Introduction: Image fusion between ultrasound (US) and computed tomography (CT) scan or magnetic resonance can increase operator accuracy in targeting liver lesions, particularly when those are undetectable with US alone. We have developed a modular gel to simulate hepatic solid lesions for educational purposes in imaging and minimally invasive ablation techniques. We aimed to assess the impact of image fusion in targeting artificial hepatic lesions during the hands-on part of 2 courses (basic and advanced) in hepatobiliary surgery.
Materials And Methods: Under US guidance, 10 fake tumors of various sizes were created in the livers of 2 pigs, by percutaneous injection of a biocompatible gel engineered to be hyperdense on CT scanning and barely detectable on US. A CT scan was obtained and a CT-US image fusion was performed using the ACUSON S3000 US system (Siemens Healthcare, Germany). A total of 12 blinded course attendants, were asked in turn to perform a 10-minute liver scan with US alone followed by a 10-minute scan using image fusion.
Results: Using US alone, the expert managed to identify all lesions successfully. The true positive rate for course attendants with US alone was 14/36 and 2/24 in the advanced and basic courses, respectively. The total number of false positives identified was 26. With image fusion, the rate of true positives significantly increased to 31/36 (P < .001) in the advanced group and 16/24 in the basic group (P < .001). The total number of false positives, considering all participants, decreased to 4 (P < .001).
Conclusions: Image fusion significantly increases accuracy in targeting hepatic lesions and might improve echo-guided procedures.
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http://dx.doi.org/10.1177/1553350615577483 | DOI Listing |
J Microsc
January 2025
Ningbo Key Laboratory of Micro-Nano Motion and Intelligent Control, Ningbo University, Ningbo, PR China.
The types and quantities of microorganisms in activated sludge are directly related to the stability and efficiency of sewage treatment systems. This paper proposes a sludge microorganism detection method based on microscopic phase contrast image optimisation and deep learning. Firstly, a dataset containing eight types of microorganisms is constructed, and an augmentation strategy based on single and multisamples processing is designed to address the issues of sample deficiency and uneven distribution.
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Objectives: The advent of O-arm navigation optimized the oblique lumbar interbody fusion (OLIF) procedure, allowing the operator to simultaneously perform OLIF and percutaneous posterior pedicle screw implantation without patient position change, thus improving the fluency and accuracy of the OLIF procedure (called as OLIF360). Nevertheless, a consensus regarding its suitability for patients with severe spinal stenosis remains elusive. This study aims to investigate the clinical efficacy of OLIF360 and its imaging changes in severe lumbar spinal stenosis cases.
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View Article and Find Full Text PDFJ Med Radiat Sci
January 2025
Department of Anatomy and Medical Imaging, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand.
Introduction: Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality worldwide. Despite advancements in early detection and treatment, postsurgical recurrence remains a significant challenge, occurring in 30%-55% of patients within 5 years after surgery. This review analysed existing studies on the utilisation of artificial intelligence (AI), incorporating CT, PET, and clinical data, for predicting recurrence risk in early-stage NSCLCs.
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