1
|
Deveshwar N, Yao J, Han M, Dwork N, Shen X, Ljungberg E, Caverzasi E, Cao P, Henry R, Green A, Larson PEZ. Quantification of the in vivo brain ultrashort-T 2* component in healthy volunteers. Magn Reson Med 2024; 91:2417-2430. [PMID: 38291598 DOI: 10.1002/mrm.30013] [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: 04/28/2023] [Revised: 12/14/2023] [Accepted: 12/29/2023] [Indexed: 02/01/2024]
Abstract
PURPOSE Recent work has shown MRI is able to measure and quantify signals of phospholipid membrane-bound protons associated with myelin in the human brain. This work seeks to develop an improved technique for characterizing this brain ultrashort-T 2 ∗ $$ {\mathrm{T}}_2\ast $$ component in vivo accounting forT 1 $$ {\mathrm{T}}_1 $$ weighting. METHODS Data from ultrashort echo time scans from 16 healthy volunteers with variable flip angles (VFA) were collected and fitted into an advanced regression model to quantify signal fraction, relaxation time, and frequency shift of the ultrashort-T 2 ∗ $$ {\mathrm{T}}_2\ast $$ component. RESULTS The fitted components show intra-subject differences of different white matter structures and significantly elevated ultrashort-T 2 ∗ $$ {\mathrm{T}}_2\ast $$ signal fraction in the corticospinal tracts measured at 0.09 versus 0.06 in other white matter structures and significantly elevated ultrashort-T 2 ∗ $$ {\mathrm{T}}_2\ast $$ frequency shift in the body of the corpus callosum at- $$ - $$ 1.5 versus- $$ - $$ 2.0 ppm in other white matter structures. CONCLUSION The significantly different measured components and measuredT 1 $$ {\mathrm{T}}_1 $$ relaxation time of the ultrashort-T 2 ∗ $$ {\mathrm{T}}_2\ast $$ component suggest that this method is picking up novel signals from phospholipid membrane-bound protons.
Collapse
Affiliation(s)
- Nikhil Deveshwar
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
- UC Berkeley - UCSF Graduate Program in Bioengineering, San Francisco, California, USA
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, California, USA
| | - Jingwen Yao
- UC Berkeley - UCSF Graduate Program in Bioengineering, San Francisco, California, USA
| | - Misung Han
- UC Berkeley - UCSF Graduate Program in Bioengineering, San Francisco, California, USA
| | - Nicholas Dwork
- Departments of Biomedical Informatics and Radiology, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Xin Shen
- UC Berkeley - UCSF Graduate Program in Bioengineering, San Francisco, California, USA
| | - Emil Ljungberg
- Department of Medical Radiation Physics, Lund University, Lund, Sweden
| | - Eduardo Caverzasi
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
| | - Peng Cao
- Department of Diagnostic Radiology, Hong Kong University, Hong Kong, China
| | - Roland Henry
- Department of Neurology, University of California, San Francisco, San Francisco, California, USA
| | - Ari Green
- Department of Neurology, University of California, San Francisco, San Francisco, California, USA
| | - Peder E Z Larson
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
- UC Berkeley - UCSF Graduate Program in Bioengineering, San Francisco, California, USA
| |
Collapse
|
2
|
Ma Y, Jang H, Jerban S, Chang EY, Chung CB, Bydder GM, Du J. Making the invisible visible-ultrashort echo time magnetic resonance imaging: Technical developments and applications. APPLIED PHYSICS REVIEWS 2022; 9:041303. [PMID: 36467869 PMCID: PMC9677812 DOI: 10.1063/5.0086459] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 09/12/2022] [Indexed: 05/25/2023]
Abstract
Magnetic resonance imaging (MRI) uses a large magnetic field and radio waves to generate images of tissues in the body. Conventional MRI techniques have been developed to image and quantify tissues and fluids with long transverse relaxation times (T2s), such as muscle, cartilage, liver, white matter, gray matter, spinal cord, and cerebrospinal fluid. However, the body also contains many tissues and tissue components such as the osteochondral junction, menisci, ligaments, tendons, bone, lung parenchyma, and myelin, which have short or ultrashort T2s. After radio frequency excitation, their transverse magnetizations typically decay to zero or near zero before the receiving mode is enabled for spatial encoding with conventional MR imaging. As a result, these tissues appear dark, and their MR properties are inaccessible. However, when ultrashort echo times (UTEs) are used, signals can be detected from these tissues before they decay to zero. This review summarizes recent technical developments in UTE MRI of tissues with short and ultrashort T2 relaxation times. A series of UTE MRI techniques for high-resolution morphological and quantitative imaging of these short-T2 tissues are discussed. Applications of UTE imaging in the musculoskeletal, nervous, respiratory, gastrointestinal, and cardiovascular systems of the body are included.
Collapse
Affiliation(s)
- Yajun Ma
- Department of Radiology, University of California, San Diego, California 92037, USA
| | - Hyungseok Jang
- Department of Radiology, University of California, San Diego, California 92037, USA
| | - Saeed Jerban
- Department of Radiology, University of California, San Diego, California 92037, USA
| | | | | | - Graeme M Bydder
- Department of Radiology, University of California, San Diego, California 92037, USA
| | - Jiang Du
- Author to whom correspondence should be addressed:. Tel.: (858) 246-2248, Fax: (858) 246-2221
| |
Collapse
|
3
|
Müller M, Egger N, Sommer S, Wilferth T, Meixner CR, Laun FB, Mennecke A, Schmidt M, Huhn K, Rothhammer V, Uder M, Dörfler A, Nagel AM. Direct imaging of white matter ultrashort T 2∗ components at 7 Tesla. Magn Reson Imaging 2021; 86:107-117. [PMID: 34906631 DOI: 10.1016/j.mri.2021.11.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 11/02/2021] [Accepted: 11/29/2021] [Indexed: 11/25/2022]
Abstract
PURPOSE To demonstrate direct imaging of the white matter ultrashort T2∗ components at 7 Tesla using inversion recovery (IR)-enhanced ultrashort echo time (UTE) MRI. To investigate its characteristics, potentials and limitations, and to establish a clinical protocol. MATERIAL AND METHODS The IR UTE technique suppresses long T2∗ signals within white matter by using adiabatic inversion in combination with dual-echo difference imaging. Artifacts arising at 7 T from long T2∗ scalp fat components were reduced by frequency shifting the IR pulse such that those frequencies were inverted likewise. For 8 healthy volunteers, the T2∗ relaxation times of white matter were then quantified. In 20 healthy volunteers, the UTE difference and fraction contrast were evaluated. Finally, in 6 patients with multiple sclerosis (MS), the performance of the technique was assessed. RESULTS A frequency shift of -1.2 ppm of the IR pulse (i.e. towards the fat frequency) provided a good suppression of artifacts. With this, an ultrashort compartment of (68 ± 6) % with a T2∗ time of (147 ± 58) μs was quantified with a chemical shift of (-3.6 ± 0.5) ppm from water. Within healthy volunteers' white matter, a stable ultrashort T2∗ fraction contrast was calculated. For the MS patients, a significant fraction reduction in the identified lesions as well as in the normal-appearing white matter was observed. CONCLUSIONS The quantification results indicate that the observed ultrashort components arise primarily from myelin tissue. Direct IR UTE imaging of the white matter ultrashort T2∗ components is thus feasible at 7 T with high quantitative inter-subject repeatability and good detection of signal loss in MS.
Collapse
Affiliation(s)
- Max Müller
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
| | - Nico Egger
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Stefan Sommer
- Siemens Healthcare, Zurich, Switzerland; Swiss Center for Musculoskeletal Imaging (SCMI), Balgrist Campus, Zurich, Switzerland
| | - Tobias Wilferth
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Christian R Meixner
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Frederik Bernd Laun
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Angelika Mennecke
- Department of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Manuel Schmidt
- Department of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Konstantin Huhn
- Department of Neurology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Veit Rothhammer
- Department of Neurology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Michael Uder
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Arnd Dörfler
- Department of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Armin M Nagel
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany; Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| |
Collapse
|
4
|
Mancini M, Karakuzu A, Cohen-Adad J, Cercignani M, Nichols TE, Stikov N. An interactive meta-analysis of MRI biomarkers of myelin. eLife 2020; 9:e61523. [PMID: 33084576 PMCID: PMC7647401 DOI: 10.7554/elife.61523] [Citation(s) in RCA: 85] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 10/20/2020] [Indexed: 12/17/2022] Open
Abstract
Several MRI measures have been proposed as in vivo biomarkers of myelin, each with applications ranging from plasticity to pathology. Despite the availability of these myelin-sensitive modalities, specificity and sensitivity have been a matter of discussion. Debate about which MRI measure is the most suitable for quantifying myelin is still ongoing. In this study, we performed a systematic review of published quantitative validation studies to clarify how different these measures are when compared to the underlying histology. We analyzed the results from 43 studies applying meta-analysis tools, controlling for study sample size and using interactive visualization (https://neurolibre.github.io/myelin-meta-analysis). We report the overall estimates and the prediction intervals for the coefficient of determination and find that MT and relaxometry-based measures exhibit the highest correlations with myelin content. We also show which measures are, and which measures are not statistically different regarding their relationship with histology.
Collapse
Affiliation(s)
- Matteo Mancini
- Department of Neuroscience, Brighton and Sussex Medical School, University of SussexBrightonUnited Kingdom
- NeuroPoly Lab, Polytechnique MontrealMontrealCanada
- CUBRIC, Cardiff UniversityCardiffUnited Kingdom
| | | | - Julien Cohen-Adad
- NeuroPoly Lab, Polytechnique MontrealMontrealCanada
- Functional Neuroimaging Unit, CRIUGM, Université de MontréalMontrealCanada
| | - Mara Cercignani
- Department of Neuroscience, Brighton and Sussex Medical School, University of SussexBrightonUnited Kingdom
- Neuroimaging Laboratory, Fondazione Santa LuciaRomeItaly
| | - Thomas E Nichols
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of OxfordOxfordUnited Kingdom
- Big Data Institute, University of OxfordOxfordUnited Kingdom
| | - Nikola Stikov
- NeuroPoly Lab, Polytechnique MontrealMontrealCanada
- Montreal Heart Institute, Université de MontréalMontrealCanada
| |
Collapse
|
5
|
Jang H, Carl M, Ma Y, Searleman AC, Jerban S, Chang EY, Corey-Bloom J, Du J. Inversion recovery zero echo time (IR-ZTE) imaging for direct myelin detection in human brain: a feasibility study. Quant Imaging Med Surg 2020; 10:895-906. [PMID: 32489915 DOI: 10.21037/qims.2020.04.13] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Background Myelin alteration is closely associated with neurological diseases such as multiple sclerosis (MS). Unfortunately, due to myelin's extremely short T2* (~0.3 ms or shorter at 3T), it cannot be directly imaged with conventional MR imaging techniques. Recently, ultrashort echo time (UTE) imaging-based methods have been proposed for direct imaging of myelin. In this study, we explore the feasibility and efficacy of inversion recovery prepared zero echo time (IR-ZTE) imaging for direct volumetric imaging of myelin in white matter of the brain in vivo. Methods In the proposed method, an adiabatic IR preparation pulse is used to suppress long T2 white matter signal, followed by dual echo ZTE imaging where the remaining long T2 components, including gray matter, are suppressed by dual echo subtraction. In the implementation of ZTE, the sampling strategy introduced in Water- and Fat-Suppressed Proton Projection MRI (WASPI) was incorporated to acquire the k-space data missing due to the radiofrequency (RF) transmit/receiver switching time. The IR-ZTE sequence was implemented on a 3T clinical MR system and evaluated using a myelin phantom composed of six different myelin concentrations (0% to 20%), a cadaveric human brain, four healthy volunteers, and seven MS patients. Results In the myelin phantom experiment, the ZTE signal intensity showed high linearity to the myelin concentrations (R2=0.98). In the ex vivo and in vivo experiments, the IR-ZTE sequence provided high contrast volumetric imaging of myelin in human brains. The IR-ZTE sequence was able to detect demyelinated foci lesions in all MS patients. Conclusions Adiabatic IR prepared dual echo ZTE imaging allows for direct, volumetric imaging of myelin in white matter of the brain in vivo.
Collapse
Affiliation(s)
- Hyungseok Jang
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | | | - Yajun Ma
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Adam C Searleman
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Saeed Jerban
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Eric Y Chang
- Department of Radiology, University of California San Diego, San Diego, CA, USA.,Radiology Service, Veterans Affairs San Diego Healthcare System, San Diego, CA, USA
| | - Jody Corey-Bloom
- Department of Neurosciences, University of California, San Diego, CA, USA
| | - Jiang Du
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| |
Collapse
|
6
|
Guglielmetti C, Boucneau T, Cao P, Van der Linden A, Larson PEZ, Chaumeil MM. Longitudinal evaluation of demyelinated lesions in a multiple sclerosis model using ultrashort echo time magnetization transfer (UTE-MT) imaging. Neuroimage 2019; 208:116415. [PMID: 31811900 DOI: 10.1016/j.neuroimage.2019.116415] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Revised: 11/16/2019] [Accepted: 11/28/2019] [Indexed: 11/27/2022] Open
Abstract
Alterations in myelin integrity are involved in many neurological disorders and demyelinating diseases, such as multiple sclerosis (MS). Although magnetic resonance imaging (MRI) is the gold standard method to diagnose and monitor MS patients, clinically available MRI protocols show limited specificity for myelin detection, notably in cerebral grey matter areas. Ultrashort echo time (UTE) MRI has shown great promise for direct imaging of lipids and myelin sheaths, and thus holds potential to improve lesion detection. In this study, we used a sequence combining magnetization transfer (MT) with UTE ("UTE-MT", TE = 76 μs) and with short TE ("STE-MT", TE = 3000 μs) to evaluate spatial and temporal changes in brain myelin content in the cuprizone mouse model for MS on a clinical 7 T scanner. During demyelination, UTE-MT ratio (UTE-MTR) and STE-MT ratio (STE-MTR) values were significantly decreased in most white matter and grey matter regions. However, only UTE-MTR detected cortical changes. After remyelination in subcortical and cortical areas, UTE-MTR values remained lower than baseline values, indicating that UTE-MT, but not STE-MT, imaging detected long-lasting changes following a demyelinating event. Next, we evaluated the potential correlations between imaging values and underlying histopathological markers. The strongest correlation was observed between UTE-MTR and percent coverage of myelin basic protein (MBP) immunostaining (r2 = 0.71). A significant, although lower, correlation was observed between STE-MTR and MBP (r2 = 0.48), and no correlation was found between UTE-MTR or STE-MTR and gliosis immunostaining. Interestingly, correlations varied across brain substructures. Altogether, our results demonstrate that UTE-MTR values significantly correlate with myelin content as measured by histopathology, not only in white matter, but also in subcortical and cortical grey matter regions in the cuprizone mouse model for MS. Readily implemented on a clinical 7 T system, this approach thus holds great potential for detecting demyelinating/remyelinating events in both white and grey matter areas in humans. When applied to patients with neurological disorders, including MS patient populations, UTE-MT methods may improve the non-invasive longitudinal monitoring of brain lesions, not only during disease progression but also in response to next generation remyelinating therapies.
Collapse
Affiliation(s)
- Caroline Guglielmetti
- Department of Physical Therapy and Rehabilitation Science, University of California, San Francisco, CA, USA; Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA; Bio-Imaging Laboratory, Department of Biomedical Sciences, University of Antwerp, 2000, Antwerp, Belgium
| | - Tanguy Boucneau
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - Peng Cao
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - Annemie Van der Linden
- Bio-Imaging Laboratory, Department of Biomedical Sciences, University of Antwerp, 2000, Antwerp, Belgium
| | - Peder E Z Larson
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA; UC Berkeley-UCSF Graduate Program in Bioengineering, Berkeley and University of California, San Francisco, CA, USA
| | - Myriam M Chaumeil
- Department of Physical Therapy and Rehabilitation Science, University of California, San Francisco, CA, USA; Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA; UC Berkeley-UCSF Graduate Program in Bioengineering, Berkeley and University of California, San Francisco, CA, USA.
| |
Collapse
|
7
|
Wei H, Cao S, Zhang Y, Guan X, Yan F, Yeom KW, Liu C. Learning-based single-step quantitative susceptibility mapping reconstruction without brain extraction. Neuroimage 2019; 202:116064. [PMID: 31377323 PMCID: PMC6819263 DOI: 10.1016/j.neuroimage.2019.116064] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Revised: 07/29/2019] [Accepted: 07/30/2019] [Indexed: 01/11/2023] Open
Abstract
Quantitative susceptibility mapping (QSM) estimates the underlying tissue magnetic susceptibility from MRI gradient-echo phase signal and typically requires several processing steps. These steps involve phase unwrapping, brain volume extraction, background phase removal and solving an ill-posed inverse problem relating the tissue phase to the underlying susceptibility distribution. The resulting susceptibility map is known to suffer from inaccuracy near the edges of the brain tissues, in part due to imperfect brain extraction, edge erosion of the brain tissue and the lack of phase measurement outside the brain. This inaccuracy has thus hindered the application of QSM for measuring susceptibility of tissues near the brain edges, e.g., quantifying cortical layers and generating superficial venography. To address these challenges, we propose a learning-based QSM reconstruction method that directly estimates the magnetic susceptibility from total phase images without the need for brain extraction and background phase removal, referred to as autoQSM. The neural network has a modified U-net structure and is trained using QSM maps computed by a two-step QSM method. 209 healthy subjects with ages ranging from 11 to 82 years were employed for patch-wise network training. The network was validated on data dissimilar to the training data, e.g., in vivo mouse brain data and brains with lesions, which suggests that the network generalized and learned the underlying mathematical relationship between magnetic field perturbation and magnetic susceptibility. Quantitative and qualitative comparisons were performed between autoQSM and other two-step QSM methods. AutoQSM was able to recover magnetic susceptibility of anatomical structures near the edges of the brain including the veins covering the cortical surface, spinal cord and nerve tracts near the mouse brain boundaries. The advantages of high-quality maps, no need for brain volume extraction, and high reconstruction speed demonstrate autoQSM's potential for future applications.
Collapse
Affiliation(s)
- Hongjiang Wei
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Steven Cao
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
| | - Yuyao Zhang
- School of Information and Science and Technology, ShanghaiTech University, Shanghai, China
| | - Xiaojun Guan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Fuhua Yan
- Department of Radiology, Rui Jin Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, China
| | - Kristen W Yeom
- Department of Radiology, Lucile Packard Children's Hospital, Stanford University, Palo Alto, CA, USA
| | - Chunlei Liu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA; Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA.
| |
Collapse
|
8
|
Extracting more for less: multi‐echo MP2RAGE for simultaneous T
1
‐weighted imaging, T
1
mapping, mapping, SWI, and QSM from a single acquisition. Magn Reson Med 2019; 83:1178-1191. [DOI: 10.1002/mrm.27975] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Revised: 08/05/2019] [Accepted: 08/06/2019] [Indexed: 12/22/2022]
|
9
|
Chen L, Wei Z, Chan K, Cai S, Liu G, Lu H, Wong PC, van Zijl PCM, Li T, Xu J. Protein aggregation linked to Alzheimer's disease revealed by saturation transfer MRI. Neuroimage 2019; 188:380-390. [PMID: 30553917 PMCID: PMC6401270 DOI: 10.1016/j.neuroimage.2018.12.018] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 12/08/2018] [Accepted: 12/10/2018] [Indexed: 12/12/2022] Open
Abstract
The goal of this study was to develop a molecular biomarker for the detection of protein aggregation involved in Alzheimer's disease (AD) by exploiting the features of the water saturation transfer spectrum (Z-spectrum), the CEST signal of which is sensitive to the molecular configuration of proteins. A radial-sampling steady-state sequence based ultrashort echo time (UTE) readout was implemented to image the Z-spectrum in the mouse brain, especially the contributions from mobile proteins at the frequency offsets for the composite protein amide proton (+3.6 ppm) and aliphatic proton (-3.6 ppm) signals. Using a relatively weak radiofrequency (RF) saturation amplitude, contributions due to strong magnetization transfer contrast (MTC) from solid-like macromolecules and direct water saturation (DS) were minimized. For practical measure of the changes in the mobile protein configuration, we defined a saturation transfer difference (ΔST) by subtracting the Z-spectral signals at ±3.6 ppm from a control signal at 8 ppm. Phantom studies of glutamate solution, protein (egg white) and hair conditioner show the capability of the proposed scheme to minimize the contributions from amine protons, DS, and MTC, respectively. The ST signal at ±3.6 ppm of the cross-linked bovine serum albumin (BSA) solutions demonstrated that the ΔST signal can be used to monitor the aggregation process of the mobile proteins. High-resolution ΔST images of AD mouse brains at ±3.6 ppm of mouse brains showed significantly reduced ΔST (-3.6) signal compared to the age-matched wild-type (WT) mice. Thus, this signal has potential to serve as a molecular biomarker for monitoring protein aggregation in AD.
Collapse
Affiliation(s)
- Lin Chen
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Zhiliang Wei
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Kannie Chan
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Shuhui Cai
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Guanshu Liu
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Hanzhang Lu
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Philip C. Wong
- Department of Pathology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neuroscience, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Peter C. M. van Zijl
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Tong Li
- Department of Pathology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jiadi Xu
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| |
Collapse
|