Principles of the magnetic resonance imaging movie method for articulatory movement : a review

Magnetic resonance imaging (MRI) has become a critical tool for dental examination. MRI has many advantages over radiographic examination methods, including the lack of a requirement for patient exposure and the ability to capture high-contrast images of various tissue and organ types. However, MRI also has several limitations, including long examination times and the existence of metallic or motion artifacts. A cardiac imaging method using cine sequences was developed in the 1990s. This technique allows for analysis of heart movement and functional blood flow. Moreover, this method has been applied in dentistry. Recent research involving 3T MRI has led to the achievement of a temporal resolution of <10 ms, surpassing the frame rate of typical video recording. The current review introduces the history and principles of the cine sequence method and its application to the oral and maxillofacial regions

Potentials and caveats of AI in Hybrid Imaging

State-of-the-art patient management frequently mandates the investigation of both anatomy and physiology of the patients. Hybrid imaging modalities such as the PET/MRI, PET/CT and SPECT/CT have the ability to provide both structural and functional information of the investigated tissues in a single examination. With the introduction of such advanced hardware fusion, new problems arise such as the exceedingly large amount of multi-modality data that requires novel approaches of how to extract a maximum of clinical information from large sets of multi-dimensional imaging data. Artificial intelligence (AI) has emerged as one of the leading technologies that has shown promise in facilitating highly integrative analysis of multi-parametric data. Specifically, the usefulness of AI algorithms in the medical imaging field has been heavily investigated in the realms of (1) image acquisition and reconstruction, (2) post-processing and (3) data mining and modelling. Here, we aim to provide an overview of the challenges encountered in hybrid imaging and discuss how AI algorithms can facilitate potential solutions. In addition, we highlight the pitfalls and challenges in using advanced AI algorithms in the context of hybrid imaging and provide suggestions for building robust AI solutions that enable reproducible and transparent research

Magnetic resonance imaging basedcomputer-guideddental implant surgery-A clinical pilot study

Background: Computer-guided implant surgery is currently based on radiographic techniques exposing patients to ionizing radiation. Purpose: To assess, whether computer-assisted 3D implant planning with template-guided placement of dental implants based on magnetic resonance imaging (MRI) is feasible. Materials and methods: 3-Tesla MRI was performed in 12 subjects as a basis for prosthetically driven virtual planning and subsequent guided implant surgery. To evaluate the transferability of the virtually planned implant position, deviations between virtually planned and resulting implant position were studied. Matching of occlusal surfaces was assessed by comparing surface scans with MRI-derived images. In addition, the overall image quality and the ability of depicting anatomically important structures were rated. Results: MRI-based guided implant surgery with subsequent prosthetic treatment was successfully performed in nine patients. Mean deviations between virtually planned and resulting implant position (error at entry point 0.8 +/- 0.3 mm, error at apex 1.2 +/- 0.6 mm, angular deviation 4.9 +/- 3.6 degrees), mean deviation of occlusal surfaces between surface scans and MRI-based tooth reconstructions (mean 0.254 +/- 0.026 mm) as well as visualization of important anatomical structures were acceptable for clinical application. Conclusion Magnetic resonance imaging (MRI) based computer-assisted implant surgery is a feasible and accurate procedure that avoids exposure to ionizing radiation

State-Of-The-Art X-Ray Digital Tomosynthesis Imaging

Digital tomosynthesis (DT) is a notable modality in medical imaging because it shows the spread of the target area with lower radiation dose relative to computed tomography. In this section, we describe the technique in two parts: (1) image quality (contrast) and (2) DT image reconstruction algorithms, including state-of-the-art total variation minimization reconstruction algorithms with single-energy X-ray conventional polychromatic imaging and novel dual-energy (DE) virtual monochromatic imaging. The novel DE virtual monochromatic image-processing algorithm provides adequate overall performance (especially, reduction of beam-hardening, reduction of noise). The DE virtual monochromatic image-processing algorithm appears to be a promising new option for imaging in DT because it provides three-dimensional visualizations of high-contrast images that are far superior to those of images processed by using conventional single-energy polychromatic image-processing algorithms

Gradient Descent Provably Solves Nonlinear Tomographic Reconstruction

In computed tomography (CT), the forward model consists of a linear Radon transform followed by an exponential nonlinearity based on the attenuation of light according to the Beer-Lambert Law. Conventional reconstruction often involves inverting this nonlinearity as a preprocessing step and then solving a convex inverse problem. However, this nonlinear measurement preprocessing required to use the Radon transform is poorly conditioned in the vicinity of high-density materials, such as metal. This preprocessing makes CT reconstruction methods numerically sensitive and susceptible to artifacts near high-density regions. In this paper, we study a technique where the signal is directly reconstructed from raw measurements through the nonlinear forward model. Though this optimization is nonconvex, we show that gradient descent provably converges to the global optimum at a geometric rate, perfectly reconstructing the underlying signal with a near minimal number of random measurements. We also prove similar results in the under-determined setting where the number of measurements is significantly smaller than the dimension of the signal. This is achieved by enforcing prior structural information about the signal through constraints on the optimization variables. We illustrate the benefits of direct nonlinear CT reconstruction with cone-beam CT experiments on synthetic and real 3D volumes. We show that this approach reduces metal artifacts compared to a commercial reconstruction of a human skull with metal dental crowns

Empirical recovery performance of fourier-based deterministic compressed sensing

Compressed sensing is a novel technique where one can recover sparse signals from the undersampled measurements. Mathematically, measuring an N-dimensional signal..