Automatic scan planning for magnetic resonance imaging of the knee aims at defining an oriented bounding box around the knee joint from sparse scout images in order to choose the optimal field of view for the diagnostic images and limit acquisition time. We propose a fast and fully automatic method to perform this task based on the standard clinical scout imaging protocol. The method is based on sequential Chamfer matching of 2D scout feature images with a three-dimensional mean model of femur and tibia. Subsequently, the joint plane separating femur and tibia, which contains both menisci, can be automatically detected using an information-augmented active shape model on the diagnostic images. This can assist the clinicians in quickly defining slices with standardized and reproducible orientation, thus increasing diagnostic accuracy and also comparability of serial examinations. The method has been evaluated on 42 knee MR images. It has the potential to be incorporated into existing systems because it does not change the current acquisition protocol.
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http://dx.doi.org/10.1007/s10439-012-0552-1 | DOI Listing |
J Pharm Anal
October 2024
National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China.
Identifying the compound formulae-related xenobiotics in bio-samples is full of challenges. Conventional strategies always exhibit the insufficiencies in overall coverage, analytical efficiency, and degree of automation, and the results highly rely on the personal knowledge and experience. The goal of this work was to establish a software-aided approach, by integrating ultra-high performance liquid chromatography/ion-mobility quadrupole time-of-flight mass spectrometry (UHPLC/IM-QTOF-MS) and in-house high-definition MS library, to enhance the identification of prototypes and metabolites of the compound formulae , taking Sishen formula (SSF) as a template.
View Article and Find Full Text PDFAnal Chem
January 2025
School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China.
Miniaturized mass spectrometers offer significant potential for in situ analysis due to their high specificity and portability. In traditional data-dependent acquisition (DDA) mode, precursor ions for tandem analysis are selected based on the full-scan mass spectrum. However, in situ applications often require the direct analysis of complex samples without extensive sample pretreatment, making them susceptible to chemical noise that can result in false negatives.
View Article and Find Full Text PDFEur Heart J Digit Health
January 2025
Department of Cardiovascular Surgery of Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Aims: Accurate heart function estimation is vital for detecting and monitoring cardiovascular diseases. While two-dimensional echocardiography (2DE) is widely accessible and used, it requires specialized training, is prone to inter-observer variability, and lacks comprehensive three-dimensional (3D) information. We introduce CardiacField, a computational echocardiography system using a 2DE probe for precise, automated left ventricular (LV) and right ventricular (RV) ejection fraction (EF) estimations, which is especially easy to use for non-cardiovascular healthcare practitioners.
View Article and Find Full Text PDFSci Rep
January 2025
Institute for X-ray Physics, Georg-August University Göttingen, Friedrich-Hund-Platz 1, 37077, Göttingen, Germany.
Imaging the entire cardiomyocyte network in entire small animal hearts at single cell resolution is a formidable challenge. Optical microscopy provides sufficient contrast and resolution in 2d, however fails to deliver non-destructive 3d reconstructions with isotropic resolution. It requires several invasive preparation steps, which introduce structural artefacts, namely dehydration, physical slicing and staining, or for the case of light sheet microscopy also clearing of the tissue.
View Article and Find Full Text PDFBackground: Currently, a majority of institution-specific automatic MRI-based contouring algorithms are trained, tested, and validated on one contrast weighting (i.e., T2-weighted), however their actual performance within this contrast weighting (i.
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