Purpose: Ultrasound is prevalent in image-guided therapy as a safe, inexpensive, and widely available imaging modality. However, extensive training in interpreting ultrasound images is essential for successful procedures. An open-source ultrasound image simulator was developed to facilitate the training of ultrasound-guided spinal intervention procedures, thereby eliminating the need for an ultrasound machine from the phantom-based training environment.
Methods: Anatomical structures and surgical tools are converted to surface meshes for data compression. Anatomical data are converted from segmented volumetric images, while the geometry of surgical tools is available as a surface mesh. The pose of the objects are either constants or coming from a pose-tracking device. Intersection points between the surface models and the ultrasound scan lines are determined with a binary space partitioning tree. The scan lines are divided into segments and filled with gray values determined by an intensity calculation accounting for material properties, reflection, and attenuation parameters defined in a configuration file. The scan lines are finally converted to a regular brightness-mode ultrasound image.
Results: The simulator was tested in a tracked ultrasound imaging system, with a mock transducer tracked with an Ascension trakSTAR electromagnetic tracker, on a spine phantom. A mesh model of the spine was created from CT data. The simulated ultrasound images were generated at a speed of 50 frames per second, and a resolution of [Formula: see text] pixels, with 256 scan lines per frame, on a PC with a 3.4 GHz processor. A human subject trial was conducted to compare the learning performance of novice trainees, with real and simulated ultrasound, in the localization of facet joints of a spine phantom. With 22 participants split into two equal groups, and each participant localizing 6 facet joints, there was no statistical difference in the performance of the two groups, indicating that simulated ultrasound could indeed replace the real ultrasound in phantom-based ultrasonography training for spinal interventions.
Conclusions: The ultrasound simulator was implemented and integrated into the open-source Public Library for Ultrasound (PLUS) toolkit.
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http://dx.doi.org/10.1007/s11548-013-0901-z | DOI Listing |
Cureus
December 2024
Department of Neurosurgery, Tohoku University Graduate School of Medicine, Sendai, JPN.
Acute epidural hematoma is one of the most serious traumatic conditions in neurosurgery, for which emergency surgery may be indicated. Injury to the middle meningeal artery (MMA) is generally the cause of hemorrhage, often accompanied by convexity fractures resulting from head trauma. However, an epidural hematoma by a contusion of the jaw is very rare.
View Article and Find Full Text PDFBMC Pulm Med
December 2024
Centre d'Atenció Primària Onze de Setembre. Gerència Territorial de Lleida, Institut Català de La Salut, Passeig 11 de Setembre,10 , 25005, Lleida, Spain.
Background: During the COVID-19 pandemia, the imaging test of choice to diagnose COVID-19 pneumonia as chest computed tomography (CT). However, access was limited in the hospital setting and patients treated in Primary Care (PC) could only access the chest x-ray as an imaging test. Several scientific articles that demonstrated the sensitivity of lung ultrasound, being superior to chest x-ray [Cleverley J et al.
View Article and Find Full Text PDFIn Vivo
December 2024
Gyula Petrányi Doctoral School of Clinical Immunology and Allergology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary.
Background/aim: Angiogenesis imaging has been a valuable complement to metabolic imaging with 2-deoxy-2-[F]fluoroglucose (FDG). In our longitudinal study, we investigated the tumour heterogeneity and the relationship between FDG and [Ga]Ga-NODAGA-c(RGDfK) (RGD) accumulation in breast cancer xenografts.
Materials And Methods: Two groups of cell lines, a fast-growing (4T1) and a slow-growing cell line (MDA-MB-HER2+), were inoculated into SCID mice.
EJNMMI Res
December 2024
Department of Radiology & Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, GD, 3015, The Netherlands.
Background: Fibroblast activation protein (FAP) is an attractive target for cancer theranostics. Although FAP-targeted nuclear imaging demonstrated promising clinical results, only sub-optimal results are reported for targeted radionuclide therapy (TRT). Preclinical research is crucial in selecting promising FAP-targeted radiopharmaceuticals and for obtaining an increased understanding of factors essential for FAP-TRT improvement.
View Article and Find Full Text PDFPLoS One
December 2024
Department of Ophthalmology, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, Tokyo, Japan.
Purpose: The objective of this study is to estimate the area of the Foveal Avascular Zone (FAZ) from B-scan OCT images using machine learning algorithms.
Methods: We developed machine learning models to predict the FAZ area from OCT B-scan images of eyes without retinal vascular diseases. The study involved three models: Model 1 predicted the FAZ length from B-scan images; Model 2 estimated the FAZ area from the predicted length using 1, 3, or 5 horizontal measurements; and Model 3 converted the FAZ area from pixels to mm2.
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