Background: Scan length optimization is a method of optimization which ensures that, imaging is performed to cover just the area of interest without unnecessarily exposing structures that would not add value to answer a given clinical question.
Purpose: This study assessed the variability and degree of redundant scan coverages along the z-axis of CT examinations of common indications and the associated radiation dose implications in CT facilities in Ghana for optimization measures to be recommended.
Methods: On reconstructed acquired CT images, the study measured extra distances covered above and below anatomical targets for common indications with calibrated calipers across 25 CT facilities. The National Cancer Institute Dosimetry System for CT (NCICT) (Monte Carlo-based-software) was used to simulate the scanning situations and organ dose implications for scans with and without the inclusion of the redundant scan areas.
Results: A total of 1,640 patients' CT data sets were used in this study. The results demonstrated that CT imaging utilized varying scan lengths (16.45±21.0-45.99±4.3 cm), and 70.6% of the scans exceeded their pre-defined anatomic boundaries by a mean range of 2.86±1.07-5.81±1.66 cm, thereby resulting in extra patient radiation dose. Hence, scanning without the redundant coverages could generate a dose length product (DLP) reduction of 17.5%, 18.8%, 15.5% and 9.0% without degrading image quality for brain lesion, lung lesion, pulmonary embolism and abdominopelvic lesion CT imaging, respectively, whilst ensuring organ dose reduction of0.8%-79.1%.
Conclusion: The study strongly recommends that radiographers should avoid the inclusion of redundant areas in CT examinations to reduce organ doses.
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http://dx.doi.org/10.1016/j.jmir.2021.10.007 | DOI Listing |
Turk J Surg
June 2024
Department of Surgical Gastroenterology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, India.
J Fungi (Basel)
November 2024
National Fungal Culture Collection of India, Biodiversity and Palaeobiology Group, MACS' Agharkar Research Institute, GG Agarkar Road, Pune 411004, India.
In this study, a new species of was isolated as an endophyte from the bark of from Mulshi, Maharashtra. The identity of this isolate was confirmed based on the asexual morphological characteristics as well as multi-gene phylogeny based on the internal transcribed spacer (ITS) and large subunit (LSU) nuclear ribosomal RNA (rRNA) regions. As this was the second species to be reported in this genus, we sequenced the genome of this species to increase our knowledge about the possible applicability of this genus to various industries.
View Article and Find Full Text PDFPlant Methods
November 2024
Changsha Vocational and Technical College of Commerce and Tourism, Changsha, 410004, China.
With the emergence of the new generation vision architecture Vmamba and the further demand for agricultural yield and efficiency, we propose an efficient and high-accuracy target detection network for automated pear picking tasks based on Vmamba, aiming to address the issue of low efficiency in current Transformer architectures. The proposed network, named SRSMamba, employs a Reward and Punishment Mechanism (RPM) to focus on important information while minimizing redundancy interference. It utilizes 3D Selective Scan (SS3D) to extend scanning dimensions and integrates global information across channel dimensions, thereby enhancing the model's robustness in complex agricultural environments and effectively adapting to the extraction of complex features in pear orchards and farmlands.
View Article and Find Full Text PDFRespir Res
October 2024
Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR.
Background: Pulmonary embolism (PE) is life-threatening and requires timely and accurate diagnosis, yet current imaging methods, like computed tomography pulmonary angiography, present limitations, particularly for patients with contraindications to iodinated contrast agents. We aimed to develop a quantitative texture analysis pipeline using machine learning (ML) based on non-contrast thoracic computed tomography (CT) scans to discover intensity and textural features correlated with regional lung perfusion (Q) physiology and pathology and synthesize voxel-wise Q surrogates to assist in PE diagnosis.
Methods: We retrospectively collected Tc-labeled macroaggregated albumin Q-SPECT/CT scans from patients suspected of PE, including an internal dataset of 76 patients (64 for training, 12 for testing) and an external testing dataset of 49 patients.
Sci Rep
October 2024
Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No.1277 Jiefang Avenue, Wuhan, 430022, Hubei, People's Republic of China.
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