Exploring polynomial based interpolation schemes for photoacoustic tomographic image reconstruction

Photoacoustic tomography (PAT) imaging employing polynomial-based interpolation methods is discussed. Nearest-neighbor, bilinear, bicubic and biquintic algorithms were implemented for the construction of the model matrix, and images were formed using the Tikhonov regularization and total variation (TV) minimization procedures. The performance of the interpolation methods was assessed by comparing the reconstructed images of three numerical and two experimental phantoms. The numerical and experimental studies demonstrate that the performance of the interpolation schemes is nearly equal for large PA sources. The simplest nearest-neighbor technique provides better image reconstruction for a sparse source compared to the others. The nearest-neighbor protocol may be adopted in practice for vascular imaging using PAT.

Panoramic radiography in dentistry

Panoramic radiography is an extraoral radiography modality that provides two-dimensional information about the teeth and the maxillofacial skeleton. It is a valuable adjunct for diagnosis and treatment planning as it facilitates one-time imaging of all teeth, the mandible, parts of maxilla including a large part of the maxillary sinus, hard palate and temporomandibular joints (TMJs). As a tomographic image is prone to errors and artefacts, a good quality radiograph in most patients can be achieved by following the standard rules and through proper patient positioning. In this article, we will discuss indications for panoramic radiography imaging, steps in taking the radiograph, as well as limitations, pitfalls and complications of the procedure. Tomographic imaging of temporomandibular joint is also discussed.

Optical Tomographic Image Reconstruction Based on Gradient Tree Calculation Method

Optical coherence tomography is a new imaging method, which is widely used in many fields. This article introduces an iterative image reconstruction algorithm based on gradient trees. It also discusses image reconstruction methods containing void-like regions. It is proved that the image reconstruction based on the transportation model can overcome the shortcomings of the diffusion equation, and it can accurately reconstruct the optical tomographic image.

Synergistic tomographic image reconstruction: part 2

This special issue is the second part of a themed issue that focuses on synergistic tomographic image reconstruction and includes a range of contributions in multiple disciplines and application areas. The primary subject of study lies within inverse problems which are tackled with various methods including statistical and computational approaches. This volume covers algorithms and methods for a wide range of imaging techniques such as spectral X-ray computed tomography (CT), positron emission tomography combined with CT or magnetic resonance imaging, bioluminescence imaging and fluorescence-mediated imaging as well as diffuse optical tomography combined with ultrasound. Some of the articles demonstrate their utility on real-world challenges, either medical applications (e.g. motion compensation for imaging patients) or applications in material sciences (e.g. material decomposition and characterization). One of the desired outcomes of the special issues is to bring together different scientific communities which do not usually interact as they do not share the same platforms such as journals and conferences. This article is part of the theme issue ‘Synergistic tomographic image reconstruction: part 2’.

(An overview of) Synergistic reconstruction for multimodality/multichannel imaging methods

Imaging is omnipresent in modern society with imaging devices based on a zoo of physical principles, probing a specimen across different wavelengths, energies and time. Recent years have seen a change in the imaging landscape with more and more imaging devices combining that which previously was used separately. Motivated by these hardware developments, an ever increasing set of mathematical ideas is appearing regarding how data from different imaging modalities or channels can be synergistically combined in the image reconstruction process, exploiting structural and/or functional correlations between the multiple images. Here we review these developments, give pointers to important challenges and provide an outlook as to how the field may develop in the forthcoming years. This article is part of the theme issue ‘Synergistic tomographic image reconstruction: part 1’.