculus and cingulum bundles can vary according to symptoms and stages of Parkinson disease. This article provides structural insight into the heterogeneous PD subtypes according to their cingulate bundle and uncinate fasciculus changes. It also examines if there is any correlation between these brain structures' structural changes with cognitive impairment or depression scales like Geriatric Depression Scale-Short (GDS). The results showed significantly lower fractional anisotropy values in the cingulum bundle compared to healthy controls as well as significant correlations between FA and GDS scores for both left and right uncinate fasciculus regions suggesting that structural damage from disease progression may be linked to cognitive impairments seen in advanced PD patients. This review help in developing more targeted treatments for different types of Parkinson's disease, as well as providing a better understanding of how cognitive impairments may be related to these structural changes. Additionally, using DTI scans can provide clinicians with valuable information about white matter tracts which is useful for diagnosing and monitoring disease progression over time.
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Affiliation(s)
- Fatemeh Rashidi
- School of Medicine, Tehran University of Medical Science, Tehran 1417613151, Iran
| | | | - Farbod Hosseinzadeh
- School of Medicine, Tehran University of Medical Science, Tehran 1417613151, Iran
| | - Zahra Kolahchi
- School of Medicine, Tehran University of Medical Science, Tehran 1417613151, Iran
| | - Niloofar Jafarimehrabady
- Department of Clinical-Surgical, Diagnostic and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy
| | - Bardia Moghisseh
- School of Medicine, Arak University of Medical Science, Arak 3848176941, Iran
| | - Mohammad Hadi Aarabi
- Department of Neuroscience (DNS), Padova Neuroscience Center, University of Padova, 35128 Padua, Italy
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13
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Morez J, Szczepankiewicz F, den Dekker AJ, Vanhevel F, Sijbers J, Jeurissen B. Optimal experimental design and estimation for q-space trajectory imaging. Hum Brain Mapp 2023; 44:1793-1809. [PMID: 36564927 PMCID: PMC9921251 DOI: 10.1002/hbm.26175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 11/10/2022] [Accepted: 11/14/2022] [Indexed: 12/25/2022] Open
Abstract
Tensor-valued diffusion encoding facilitates data analysis by q-space trajectory imaging. By modeling the diffusion signal of heterogeneous tissues with a diffusion tensor distribution (DTD) and modulating the encoding tensor shape, this novel approach allows disentangling variations in diffusivity from microscopic anisotropy, orientation dispersion, and mixtures of multiple isotropic diffusivities. To facilitate the estimation of the DTD parameters, a parsimonious acquisition scheme coupled with an accurate and precise estimation of the DTD is needed. In this work, we create two precision-optimized acquisition schemes: one that maximizes the precision of the raw DTD parameters, and another that maximizes the precision of the scalar measures derived from the DTD. The improved precision of these schemes compared to a naïve sampling scheme is demonstrated in both simulations and real data. Furthermore, we show that the weighted linear least squares (WLLS) estimator that uses the squared reciprocal of the noisy signal as weights can be biased, whereas the iteratively WLLS estimator with the squared reciprocal of the predicted signal as weights outperforms the conventional unweighted linear LS and nonlinear LS estimators in terms of accuracy and precision. Finally, we show that the use of appropriate constraints can considerably increase the precision of the estimator with only a limited decrease in accuracy.
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Affiliation(s)
- Jan Morez
- imec‐Vision Lab, Department of PhysicsUniversity of AntwerpAntwerpBelgium
- μNEURO Research Centre of ExcellenceUniversity of AntwerpAntwerpBelgium
| | | | - Arnold J. den Dekker
- imec‐Vision Lab, Department of PhysicsUniversity of AntwerpAntwerpBelgium
- μNEURO Research Centre of ExcellenceUniversity of AntwerpAntwerpBelgium
| | - Floris Vanhevel
- Department of RadiologyUniversity Hospital AntwerpAntwerpBelgium
| | - Jan Sijbers
- imec‐Vision Lab, Department of PhysicsUniversity of AntwerpAntwerpBelgium
- μNEURO Research Centre of ExcellenceUniversity of AntwerpAntwerpBelgium
| | - Ben Jeurissen
- imec‐Vision Lab, Department of PhysicsUniversity of AntwerpAntwerpBelgium
- μNEURO Research Centre of ExcellenceUniversity of AntwerpAntwerpBelgium
- Lab for Equilibrium Investigations and Aerospace, Department of PhysicsUniversity of AntwerpAntwerpBelgium
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14
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Brynolfsson P, Lerner M, Sundgren PC, Jamtheim Gustafsson C, Nilsson M, Szczepankiewicz F, Olsson LE. Tensor-valued diffusion magnetic resonance imaging in a radiotherapy setting. Phys Imaging Radiat Oncol 2022; 24:144-151. [DOI: 10.1016/j.phro.2022.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 11/02/2022] [Accepted: 11/03/2022] [Indexed: 11/11/2022] Open
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Lee W, Choi G, Lee J, Park H. Registration and quantification network (RQnet) for IVIM-DKI analysis in MRI. Magn Reson Med 2022; 89:250-261. [PMID: 36121205 DOI: 10.1002/mrm.29454] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 08/22/2022] [Accepted: 08/22/2022] [Indexed: 11/08/2022]
Abstract
PURPOSE A deep learning method is proposed for aligning diffusion weighted images (DWIs) and estimating intravoxel incoherent motion-diffusion kurtosis imaging parameters simultaneously. METHODS We propose an unsupervised deep learning method that performs 2 tasks: registration and quantification for intravoxel incoherent motion-diffusion kurtosis imaging analysis. A common registration method in diffusion MRI is based on minimizing dissimilarity between various DWIs, which may result in registration errors due to different contrasts in different DWIs. We designed a novel unsupervised deep learning method for both accurate registration and quantification of various diffusion parameters. In order to generate motion-simulated training data and test data, 17 volunteers were scanned without moving their heads, and 4 volunteers moved their heads during the scan in a 3 Tesla MRI. In order to investigate the applicability of the proposed method to other organs, kidney images were also obtained. We compared the registration accuracy of the proposed method, statistical parametric mapping, and a deep learning method with a normalized cross-correlation loss. In the quantification part of the proposed method, a deep learning method that considered the diffusion gradient direction was used. RESULTS Simulations and experimental results showed that the proposed method accurately performed registration and quantification for intravoxel incoherent motion-diffusion kurtosis imaging analysis. The registration accuracy of the proposed method was high for all b values. Furthermore, quantification performance was analyzed through simulations and in vivo experiments, where the proposed method showed the best performance among the compared methods. CONCLUSION The proposed method aligns the DWIs and accurately quantifies the intravoxel incoherent motion-diffusion kurtosis imaging parameters.
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Affiliation(s)
- Wonil Lee
- Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Giyong Choi
- Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Jongyeon Lee
- Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - HyunWook Park
- Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
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16
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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] [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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17
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Brabec J, Durmo F, Szczepankiewicz F, Brynolfsson P, Lampinen B, Rydelius A, Knutsson L, Westin CF, Sundgren PC, Nilsson M. Separating Glioma Hyperintensities From White Matter by Diffusion-Weighted Imaging With Spherical Tensor Encoding. Front Neurosci 2022; 16:842242. [PMID: 35527815 PMCID: PMC9069143 DOI: 10.3389/fnins.2022.842242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 03/01/2022] [Indexed: 11/13/2022] Open
Abstract
Background Tumor-related hyperintensities in high b-value diffusion-weighted imaging (DWI) are radiologically important in the workup of gliomas. However, the white matter may also appear as hyperintense, which may conflate interpretation. Purpose To investigate whether DWI with spherical b-tensor encoding (STE) can be used to suppress white matter and enhance the conspicuity of glioma hyperintensities unrelated to white matter. Materials and Methods Twenty-five patients with a glioma tumor and at least one pathology-related hyperintensity on DWI underwent conventional MRI at 3 T. The DWI was performed both with linear and spherical tensor encoding (LTE-DWI and STE-DWI). The LTE-DWI here refers to the DWI obtained with conventional diffusion encoding and averaged across diffusion-encoding directions. Retrospectively, the differences in contrast between LTE-DWI and STE-DWI, obtained at a b-value of 2,000 s/mm2, were evaluated by comparing hyperintensities and contralateral normal-appearing white matter (NAWM) both visually and quantitatively in terms of the signal intensity ratio (SIR) and contrast-to-noise ratio efficiency (CNReff). Results The spherical tensor encoding DWI was more effective than LTE-DWI at suppressing signals from white matter and improved conspicuity of pathology-related hyperintensities. The median SIR improved in all cases and on average by 28%. The median (interquartile range) SIR was 1.9 (1.6 – 2.1) for STE and 1.4 (1.3 – 1.7) for LTE, with a significant difference of 0.4 (0.3 –0.5) (p < 10–4, paired U-test). In 40% of the patients, the SIR was above 2 for STE-DWI, but with LTE-DWI, the SIR was below 2 for all patients. The CNReff of STE-DWI was significantly higher than of LTE-DWI: 2.5 (2 – 3.5) vs. 2.3 (1.7 – 3.1), with a significant difference of 0.4 (−0.1 –0.6) (p < 10–3, paired U-test). The STE improved CNReff in 70% of the cases. We illustrate the benefits of STE-DWI in three patients, where STE-DWI may facilitate an improved radiological description of tumor-related hyperintensity, including one case that could have been missed out if only LTE-DWI was inspected. Conclusion The contrast mechanism of high b-value STE-DWI results in a stronger suppression of white matter than conventional LTE-DWI, and may, therefore, be more sensitive and specific for assessment of glioma tumors and DWI-hyperintensities.
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Affiliation(s)
- Jan Brabec
- Medical Radiation Physics, Lund University, Lund, Sweden
- *Correspondence: Jan Brabec,
| | - Faris Durmo
- Diagnostic Radiology, Lund University, Lund, Sweden
| | - Filip Szczepankiewicz
- Diagnostic Radiology, Lund University, Lund, Sweden
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Patrik Brynolfsson
- Division of Medical Radiation Physics, Department of Translational Medicine, Lund University, Lund, Sweden
| | - Björn Lampinen
- Medical Radiation Physics, Lund University, Lund, Sweden
| | - Anna Rydelius
- Department of Neurology, Lund University, Lund, Sweden
| | - Linda Knutsson
- 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 for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Carl-Fredrik Westin
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Pia C. Sundgren
- Diagnostic Radiology, Lund University, Lund, Sweden
- Lund University Bioimaging Center, Lund University, Lund, Sweden
- Department of Imaging and Physiology, Skåne University Hospital, Lund University, Lund, Sweden
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18
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Brabec J, Szczepankiewicz F, Lennartsson F, Englund E, Pebdani H, Bengzon J, Knutsson L, Westin CF, Sundgren PC, Nilsson M. Histogram analysis of tensor-valued diffusion MRI in meningiomas: Relation to consistency, histological grade and type. Neuroimage Clin 2022; 33:102912. [PMID: 34922122 PMCID: PMC8688887 DOI: 10.1016/j.nicl.2021.102912] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 11/30/2021] [Accepted: 12/08/2021] [Indexed: 01/18/2023]
Abstract
Tensor-valued dMRI facilitates prediction of meningioma consistency, grade and type. Tensor-valued dMRI corroborates findings of diffusion tensor and kurtosis imaging. MK and MKA is associated with firm and MD with variable meningioma consistency. Variability of MKI in the vicinity of the tumor is associated with meningioma grade. MKA 50 and MKI 50 separates psammomatous meningiomas from other meningioma types.
Background Preoperative radiological assessment of meningioma characteristics is of value for pre- and post-operative patient management, counselling, and surgical approach. Purpose To investigate whether tensor-valued diffusion MRI can add to the preoperative prediction of meningioma consistency, grade and type. Materials and methods 30 patients with intracranial meningiomas (22 WHO grade I, 8 WHO grade II) underwent MRI prior to surgery. Diffusion MRI was performed with linear and spherical b-tensors with b-values up to 2000 s/mm2. The data were used to estimate mean diffusivity (MD), fractional anisotropy (FA), mean kurtosis (MK) and its components—the anisotropic and isotropic kurtoses (MKA and MKI). Meningioma consistency was estimated for 16 patients during resection based on ultrasonic aspiration intensity, ease of resection with instrumentation or suction. Grade and type were determined by histopathological analysis. The relation between consistency, grade and type and dMRI parameters was analyzed inside the tumor (“whole-tumor”) and within brain tissue in the immediate periphery outside the tumor (“rim”) by histogram analysis. Results Lower 10th percentiles of MK and MKA in the whole-tumor were associated with firm consistency compared with pooled soft and variable consistency (n = 7 vs 9; U test, p = 0.02 for MKA 10 and p = 0.04 for MK10) and lower 10th percentile of MD with variable against soft and firm (n = 5 vs 11; U test, p = 0.02). Higher standard deviation of MKI in the rim was associated with lower grade (n = 22 vs 8; U test, p = 0.04) and in the MKI maps we observed elevated rim-like structure that could be associated with grade. Higher median MKA and lower median MKI distinguished psammomatous type from other pooled meningioma types (n = 5 vs 25; U test; p = 0.03 for MKA 50 and p = 0.03 and p = 0.04 for MKI 50). Conclusion Parameters from tensor-valued dMRI can facilitate prediction of consistency, grade and type.
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Affiliation(s)
- Jan Brabec
- Medical Radiation Physics, Clinical Sciences, Lund University, Lund, Sweden.
| | | | - Finn Lennartsson
- Diagnostic Radiology, Clinical Sciences, Lund University, Lund, Sweden
| | | | - Houman Pebdani
- Department of Neurosurgery, Clinical Sciences, Lund University, Lund, Sweden
| | - Johan Bengzon
- Department of Neurosurgery, Clinical Sciences, Lund University, Lund, Sweden; Lund Stem Cell Center, Clinical Sciences, Lund University, Lund, Sweden
| | - Linda Knutsson
- Medical Radiation Physics, Clinical Sciences, Lund University, Lund, Sweden; Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Carl-Fredrik Westin
- Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Pia C Sundgren
- Diagnostic Radiology, Clinical Sciences, Lund University, Lund, Sweden; Lund University Bioimaging Center, Lund University, Lund, Sweden; Department for Imaging and Function, Skåne University Hospital, Lund University, Lund, Sweden
| | - Markus Nilsson
- Diagnostic Radiology, Clinical Sciences, Lund University, Lund, Sweden
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What's New and What's Next in Diffusion MRI Preprocessing. Neuroimage 2021; 249:118830. [PMID: 34965454 PMCID: PMC9379864 DOI: 10.1016/j.neuroimage.2021.118830] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 10/26/2021] [Accepted: 12/15/2021] [Indexed: 02/07/2023] Open
Abstract
Diffusion MRI (dMRI) provides invaluable information for the study of tissue microstructure and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the analysis of results and their interpretability if not appropriately accounted for. This review will cover dMRI artifacts and preprocessing steps, some of which have not typically been considered in existing pipelines or reviews, or have only gained attention in recent years: brain/skull extraction, B-matrix incompatibilities w.r.t the imaging data, signal drift, Gibbs ringing, noise distribution bias, denoising, between- and within-volumes motion, eddy currents, outliers, susceptibility distortions, EPI Nyquist ghosts, gradient deviations, B1 bias fields, and spatial normalization. The focus will be on “what’s new” since the notable advances prior to and brought by the Human Connectome Project (HCP), as presented in the predecessing issue on “Mapping the Connectome” in 2013. In addition to the development of novel strategies for dMRI preprocessing, exciting progress has been made in the availability of open source tools and reproducible pipelines, databases and simulation tools for the evaluation of preprocessing steps, and automated quality control frameworks, amongst others. Finally, this review will consider practical considerations and our view on “what’s next” in dMRI preprocessing.
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20
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Nonparametric D-R 1-R 2 distribution MRI of the living human brain. Neuroimage 2021; 245:118753. [PMID: 34852278 DOI: 10.1016/j.neuroimage.2021.118753] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 11/17/2021] [Accepted: 11/22/2021] [Indexed: 11/23/2022] Open
Abstract
Diffusion-relaxation correlation NMR can simultaneously characterize both the microstructure and the local chemical composition of complex samples that contain multiple populations of water. Recent developments on tensor-valued diffusion encoding and Monte Carlo inversion algorithms have made it possible to transfer diffusion-relaxation correlation NMR from small-bore scanners to clinical MRI systems. Initial studies on clinical MRI systems employed 5D D-R1 and D-R2 correlation to characterize healthy brain in vivo. However, these methods are subject to an inherent bias that originates from not including R2 or R1 in the analysis, respectively. This drawback can be remedied by extending the concept to 6D D-R1-R2 correlation. In this work, we present a sparse acquisition protocol that records all data necessary for in vivo 6D D-R1-R2 correlation MRI across 633 individual measurements within 25 min-a time frame comparable to previous lower-dimensional acquisition protocols. The data were processed with a Monte Carlo inversion algorithm to obtain nonparametric 6D D-R1-R2 distributions. We validated the reproducibility of the method in repeated measurements of healthy volunteers. For a post-therapy glioblastoma case featuring cysts, edema, and partially necrotic remains of tumor, we present representative single-voxel 6D distributions, parameter maps, and artificial contrasts over a wide range of diffusion-, R1-, and R2-weightings based on the rich information contained in the D-R1-R2 distributions.
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21
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Multi-tissue spherical deconvolution of tensor-valued diffusion MRI. Neuroimage 2021; 245:118717. [PMID: 34775006 DOI: 10.1016/j.neuroimage.2021.118717] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 10/28/2021] [Accepted: 11/08/2021] [Indexed: 12/18/2022] Open
Abstract
Multi-tissue constrained spherical deconvolution (MT-CSD) leverages the characteristic b-value dependency of each tissue type to estimate both the apparent tissue densities and the white matter fiber orientation distribution function from diffusion MRI data. In this work, we generalize MT-CSD to tensor-valued diffusion encoding with arbitrary b-tensor shapes. This enables the use of data encoded with mixed b-tensors, rather than being limited to the subset of linear (conventional) b-tensors. Using the complete set of data, including all b-tensor shapes, provides a categorical improvement in the estimation of apparent tissue densities, fiber ODF, and resulting tractography. Furthermore, we demonstrate that including multiple b-tensor shapes in the analysis provides improved contrast between tissue types, in particular between gray matter and white matter. We also show that our approach provides high-quality apparent tissue density maps and high-quality fiber tracking from data, even with sparse sampling across b-tensors that yield whole-brain coverage at 2 mm isotropic resolution in approximately 5:15 min.
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22
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de Almeida Martins JP, Nilsson M, Lampinen B, Palombo M, While PT, Westin CF, Szczepankiewicz F. Neural networks for parameter estimation in microstructural MRI: Application to a diffusion-relaxation model of white matter. Neuroimage 2021; 244:118601. [PMID: 34562578 PMCID: PMC9651573 DOI: 10.1016/j.neuroimage.2021.118601] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 08/26/2021] [Accepted: 09/18/2021] [Indexed: 12/14/2022] Open
Abstract
Specific features of white matter microstructure can be investigated by using biophysical models to interpret relaxation-diffusion MRI brain data. Although more intricate models have the potential to reveal more details of the tissue, they also incur time-consuming parameter estimation that may converge to inaccurate solutions due to a prevalence of local minima in a degenerate fitting landscape. Machine-learning fitting algorithms have been proposed to accelerate the parameter estimation and increase the robustness of the attained estimates. So far, learning-based fitting approaches have been restricted to microstructural models with a reduced number of independent model parameters where dense sets of training data are easy to generate. Moreover, the degree to which machine learning can alleviate the degeneracy problem is poorly understood. For conventional least-squares solvers, it has been shown that degeneracy can be avoided by acquisition with optimized relaxation-diffusion-correlation protocols that include tensor-valued diffusion encoding. Whether machine-learning techniques can offset these acquisition requirements remains to be tested. In this work, we employ artificial neural networks to vastly accelerate the parameter estimation for a recently introduced relaxation-diffusion model of white matter microstructure. We also develop strategies for assessing the accuracy and sensitivity of function fitting networks and use those strategies to explore the impact of the acquisition protocol. The developed learning-based fitting pipelines were tested on relaxation-diffusion data acquired with optimal and sub-optimal acquisition protocols. Networks trained with an optimized protocol were observed to provide accurate parameter estimates within short computational times. Comparing neural networks and least-squares solvers, we found the performance of the former to be less affected by sub-optimal protocols; however, model fitting networks were still susceptible to degeneracy issues and their use could not fully replace a careful design of the acquisition protocol.
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Affiliation(s)
- João P de Almeida Martins
- Department of Clinical Sciences, Radiology, Lund University, Lund, Sweden; Department of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway.
| | - Markus Nilsson
- Department of Clinical Sciences, Radiology, Lund University, Lund, Sweden
| | - Björn Lampinen
- Department of Clinical Sciences, Medical Radiation Physics, Lund University, Lund, Sweden
| | - Marco Palombo
- Centre for Medical Image Computing and Department of Computer Science, University College London, London, United Kingdom
| | - Peter T While
- Department of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway; Department of Circulation and Medical Imaging, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
| | - Carl-Fredrik Westin
- Radiology, Brigham and Women's Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Filip Szczepankiewicz
- Department of Clinical Sciences, Radiology, Lund University, Lund, Sweden; Radiology, Brigham and Women's Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
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23
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Yu B, Li L, Guan X, Xu X, Liu X, Yang Q, Wei H, Zuo C, Zhang Y. HybraPD atlas: Towards precise subcortical nuclei segmentation using multimodality medical images in patients with Parkinson disease. Hum Brain Mapp 2021; 42:4399-4421. [PMID: 34101297 PMCID: PMC8357000 DOI: 10.1002/hbm.25556] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 05/27/2021] [Accepted: 05/30/2021] [Indexed: 12/29/2022] Open
Abstract
Human brain atlases are essential for research and surgical treatment of Parkinson's disease (PD). For example, deep brain stimulation for PD often requires human brain atlases for brain structure identification. However, few atlases can provide disease-specific subcortical structures for PD, and most of them are based on T1w and T2w images. In this work, we construct a HybraPD atlas using fused quantitative susceptibility mapping (QSM) and T1w images from 87 patients with PD. The constructed HybraPD atlas provides a series of templates, that is, T1w, GRE magnitude, QSM, R2*, and brain tissue probabilistic maps. Then, we manually delineate a parcellation map with 12 bilateral subcortical nuclei, which are highly related to PD pathology, such as sub-regions in globus pallidus and substantia nigra. Furthermore, we build a whole-brain parcellation map by combining existing cortical parcellation and white-matter segmentation with the proposed subcortical nuclei map. Considering the multimodality of the HybraPD atlas, the segmentation accuracy of each nucleus is evaluated using T1w and QSM templates, respectively. The results show that the HybraPD atlas provides more accurate segmentation than existing atlases. Moreover, we analyze the metabolic difference in subcortical nuclei between PD patients and healthy control subjects by applying the HybraPD atlas to calculate uptake values of contrast agents on positron emission tomography (PET) images. The atlas-based analysis generates accurate disease-related brain nuclei segmentation on PET images. The newly developed HybraPD atlas could serve as an efficient template to study brain pathological alterations in subcortical regions for PD research.
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Affiliation(s)
- Boliang Yu
- School of Information Science and TechnologyShanghaiTech UniversityShanghaiChina
| | - Ling Li
- PET Center, Huashan HospitalFudan UniversityShanghaiChina
| | - Xiaojun Guan
- Department of Radiology, The Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Xueling Liu
- Department of Radiology, Huashan HospitalFudan UniversityShanghaiChina
| | - Qing Yang
- Institute of Brain‐Intelligence Technology, Zhangjiang LaboratoryShanghaiChina
| | - Hongjiang Wei
- Institute for Medicine Imaging Technology, School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghaiChina
| | - Chuantao Zuo
- PET Center, Huashan HospitalFudan UniversityShanghaiChina
| | - Yuyao Zhang
- School of Information Science and TechnologyShanghaiTech UniversityShanghaiChina
- Shanghai Engineering Research Center of Intelligent Vision and ImagingShanghaiTech UniversityShanghaiChina
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24
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DelPriore MR, Biswas D, Hippe DS, Zecevic M, Parsian S, Scheel JR, Rahbar H, Partridge SC. Breast Cancer Conspicuity on Computed Versus Acquired High b-Value Diffusion-Weighted MRI. Acad Radiol 2021; 28:1108-1117. [PMID: 32307271 DOI: 10.1016/j.acra.2020.03.011] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 03/07/2020] [Accepted: 03/08/2020] [Indexed: 12/30/2022]
Abstract
RATIONALE AND OBJECTIVES On unenhanced diffusion-weighted imaging (DWI), computing or synthesizing high b-value images from lower b-value acquisitions can enhance breast cancer visibility. This study aimed to evaluate relative lesion conspicuity on computed versus acquired diffusion-weighted images and investigate clinical characteristics influencing optimal b-values. MATERIALS AND METHODS Women with newly diagnosed breast cancer were prospectively enrolled and underwent 3T breast MRI with DWI. Lesion contrast-to-noise ratio (CNR) was measured across a range of b-values (0-2500 s/mm2) for computed and acquired DWI. Three readers independently compared lesion visibility between computed and acquired DWI and selected the optimal b-value. Computed versus acquired DWI was compared quantitatively based on CNR by paired t-test and qualitatively based on reader preference using a sign test. Optimal b-values by qualitative and quantitative assessment were compared by paired t-test, and associations with clinical characteristics were assessed by Wilcoxon rank sum test. RESULTS The study included 30 women (median age, 48 years); 28 with invasive carcinoma, 2 DCIS. Lesion CNR was higher on acquired versus computed images (p = 0.018), while lesion visibility by reader assessment was not different (p = 0.36). Optimal b-values selected by readers (mean, b = 1411 ± 383 s/mm2) were slightly higher than those based on peak CNR (b = 1233 ± 463 s/mm2, p = 0.023), and were higher for younger (≤50 years) versus older women (p = 0.002) and dense versus nondense breasts (p = 0.015). CONCLUSION Lesion CNR on computed high b-value images was slightly reduced versus acquired images, but our study suggests that this did not significantly impact lesion visibility. Computing high b-value images offers extra flexibility to adjust b-value during interpretation.
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25
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Novén M, Olsson H, Helms G, Horne M, Nilsson M, Roll M. Cortical and white matter correlates of language-learning aptitudes. Hum Brain Mapp 2021; 42:5037-5050. [PMID: 34288240 PMCID: PMC8449104 DOI: 10.1002/hbm.25598] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 06/13/2021] [Accepted: 07/08/2021] [Indexed: 11/30/2022] Open
Abstract
People learn new languages with varying degrees of success but what are the neuroanatomical correlates of the difference in language‐learning aptitude? In this study, we set out to investigate how differences in cortical morphology and white matter microstructure correlate with aptitudes for vocabulary learning, phonetic memory, and grammatical inferencing as measured by the first‐language neutral LLAMA test battery. We used ultra‐high field (7T) magnetic resonance imaging to estimate the cortical thickness and surface area from sub‐millimeter resolved image volumes. Further, diffusion kurtosis imaging was used to map diffusion properties related to the tissue microstructure from known language‐related white matter tracts. We found a correlation between cortical surface area in the left posterior‐inferior precuneus and vocabulary learning aptitude, possibly indicating a greater predisposition for storing word‐figure associations. Moreover, we report negative correlations between scores for phonetic memory and axial kurtosis in left arcuate fasciculus as well as mean kurtosis, axial kurtosis, and radial kurtosis of the left superior longitudinal fasciculus III, which are tracts connecting cortical areas important for phonological working memory.
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Affiliation(s)
- Mikael Novén
- Department of Linguistics and Phonetics, Lund University, Lund, Sweden
| | - Hampus Olsson
- Department of Clinical Sciences Lund, Medical Radiation Physics, Lund University, Lund, Sweden
| | - Gunther Helms
- Department of Clinical Sciences Lund, Medical Radiation Physics, Lund University, Lund, Sweden
| | - Merle Horne
- Department of Linguistics and Phonetics, Lund University, Lund, Sweden
| | - Markus Nilsson
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
| | - Mikael Roll
- Department of Linguistics and Phonetics, Lund University, Lund, Sweden
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26
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Li S, Zheng Y, Sun W, Lasič S, Szczepankiewicz F, Wei Q, Han S, Zhang S, Zhong X, Wang L, Li H, Cai Y, Xu D, Li Z, He Q, van Westen D, Bryskhe K, Topgaard D, Xu H. Glioma grading, molecular feature classification, and microstructural characterization using MR diffusional variance decomposition (DIVIDE) imaging. Eur Radiol 2021; 31:8197-8207. [PMID: 33914116 DOI: 10.1007/s00330-021-07959-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 03/10/2021] [Accepted: 03/29/2021] [Indexed: 12/19/2022]
Abstract
OBJECTIVE To evaluate the potential of diffusional variance decomposition (DIVIDE) for grading, molecular feature classification, and microstructural characterization of gliomas. MATERIALS AND METHODS Participants with suspected gliomas underwent DIVIDE imaging, yielding parameter maps of fractional anisotropy (FA), mean diffusivity (MD), anisotropic mean kurtosis (MKA), isotropic mean kurtosis (MKI), total mean kurtosis (MKT), MKA/MKT, and microscopic fractional anisotropy (μFA). Tumor type and grade, isocitrate dehydrogenase (IDH) 1/2 mutant status, and the Ki-67 labeling index (Ki-67 LI) were determined after surgery. Statistical analysis included 33 high-grade gliomas (HGG) and 17 low-grade gliomas (LGG). Tumor diffusion metrics were compared between HGG and LGG, among grades, and between wild and mutated IDH types using appropriate tests according to normality assessment results. Receiver operating characteristic and Spearman correlation analysis were also used for statistical evaluations. RESULTS FA, MD, MKA, MKI, MKT, μFA, and MKA/MKT differed between HGG and LGG (FA: p = 0.047; MD: p = 0.037, others p < 0.001), and among glioma grade II, III, and IV (FA: p = 0.048; MD: p = 0.038, others p < 0.001). All diffusion metrics differed between wild-type and mutated IDH tumors (MKI: p = 0.003; others: p < 0.001). The metrics that best discriminated between HGG and LGGs and between wild-type and mutated IDH tumors were MKT and FA respectively (area under the curve 0.866 and 0.881). All diffusion metrics except FA showed significant correlation with Ki-67 LI, and MKI had the highest correlation coefficient (rs = 0.618). CONCLUSION DIVIDE is a promising technique for glioma characterization and diagnosis. KEY POINTS • DIVIDE metrics MKI is related to cell density heterogeneity while MKA and μFA are related to cell eccentricity. • DIVIDE metrics can effectively differentiate LGG from HGG and IDH mutation from wild-type tumor, and showed significant correlation with the Ki-67 labeling index. • MKI was larger than MKA which indicates predominant cell density heterogeneity in gliomas. • MKA and MKI increased with grade or degree of malignancy, however with a relatively larger increase in the cell eccentricity metric MKA in relation to the cell density heterogeneity metric MKI.
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Affiliation(s)
- Sirui Li
- Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | | | - Wenbo Sun
- Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | | | | | - Qing Wei
- United Imaging Healthcare, Shanghai, China
| | | | | | - Xiaoli Zhong
- Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | - Liang Wang
- Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | - Huan Li
- Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | - Yuxiang Cai
- Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | - Dan Xu
- Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | - Zhiqiang Li
- Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | - Qiang He
- United Imaging Healthcare, Shanghai, China
| | | | | | | | - Haibo Xu
- Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China.
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Naranjo ID, Reymbaut A, Brynolfsson P, Lo Gullo R, Bryskhe K, Topgaard D, Giri DD, Reiner JS, Thakur SB, Pinker-Domenig K. Multidimensional Diffusion Magnetic Resonance Imaging for Characterization of Tissue Microstructure in Breast Cancer Patients: A Prospective Pilot Study. Cancers (Basel) 2021; 13:1606. [PMID: 33807205 PMCID: PMC8037718 DOI: 10.3390/cancers13071606] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 03/29/2021] [Indexed: 12/19/2022] Open
Abstract
Diffusion-weighted imaging is a non-invasive functional imaging modality for breast tumor characterization through apparent diffusion coefficients. Yet, it has so far been unable to intuitively inform on tissue microstructure. In this IRB-approved prospective study, we applied novel multidimensional diffusion (MDD) encoding across 16 patients with suspected breast cancer to evaluate its potential for tissue characterization in the clinical setting. Data acquired via custom MDD sequences was processed using an algorithm estimating non-parametric diffusion tensor distributions. The statistical descriptors of these distributions allow us to quantify tissue composition in terms of metrics informing on cell densities, shapes, and orientations. Additionally, signal fractions from specific cell types, such as elongated cells (bin1), isotropic cells (bin2), and free water (bin3), were teased apart. Histogram analysis in cancers and healthy breast tissue showed that cancers exhibited lower mean values of "size" (1.43 ± 0.54 × 10-3 mm2/s) and higher mean values of "shape" (0.47 ± 0.15) corresponding to bin1, while FGT (fibroglandular breast tissue) presented higher mean values of "size" (2.33 ± 0.22 × 10-3 mm2/s) and lower mean values of "shape" (0.27 ± 0.11) corresponding to bin3 (p < 0.001). Invasive carcinomas showed significant differences in mean signal fractions from bin1 (0.64 ± 0.13 vs. 0.4 ± 0.25) and bin3 (0.18 ± 0.08 vs. 0.42 ± 0.21) compared to ductal carcinomas in situ (DCIS) and invasive carcinomas with associated DCIS (p = 0.03). MDD enabled qualitative and quantitative evaluation of the composition of breast cancers and healthy glands.
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Affiliation(s)
- Isaac Daimiel Naranjo
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, 300 E 66th Street, New York, NY 10065, USA; (I.D.N.); (R.L.G.); (J.S.R.); (S.B.T.)
- Department of Radiology, Breast Imaging Service, Guy’s and St. Thomas’ NHS Trust, Great Maze Pond, London SE1 9RT, UK
| | - Alexis Reymbaut
- Random Walk Imaging AB, SE-22002 Lund, Sweden; (A.R.); (P.B.); (K.B.)
| | - Patrik Brynolfsson
- Random Walk Imaging AB, SE-22002 Lund, Sweden; (A.R.); (P.B.); (K.B.)
- NONPI Medical AB, SE-90738 Umeå, Sweden
| | - Roberto Lo Gullo
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, 300 E 66th Street, New York, NY 10065, USA; (I.D.N.); (R.L.G.); (J.S.R.); (S.B.T.)
| | - Karin Bryskhe
- Random Walk Imaging AB, SE-22002 Lund, Sweden; (A.R.); (P.B.); (K.B.)
| | - Daniel Topgaard
- Department of Chemistry, Lund University, SE-22100 Lund, Sweden;
| | - Dilip D. Giri
- Memorial Sloan Kettering Cancer Center, Department of Pathology, 1275 York Ave, New York, NY 10065, USA;
| | - Jeffrey S. Reiner
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, 300 E 66th Street, New York, NY 10065, USA; (I.D.N.); (R.L.G.); (J.S.R.); (S.B.T.)
| | - Sunitha B. Thakur
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, 300 E 66th Street, New York, NY 10065, USA; (I.D.N.); (R.L.G.); (J.S.R.); (S.B.T.)
- Memorial Sloan Kettering Cancer Center, Department of Medical Physics, 1275 York Ave, New York, NY 10065, USA
| | - Katja Pinker-Domenig
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, 300 E 66th Street, New York, NY 10065, USA; (I.D.N.); (R.L.G.); (J.S.R.); (S.B.T.)
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28
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Lampinen B, Lätt J, Wasselius J, van Westen D, Nilsson M. Time dependence in diffusion MRI predicts tissue outcome in ischemic stroke patients. Magn Reson Med 2021; 86:754-764. [PMID: 33755261 PMCID: PMC8445077 DOI: 10.1002/mrm.28743] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 01/29/2021] [Accepted: 01/30/2021] [Indexed: 12/18/2022]
Abstract
Purpose: Reperfusion therapy enables effective treatment of ischemic stroke presenting within 4–6 hours. However, tissue progression from ischemia to infarction is variable, and some patients benefit from treatment up until 24 hours. Improved imaging techniques are needed to identify these patients. Here, it was hypothesized that time dependence in diffusion MRI may predict tissue outcome in ischemic stroke. Methods: Diffusion MRI data were acquired with multiple diffusion times in five non-reperfused patients at 2, 9, and 100 days after stroke onset. Maps of “rate of kurtosis change” (k), mean kurtosis, ADC, and fractional anisotropy were derived. The ADC maps defined lesions, normal-appearing tissue, and the lesion tissue that would either be infarcted or remain viable by day 100. Diffusion parameters were compared (1) between lesions and normal-appearing tissue, and (2) between lesion tissue that would be infarcted or remain viable. Results: Positive values of k were observed within stroke lesions on day 2 (P = .001) and on day 9 (P = .023), indicating diffusional exchange. On day 100, high ADC values indicated infarction of 50 ± 20% of the lesion volumes. Tissue infarction was predicted by high k values both on day 2 (P = .026) and on day 9 (P = .046), by low mean kurtosis values on day 2 (P = .043), and by low fractional anisotropy values on day 9 (P = .029), but not by low ADC values. Conclusions: Diffusion time dependence predicted tissue outcome in ischemic stroke more accurately than the ADC, and may be useful for predicting reperfusion benefit.
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Affiliation(s)
- Björn Lampinen
- Clinical Sciences Lund, Medical Radiation Physics, Lund University, Lund, Sweden
| | - Jimmy Lätt
- Center for Medical Imaging and Physiology, Skåne University Hospital Lund, Lund, Sweden
| | - Johan Wasselius
- Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
| | | | - Markus Nilsson
- Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
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29
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Brain tissues have single-voxel signatures in multi-spectral MRI. Neuroimage 2021; 234:117986. [PMID: 33757906 DOI: 10.1016/j.neuroimage.2021.117986] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 03/03/2021] [Accepted: 03/15/2021] [Indexed: 12/20/2022] Open
Abstract
Since the seminal works by Brodmann and contemporaries, it is well-known that different brain regions exhibit unique cytoarchitectonic and myeloarchitectonic features. Transferring the approach of classifying brain tissues - and other tissues - based on their intrinsic features to the realm of magnetic resonance (MR) is a longstanding endeavor. In the 1990s, atlas-based segmentation replaced earlier multi-spectral classification approaches because of the large overlap between the class distributions. Here, we explored the feasibility of performing global brain classification based on intrinsic MR features, and used several technological advances: ultra-high field MRI, q-space trajectory diffusion imaging revealing voxel-intrinsic diffusion properties, chemical exchange saturation transfer and semi-solid magnetization transfer imaging as a marker of myelination and neurochemistry, and current neural network architectures to analyze the data. In particular, we used the raw image data as well to increase the number of input features. We found that a global brain classification of roughly 97 brain regions was feasible with gross classification accuracy of 60%; and that mapping from voxel-intrinsic MR data to the brain region to which the data belongs is possible. This indicates the presence of unique MR signals of different brain regions, similar to their cytoarchitectonic and myeloarchitectonic fingerprints.
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30
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Ahn HS, Kim SH, Kim JY, Park CS, Grimm R, Son Y. Image quality and diagnostic value of diffusion-weighted breast magnetic resonance imaging: Comparison of acquired and computed images. PLoS One 2021; 16:e0247379. [PMID: 33617567 PMCID: PMC7899336 DOI: 10.1371/journal.pone.0247379] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 02/06/2021] [Indexed: 11/18/2022] Open
Abstract
PURPOSE To compare the image quality of acquired diffusion-weighted imaging (DWI) and computed DWI and evaluate the lesion detectability and likelihood of malignancy in these datasets. MATERIALS AND METHODS This prospective study was approved by our institutional review board. A total of 29 women (mean age, 43.5 years) underwent DWI between August 2018 and April 2019 for 32 breast cancers and 16 benign breast lesions. Three radiologists independently reviewed the acquired DWI with b-values of 1000 and 2000 s/mm2 (A-b1000 and A-b2000) and the computed DWI with a b-value of 2000 s/mm2 (C-b2000). Image quality was scored and compared between the three DWI datasets. Lesion detectability was recorded, and the lesion's likelihood for malignancy was scored using a five-point scale. RESULTS The A-b1000 images were superior to the A-b2000 and C-b2000 images in chest distinction, fat suppression, and overall image quality. The A-b2000 and C-b2000 images showed comparable scores for all image quality parameters. C-b2000 showed the highest values for lesion detection among all readers, although there was no statistical difference in sensitivity, specificity, positive predictive value, negative predictive value, and accuracy between the DWI datasets. The malignancy scores of the DWI images were not significantly different among the three readers. CONCLUSIONS A-b1000 DWI is suitable for breast lesion evaluations, considering its better image quality and comparable diagnostic values compared to that of A-b2000 and C-b2000 images. The additional use of computed high b-value DWI may have the potential to increase the detectability of breast masses.
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Affiliation(s)
- Hye Shin Ahn
- Department of Radiology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, Republic of Korea
| | - Sung Hun Kim
- Department of Radiology, College of Medicine, Seoul St. Mary’s Hospital, The Catholic University of Korea, Seoul, Republic of Korea
- * E-mail:
| | - Ji Youn Kim
- Department of Radiology, College of Medicine, Yeouido St. Mary’s Hospital, The Catholic University of Korea, Seoul, Republic of Korea
| | - Chang Suk Park
- Department of Radiology, College of Medicine, Incheon St. Mary’s Hospital, The Catholic University of Korea, Icheon, Republic of Korea
| | - Robert Grimm
- MR Applications Development, Siemens Healthcare, Erlangen, Germany
| | - Yohan Son
- Siemens Healthineers Ltd., Seoul, Republic of Korea
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31
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Reymbaut A, Caron AV, Gilbert G, Szczepankiewicz F, Nilsson M, Warfield SK, Descoteaux M, Scherrer B. Magic DIAMOND: Multi-fascicle diffusion compartment imaging with tensor distribution modeling and tensor-valued diffusion encoding. Med Image Anal 2021; 70:101988. [PMID: 33611054 DOI: 10.1016/j.media.2021.101988] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 01/25/2021] [Accepted: 01/29/2021] [Indexed: 01/05/2023]
Abstract
Diffusion tensor imaging provides increased sensitivity to microstructural tissue changes compared to conventional anatomical imaging but also presents limited specificity. To tackle this problem, the DIAMOND model subdivides the voxel content into diffusion compartments and draws from diffusion-weighted data to estimate compartmental non-central matrix-variate Gamma distributions of diffusion tensors. It models each sub-voxel fascicle separately, resolving crossing white-matter pathways and allowing for a fascicle-element (fixel) based analysis of microstructural features. Alternatively, specific features of the intra-voxel diffusion tensor distribution can be selectively measured using tensor-valued diffusion-weighted acquisition schemes. However, the impact of such schemes on estimating brain microstructural features has only been studied in a handful of parametric single-fascicle models. In this work, we derive a general Laplace transform for the non-central matrix-variate Gamma distribution, which enables the extension of DIAMOND to tensor-valued encoded data. We then evaluate this "Magic DIAMOND" model in silico and in vivo on various combinations of tensor-valued encoded data. Assessing uncertainty on parameter estimation via stratified bootstrap, we investigate both voxel-based and fixel-based metrics by carrying out multi-peak tractography. We demonstrate using in silico evaluations that tensor-valued diffusion encoding significantly improves Magic DIAMOND's accuracy. Most importantly, we show in vivo that our estimated metrics can be robustly mapped along tracks across regions of fiber crossing, which opens new perspectives for tractometry and microstructure mapping along specific white-matter tracts.
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Affiliation(s)
| | | | - Guillaume Gilbert
- MR Clinical Science, Philips Healthcare Canada, Markham, ON L6C 2S3, Canada
| | - Filip Szczepankiewicz
- Department of Clinical Sciences, Lund University, 22184, Lund, Sweden; Random Walk Imaging AB, 22224, Lund, Sweden
| | - Markus Nilsson
- Department of Clinical Sciences, Lund University, 22184, Lund, Sweden
| | - Simon K Warfield
- Department of Radiology, Boston Children's Hospital, Boston, MA 02115, United States
| | | | - Benoit Scherrer
- Department of Radiology, Boston Children's Hospital, Boston, MA 02115, United States
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de Almeida Martins JP, Tax CMW, Reymbaut A, Szczepankiewicz F, Chamberland M, Jones DK, Topgaard D. Computing and visualising intra-voxel orientation-specific relaxation-diffusion features in the human brain. Hum Brain Mapp 2021; 42:310-328. [PMID: 33022844 PMCID: PMC7776010 DOI: 10.1002/hbm.25224] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 09/04/2020] [Accepted: 09/22/2020] [Indexed: 12/12/2022] Open
Abstract
Diffusion MRI techniques are used widely to study the characteristics of the human brain connectome in vivo. However, to resolve and characterise white matter (WM) fibres in heterogeneous MRI voxels remains a challenging problem typically approached with signal models that rely on prior information and constraints. We have recently introduced a 5D relaxation-diffusion correlation framework wherein multidimensional diffusion encoding strategies are used to acquire data at multiple echo-times to increase the amount of information encoded into the signal and ease the constraints needed for signal inversion. Nonparametric Monte Carlo inversion of the resulting datasets yields 5D relaxation-diffusion distributions where contributions from different sub-voxel tissue environments are separated with minimal assumptions on their microscopic properties. Here, we build on the 5D correlation approach to derive fibre-specific metrics that can be mapped throughout the imaged brain volume. Distribution components ascribed to fibrous tissues are resolved, and subsequently mapped to a dense mesh of overlapping orientation bins to define a smooth orientation distribution function (ODF). Moreover, relaxation and diffusion measures are correlated to each independent ODF coordinate, thereby allowing the estimation of orientation-specific relaxation rates and diffusivities. The proposed method is tested on a healthy volunteer, where the estimated ODFs were observed to capture major WM tracts, resolve fibre crossings, and, more importantly, inform on the relaxation and diffusion features along with distinct fibre bundles. If combined with fibre-tracking algorithms, the methodology presented in this work has potential for increasing the depth of characterisation of microstructural properties along individual WM pathways.
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Affiliation(s)
- João P. de Almeida Martins
- Division of Physical Chemistry, Department of ChemistryLund UniversityLundSweden
- Random Walk Imaging ABLundSweden
| | - Chantal M. W. Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff UniversityCardiffUK
- University Medical Center Utrecht, Utrecht UniversityUtrechtThe Netherlands
| | - Alexis Reymbaut
- Division of Physical Chemistry, Department of ChemistryLund UniversityLundSweden
- Random Walk Imaging ABLundSweden
| | - Filip Szczepankiewicz
- Department of Clinical SciencesLund UniversityLundSweden
- Harvard Medical SchoolBostonMassachusettsUSA
- Radiology, Brigham and Women's HospitalBostonMassachusettsUSA
| | - Maxime Chamberland
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff UniversityCardiffUK
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff UniversityCardiffUK
- Mary MacKillop Institute for Health Research, Australian Catholic UniversityMelbourneAustralia
| | - Daniel Topgaard
- Division of Physical Chemistry, Department of ChemistryLund UniversityLundSweden
- Random Walk Imaging ABLundSweden
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Szczepankiewicz F, Westin CF, Nilsson M. Gradient waveform design for tensor-valued encoding in diffusion MRI. J Neurosci Methods 2021; 348:109007. [PMID: 33242529 PMCID: PMC8443151 DOI: 10.1016/j.jneumeth.2020.109007] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 11/17/2020] [Accepted: 11/19/2020] [Indexed: 12/13/2022]
Abstract
Diffusion encoding along multiple spatial directions per signal acquisition can be described in terms of a b-tensor. The benefit of tensor-valued diffusion encoding is that it unlocks the 'shape of the b-tensor' as a new encoding dimension. By modulating the b-tensor shape, we can control the sensitivity to microscopic diffusion anisotropy which can be used as a contrast mechanism; a feature that is inaccessible by conventional diffusion encoding. Since imaging methods based on tensor-valued diffusion encoding are finding an increasing number of applications we are prompted to highlight the challenge of designing the optimal gradient waveforms for any given application. In this review, we first establish the basic design objectives in creating field gradient waveforms for tensor-valued diffusion MRI. We also survey additional design considerations related to limitations imposed by hardware and physiology, potential confounding effects that cannot be captured by the b-tensor, and artifacts related to the diffusion encoding waveform. Throughout, we discuss the expected compromises and tradeoffs with an aim to establish a more complete understanding of gradient waveform design and its impact on accurate measurements and interpretations of data.
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Affiliation(s)
- Filip Szczepankiewicz
- Radiology, Brigham and Women's Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Clinical Sciences, Lund University, Lund, Sweden.
| | - Carl-Fredrik Westin
- Radiology, Brigham and Women's Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
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Probing tissue microstructure by diffusion skewness tensor imaging. Sci Rep 2021; 11:135. [PMID: 33420140 PMCID: PMC7794496 DOI: 10.1038/s41598-020-79748-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 11/30/2020] [Indexed: 01/29/2023] Open
Abstract
Probing the cellular structure of in vivo biological tissue is a fundamental problem in biomedical imaging and medical science. This work introduces an approach for analyzing diffusion magnetic resonance imaging data acquired by the novel tensor-valued encoding technique for characterizing tissue microstructure. Our approach first uses a signal model to estimate the variance and skewness of the distribution of apparent diffusion tensors modeling the underlying tissue. Then several novel imaging indices, such as weighted microscopic anisotropy and microscopic skewness, are derived to characterize different ensembles of diffusion processes that are indistinguishable by existing techniques. The contributions of this work also include a theoretical proof that shows that, to estimate the skewness of a diffusion tensor distribution, the encoding protocol needs to include full-rank tensor diffusion encoding. This proof provides a guideline for the application of this technique. The properties of the proposed indices are illustrated using both synthetic data and in vivo data acquired from a human brain.
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35
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Spotorno N, Lindberg O, Nilsson C, Landqvist Waldö M, van Westen D, Nilsson K, Vestberg S, Englund E, Zetterberg H, Blennow K, Lätt J, Markus N, Lars-Olof W, Alexander S. Plasma neurofilament light protein correlates with diffusion tensor imaging metrics in frontotemporal dementia. PLoS One 2020; 15:e0236384. [PMID: 33108404 PMCID: PMC7591030 DOI: 10.1371/journal.pone.0236384] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 10/12/2020] [Indexed: 12/02/2022] Open
Abstract
Neurofilaments are structural components of neurons and are particularly abundant in highly myelinated axons. The levels of neurofilament light chain (NfL) in both cerebrospinal fluid (CSF) and plasma have been related to degeneration in several neurodegenerative conditions including frontotemporal dementia (FTD) and NfL is currently considered as the most promising diagnostic and prognostic fluid biomarker in FTD. Although the location and function of filaments in the healthy nervous system suggests a link between increased NfL and white matter degeneration, such a claim has not been fully elucidated in vivo, especially in the context of FTD. The present study provides evidence of an association between the plasma levels of NfL and white matter involvement in behavioral variant FTD (bvFTD) by relating plasma concentration of NfL to diffusion tensor imaging (DTI) metrics in a group of 20 bvFTD patients. The results of both voxel-wise and tract specific analysis showed that increased plasma NfL concentration is associated with a reduction in fractional anisotropy (FA) in a widespread set of white matter tracts including the superior longitudinal fasciculus, the fronto-occipital fasciculus the anterior thalamic radiation and the dorsal cingulum bundle. Plasma NfL concentration also correlated with cortical thinning in a portion of the right medial prefrontal cortex and of the right lateral orbitofrontal cortex. These results support the hypothesis that blood NfL levels reflect the global level of neurodegeneration in bvFTD and help to advance our understanding of the association between this blood biomarker for FTD and the disease process.
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Affiliation(s)
- Nicola Spotorno
- Department of Neurology, Penn Frontotemporal Degeneration Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States of America
- Department of Clinical Sciences, Clinical Memory Research Unit, Lund University, Malmö, Sweden
| | - Olof Lindberg
- Division of Clinical Geriatrics, Karolinska Institute, Stockholm, Sweden
| | - Christer Nilsson
- Division of Neurology, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Maria Landqvist Waldö
- Department of clinical Sciences, Clinical Sciences Helsingborg, Lund, Lund University, Lund, Sweden
| | - Danielle van Westen
- Department of Diagnostic Radiology, Clinical Sciences, Lund University, Lund, Sweden
| | - Karin Nilsson
- Department of Clinical Sciences, Clinical Memory Research Unit, Lund University, Malmö, Sweden
| | | | - Elisabet Englund
- Division of Pathology, Department of Clinical Sciences, Lund, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience & Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, United Kingdom
- UK Dementia Research Institute at UCL, London, United Kingdom
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience & Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Jimmy Lätt
- Center for Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden
| | - Nilsson Markus
- Department of Diagnostic Radiology, Clinical Sciences, Lund University, Lund, Sweden
| | - Wahlund Lars-Olof
- Division of Clinical Geriatrics, Karolinska Institute, Stockholm, Sweden
| | - Santillo Alexander
- Department of Clinical Sciences, Clinical Memory Research Unit, Lund University, Malmö, Sweden
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36
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Lampinen B, Zampeli A, Björkman-Burtscher IM, Szczepankiewicz F, Källén K, Compagno Strandberg M, Nilsson M. Tensor-valued diffusion MRI differentiates cortex and white matter in malformations of cortical development associated with epilepsy. Epilepsia 2020; 61:1701-1713. [PMID: 32667688 PMCID: PMC7963222 DOI: 10.1111/epi.16605] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 05/21/2020] [Accepted: 06/15/2020] [Indexed: 12/24/2022]
Abstract
Objective: Delineation of malformations of cortical development (MCD) is central in presurgical evaluation of drug-resistant epilepsy. Delineation using magnetic resonance imaging (MRI) can be ambiguous, however, because the conventional T1- and T2-weighted contrasts depend strongly on myelin for differentiation of cortical tissue and white matter. Variations in myelin content within both cortex and white matter may cause MCD findings on MRI to change size, become undetectable, or disagree with histopathology. The novel tensor-valued diffusion MRI (dMRI) technique maps microscopic diffusion anisotropy, which is sensitive to axons rather than myelin. This work investigated whether tensor-valued dMRI may improve differentiation of cortex and white matter in the delineation of MCD. Methods: Tensor-valued dMRI was performed on a 7 T MRI scanner in 13 MCD patients (age = 32 ± 13 years) featuring periventricular heterotopia, subcortical heterotopia, focal cortical dysplasia, and polymicrogyria. Data analysis yielded maps of microscopic anisotropy that were compared with T1-weighted and T2-fluid-attenuated inversion recovery images and with the fractional anisotropy from diffusion tensor imaging. Results: Maps of microscopic anisotropy revealed large white matter-like regions within MCD that were uniformly cortex-like in the conventional MRI contrasts. These regions were seen particularly in the deep white matter parts of subcortical heterotopias and near the gray-white boundaries of focal cortical dysplasias and polymicrogyrias. Significance: By being sensitive to axons rather than myelin, mapping of microscopic anisotropy may yield a more robust differentiation of cortex and white matter and improve MCD delineation in presurgical evaluation of epilepsy.
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Affiliation(s)
- Björn Lampinen
- Clinical Sciences Lund, Medical Radiation Physics, Lund University, Lund, Sweden
| | - Ariadne Zampeli
- Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden
| | | | - Filip Szczepankiewicz
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden.,Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Kristina Källén
- Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden.,Clinical Sciences Lund, Department of Clinical Sciences Helsingborg, Lund University, Lund, Sweden
| | | | - Markus Nilsson
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden
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37
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Andersen KW, Lasič S, Lundell H, Nilsson M, Topgaard D, Sellebjerg F, Szczepankiewicz F, Siebner HR, Blinkenberg M, Dyrby TB. Disentangling white-matter damage from physiological fibre orientation dispersion in multiple sclerosis. Brain Commun 2020; 2:fcaa077. [PMID: 32954329 PMCID: PMC7472898 DOI: 10.1093/braincomms/fcaa077] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2020] [Revised: 04/20/2020] [Accepted: 05/07/2020] [Indexed: 01/23/2023] Open
Abstract
Multiple sclerosis leads to diffuse damage of the central nervous system, affecting also the normal-appearing white matter. Demyelination and axonal degeneration reduce regional fractional anisotropy in normal-appearing white matter, which can be routinely mapped with diffusion tensor imaging. However, the standard fractional anisotropy metric is also sensitive to physiological variations in orientation dispersion of white matter fibres. This complicates the detection of disease-related damage in large parts of cerebral white matter where microstructure physiologically displays a high degree of fibre dispersion. To resolve this ambiguity, we employed a novel tensor-valued encoding method for diffusion MRI, which yields a microscopic fractional anisotropy metric that is unaffected by regional variations in orientation dispersion. In 26 patients with relapsing-remitting multiple sclerosis, 14 patients with primary-progressive multiple sclerosis and 27 age-matched healthy controls, we compared standard fractional anisotropy mapping with the novel microscopic fractional anisotropy mapping method, focusing on normal-appearing white matter. Mean microscopic fractional anisotropy and standard fractional anisotropy of normal-appearing white matter were significantly reduced in both patient groups relative to healthy controls, but microscopic fractional anisotropy yielded a better reflection of disease-related white-matter alterations. The reduction in mean microscopic fractional anisotropy showed a significant positive linear relationship with physical disability, as reflected by the expanded disability status scale. Mean reduction of microscopic fractional anisotropy in normal-appearing white matter also scaled positively with individual cognitive dysfunction, as measured with the symbol digit modality test. Mean microscopic fractional anisotropy reduction in normal-appearing white matter also showed a positive relationship with total white-matter lesion load as well as lesion load in specific tract systems. None of these relationships between normal-appearing white-matter microstructure and clinical, cognitive or structural measures emerged when using mean fractional anisotropy. Together, the results provide converging evidence that microscopic fractional anisotropy mapping substantially advances the assessment of cerebral white matter in multiple sclerosis by disentangling microstructure damage from variations in physiological fibre orientation dispersion at the stage of data acquisition. Since tensor-valued encoding can be implemented in routine diffusion MRI, microscopic fractional anisotropy mapping bears considerable potential for the future assessment of disease progression in normal-appearing white matter in both relapsing-remitting and progressive forms of multiple sclerosis as well as other white-matter-related brain diseases.
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Affiliation(s)
- Kasper Winther Andersen
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, 2650 Hvidovre, Denmark
| | - Samo Lasič
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, 2650 Hvidovre, Denmark
- Random Walk Imaging, AB, 222 24 Lund, Sweden
| | - Henrik Lundell
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, 2650 Hvidovre, Denmark
| | - Markus Nilsson
- Department of Radiology, Clinical Sciences, Lund, Lund University, 221 00 Lund, Sweden
| | - Daniel Topgaard
- Division of Physical Chemistry, Department of Chemistry, Lund University, 221 00 Lund, Sweden
| | - Finn Sellebjerg
- Danish Multiple Sclerosis Center, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark
| | - Filip Szczepankiewicz
- Department of Medical Radiation Physics, Clinical Sciences, Lund, Lund University, 221 00 Lund, Sweden
| | - Hartwig Roman Siebner
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, 2650 Hvidovre, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, Denmark
- Department of Neurology, Copenhagen University Hospital Bispebjerg, 2400 Copenhagen NV, Denmark
| | - Morten Blinkenberg
- Danish Multiple Sclerosis Center, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark
| | - Tim B Dyrby
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, 2650 Hvidovre, Denmark
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2700 Kongens Lyngby, Denmark
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38
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Lampinen B, Szczepankiewicz F, Mårtensson J, van Westen D, Hansson O, Westin CF, Nilsson M. Towards unconstrained compartment modeling in white matter using diffusion-relaxation MRI with tensor-valued diffusion encoding. Magn Reson Med 2020; 84:1605-1623. [PMID: 32141131 DOI: 10.1002/mrm.28216] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 01/27/2020] [Accepted: 01/28/2020] [Indexed: 01/05/2023]
Abstract
PURPOSE To optimize diffusion-relaxation MRI with tensor-valued diffusion encoding for precise estimation of compartment-specific fractions, diffusivities, and T2 values within a two-compartment model of white matter, and to explore the approach in vivo. METHODS Sampling protocols featuring different b-values (b), b-tensor shapes (bΔ ), and echo times (TE) were optimized using Cramér-Rao lower bounds (CRLB). Whole-brain data were acquired in children, adults, and elderly with white matter lesions. Compartment fractions, diffusivities, and T2 values were estimated in a model featuring two microstructural compartments represented by a "stick" and a "zeppelin." RESULTS Precise parameter estimates were enabled by sampling protocols featuring seven or more "shells" with unique b/bΔ /TE-combinations. Acquisition times were approximately 15 minutes. In white matter of adults, the "stick" compartment had a fraction of approximately 0.5 and, compared with the "zeppelin" compartment, featured lower isotropic diffusivities (0.6 vs. 1.3 μm2 /ms) but higher T2 values (85 vs. 65 ms). Children featured lower "stick" fractions (0.4). White matter lesions exhibited high "zeppelin" isotropic diffusivities (1.7 μm2 /ms) and T2 values (150 ms). CONCLUSIONS Diffusion-relaxation MRI with tensor-valued diffusion encoding expands the set of microstructure parameters that can be precisely estimated and therefore increases their specificity to biological quantities.
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Affiliation(s)
- Björn Lampinen
- Clinical Sciences Lund, Medical Radiation Physics, Lund University, Lund, Sweden
| | - Filip Szczepankiewicz
- Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden.,Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Johan Mårtensson
- Clinical Sciences Lund, Department of Logopedics, Phoniatrics and Audiology, Lund University, Lund, Sweden
| | | | - Oskar Hansson
- Clinical Sciences Malmö, Clinical Memory Research Unit, Lund University, Lund, Sweden
| | - Carl-Fredrik Westin
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Markus Nilsson
- Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
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39
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de Almeida Martins J, Tax C, Szczepankiewicz F, Jones D, Westin CF, Topgaard D. Transferring principles of solid-state and Laplace NMR to the field of in vivo brain MRI. MAGNETIC RESONANCE (GOTTINGEN, GERMANY) 2020; 1:27-43. [PMID: 37904884 PMCID: PMC10500744 DOI: 10.5194/mr-1-27-2020] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 02/18/2020] [Indexed: 11/01/2023]
Abstract
Magnetic resonance imaging (MRI) is the primary method for noninvasive investigations of the human brain in health, disease, and development but yields data that are difficult to interpret whenever the millimeter-scale voxels contain multiple microscopic tissue environments with different chemical and structural properties. We propose a novel MRI framework to quantify the microscopic heterogeneity of the living human brain as spatially resolved five-dimensional relaxation-diffusion distributions by augmenting a conventional diffusion-weighted imaging sequence with signal encoding principles from multidimensional solid-state nuclear magnetic resonance (NMR) spectroscopy, relaxation-diffusion correlation methods from Laplace NMR of porous media, and Monte Carlo data inversion. The high dimensionality of the distribution space allows resolution of multiple microscopic environments within each heterogeneous voxel as well as their individual characterization with novel statistical measures that combine the chemical sensitivity of the relaxation rates with the link between microstructure and the anisotropic diffusivity of tissue water. The proposed framework is demonstrated on a healthy volunteer using both exhaustive and clinically viable acquisition protocols.
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Affiliation(s)
- João P. de Almeida Martins
- Division of Physical Chemistry, Department of Chemistry, Lund
University, Lund, Sweden
- Random Walk Imaging AB, Lund, Sweden
| | - Chantal M. W. Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff
University, Cardiff, UK
| | - Filip Szczepankiewicz
- Harvard Medical School, Boston, MA, USA
- Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff
University, Cardiff, UK
- Mary MacKillop Institute for Health Research, Australian Catholic
University, Melbourne, Australia
| | - Carl-Fredrik Westin
- Harvard Medical School, Boston, MA, USA
- Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Daniel Topgaard
- Division of Physical Chemistry, Department of Chemistry, Lund
University, Lund, Sweden
- Random Walk Imaging AB, Lund, Sweden
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40
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Martin J, Endt S, Wetscherek A, Kuder TA, Doerfler A, Uder M, Hensel B, Laun FB. Contrast-to-noise ratio analysis of microscopic diffusion anisotropy indices in q-space trajectory imaging. Z Med Phys 2020; 30:4-16. [DOI: 10.1016/j.zemedi.2019.01.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Revised: 12/20/2018] [Accepted: 01/29/2019] [Indexed: 12/26/2022]
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41
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Tax CMW, Szczepankiewicz F, Nilsson M, Jones DK. The dot-compartment revealed? Diffusion MRI with ultra-strong gradients and spherical tensor encoding in the living human brain. Neuroimage 2020; 210:116534. [PMID: 31931157 PMCID: PMC7429990 DOI: 10.1016/j.neuroimage.2020.116534] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 12/12/2019] [Accepted: 01/09/2020] [Indexed: 11/29/2022] Open
Abstract
The so-called “dot-compartment” is conjectured in diffusion MRI to represent small spherical spaces, such as cell bodies, in which the diffusion is restricted in all directions. Previous investigations inferred its existence from data acquired with directional diffusion encoding which does not permit a straightforward separation of signals from ‘sticks’ (axons) and signals from ‘dots’. Here we combine isotropic diffusion encoding with ultra-strong diffusion gradients (240 mT/m) to achieve high diffusion-weightings with high signal to noise ratio, while suppressing signal arising from anisotropic water compartments with significant mobility along at least one axis (e.g., axons). A dot-compartment, defined to have apparent diffusion coefficient equal to zero and no exchange, would result in a non-decaying signal at very high b-values (b≳7000s/mm2). With this unique experimental setup, a residual yet slowly decaying signal above the noise floor for b-values as high as 15000s/mm2 was seen clearly in the cerebellar grey matter (GM), and in several white matter (WM) regions to some extent. Upper limits of the dot-signal-fraction were estimated to be 1.8% in cerebellar GM and 0.5% in WM. By relaxing the assumption of zero diffusivity, the signal at high b-values in cerebellar GM could be represented more accurately by an isotropic water pool with a low apparent diffusivity of 0.12 μm2/ms and a substantial signal fraction of 9.7%. The T2 of this component was estimated to be around 61ms. This remaining signal at high b-values has potential to serve as a novel and simple marker for isotropically-restricted water compartments in cerebellar GM. The “dot-compartment” is conjectured in diffusion MRI to represent e.g. cell bodies. We combine isotropic encoding with ultra-strong gradients to study the dot-compartment. A slowly decaying signal for high b-values was seen in cerebellar GM. An apparent diffusivity of 0.12 and signal fraction of 9.7% were estimated. The signal could serve as a novel and simple marker for spherical compartments.
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Affiliation(s)
- Chantal M W Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK.
| | - Filip Szczepankiewicz
- Radiology, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Medical Radiation Physics, Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Markus Nilsson
- Radiology, Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK; Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, Australia
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42
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Spotorno N, Hall S, Irwin DJ, Rumetshofer T, Acosta-Cabronero J, Deik AF, Spindler MA, Lee EB, Trojanowski JQ, van Westen D, Nilsson M, Grossman M, Nestor PJ, McMillan CT, Hansson O. Diffusion Tensor MRI to Distinguish Progressive Supranuclear Palsy from α-Synucleinopathies. Radiology 2019; 293:646-653. [PMID: 31617796 PMCID: PMC6889922 DOI: 10.1148/radiol.2019190406] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2019] [Revised: 07/21/2019] [Accepted: 08/21/2019] [Indexed: 01/25/2023]
Abstract
Background The differential diagnosis of progressive supranuclear palsy (PSP) and Lewy body disorders, which include Parkinson disease and dementia with Lewy bodies, is often challenging due to the overlapping symptoms. Purpose To develop a diagnostic tool based on diffusion tensor imaging (DTI) to distinguish between PSP and Lewy body disorders at the individual-subject level. Materials and Methods In this retrospective study, skeletonized DTI metrics were extracted from two independent data sets: the discovery cohort from the Swedish BioFINDER study and the validation cohort from the Penn Frontotemporal Degeneration Center (data collected between 2010 and 2018). Based on previous neuroimaging studies and neuropathologic evidence, a combination of regions hypothesized to be sensitive to pathologic features of PSP were identified (ie, the superior cerebellar peduncle and frontal white matter) and fractional anisotropy (FA) was used to compute an FA score for each individual. Classification performances were assessed by using logistic regression and receiver operating characteristic analysis. Results In the discovery cohort, 16 patients with PSP (mean age ± standard deviation, 73 years ± 5; eight women, eight men), 34 patients with Lewy body disorders (mean age, 71 years ± 6; 14 women, 20 men), and 44 healthy control participants (mean age, 66 years ± 8; 26 women, 18 men) were evaluated. The FA score distinguished between clinical PSP and Lewy body disorders with an area under the curve of 0.97 ± 0.04, a specificity of 91% (31 of 34), and a sensitivity of 94% (15 of 16). In the validation cohort, 34 patients with PSP (69 years ± 7; 22 women, 12 men), 25 patients with Lewy body disorders (70 years ± 7; nine women, 16 men), and 32 healthy control participants (64 years ± 7; 22 women, 10 men) were evaluated. The accuracy of the FA score was confirmed (area under the curve, 0.96 ± 0.04; specificity, 96% [24 of 25]; and sensitivity, 85% [29 of 34]). Conclusion These cross-validated findings lay the foundation for a clinical test to distinguish progressive supranuclear palsy from Lewy body disorders. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Shah in this issue.
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Affiliation(s)
- Nicola Spotorno
- From the Clinical Memory Research Unit, Department of Clinical
Sciences, Malmö, Lund University, Sölvegatan 19, 22100 Lund, Sweden
(N.S., S.H., D.v.W., O.H.); Penn Frontotemporal Degeneration Center, Department
of Neurology, Perelman School of Medicine, University of Pennsylvania,
Philadelphia, Pa (N.S., D.J.I., M.G., C.T.M.); Memory Clinic, Skåne
University Hospital, Malmö, Sweden (S.H., O.H.); Center for
Neurodegenerative Disease Research, Perelman School of Medicine, University of
Pennsylvania, Philadelphia, Pa (D.J.I., E.B.L., J.Q.T.); Department of
Diagnostic Radiology, Lund University, Lund, Sweden (T.R., D.v.W., M.N.);
Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology,
University College London, London, England (J.A.C.); Parkinson’s Disease
and Movement Disorders Center, Department of Neurology, Perelman School of
Medicine, University of Pennsylvania, Philadelphia, Pa (A.F.D., M.A.S.);
Alzheimer’s Disease Core Center, Department of Pathology and Laboratory
Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia,
Pa (E.B.L., J.Q.T.); and Queensland Brain Institute, University of Queensland
and Mater Misericordiae, Brisbane, Queensland, Australia (P.J.N.)
| | - Sara Hall
- From the Clinical Memory Research Unit, Department of Clinical
Sciences, Malmö, Lund University, Sölvegatan 19, 22100 Lund, Sweden
(N.S., S.H., D.v.W., O.H.); Penn Frontotemporal Degeneration Center, Department
of Neurology, Perelman School of Medicine, University of Pennsylvania,
Philadelphia, Pa (N.S., D.J.I., M.G., C.T.M.); Memory Clinic, Skåne
University Hospital, Malmö, Sweden (S.H., O.H.); Center for
Neurodegenerative Disease Research, Perelman School of Medicine, University of
Pennsylvania, Philadelphia, Pa (D.J.I., E.B.L., J.Q.T.); Department of
Diagnostic Radiology, Lund University, Lund, Sweden (T.R., D.v.W., M.N.);
Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology,
University College London, London, England (J.A.C.); Parkinson’s Disease
and Movement Disorders Center, Department of Neurology, Perelman School of
Medicine, University of Pennsylvania, Philadelphia, Pa (A.F.D., M.A.S.);
Alzheimer’s Disease Core Center, Department of Pathology and Laboratory
Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia,
Pa (E.B.L., J.Q.T.); and Queensland Brain Institute, University of Queensland
and Mater Misericordiae, Brisbane, Queensland, Australia (P.J.N.)
| | - David J. Irwin
- From the Clinical Memory Research Unit, Department of Clinical
Sciences, Malmö, Lund University, Sölvegatan 19, 22100 Lund, Sweden
(N.S., S.H., D.v.W., O.H.); Penn Frontotemporal Degeneration Center, Department
of Neurology, Perelman School of Medicine, University of Pennsylvania,
Philadelphia, Pa (N.S., D.J.I., M.G., C.T.M.); Memory Clinic, Skåne
University Hospital, Malmö, Sweden (S.H., O.H.); Center for
Neurodegenerative Disease Research, Perelman School of Medicine, University of
Pennsylvania, Philadelphia, Pa (D.J.I., E.B.L., J.Q.T.); Department of
Diagnostic Radiology, Lund University, Lund, Sweden (T.R., D.v.W., M.N.);
Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology,
University College London, London, England (J.A.C.); Parkinson’s Disease
and Movement Disorders Center, Department of Neurology, Perelman School of
Medicine, University of Pennsylvania, Philadelphia, Pa (A.F.D., M.A.S.);
Alzheimer’s Disease Core Center, Department of Pathology and Laboratory
Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia,
Pa (E.B.L., J.Q.T.); and Queensland Brain Institute, University of Queensland
and Mater Misericordiae, Brisbane, Queensland, Australia (P.J.N.)
| | - Theodor Rumetshofer
- From the Clinical Memory Research Unit, Department of Clinical
Sciences, Malmö, Lund University, Sölvegatan 19, 22100 Lund, Sweden
(N.S., S.H., D.v.W., O.H.); Penn Frontotemporal Degeneration Center, Department
of Neurology, Perelman School of Medicine, University of Pennsylvania,
Philadelphia, Pa (N.S., D.J.I., M.G., C.T.M.); Memory Clinic, Skåne
University Hospital, Malmö, Sweden (S.H., O.H.); Center for
Neurodegenerative Disease Research, Perelman School of Medicine, University of
Pennsylvania, Philadelphia, Pa (D.J.I., E.B.L., J.Q.T.); Department of
Diagnostic Radiology, Lund University, Lund, Sweden (T.R., D.v.W., M.N.);
Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology,
University College London, London, England (J.A.C.); Parkinson’s Disease
and Movement Disorders Center, Department of Neurology, Perelman School of
Medicine, University of Pennsylvania, Philadelphia, Pa (A.F.D., M.A.S.);
Alzheimer’s Disease Core Center, Department of Pathology and Laboratory
Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia,
Pa (E.B.L., J.Q.T.); and Queensland Brain Institute, University of Queensland
and Mater Misericordiae, Brisbane, Queensland, Australia (P.J.N.)
| | - Julio Acosta-Cabronero
- From the Clinical Memory Research Unit, Department of Clinical
Sciences, Malmö, Lund University, Sölvegatan 19, 22100 Lund, Sweden
(N.S., S.H., D.v.W., O.H.); Penn Frontotemporal Degeneration Center, Department
of Neurology, Perelman School of Medicine, University of Pennsylvania,
Philadelphia, Pa (N.S., D.J.I., M.G., C.T.M.); Memory Clinic, Skåne
University Hospital, Malmö, Sweden (S.H., O.H.); Center for
Neurodegenerative Disease Research, Perelman School of Medicine, University of
Pennsylvania, Philadelphia, Pa (D.J.I., E.B.L., J.Q.T.); Department of
Diagnostic Radiology, Lund University, Lund, Sweden (T.R., D.v.W., M.N.);
Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology,
University College London, London, England (J.A.C.); Parkinson’s Disease
and Movement Disorders Center, Department of Neurology, Perelman School of
Medicine, University of Pennsylvania, Philadelphia, Pa (A.F.D., M.A.S.);
Alzheimer’s Disease Core Center, Department of Pathology and Laboratory
Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia,
Pa (E.B.L., J.Q.T.); and Queensland Brain Institute, University of Queensland
and Mater Misericordiae, Brisbane, Queensland, Australia (P.J.N.)
| | - Andres F. Deik
- From the Clinical Memory Research Unit, Department of Clinical
Sciences, Malmö, Lund University, Sölvegatan 19, 22100 Lund, Sweden
(N.S., S.H., D.v.W., O.H.); Penn Frontotemporal Degeneration Center, Department
of Neurology, Perelman School of Medicine, University of Pennsylvania,
Philadelphia, Pa (N.S., D.J.I., M.G., C.T.M.); Memory Clinic, Skåne
University Hospital, Malmö, Sweden (S.H., O.H.); Center for
Neurodegenerative Disease Research, Perelman School of Medicine, University of
Pennsylvania, Philadelphia, Pa (D.J.I., E.B.L., J.Q.T.); Department of
Diagnostic Radiology, Lund University, Lund, Sweden (T.R., D.v.W., M.N.);
Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology,
University College London, London, England (J.A.C.); Parkinson’s Disease
and Movement Disorders Center, Department of Neurology, Perelman School of
Medicine, University of Pennsylvania, Philadelphia, Pa (A.F.D., M.A.S.);
Alzheimer’s Disease Core Center, Department of Pathology and Laboratory
Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia,
Pa (E.B.L., J.Q.T.); and Queensland Brain Institute, University of Queensland
and Mater Misericordiae, Brisbane, Queensland, Australia (P.J.N.)
| | - Meredith A. Spindler
- From the Clinical Memory Research Unit, Department of Clinical
Sciences, Malmö, Lund University, Sölvegatan 19, 22100 Lund, Sweden
(N.S., S.H., D.v.W., O.H.); Penn Frontotemporal Degeneration Center, Department
of Neurology, Perelman School of Medicine, University of Pennsylvania,
Philadelphia, Pa (N.S., D.J.I., M.G., C.T.M.); Memory Clinic, Skåne
University Hospital, Malmö, Sweden (S.H., O.H.); Center for
Neurodegenerative Disease Research, Perelman School of Medicine, University of
Pennsylvania, Philadelphia, Pa (D.J.I., E.B.L., J.Q.T.); Department of
Diagnostic Radiology, Lund University, Lund, Sweden (T.R., D.v.W., M.N.);
Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology,
University College London, London, England (J.A.C.); Parkinson’s Disease
and Movement Disorders Center, Department of Neurology, Perelman School of
Medicine, University of Pennsylvania, Philadelphia, Pa (A.F.D., M.A.S.);
Alzheimer’s Disease Core Center, Department of Pathology and Laboratory
Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia,
Pa (E.B.L., J.Q.T.); and Queensland Brain Institute, University of Queensland
and Mater Misericordiae, Brisbane, Queensland, Australia (P.J.N.)
| | - Edward B. Lee
- From the Clinical Memory Research Unit, Department of Clinical
Sciences, Malmö, Lund University, Sölvegatan 19, 22100 Lund, Sweden
(N.S., S.H., D.v.W., O.H.); Penn Frontotemporal Degeneration Center, Department
of Neurology, Perelman School of Medicine, University of Pennsylvania,
Philadelphia, Pa (N.S., D.J.I., M.G., C.T.M.); Memory Clinic, Skåne
University Hospital, Malmö, Sweden (S.H., O.H.); Center for
Neurodegenerative Disease Research, Perelman School of Medicine, University of
Pennsylvania, Philadelphia, Pa (D.J.I., E.B.L., J.Q.T.); Department of
Diagnostic Radiology, Lund University, Lund, Sweden (T.R., D.v.W., M.N.);
Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology,
University College London, London, England (J.A.C.); Parkinson’s Disease
and Movement Disorders Center, Department of Neurology, Perelman School of
Medicine, University of Pennsylvania, Philadelphia, Pa (A.F.D., M.A.S.);
Alzheimer’s Disease Core Center, Department of Pathology and Laboratory
Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia,
Pa (E.B.L., J.Q.T.); and Queensland Brain Institute, University of Queensland
and Mater Misericordiae, Brisbane, Queensland, Australia (P.J.N.)
| | - John Q. Trojanowski
- From the Clinical Memory Research Unit, Department of Clinical
Sciences, Malmö, Lund University, Sölvegatan 19, 22100 Lund, Sweden
(N.S., S.H., D.v.W., O.H.); Penn Frontotemporal Degeneration Center, Department
of Neurology, Perelman School of Medicine, University of Pennsylvania,
Philadelphia, Pa (N.S., D.J.I., M.G., C.T.M.); Memory Clinic, Skåne
University Hospital, Malmö, Sweden (S.H., O.H.); Center for
Neurodegenerative Disease Research, Perelman School of Medicine, University of
Pennsylvania, Philadelphia, Pa (D.J.I., E.B.L., J.Q.T.); Department of
Diagnostic Radiology, Lund University, Lund, Sweden (T.R., D.v.W., M.N.);
Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology,
University College London, London, England (J.A.C.); Parkinson’s Disease
and Movement Disorders Center, Department of Neurology, Perelman School of
Medicine, University of Pennsylvania, Philadelphia, Pa (A.F.D., M.A.S.);
Alzheimer’s Disease Core Center, Department of Pathology and Laboratory
Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia,
Pa (E.B.L., J.Q.T.); and Queensland Brain Institute, University of Queensland
and Mater Misericordiae, Brisbane, Queensland, Australia (P.J.N.)
| | - Danielle van Westen
- From the Clinical Memory Research Unit, Department of Clinical
Sciences, Malmö, Lund University, Sölvegatan 19, 22100 Lund, Sweden
(N.S., S.H., D.v.W., O.H.); Penn Frontotemporal Degeneration Center, Department
of Neurology, Perelman School of Medicine, University of Pennsylvania,
Philadelphia, Pa (N.S., D.J.I., M.G., C.T.M.); Memory Clinic, Skåne
University Hospital, Malmö, Sweden (S.H., O.H.); Center for
Neurodegenerative Disease Research, Perelman School of Medicine, University of
Pennsylvania, Philadelphia, Pa (D.J.I., E.B.L., J.Q.T.); Department of
Diagnostic Radiology, Lund University, Lund, Sweden (T.R., D.v.W., M.N.);
Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology,
University College London, London, England (J.A.C.); Parkinson’s Disease
and Movement Disorders Center, Department of Neurology, Perelman School of
Medicine, University of Pennsylvania, Philadelphia, Pa (A.F.D., M.A.S.);
Alzheimer’s Disease Core Center, Department of Pathology and Laboratory
Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia,
Pa (E.B.L., J.Q.T.); and Queensland Brain Institute, University of Queensland
and Mater Misericordiae, Brisbane, Queensland, Australia (P.J.N.)
| | - Markus Nilsson
- From the Clinical Memory Research Unit, Department of Clinical
Sciences, Malmö, Lund University, Sölvegatan 19, 22100 Lund, Sweden
(N.S., S.H., D.v.W., O.H.); Penn Frontotemporal Degeneration Center, Department
of Neurology, Perelman School of Medicine, University of Pennsylvania,
Philadelphia, Pa (N.S., D.J.I., M.G., C.T.M.); Memory Clinic, Skåne
University Hospital, Malmö, Sweden (S.H., O.H.); Center for
Neurodegenerative Disease Research, Perelman School of Medicine, University of
Pennsylvania, Philadelphia, Pa (D.J.I., E.B.L., J.Q.T.); Department of
Diagnostic Radiology, Lund University, Lund, Sweden (T.R., D.v.W., M.N.);
Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology,
University College London, London, England (J.A.C.); Parkinson’s Disease
and Movement Disorders Center, Department of Neurology, Perelman School of
Medicine, University of Pennsylvania, Philadelphia, Pa (A.F.D., M.A.S.);
Alzheimer’s Disease Core Center, Department of Pathology and Laboratory
Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia,
Pa (E.B.L., J.Q.T.); and Queensland Brain Institute, University of Queensland
and Mater Misericordiae, Brisbane, Queensland, Australia (P.J.N.)
| | - Murray Grossman
- From the Clinical Memory Research Unit, Department of Clinical
Sciences, Malmö, Lund University, Sölvegatan 19, 22100 Lund, Sweden
(N.S., S.H., D.v.W., O.H.); Penn Frontotemporal Degeneration Center, Department
of Neurology, Perelman School of Medicine, University of Pennsylvania,
Philadelphia, Pa (N.S., D.J.I., M.G., C.T.M.); Memory Clinic, Skåne
University Hospital, Malmö, Sweden (S.H., O.H.); Center for
Neurodegenerative Disease Research, Perelman School of Medicine, University of
Pennsylvania, Philadelphia, Pa (D.J.I., E.B.L., J.Q.T.); Department of
Diagnostic Radiology, Lund University, Lund, Sweden (T.R., D.v.W., M.N.);
Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology,
University College London, London, England (J.A.C.); Parkinson’s Disease
and Movement Disorders Center, Department of Neurology, Perelman School of
Medicine, University of Pennsylvania, Philadelphia, Pa (A.F.D., M.A.S.);
Alzheimer’s Disease Core Center, Department of Pathology and Laboratory
Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia,
Pa (E.B.L., J.Q.T.); and Queensland Brain Institute, University of Queensland
and Mater Misericordiae, Brisbane, Queensland, Australia (P.J.N.)
| | - Peter J. Nestor
- From the Clinical Memory Research Unit, Department of Clinical
Sciences, Malmö, Lund University, Sölvegatan 19, 22100 Lund, Sweden
(N.S., S.H., D.v.W., O.H.); Penn Frontotemporal Degeneration Center, Department
of Neurology, Perelman School of Medicine, University of Pennsylvania,
Philadelphia, Pa (N.S., D.J.I., M.G., C.T.M.); Memory Clinic, Skåne
University Hospital, Malmö, Sweden (S.H., O.H.); Center for
Neurodegenerative Disease Research, Perelman School of Medicine, University of
Pennsylvania, Philadelphia, Pa (D.J.I., E.B.L., J.Q.T.); Department of
Diagnostic Radiology, Lund University, Lund, Sweden (T.R., D.v.W., M.N.);
Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology,
University College London, London, England (J.A.C.); Parkinson’s Disease
and Movement Disorders Center, Department of Neurology, Perelman School of
Medicine, University of Pennsylvania, Philadelphia, Pa (A.F.D., M.A.S.);
Alzheimer’s Disease Core Center, Department of Pathology and Laboratory
Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia,
Pa (E.B.L., J.Q.T.); and Queensland Brain Institute, University of Queensland
and Mater Misericordiae, Brisbane, Queensland, Australia (P.J.N.)
| | - Corey T. McMillan
- From the Clinical Memory Research Unit, Department of Clinical
Sciences, Malmö, Lund University, Sölvegatan 19, 22100 Lund, Sweden
(N.S., S.H., D.v.W., O.H.); Penn Frontotemporal Degeneration Center, Department
of Neurology, Perelman School of Medicine, University of Pennsylvania,
Philadelphia, Pa (N.S., D.J.I., M.G., C.T.M.); Memory Clinic, Skåne
University Hospital, Malmö, Sweden (S.H., O.H.); Center for
Neurodegenerative Disease Research, Perelman School of Medicine, University of
Pennsylvania, Philadelphia, Pa (D.J.I., E.B.L., J.Q.T.); Department of
Diagnostic Radiology, Lund University, Lund, Sweden (T.R., D.v.W., M.N.);
Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology,
University College London, London, England (J.A.C.); Parkinson’s Disease
and Movement Disorders Center, Department of Neurology, Perelman School of
Medicine, University of Pennsylvania, Philadelphia, Pa (A.F.D., M.A.S.);
Alzheimer’s Disease Core Center, Department of Pathology and Laboratory
Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia,
Pa (E.B.L., J.Q.T.); and Queensland Brain Institute, University of Queensland
and Mater Misericordiae, Brisbane, Queensland, Australia (P.J.N.)
| | - Oskar Hansson
- From the Clinical Memory Research Unit, Department of Clinical
Sciences, Malmö, Lund University, Sölvegatan 19, 22100 Lund, Sweden
(N.S., S.H., D.v.W., O.H.); Penn Frontotemporal Degeneration Center, Department
of Neurology, Perelman School of Medicine, University of Pennsylvania,
Philadelphia, Pa (N.S., D.J.I., M.G., C.T.M.); Memory Clinic, Skåne
University Hospital, Malmö, Sweden (S.H., O.H.); Center for
Neurodegenerative Disease Research, Perelman School of Medicine, University of
Pennsylvania, Philadelphia, Pa (D.J.I., E.B.L., J.Q.T.); Department of
Diagnostic Radiology, Lund University, Lund, Sweden (T.R., D.v.W., M.N.);
Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology,
University College London, London, England (J.A.C.); Parkinson’s Disease
and Movement Disorders Center, Department of Neurology, Perelman School of
Medicine, University of Pennsylvania, Philadelphia, Pa (A.F.D., M.A.S.);
Alzheimer’s Disease Core Center, Department of Pathology and Laboratory
Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia,
Pa (E.B.L., J.Q.T.); and Queensland Brain Institute, University of Queensland
and Mater Misericordiae, Brisbane, Queensland, Australia (P.J.N.)
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43
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Amornsiripanitch N, Bickelhaupt S, Shin HJ, Dang M, Rahbar H, Pinker K, Partridge SC. Diffusion-weighted MRI for Unenhanced Breast Cancer Screening. Radiology 2019; 293:504-520. [PMID: 31592734 PMCID: PMC6884069 DOI: 10.1148/radiol.2019182789] [Citation(s) in RCA: 80] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Revised: 06/18/2019] [Accepted: 07/10/2019] [Indexed: 01/12/2023]
Abstract
Diffusion-weighted (DW) MRI is a rapid technique that measures the mobility of water molecules within tissue, reflecting the cellular microenvironment. At DW MRI, breast cancers typically exhibit reduced diffusivity and appear hyperintense to surrounding tissues. On the basis of this characteristic, DW MRI may offer an unenhanced method to detect breast cancer without the costs and safety concerns associated with dynamic contrast material-enhanced MRI, the current reference standard in the setting of high-risk screening. This application of DW MRI has not been widely explored but is particularly timely given the growing health concerns related to the long-term use of gadolinium-based contrast material. Moreover, increasing breast density notification legislation across the United States is raising awareness of the limitations of mammography in women with dense breasts, emphasizing the need for additional cost-effective supplemental screening examinations. Preliminary studies suggest unenhanced MRI with DW MRI may provide higher sensitivity than screening mammography for the detection of breast malignancies. Larger prospective multicenter trials are needed to validate single-center findings and assess the performance of DW MRI for generalized breast cancer screening. Standardization of DW MRI acquisition and interpretation is essential to ensure reliable sensitivity and specificity, and an optimal approach for screening using readily available techniques is proposed here.
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Affiliation(s)
- Nita Amornsiripanitch
- From the Department of Breast Imaging, University of Massachusetts Memorial Medical Center, Worcester, Mass (N.A.); Medical Imaging and Radiology–Cancer Prevention, German Cancer Research Center, Heidelberg, Germany (S.B.); Department of Radiology, Asan Medical Center, University of Ulsan, Seoul, South Korea (H.J.S.); Department of Radiology, University of Washington, 825 Eastlake Ave E, G2-600, Seattle, WA 98109 (M.D., H.R., S.C.P.); Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (K.P.); and Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria (K.P.)
| | - Sebastian Bickelhaupt
- From the Department of Breast Imaging, University of Massachusetts Memorial Medical Center, Worcester, Mass (N.A.); Medical Imaging and Radiology–Cancer Prevention, German Cancer Research Center, Heidelberg, Germany (S.B.); Department of Radiology, Asan Medical Center, University of Ulsan, Seoul, South Korea (H.J.S.); Department of Radiology, University of Washington, 825 Eastlake Ave E, G2-600, Seattle, WA 98109 (M.D., H.R., S.C.P.); Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (K.P.); and Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria (K.P.)
| | - Hee Jung Shin
- From the Department of Breast Imaging, University of Massachusetts Memorial Medical Center, Worcester, Mass (N.A.); Medical Imaging and Radiology–Cancer Prevention, German Cancer Research Center, Heidelberg, Germany (S.B.); Department of Radiology, Asan Medical Center, University of Ulsan, Seoul, South Korea (H.J.S.); Department of Radiology, University of Washington, 825 Eastlake Ave E, G2-600, Seattle, WA 98109 (M.D., H.R., S.C.P.); Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (K.P.); and Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria (K.P.)
| | - Madeline Dang
- From the Department of Breast Imaging, University of Massachusetts Memorial Medical Center, Worcester, Mass (N.A.); Medical Imaging and Radiology–Cancer Prevention, German Cancer Research Center, Heidelberg, Germany (S.B.); Department of Radiology, Asan Medical Center, University of Ulsan, Seoul, South Korea (H.J.S.); Department of Radiology, University of Washington, 825 Eastlake Ave E, G2-600, Seattle, WA 98109 (M.D., H.R., S.C.P.); Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (K.P.); and Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria (K.P.)
| | - Habib Rahbar
- From the Department of Breast Imaging, University of Massachusetts Memorial Medical Center, Worcester, Mass (N.A.); Medical Imaging and Radiology–Cancer Prevention, German Cancer Research Center, Heidelberg, Germany (S.B.); Department of Radiology, Asan Medical Center, University of Ulsan, Seoul, South Korea (H.J.S.); Department of Radiology, University of Washington, 825 Eastlake Ave E, G2-600, Seattle, WA 98109 (M.D., H.R., S.C.P.); Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (K.P.); and Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria (K.P.)
| | - Katja Pinker
- From the Department of Breast Imaging, University of Massachusetts Memorial Medical Center, Worcester, Mass (N.A.); Medical Imaging and Radiology–Cancer Prevention, German Cancer Research Center, Heidelberg, Germany (S.B.); Department of Radiology, Asan Medical Center, University of Ulsan, Seoul, South Korea (H.J.S.); Department of Radiology, University of Washington, 825 Eastlake Ave E, G2-600, Seattle, WA 98109 (M.D., H.R., S.C.P.); Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (K.P.); and Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria (K.P.)
| | - Savannah C. Partridge
- From the Department of Breast Imaging, University of Massachusetts Memorial Medical Center, Worcester, Mass (N.A.); Medical Imaging and Radiology–Cancer Prevention, German Cancer Research Center, Heidelberg, Germany (S.B.); Department of Radiology, Asan Medical Center, University of Ulsan, Seoul, South Korea (H.J.S.); Department of Radiology, University of Washington, 825 Eastlake Ave E, G2-600, Seattle, WA 98109 (M.D., H.R., S.C.P.); Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (K.P.); and Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria (K.P.)
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44
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Martin J, Endt S, Wetscherek A, Kuder TA, Doerfler A, Uder M, Hensel B, Laun FB. Twice‐refocused stimulated echo diffusion imaging: Measuring diffusion time dependence at constant
T
1
weighting. Magn Reson Med 2019; 83:1741-1749. [DOI: 10.1002/mrm.28046] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2019] [Revised: 09/30/2019] [Accepted: 09/30/2019] [Indexed: 02/04/2023]
Affiliation(s)
- Jan Martin
- Institute of Radiology University Hospital ErlangenFriedrich‐Alexander‐Universität Erlangen‐Nürnberg Erlangen Germany
| | - Sebastian Endt
- Institute of Radiology University Hospital ErlangenFriedrich‐Alexander‐Universität Erlangen‐Nürnberg Erlangen Germany
- Department of Computer Science Technical University of Munich Garching Germany
| | - Andreas Wetscherek
- Joint Department of Physics The Institute of Cancer Research and the Royal Marsden NHS Foundation Trust London United Kingdom
| | - Tristan Anselm Kuder
- Department Medical Physics in Radiology German Cancer Research Center Heidelberg Germany
| | - Arnd Doerfler
- Institute of Neuroradiology University Hospital ErlangenFriedrich‐Alexander‐Universität Erlangen‐Nürnberg Erlangen Germany
| | - Michael Uder
- Institute of Radiology University Hospital ErlangenFriedrich‐Alexander‐Universität Erlangen‐Nürnberg Erlangen Germany
| | - Bernhard Hensel
- Center for Medical Physics and Engineering Friedrich‐Alexander‐Universität Erlangen‐Nürnberg Erlangen Germany
| | - Frederik Bernd Laun
- Institute of Radiology University Hospital ErlangenFriedrich‐Alexander‐Universität Erlangen‐Nürnberg Erlangen Germany
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Szczepankiewicz F, Westin CF, Nilsson M. Maxwell-compensated design of asymmetric gradient waveforms for tensor-valued diffusion encoding. Magn Reson Med 2019; 82:1424-1437. [PMID: 31148245 PMCID: PMC6626569 DOI: 10.1002/mrm.27828] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 05/03/2019] [Accepted: 05/06/2019] [Indexed: 01/13/2023]
Abstract
PURPOSE Diffusion encoding with asymmetric gradient waveforms is appealing because the asymmetry provides superior efficiency. However, concomitant gradients may cause a residual gradient moment at the end of the waveform, which can cause significant signal error and image artifacts. The purpose of this study was to develop an asymmetric waveform designs for tensor-valued diffusion encoding that is not sensitive to concomitant gradients. METHODS The "Maxwell index" was proposed as a scalar invariant to capture the effect of concomitant gradients. Optimization of "Maxwell-compensated" waveforms was performed in which this index was constrained. Resulting waveforms were compared to waveforms from literature, in terms of the measured and predicted impact of concomitant gradients, by numerical analysis as well as experiments in a phantom and in a healthy human brain. RESULTS Maxwell-compensated waveforms with Maxwell indices below 100 (mT/m)2 ms showed negligible signal bias in both numerical analysis and experiments. By contrast, several waveforms from literature showed gross signal bias under the same conditions, leading to a signal bias that was large enough to markedly affect parameter maps. Experimental results were accurately predicted by theory. CONCLUSION Constraining the Maxwell index in the optimization of asymmetric gradient waveforms yields efficient diffusion encoding that negates the effects of concomitant fields while enabling arbitrary shapes of the b-tensor. This waveform design is especially useful in combination with strong gradients, long encoding times, thick slices, simultaneous multi-slice acquisition, and large FOVs.
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Affiliation(s)
- Filip Szczepankiewicz
- Radiology, Brigham and Women’s Hospital, Boston, MA, US
- Harvard Medical School, Boston, MA, US
| | - Carl-Fredrik Westin
- Radiology, Brigham and Women’s Hospital, Boston, MA, US
- Harvard Medical School, Boston, MA, US
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Nilsson M, Szczepankiewicz F, Brabec J, Taylor M, Westin CF, Golby A, van Westen D, Sundgren PC. Tensor-valued diffusion MRI in under 3 minutes: an initial survey of microscopic anisotropy and tissue heterogeneity in intracranial tumors. Magn Reson Med 2019; 83:608-620. [PMID: 31517401 PMCID: PMC6900060 DOI: 10.1002/mrm.27959] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Revised: 07/05/2019] [Accepted: 07/30/2019] [Indexed: 12/23/2022]
Abstract
PURPOSE To evaluate the feasibility of a 3-minutes protocol for assessment of the microscopic anisotropy and tissue heterogeneity based on tensor-valued diffusion MRI in a wide range of intracranial tumors. METHODS B-tensor encoding was performed in 42 patients with intracranial tumors (gliomas, meningiomas, adenomas, and metastases). Microscopic anisotropy and tissue heterogeneity were evaluated by estimating the anisotropic kurtosis (MKA ) and isotropic kurtosis (MKI ), respectively. An extensive imaging protocol was compared with a 3-minutes protocol. RESULTS The fast imaging protocol yielded parameters with characteristics in terms of bias and precision similar to the full protocol. Glioblastomas had lower microscopic anisotropy than meningiomas (MKA = 0.29 ± 0.06 vs. 0.45 ± 0.08, P = 0.003). Metastases had higher tissue heterogeneity (MKI = 0.57 ± 0.07) than both the glioblastomas (0.44 ± 0.06, P < 0.001) and meningiomas (0.46 ± 0.06, P = 0.03). CONCLUSION Evaluation of the microscopic anisotropy and tissue heterogeneity in intracranial tumor patients is feasible in clinically relevant times frames.
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Affiliation(s)
- Markus Nilsson
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
| | | | - Jan Brabec
- Department of Clinical Sciences Lund, Medical Radiation Physics, Lund University, Lund, Sweden
| | - Marie Taylor
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
| | | | - Alexandra Golby
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Danielle van Westen
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
| | - Pia C Sundgren
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden.,Lund University Bioimaging Center (LBIC), Lund University, Lund, Sweden
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Szczepankiewicz F, Hoge S, Westin CF. Linear, planar and spherical tensor-valued diffusion MRI data by free waveform encoding in healthy brain, water, oil and liquid crystals. Data Brief 2019; 25:104208. [PMID: 31338402 PMCID: PMC6626882 DOI: 10.1016/j.dib.2019.104208] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Revised: 06/06/2019] [Accepted: 06/25/2019] [Indexed: 11/25/2022] Open
Abstract
Recently, several biophysical models and signal representations have been proposed for microstructure imaging based on tensor-valued, or multidimensional, diffusion MRI. The acquisition of the necessary data requires non-conventional pulse sequences, and data is therefore not available to the wider diffusion MRI community. To facilitate exploration and development of analysis techniques based on tensor-valued diffusion encoding, we share a comprehensive data set acquired in a healthy human brain. The data encompasses diffusion weighted images using linear, planar and spherical diffusion tensor encoding at multiple b-values and diffusion encoding directions. We also supply data acquired in several phantoms that may support validation. The data is hosted by GitHub: https://github.com/filip-szczepankiewicz/Szczepankiewicz_DIB_2019.
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Affiliation(s)
- Filip Szczepankiewicz
- Radiology, Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Scott Hoge
- Radiology, Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Carl-Fredrik Westin
- Radiology, Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
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Brun L, Pron A, Sein J, Deruelle C, Coulon O. Diffusion MRI: Assessment of the Impact of Acquisition and Preprocessing Methods Using the BrainVISA-Diffuse Toolbox. Front Neurosci 2019; 13:536. [PMID: 31275091 PMCID: PMC6593278 DOI: 10.3389/fnins.2019.00536] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Accepted: 05/08/2019] [Indexed: 12/28/2022] Open
Abstract
Diffusion MR images are prone to severe geometric distortions induced by head movement, eddy-current and inhomogeneity of magnetic susceptibility. Various correction methods have been proposed that depend on the choice of the acquisition settings and potentially provide highly different data quality. However, the impact of this choice has not been evaluated in terms of the ratio between scan time and preprocessed data quality. This study aims at investigating the impact of six well-known preprocessing methods, each associated to specific acquisition settings, on the outcome of diffusion analyses. For this purpose, we developed a comprehensive toolbox called Diffuse which automatically guides the user to the best preprocessing pipeline according to the input data. Using MR images of 20 subjects from the HCP dataset, we compared the six pre-processing pipelines regarding the following criteria: the ability to recover brain’s true geometry, the tensor model estimation and derived indices in the white matter, and finally the spatial dispersion of six well known connectivity pathways. As expected the pipeline associated to the longer acquisition fully repeated with reversed phase-encoding (RPE) yielded the higher data quality and was used as a reference to evaluate the other pipelines. In this way, we highlighted several significant aspects of other pre-processing pipelines. Our results first established that eddy-current correction improves the tensor-fitting performance with a localized impact especially in the corpus callosum. Concerning susceptibility distortions, we showed that the use of a field map is not sufficient and involves additional smoothing, yielding to an artificial decrease of tensor-fitting error. Of most importance, our findings demonstrate that, for an equivalent scan time, the acquisition of a b0 volume with RPE ensures a better brain’s geometry reconstruction and local improvement of tensor quality, without any smoothing of the image. This was found to be the best scan time/data quality compromise. To conclude, this study highlights and attempts to quantify the strong dependence of diffusion metrics on acquisition settings and preprocessing methods.
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Affiliation(s)
- Lucile Brun
- Institut de Neurosciences de La Timone, Aix-Marseille University, CNRS, UMR 7289, Marseille, France
| | - Alexandre Pron
- Institut de Neurosciences de La Timone, Aix-Marseille University, CNRS, UMR 7289, Marseille, France
| | - Julien Sein
- Institut de Neurosciences de La Timone, Aix-Marseille University, CNRS, UMR 7289, Marseille, France
| | - Christine Deruelle
- Institut de Neurosciences de La Timone, Aix-Marseille University, CNRS, UMR 7289, Marseille, France
| | - Olivier Coulon
- Institut de Neurosciences de La Timone, Aix-Marseille University, CNRS, UMR 7289, Marseille, France
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Lampinen B, Szczepankiewicz F, Novén M, van Westen D, Hansson O, Englund E, Mårtensson J, Westin C, Nilsson M. Searching for the neurite density with diffusion MRI: Challenges for biophysical modeling. Hum Brain Mapp 2019; 40:2529-2545. [PMID: 30802367 PMCID: PMC6503974 DOI: 10.1002/hbm.24542] [Citation(s) in RCA: 83] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Revised: 01/17/2019] [Accepted: 02/03/2019] [Indexed: 12/19/2022] Open
Abstract
In vivo mapping of the neurite density with diffusion MRI (dMRI) is a high but challenging aim. First, it is unknown whether all neurites exhibit completely anisotropic ("stick-like") diffusion. Second, the "density" of tissue components may be confounded by non-diffusion properties such as T2 relaxation. Third, the domain of validity for the estimated parameters to serve as indices of neurite density is incompletely explored. We investigated these challenges by acquiring data with "b-tensor encoding" and multiple echo times in brain regions with low orientation coherence and in white matter lesions. Results showed that microscopic anisotropy from b-tensor data is associated with myelinated axons but not with dendrites. Furthermore, b-tensor data together with data acquired for multiple echo times showed that unbiased density estimates in white matter lesions require data-driven estimates of compartment-specific T2 values. Finally, the "stick" fractions of different biophysical models could generally not serve as neurite density indices across the healthy brain and white matter lesions, where outcomes of comparisons depended on the choice of constraints. In particular, constraining compartment-specific T2 values was ambiguous in the healthy brain and had a large impact on estimated values. In summary, estimating neurite density generally requires accounting for different diffusion and/or T2 properties between axons and dendrites. Constrained "index" parameters could be valid within limited domains that should be delineated by future studies.
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Affiliation(s)
- Björn Lampinen
- Clinical Sciences Lund, Medical Radiation PhysicsLund UniversityLundSweden
| | - Filip Szczepankiewicz
- Clinical Sciences Lund, Medical Radiation PhysicsLund UniversityLundSweden
- Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUS
| | - Mikael Novén
- Centre for Languages and LiteratureLund UniversityLundSweden
| | | | - Oskar Hansson
- Clinical Sciences Malmö, Clinical Memory Research UnitLund UniversityLundSweden
| | - Elisabet Englund
- Clinical Sciences Lund, Oncology and PathologyLund UniversityLundSweden
| | - Johan Mårtensson
- Clinical Sciences Lund, Department of Logopedics, Phoniatrics and AudiologyLund UniversityLundSweden
| | | | - Markus Nilsson
- Clinical Sciences Lund, RadiologyLund UniversityLundSweden
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Szczepankiewicz F, Sjölund J, Ståhlberg F, Lätt J, Nilsson M. Tensor-valued diffusion encoding for diffusional variance decomposition (DIVIDE): Technical feasibility in clinical MRI systems. PLoS One 2019; 14:e0214238. [PMID: 30921381 PMCID: PMC6438503 DOI: 10.1371/journal.pone.0214238] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Accepted: 03/08/2019] [Indexed: 11/18/2022] Open
Abstract
Microstructure imaging techniques based on tensor-valued diffusion encoding have gained popularity within the MRI research community. Unlike conventional diffusion encoding-applied along a single direction in each shot-tensor-valued encoding employs diffusion encoding along multiple directions within a single preparation of the signal. The benefit is that such encoding may probe tissue features that are not accessible by conventional encoding. For example, diffusional variance decomposition (DIVIDE) takes advantage of tensor-valued encoding to probe microscopic diffusion anisotropy independent of orientation coherence. The drawback is that tensor-valued encoding generally requires gradient waveforms that are more demanding on hardware; it has therefore been used primarily in MRI systems with relatively high performance. The purpose of this work was to explore tensor-valued diffusion encoding on clinical MRI systems with varying performance to test its technical feasibility within the context of DIVIDE. We performed whole-brain imaging with linear and spherical b-tensor encoding at field strengths between 1.5 and 7 T, and at maximal gradient amplitudes between 45 and 80 mT/m. Asymmetric gradient waveforms were optimized numerically to yield b-values up to 2 ms/μm2. Technical feasibility was assessed in terms of the repeatability, SNR, and quality of DIVIDE parameter maps. Variable system performance resulted in echo times between 83 to 115 ms and total acquisition times of 6 to 9 minutes when using 80 signal samples and resolution 2×2×4 mm3. As expected, the repeatability, signal-to-noise ratio and parameter map quality depended on hardware performance. We conclude that tensor-valued encoding is feasible for a wide range of MRI systems-even at 1.5 T with maximal gradient waveform amplitudes of 33 mT/m-and baseline experimental design and quality parameters for all included configurations. This demonstrates that tissue features, beyond those accessible by conventional diffusion encoding, can be explored on a wide range of MRI systems.
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Affiliation(s)
- Filip Szczepankiewicz
- Lund University, Department of Clinical Sciences Lund, Medical Radiation Physics, Lund, Sweden
| | - Jens Sjölund
- Elekta Instrument AB, Kungstensgatan 18, Stockholm, Sweden
- Linköping University, Department of Biomedical Engineering, Linköping, Sweden
- Linköping University, Center for Medical Image Science and Visualization (CMIV), Linköping, Sweden
| | - Freddy Ståhlberg
- Lund University, Department of Clinical Sciences Lund, Medical Radiation Physics, Lund, Sweden
- Lund University, Department of Clinical Sciences Lund, Diagnostic Radiology, Lund, Sweden
| | - Jimmy Lätt
- Skåne University Hospital, Department of Imaging and Function, Lund, Sweden
| | - Markus Nilsson
- Lund University, Department of Clinical Sciences Lund, Diagnostic Radiology, Lund, Sweden
- Lund University, Lund University Bioimaging Center, Lund, Sweden
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