Spectrally Extended Line Field Optical Coherence Tomography Angiography

The widespread usage of optical coherence tomography angiography (OCTA) is hindered by technical gaps including limited field of view (FOV), lack of quantitative flow information, and suboptimal motion correction. We introduce spectrally extended line field (SELF) OCTA that provides advanced solutions to these challenges. SELF-OCTA breaks the speed limitation and achieves two-fold gain in FOV without sacrificing microvascular details and signal strength through parallel imaging. It also relieves the requirement for shorter exposure time in wide-field applications, so that sufficient sensitivity to slow flow is maintained, particularly in spectral-domain OCT. Towards quantitative angiography, the ‘frequency flow’ mechanism overcomes the speed bottleneck by obviating the requirement for superfluous B-scans. In addition, this mechanism facilitates OCTA-data based motion tracking. Since it can be implemented in existing OCT devices without significant hardware modification or affecting existing functions, SELF-OCTA will make non-invasive, wide-field, quantitative, and low-cost angiographic imaging available to larger patient populations.

Three-dimensional SPACE MR with CAIPIRINHA fourfold acceleration for assessing long head of biceps tendon

Background Data regarding controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA) T2-weighted sampling perfection with application optimized contrast evolution (SPACE) with fourfold acceleration factor for assessing long head of biceps tendon (LHBT) disorder is lacking. Purpose To investigate the feasibility of 3D CAIPIRINHA SPACE with fourfold acceleration in assessing LHBT disorder. Material and Methods A total of 42 consecutive patients underwent shoulder magnetic resonance (MR) examinations including CAIPIRINHA SPACE with fourfold acceleration, and non-CAIPIRINHA SPACE with twofold acceleration, and 2D fast spin echo (FSE). A subjective score of depiction of LHBT was given to 3D sequence according to a 4-point scale (0–3, “poor” to “excellent”). The Wilcoxon signed rank test was used to compare depiction scores between 3D sequences. Three statuses of LHBT were defined in the study: normal, tendonitis, and tear. McNemar’s test was used compare diagnostic accuracy. Results LHBT was better depicted with CAIPIRINHA SPACE versus non-CAIPIRINHA SPACE (2.1 ± 0.4 vs. 1.5 ± 0.4; P < 0.001). Inter-modality agreement between CAIPIRINHA SPACE and 2D FSE was almost perfect (kappa = 0.884 ± 0.064). The sensitivity and specificity in detecting LHBT disorder were 95% (20/21) and 95% (20/21), respectively, for CAIPIRINHA SPACE, and 71% (15/21) and 76% (16/21), respectively, for non-CAIPIRINHA SPACE ( P = 0.039). Conclusion Fourfold acceleration CAIPIRINHA is feasible in reducing the acquisition time of SPACE MR in the shoulder. 3D CAIPIRINHA SPACE with fourfold acceleration is highly accurate in detecting LHBT disorder.

Joint reconstruction framework of compressed sensing and nonlinear parallel imaging for dynamic cardiac magnetic resonance imaging

Compressed Sensing (CS) and parallel imaging are two promising techniques that accelerate the MRI acquisition process. Combining these two techniques is of great interest due to the complementary information used in each. In this study, we proposed a novel reconstruction framework that effectively combined compressed sensing and nonlinear parallel imaging technique for dynamic cardiac imaging. Specifically, the proposed method decouples the reconstruction process into two sequential steps: In the first step, a series of aliased dynamic images were reconstructed from the highly undersampled k-space data using compressed sensing; In the second step, nonlinear parallel imaging technique, i.e. nonlinear GRAPPA, was utilized to reconstruct the original dynamic images from the reconstructed k-space data obtained from the first step. In addition, we also proposed a tailored k-space down-sampling scheme that satisfies both the incoherent undersampling requirement for CS and the structured undersampling requirement for nonlinear parallel imaging. The proposed method was validated using four in vivo experiments of dynamic cardiac cine MRI with retrospective undersampling. Experimental results showed that the proposed method is superior at reducing aliasing artifacts and preserving the spatial details and temporal variations, compared with the competing k-t FOCUSS and k-t FOCUSS with sensitivity encoding methods, with the same numbers of measurements.

A review on deep learning MRI reconstruction without fully sampled k-space

Background Magnetic resonance imaging (MRI) is an effective auxiliary diagnostic method in clinical medicine, but it has always suffered from the problem of long acquisition time. Compressed sensing and parallel imaging are two common techniques to accelerate MRI reconstruction. Recently, deep learning provides a new direction for MRI, while most of them require a large number of data pairs for training. However, there are many scenarios where fully sampled k-space data cannot be obtained, which will seriously hinder the application of supervised learning. Therefore, deep learning without fully sampled data is indispensable. Main text In this review, we first introduce the forward model of MRI as a classic inverse problem, and briefly discuss the connection of traditional iterative methods to deep learning. Next, we will explain how to train reconstruction network without fully sampled data from the perspective of obtaining prior information. Conclusion Although the reviewed methods are used for MRI reconstruction, they can also be extended to other areas where ground-truth is not available. Furthermore, we may anticipate that the combination of traditional methods and deep learning will produce better reconstruction results.

Application of the SENSE Algorithm to Multimodal Switchable Metasurface Imaging

This work aims to provide a way of performing parallel imaging with a single-channel variable-frequency resonant device. Different spatial sensitivity profiles required for SENSE reconstruction are achieved by switching between the device eigenmodes. A device capable of such switching is manufactured and several k-spaces are acquired using the device different eigenmodes. The k-spaces are then subject to downsampling to obtain the sensitivity profiles of each eigenmode and then - to perform SENSE-based reconstruction of the unaliased images with different acceleration factors.

A high temporal/spatial resolution neuro-architecture study of rodent brain by wideband echo planar imaging

Latest simultaneous multi-slice (SMS) methods greatly benefit MR efficiency for recent studies using parallel imaging technique. However, these methods are limited by the requirement of array coils. The proposed Coherent Wideband method, which employs an extended field of view to separate multiple excited slices, can be applied to any existing MRI instrument, even those without array coils. In this study, the Coherent Wideband echo-planar imaging method was implemented on 7 T animal MRI to exhibit comprehensive enhancements in neuro-architecture, including diffusion tensor imaging (DTI) and functional MR studies (fMRI). Under the same scan time, the time-saving effect can be manipulated to increase the number of averages for DTI SNR improvement, reducing fractional anisotropy difference by 56.9% (from 0.072 to 0.041) and the deviation angle by 64% (from 25.3° to 16.2°). In summary, Coherent Wideband Echo Planar Imaging (EPI) will provide faster, higher resolution, thinner slice, or higher SNR imaging for precision neuro-architecture studies.