Background: Measuring diffusing capacity of the lung for carbon monoxide (DLCO) is complex and associated with wide intra- and inter-laboratory variability. Increased D(LCO) variability may have important clinical consequences. The objective of the study was to assess instrument performance across hospital pulmonary function testing laboratories using a D(LCO) simulator that produces precise and repeatable D(LCO) values.
Methods: D(LCO) instruments were tested with CO gas concentrations representing medium and high range D(LCO) values. The absolute difference between observed and target D(LCO) value was used to determine measurement accuracy; accuracy was defined as an average deviation from the target value of < 2.0 mL/min/mm Hg. Accuracy of inspired volume measurement and gas sensors were also determined.
Results: Twenty-three instruments were tested across 3 healthcare systems. The mean absolute deviation from the target value was 1.80 mL/min/mm Hg (range 0.24-4.23) with 10 of 23 instruments (43%) being inaccurate. High volume laboratories performed better than low volume laboratories, although the difference was not significant. There was no significant difference among the instruments by manufacturers. Inspired volume was not accurate in 48% of devices; mean absolute deviation from target value was 3.7%. Instrument gas analyzers performed adequately in all instruments.
Conclusions: D(LCO) instrument accuracy was unacceptable in 43% of devices. Instrument inaccuracy can be primarily attributed to errors in inspired volume measurement and not gas analyzer performance. D(LCO) instrument performance may be improved by regular testing with a simulator. Caution should be used when comparing D(LCO) results reported from different laboratories.
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http://dx.doi.org/10.4187/respcare.03512 | DOI Listing |
J Vasc Interv Radiol
January 2025
Department of Vascular and Interventional Radiology, Singapore General Hospital, Outram Rd, Singapore 169608, Singapore.
Purpose: To investigate the feasibility of a robotic system with artificial intelligence-based lesion detection and path planning for CT-guided biopsy, compared to the conventional freehand technique.
Materials And Methods: Eight nodules within an abdominal phantom, incorporating the simulated vertebrae and ribs, were designated as targets. A robotic system was used for lesion detection, trajectory generation, and needle-holder positioning.
J Vasc Interv Radiol
January 2025
Gustave Roussy (GR), Département d'Anesthésie Chirurgie et Interventionnelle (DACI), Service d'Imagerie Thérapeutique, Villejuif France; Centre d'Investigation Clinique BIOTHERIS, INSERM CIC1428, Villejuif, France; Radiologie Interventionnelle, Gustave Roussy, Villejuif, France; Laboratoire de Recherche Translationnelle en Immunothérapie (LRTI), INSERM U1015, Villejuif, France; Faculté de Médecine, Paris-Saclay Université, F-94276 Le Kremlin Bicêtre, France.
Purpose: To evaluate the feasibility and accuracy of a robotic device used clinically in soft tissues (abdomen and lung), modified in design and workflow, to perform needle insertion in percutaneous bone procedures.
Methods: The primary objective was safety (severe complications) of robotic-assisted insertion in this new application. Secondary objectives were feasibility (placement technical success), performance (acceptable insertions rate), accuracy (lateral deviation), number of intermediate CT-scans and tolerance (minor/moderate complications).
Clin Oncol (R Coll Radiol)
January 2025
Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China; Department of Radiotherapy Physics & Technology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
Aims: To assess the robustness of 4D-optimised IMPT and PAT plans against interplay effects in non-small cell lung cancer (NSCLC) patients with respiratory motion over 10 mm, and to provide insights into the use of proton-based stereotactic body radiotherapy (SBRT) for lung cancer with significant tumour movement.
Materials And Methods: Fourteen patients with early-stage NSCLC and tumour motion >10 mm were selected. Three hypofraction regimens were generated using 4D robust optimisation with the IMPT and PAT techniques.
Invest Radiol
January 2025
From the Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany (A. Schwarz, A. Simon, A.M.); Siemens Healthineers AG, Forchheim, Germany (A. Schwarz, C.H., J.D., A. Simon); Institute for Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany (F.K.W., S.G., M.S.); and Institut for Radiology, Pediatric and Neuroradiology, Helios Hospital, Schwerin, Germany (H.-J.R.).
Objective: Respiratory motion can affect image quality and thus affect the diagnostic accuracy of CT images by masking or mimicking relevant lung pathologies. CT examinations are often performed during deep inspiration and breath-hold to achieve optimal image quality. However, this can be challenging for certain patient groups, such as children, the elderly, or sedated patients.
View Article and Find Full Text PDFJ 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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