The prevalence of diabetes is increasing globally. More than 690 million cases of diabetes are expected worldwide by 2045. Continuous blood glucose monitoring is essential to control the disease and avoid long-term complications. Diabetics suffer on a daily basis with the traditional glucose monitors currently in use, which are invasive, painful, and cost-intensive. Therefore, the demand for non-invasive, painless, economical, and reliable approaches to monitor glucose levels is increasing. Since the last decades, many glucose sensing technologies have been developed. Researchers and scientists have been working on the enhancement of these technologies to achieve better results. This paper provides an updated review of some of the pioneering non-invasive optical techniques for monitoring blood glucose levels that have been proposed in the last six years, including a summary of state-of-the-art error analysis and validation techniques.

With recent advances in nanotechnology, various nanomaterials have been used as drug carriers in molecular imaging for the treatment of cancer. The unique physiochemical properties and biocompatibility of gold nanoparticles have developed a breakthrough in molecular imaging, which allows exploration of gold nanoparticles in drug delivery for diagnostic purpose. The conventional gold nanoparticles synthetisation methods have limitations with chemical contaminations during the synthesisation process and the use of higher energy. Thus, various innovative approaches in gold nanoparticles synthetisation are under development. Recently, studies have been focused on the development of eco-friendly, non-toxic, cost-effective and simple gold nanoparticle synthesisation. The pulsed laser ablation in liquid (PLAL) technique is a versatile synthetic and convincing technique due to its high efficiency, eco-friendly and facile method to produce gold nanoparticle. Therefore, this study aimed to review the eco-friendly gold nanoparticle synthesisation method via the PLAL method and to characterise the gold nanoparticles properties for molecular imaging. This review paper provides new insight to understand the PLAL technique in producing gold nanoparticles and the PLAL parameters that affect gold nanoparticle properties to meet the desired needs in molecular imaging.

Accurate, robust and reproducible delineation of tumour in Positron Emission Tomography (PET) is essential for diagnosis, treatment planning and response assessment. Since standardized uptake value (SUV) – a normalized semiquantitative parameter used in PET is represented by the intensity of the PET images and related to the radiotracer uptake, a SUV based threshold method is a natural choice to delineate the tumour. However, determination of an optimum threshold value is a challenging task due to low spatial resolution, and signal-to-noise ratio (SNR) along with finite image sampling constraint. The aim of the review is to summarize different fixed and adaptive threshold-based PET image segmentation approaches under a common mathematical framework Advantages and disadvantages of different threshold based methods are also highlighted from the perspectives of diagnosis, treatment planning and response assessment. Several fixed threshold values (30%–70% of the maximum SUV of the tumour (SUVmaxT)) have been investigated. It has been reported that the fixed threshold-based method is very much dependent on the SNR, tumour to background ratio (TBR) and the size of the tumour. Adaptive threshold-based method, an alternative to fixed threshold, can minimize these dependencies by accounting for tumour to background ratio (TBR) and tumour size. However, the parameters for the adaptive methods need to be calibrated for each PET camera system (e.g., scanner geometry, image acquisition protocol, reconstruction algorithm etc.) and it is not straight forward to implement the same procedure to other PET systems to obtain similar results. It has been reported that the performance of the adaptive methods is also not optimum for smaller volumes with lower TBR and SNR. Statistical analysis carried out on the NEMA thorax phantom images also indicates that regions segmented by the fixed threshold method are significantly different for all cases. On the other hand, the adaptive method provides significantly different segmented regions only for low TBR with different SNR. From this viewpoint, a robust threshold based segmentation method that will be less sensitive to SUVmaxT, SNR, TBR and volume needs to be developed. It was really challenging to compare the performance of different threshold-based methods because the performance of each method was tested on dissimilar data set with different data acquisition and reconstruction protocols along with different TBR, SNR and volumes. To avoid such difficulties, it will be desirable to have a common database of clinical PET images acquired with different image acquisition protocols and different PET cameras to compare the performance of automatic segmentation methods. It is also suggested to report the changes in SNR and TBR while reporting the response using threshold based methods.

Conventional methods for dealing with attenuation and scatter in PET can limit the reconstructed image quality, particularly if the attenuating medium is large (as in whole body 3D PET). In such cases, often a substantial scatter subtraction is performed followed by amplification of the remaining data (to correct for attenuation) resulting in noisy reconstructions. More recent iterative reconstruction methods include the attenuation in the system model in conjunction with either pre-scatter subtraction or a separate addition of the scatter component after each application of the forward model. This work compares these more conventional approaches of including attenuation and scatter to the case where attenuation and scatter information are both included within the system matrix used by the expectation maximization maximum likelihood (EM-ML) algorithm. For this case all acquired data are used and regarded as a source of information by the reconstruction algorithm. Multiple realisations of simulated data sets have been used to compare the performance of the unified attenuation and scatter model with other methods. For a large attenuating medium and low counts there are notable differences between the four main ways of including attenuation and scatter within the reconstruction-with full pre-correction of the data being inferior compared to all the other methods, and the method which models scatter and attenuation within the system matrix showing some advantages. This work suggests that if regularisation of the EM algorithm is carried out by early termination of the iterative process, the subtraction method is the better approach among the techniques considered. In contrast, if a post-reconstruction smoothing approach to regularisation is to be used (whereby highly iterated, noisy image estimates are smoothed), the full modeling method for attenuation and scatter yields the better results, albeit at the computational cost of many more iterations being required

Positron Emission Tomography (PET) allows tumour microenvironment to be studied in vivo with high sensitivity and specificity. Inter- and intra-tumour morphological and phenotypic heterogeneity or pattern provided by PET images are of critical importance. The traditional practice of visual interpretation of these images are not sufficient enough to extract all the information embedded in the images. On the other hand, simultaneous development of automated and reproducible analysis methodologies makes it possible to extract large amount of quantitative features from these images which is termed as radiomics. Analysis of these radiomics feature using artificial intelligence (AI) can significantly improve individualized treatment selection and monitoring. Grey level co-occurrence matrix (GLCM), a member of texture based radiomics feature family is widely used as a biomarker of heterogeneity and can provide information of the tumour microenvironment. The GLCM can subsequently be used for artificial intelligence (AI) assisted tumour diagnosis, monitoring of progression and treatment planning as well as for monitoring response to therapeutic intervention. This aim of the study was to investigate the accuracy and robustness of PET based GLCM in varying image acquisition and analysis conditions using phantom data. It has been observed that GLCM based textural features (e.g., correlation, entropy, homogeneity, energy contrast and dissimilarity) are not only dependent on the volume but also on the quantization level. They are also dependent on signal-to-noise ratio (SNR) and image contrast. The dependencies of these features to the varying imaging conditions are also not linear and cannot always be directly related. To use these GLCM derived textural features as biomarkers for AI assisted analysis, all the information regarding the textural features should always be included along with the changes in volumes and contrast of the PET images in the training dataset.