Approaches for Image Reconstruction in Low-Field Magnetic Resonance Imaging

Background: Magnetic Resonance Imaging (MRI) and spectroscopic techniques are frequently employed for clinical diagnostics as well as basic research in areas like cognitive neuroimaging. MRI is a widely used imaging modality for intracranial diseases. However, conventional MRI is expensive to purchase, maintain and sustain, limiting their use in low-income countries. Low field MRI can provide an economical, long-term, and safe imaging option to high-field MRI and computed tomography (CT) for brain imaging. This paper offers a review of the image reconstruction techniques used in low field magnetic resonance imaging (MRI). It is aimed at familiarizing the readers with the relevant knowledge, literature, and the latest updates on the state-of-art image reconstruction techniques that have been used in low field MRI citing their strengths, and areas for improvement. Methods: An in-depth keyword-based search was undertaken for publications on image reconstruction approaches in low-field MRI in the top scientific databases such as Google Scholar, Wiley, Science Direct, Springer, IEEE, Scopus, Nature, Elsevier, and PubMed throughout this study. This research also contained relevant postgraduate theses. For the selection of relevant research publications, the PRISMA flow diagram and protocol were also used.Results: Studies revealed that Inhomogeneities are present in low field MRI, implying that the traditional method of acquiring the image, using the inverse Fourier Transform, is no longer viable. The image reconstruction techniques reviewed include iterative methods, dictionary learning methods, and deep learning methods. Experimental results from the literature revealed improved image quality of the reconstructed images using data driven and learning based methods (deep learning and dictionary learning methods). Conclusion: The study revealed that there is limited literature on the image reconstruction approaches in low field MRI even if though there are sufficient studies on the subject in high field MRI. Data driven and learning based methods improves image reconstruction quality when compared to analytic and iterative approaches.

The Daniel K. Inouye Solar Telescope (DKIST)/Visible Broadband Imager (VBI)

The Daniel K. Inouye Solar Telescope (DKIST) is a ground-based observatory for observations of the solar atmosphere featuring an unprecedented entrance aperture of four meters. To address its demanding scientific goals, DKIST features innovative and state-of-the-art instrument subsystems that are fully integrated with the facility and designed to be capable of operating mostly simultaneously. An important component of DKIST’s first-light instrument suite is the Visible Broadband Imager (VBI). The VBI is an imaging instrument that aims to acquire images of the solar photosphere and chromosphere with high spatial resolution and high temporal cadence to investigate the to-date smallest detectable features and their dynamics in the solar atmosphere. VBI observations of unprecedented spatial resolution ultimately will be able to inform modern numerical models and thereby allow new insights into the physics of the plasma motion at the smallest scales measurable by DKIST. The VBI was designed to deliver images at various wavelengths and at the diffraction limit of DKIST. The diffraction limit is achieved by using adaptive optics in conjunction with post-facto image-reconstruction techniques to remove residual effects of the terrestrial atmosphere. The first images of the VBI demonstrate that DKIST’s optical system enables diffraction-limited imaging across a large field of view of various layers in the solar atmosphere. These images allow a first glimpse at the exciting scientific discoveries that will be possible with DKIST’s VBI.

Plane wave ultrasound imaging using synthetic aperture image reconstruction techniques

Ultrasound imaging based on transmitting plane waves (PW) enables ultrafast imaging. Coherent PW compounding ultrasound imaging can reach the image quality of optimal multifocus image. In the image reconstruction, it was assumed that an infinite extent PWs was emitted. In this thesis, we propose a new image reconstruction algorithm – Synthetic-aperture plane-wave (SAPW) imaging – without using this assumption. The SAPW imaging was compared with the PWs imaging in numerical simulations and experimental measurements. The measured RF data in PW imaging was first decoded in the frequency domain using a pseudoinverse algorithm to estimate the RF data Then, SAPW RF data were used to reconstruct images through the standard synthetic transit aperture (STA) method. Main improvements in the image quality of the SAPW imaging in comparison with the PWs imaging are increases in the depth of penetration and the field of view when contrast-to-noise ratio (CNR) was used as a quantitative metric.

Plane wave ultrasound imaging using synthetic aperture image reconstruction techniques

Ultrasound imaging based on transmitting plane waves (PW) enables ultrafast imaging. Coherent PW compounding ultrasound imaging can reach the image quality of optimal multifocus image. In the image reconstruction, it was assumed that an infinite extent PWs was emitted. In this thesis, we propose a new image reconstruction algorithm – Synthetic-aperture plane-wave (SAPW) imaging – without using this assumption. The SAPW imaging was compared with the PWs imaging in numerical simulations and experimental measurements. The measured RF data in PW imaging was first decoded in the frequency domain using a pseudoinverse algorithm to estimate the RF data Then, SAPW RF data were used to reconstruct images through the standard synthetic transit aperture (STA) method. Main improvements in the image quality of the SAPW imaging in comparison with the PWs imaging are increases in the depth of penetration and the field of view when contrast-to-noise ratio (CNR) was used as a quantitative metric.

A novel framework for compressed sensing brain MRI edge detection and de-noising

In this thesis a novel edge detection technique is developed that employs compressed sensing image reconstruction techniques. The ability of compressed sensing noise reduction is combined with wavelet transforms, acting both as a sparsifying transform as well as an edge detection media. The proposed design was implemented and simulated on a brain phantom. The simulation results were provided for a variety of different sets of variables, and the differences were explained. The results obtained are compared with other edge detection techniques already in use. One important comparison criteria is the visual quality of images; according to which the proposed technique presents improved noise reduction and edge preservation. In addition to qualitative evaluation a method of quantitative measurement based on structural content is also utilized. It is found that the values for such a measure of the proposed method is 1.0755, 1.0174 and 0.5590 for Gaussian, Speckle, and Salt & Pepper noise types respectively. These results indicate that this novel method also improves edge preservation, while the visual quality inspection indicates how much noise has been suppressed.

A novel framework for compressed sensing brain MRI edge detection and de-noising

In this thesis a novel edge detection technique is developed that employs compressed sensing image reconstruction techniques. The ability of compressed sensing noise reduction is combined with wavelet transforms, acting both as a sparsifying transform as well as an edge detection media. The proposed design was implemented and simulated on a brain phantom. The simulation results were provided for a variety of different sets of variables, and the differences were explained. The results obtained are compared with other edge detection techniques already in use. One important comparison criteria is the visual quality of images; according to which the proposed technique presents improved noise reduction and edge preservation. In addition to qualitative evaluation a method of quantitative measurement based on structural content is also utilized. It is found that the values for such a measure of the proposed method is 1.0755, 1.0174 and 0.5590 for Gaussian, Speckle, and Salt & Pepper noise types respectively. These results indicate that this novel method also improves edge preservation, while the visual quality inspection indicates how much noise has been suppressed.

Monte-Carlo-Based Estimation of the X-ray Energy Spectrum for CT Artifact Reduction

Beam hardening and scattering effects can seriously degrade image quality in polychromatic X-ray CT imaging. In recent years, polychromatic image reconstruction techniques and scatter estimation using Monte Carlo simulation have been developed to compensate for beam hardening and scattering CT artifacts, respectively. Both techniques require knowledge of the X-ray tube energy spectrum. In this work, Monte Carlo simulations were used to calculate the X-ray energy spectrum of FleXCT, a novel prototype industrial micro-CT scanner, enabling beam hardening and scatter reduction for CT experiments. Both source and detector were completely modeled by Monte Carlo simulation. In order to validate the energy spectra obtained via Monte Carlo simulation, they were compared with energy spectra obtained via a second method. Here, energy spectra were calculated from empirical measurements using a step wedge sample, in combination with the Maximum Likelihood Expectation Maximization (MLEM) method. Good correlation was achieved between both approaches, confirming the correct modeling of the FleXCT system by Monte Carlo simulation. After validation of the modeled FleXCT system through comparing the X-ray spectra for different tube voltages inside the detector, we calculated the X-ray spectrum of the FleXCT X-ray tube, independent of the flat panel detector response, which is a prerequisite for beam hardening and scattering CT artifacts.