Abstract:Recent development of ultra-low-field (ULF) MRI presents opportunities for low-power, shielding-free, and portable clinical applications at a fraction of the cost. However, its performance remains limited by poor image quality. Here, a computational approach is formulated to advance ULF MR brain imaging through deep learning of large-scale publicly available 3T brain data. Methods: A dual-acquisition 3D superresolution model is developed for ULF brain MRI at 0.055 T. It consists of deep cross-scale feature ext… Show more
“…In this study, we have implemented and demonstrated such an image formation method, PF-SR (26), applied to brain, spine, liver, and knee imaging, illustrating the ability of such data-driven image formation in enhancing image resolution while suppressing noise and artifacts. Our previous studies (23,26) and the preliminary brain and spine tests using synthetic datasets in this study have also shown the potential of applying this approach to datasets that contain brain and spine lesions.…”
Section: Discussionmentioning
confidence: 74%
“…Utilizing deep learning for enhanced image formation at 0.05 Tesla MR signal at 0.05 T is several orders of magnitude weaker than at 3 T, the standard highfield strength, due to its proportionality to field strength squared (B 0 2 ) (32), causing high image noise and poor resolution in ULF MRI. To overcome this challenge, we turned to computing and devised deep learning-based reconstruction methods for ULF MRI image formation that are driven by the large-scale high-field MRI data (23,26). We designed a partial Fourier super-resolution (PF-SR) method that integrates image reconstruction and super-resolution (fig.…”
Section: Shielding-free 005 Tesla Whole-body Mri Scanner Designmentioning
confidence: 99%
“…S3. In brief, the model applied multi-scale feature extraction with a residual group (RG) inspired by the residual channel attention network (75) and a modified residual channel attention block for extracting multiscale high-level features (23). Small kernel sizes at the top scale level enabled local image feature extraction, whereas an increased receptive field of 3D convolution layers at middle to bottom scale levels facilitated semi-global image feature learning (76)(77)(78).…”
Section: Deep Learning 3d Pf-sr Image Reconstructionmentioning
confidence: 99%
“…Channel and spatial attentions were utilized to modulate highlevel features based on their inter-channel and inter-spatial relationships (79). The modulated features were then fed into a cascade of RGs, up-sampled to a high-resolution feature space using a 3D sub-pixel convolution layer, and transformed into a high-resolution 3D image residue using a 3D convolution layer (23). The final high-resolution 3D image output was generated by combining the image residue and trilinearly up-sampled model input.…”
Section: Deep Learning 3d Pf-sr Image Reconstructionmentioning
confidence: 99%
“…Concurrently, deep learning advances offer exceptional capabilities for multidimensional feature extraction ( 20 , 21 ), presenting approaches to address the low magnetic resonance (MR) signal-to-noise ratio (SNR) inherent to ULF. For example, deep learning super-resolution strategies have been recently pursued for brain ULF MRI to suppress image noise and boost resolution by leveraging the homogeneous brain structures and image contrasts available in human brain high-field MRI data ( 22 , 23 ). However, these developments have been confined to imaging of the brain ( 9 – 14 ) and extremities ( 24 ).…”
Despite a half-century of advancements, global magnetic resonance imaging (MRI) accessibility remains limited and uneven, hindering its full potential in health care. Initially, MRI development focused on low fields around 0.05 Tesla, but progress halted after the introduction of the 1.5 Tesla whole-body superconducting scanner in 1983. Using a permanent 0.05 Tesla magnet and deep learning for electromagnetic interference elimination, we developed a whole-body scanner that operates using a standard wall power outlet and without radiofrequency and magnetic shielding. We demonstrated its wide-ranging applicability for imaging various anatomical structures. Furthermore, we developed three-dimensional deep learning reconstruction to boost image quality by harnessing extensive high-field MRI data. These advances pave the way for affordable deep learning–powered ultra-low-field MRI scanners, addressing unmet clinical needs in diverse health care settings worldwide.
“…In this study, we have implemented and demonstrated such an image formation method, PF-SR (26), applied to brain, spine, liver, and knee imaging, illustrating the ability of such data-driven image formation in enhancing image resolution while suppressing noise and artifacts. Our previous studies (23,26) and the preliminary brain and spine tests using synthetic datasets in this study have also shown the potential of applying this approach to datasets that contain brain and spine lesions.…”
Section: Discussionmentioning
confidence: 74%
“…Utilizing deep learning for enhanced image formation at 0.05 Tesla MR signal at 0.05 T is several orders of magnitude weaker than at 3 T, the standard highfield strength, due to its proportionality to field strength squared (B 0 2 ) (32), causing high image noise and poor resolution in ULF MRI. To overcome this challenge, we turned to computing and devised deep learning-based reconstruction methods for ULF MRI image formation that are driven by the large-scale high-field MRI data (23,26). We designed a partial Fourier super-resolution (PF-SR) method that integrates image reconstruction and super-resolution (fig.…”
Section: Shielding-free 005 Tesla Whole-body Mri Scanner Designmentioning
confidence: 99%
“…S3. In brief, the model applied multi-scale feature extraction with a residual group (RG) inspired by the residual channel attention network (75) and a modified residual channel attention block for extracting multiscale high-level features (23). Small kernel sizes at the top scale level enabled local image feature extraction, whereas an increased receptive field of 3D convolution layers at middle to bottom scale levels facilitated semi-global image feature learning (76)(77)(78).…”
Section: Deep Learning 3d Pf-sr Image Reconstructionmentioning
confidence: 99%
“…Channel and spatial attentions were utilized to modulate highlevel features based on their inter-channel and inter-spatial relationships (79). The modulated features were then fed into a cascade of RGs, up-sampled to a high-resolution feature space using a 3D sub-pixel convolution layer, and transformed into a high-resolution 3D image residue using a 3D convolution layer (23). The final high-resolution 3D image output was generated by combining the image residue and trilinearly up-sampled model input.…”
Section: Deep Learning 3d Pf-sr Image Reconstructionmentioning
confidence: 99%
“…Concurrently, deep learning advances offer exceptional capabilities for multidimensional feature extraction ( 20 , 21 ), presenting approaches to address the low magnetic resonance (MR) signal-to-noise ratio (SNR) inherent to ULF. For example, deep learning super-resolution strategies have been recently pursued for brain ULF MRI to suppress image noise and boost resolution by leveraging the homogeneous brain structures and image contrasts available in human brain high-field MRI data ( 22 , 23 ). However, these developments have been confined to imaging of the brain ( 9 – 14 ) and extremities ( 24 ).…”
Despite a half-century of advancements, global magnetic resonance imaging (MRI) accessibility remains limited and uneven, hindering its full potential in health care. Initially, MRI development focused on low fields around 0.05 Tesla, but progress halted after the introduction of the 1.5 Tesla whole-body superconducting scanner in 1983. Using a permanent 0.05 Tesla magnet and deep learning for electromagnetic interference elimination, we developed a whole-body scanner that operates using a standard wall power outlet and without radiofrequency and magnetic shielding. We demonstrated its wide-ranging applicability for imaging various anatomical structures. Furthermore, we developed three-dimensional deep learning reconstruction to boost image quality by harnessing extensive high-field MRI data. These advances pave the way for affordable deep learning–powered ultra-low-field MRI scanners, addressing unmet clinical needs in diverse health care settings worldwide.
PurposeTo develop a new electromagnetic interference (EMI) elimination strategy for RF shielding‐free MRI via active EMI sensing and deep learning direct MR signal prediction (Deep‐DSP).MethodsDeep‐DSP is proposed to directly predict EMI‐free MR signals. During scanning, MRI receive coil and EMI sensing coils simultaneously sample data within two windows (i.e., for MR data and EMI characterization data acquisition, respectively). Afterward, a residual U‐Net model is trained using synthetic MRI receive coil data and EMI sensing coil data acquired during EMI signal characterization window, to predict EMI‐free MR signals from signals acquired by MRI receive and EMI sensing coils. The trained model is then used to directly predict EMI‐free MR signals from data acquired by MRI receive and sensing coils during the MR signal‐acquisition window. This strategy was evaluated on an ultralow‐field 0.055T brain MRI scanner without any RF shielding and a 1.5T whole‐body scanner with incomplete RF shielding.ResultsDeep‐DSP accurately predicted EMI‐free MR signals in presence of strong EMI. It outperformed recently developed EDITER and convolutional neural network methods, yielding better EMI elimination and enabling use of few EMI sensing coils. Furthermore, it could work well without dedicated EMI characterization data.ConclusionDeep‐DSP presents an effective EMI elimination strategy that outperforms existing methods, advancing toward truly portable and patient‐friendly MRI. It exploits electromagnetic coupling between MRI receive and EMI sensing coils as well as typical MR signal characteristics. Despite its deep learning nature, Deep‐DSP framework is computationally simple and efficient.
PurposeTo demonstrate magnetization transfer (MT) effects with low specific absorption rate (SAR) on ultra‐low‐field (ULF) MRI.MethodsMT imaging was implemented by using sinc‐modulated RF pulse train (SPT) modules to provide bilateral off‐resonance irradiation. They were incorporated into 3D gradient echo (GRE) and fast spin echo (FSE) protocols on a shielding‐free 0.055T head scanner. MT effects were first verified using phantoms. Brain MT imaging was conducted in both healthy subjects and patients.ResultsMT effects were clearly observed in phantoms using six SPT modules with total flip angle 3600° at central primary saturation bands of approximate offset ±786 Hz, even in the presence of large relative B0 inhomogeneity. For brain, strong MT effects were observed in gray matter, white matter, and muscle in 3D GRE and FSE imaging using six and sixteen SPT modules with total flip angle 3600° and 9600°, respectively. Fat, cerebrospinal fluid, and blood exhibited relatively weak MT effects. MT preparation enhanced tissue contrasts in T2‐weighted and FLAIR‐like images, and improved brain lesion delineation. The estimated MT SAR was 0.0024 and 0.0008 W/kg for two protocols, respectively, which is far below the US Food and Drug Administration (FDA) limit of 3.0 W/kg.ConclusionRobust MT effects can be readily obtained at ULF with extremely low SAR, despite poor relative B0 homogeneity in ppm. This unique advantage enables flexible MT pulse design and implementation on low‐cost ULF MRI platforms to achieve strong MT effects in brain and beyond, potentially augmenting their clinical utility in the future.
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