Abstract
ventilation/perfusion scintigraphy (V/PSCAN) was the procedure of choice for studying patients with suspected pulmonary embolism (PE) until the prospective evaluation of V/P imaging in the study known as PIOPED I revealed high number of non diagnostic findings. This has challenged development of computed tomography (CT) during 1990s. Since beginning of 2000, multidetector CT (MDCT) is widely applied and has become the most commonly used technique in patients with suspected PE. The current MDCT have very advanced technology and have improved ability to detect small emboli. Unfortunately, results of the most advanced MDCT are still compared with V/PSCAN results performed in PIOPED I 20 years ago. However V/PSCAN has further developed. Tomographic technique V/PSPECT is taking over V/PPLANAR. Probabilistic criteria are replaced by holistic criteria for interpretation, taking lessons learned from PIOPED I. Discussion is focused on diagnostic value for MDCT and V/PSPECT. MDCT and V/PSPECT are both indispensible imaging techniques to study patients with suspected PE. However, when available, V/PSPECT should be the preferred method for diagnosis of PE, primarily due to the lower radiation exposure and applicability to all patients. Therefore it is our duty to make V/PSPECT generally more available, to use holistic interpretation knowledge and to communicate daily with the clinicians, so that the best technique is used in all patients. In conclusion MDCT cannot replace V/PSPECT. MDCT and V/PSPECT are complementary rather the competitive!
Call for Papers in Thematic Issues
Intelligent Information Retrieval for Multispectral and Hyperspectral Imaging
Multispectral and hyperspectral imaging is an interdisciplinary research area, widely focuses on spectral, spatial, and temporal data. It offers a plethora of opportunities to efficiently analyze the vast area of the earth's surface. However, there are numerous factors such as dimensionality and size of the hyperspectral data, lesser training samples, ...read more
Machine and Deep Learning in Medical Image Diagnosis
Medical Image Processing regards a set of methodologies that have been developed over recent years with the purpose of improving medical image quality, improving medical data visualization, understanding, and assisting medical diagnosis, and so on. The application of machine and deep learning methods in sensing and imaging can potentially have ...read more
Quantum Machine Learning for Medical Data and Imaging
Quantum computing has promised a significant speedup in certain computationally intensive tasks that are intractable on classical computers. Researchers in quantum machine learning, such as those working in computer vision, image processing, biomedical analysis, and related topics, may play an important role in comprehending and working on complicated medical data, ...read more
2