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Aims The development of wire mesh collimator (WMC), has improved the performance of Single Photon Emission Computed tomography (SPECT) because of its higher sensitivity whilst maintaining the same resolution. Consequently, the WMC allows better detection of early-stage cancer. The purpose of this study was to find an optimal filter for image reconstruction for breast SPECT imaging. Methods Half-ellipsoidal breast phantom in the prone position was simulated with dual-head SPECT camera by Monte Carlo N-Particle Transport Code, version 5 (MCNP5). Six different filters were compared with 17 different cutoff frequencies (Fc), ranging from 0.2 Nyquist frequency (Nq) to 1.0 Nq, with step 0.05. For Butterworth filter, order was from 3 to 12 with step 1. A total of 255 central slices with different parameters were reconstructed by filtered back projection (FBP) to compare the image performance in terms of contrast, noise level and tumour size. Results The values of tumour size, contrast and noise level were greatly influenced by different filter types and the value of Fc. Ramp and Butterworth produced the best value of tumour size and contrast, whilst Parzen, Hann and Hamming filters gave smoother images. Overall, results showed that Butterworth filter with the highest mean score. Conclusion Butterworth filter proved to be the provided the best image quality at a higher sensitivity and was found to be the optimum filter for quantitative analysis. [ABSTRACT FROM AUTHOR]

The field of space weather research has witnessed growing interest in the use of machine learning techniques. This could be attributed to the increasing accessibility of data, which has created a high demand for investigating scientific phenomena using data-driven methods. The dataset, which is based on bibliographic records from the Web of Science (WoS) and Scopus, was compiled over the last several decades and discusses multidisciplinary trends in this topic while revealing significant advances in current knowledge. It provides a comprehensive examination of trends in publication characteristics, with a focus on publications, document sources, authors, affiliations, and frequent word analysis as bibliometric indicators, all of which were analysed using the Biblioshiny application on the web. This dataset serves as the document profile metrics for emphasising the breadth and progress of current and previous studies, providing useful insights into hotspots for projection research subjects and influential entities that can be identified for future research., (© 2023 The Author(s).)

Tumorous cancer has been a widely known and well-studied medical phenomenon; however, rare diseases like Myeloproliferative Neoplasm (MPN) have received less attention, leading to delayed diagnosis. Despite the availability of advanced technology in diagnostic tools that can boost the procedure, the morphological assessment of bone marrow trephine (BMT) images remains critical to confirm and differentiate MPN subtypes. This paper reports a histopathological imagery dataset that was created to focus on the most common MPN from the Philadelphia Chromosome (Ph)-negative type, namely Essential Thrombocythemia (ET), Polycythemia Vera (PV), and Primary Myelofibrosis (MF). The dataset consisted of 300 BMT images that can be used to enable computer vision applications, such as image segmentation, disease classification, and object recognition, in assisting the classification of the MPN disease. Ethical approval was obtained from the Ministry of Health, Malaysia and the bone marrow trephine images were captured using a digital microscope from the Olympus model (BX41 Dual head microscope) with x10, x20, and x40 lens types. The development of comprehensive tools deployed from this dataset can assist medical practitioners in diagnosing diseases, thus overcoming the current challenges., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors. Published by Elsevier Inc.)

Monte Carlo N-Particle (MCNP) simulation has been extensively proven in nuclear medicine imaging systems, most notably in designing and optimizing new medical imaging tools. It enables more complicated geometries and the simulation of particles passing through and interacting with materials. However, a relatively long simulation time is a drawback of Monte Carlo simulation, mainly when complex geometry exists. The current study presents an alternative variance reduction technique for a modeled positron emission tomography (PET) camera by reducing the height of the source volume definition while maintaining the geometry of the simulated model. The National Electrical Manufacturers Association (NEMA) of the International Electrotechnical Commission (IEC) PET's phantom was used with a 1 cm diameter and 7 cm height of line source placed in the middle. The first geometry was fully filled the line source with 0.50 mCi radioactivity. In contrast, the second geometry decreased the source definition to 2.4 cm in height, covering 1 cm above and below the sub-block detector level. The source volume definition approach led to a 71% reduction in the total photons to be simulated. Results showed that the proposed variance reduction strategy could produce spatial resolution as precise as fully filled geometry and sped up the simulation time by approximately 65%. Hence, this strategy can be utilized for further PET optimizing simulation studies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Magnetic resonance imaging (MRI) provides a significant key to diagnose and monitor the progression of multiple sclerosis (MS) disease. Manual MS-lesion segmentation, expanded disability status scale (EDSS) and patient's meta information can provide a gold standard for research in terms of automated MS-lesion quantification, automated EDSS prediction and identification of the correlation between MS-lesion and patient disability. In this dataset, we provide a novel multi-sequence MRI dataset of 60 MS patients with consensus manual lesion segmentation, EDSS, general patient information and clinical information. On this dataset, three radiologists and neurologist experts segmented and validated the manual MS-lesion segmentation for three MRI sequences T1-weighted, T2-weighted and fluid-attenuated inversion recovery (FLAIR). The dataset can be used to study the relationship between MS-lesion, EDSS and patient clinical information. Furthermore, it also can be used for the development of automated MS-lesion segmentation, patient disability prediction using MRI and correlation analysis between patient disability and MRI brain abnormalities include MS lesion location, size, number and type., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Author(s). Published by Elsevier Inc.)