Background Computed tomography (CT) is a central modality in modern radiology contributing to diagnostic medicine in almost every medical subspecialty, but particularly in emergency services. To solve the inverse problem of reconstructing anatomical slice images from the raw output the scanner measures, several methods have been developed, with filtered back projection (FBP) and iterative reconstruction (IR) subsequently providing criterion standards. Currently there are new approaches to reconstruction in the field of artificial intelligence utilizing the upcoming possibilities of machine learning (ML), or more specifically, deep learning (DL). Method This review covers the principles of present CT image reconstruction as well as the basic concepts of DL and its implementation in reconstruction. Subsequently commercially available algorithms and current limitations are being discussed. Results and Conclusion DL is an ML method that utilizes a trained artificial neural network to solve specific problems. Currently two vendors are providing DL image reconstruction algorithms for the clinical routine. For these algorithms, a decrease in image noise and an increase in overall image quality that could potentially facilitate the diagnostic confidence in lesion conspicuity or may translate to dose reduction for given clinical tasks have been shown. One study showed equal diagnostic accuracy in the detection of coronary artery stenosis for DL reconstructed images compared to IR at higher image quality levels. Consequently, a lot more research is necessary and should aim at diagnostic superiority in the clinical context covering a broadness of pathologies to demonstrate the reliability of such DL approaches. Key Points: Citation Format
Non-contrast-enhanced imaging of the lower limb arteries using a TRANCE-sequence in a 1.0 T open MRI system is feasible with the protocol presented; however, TRANCE tends to underestimate larger vessels and overestimate smaller vessels compared to DSA.
ObjectiveTo apply the process mapping technique in an interdisciplinary approach in order to visualize, better understand, and efficiently organize percutaneous transluminal angioplasty (PTA) and stent placement procedures in a university hospital’s interventional radiology department.MethodsAfter providing an overview of seven established mapping techniques for medical professionals, the process mapping technique was chosen and applied in an interdisciplinary approach including referrers (physicians, nurses, and other staff in referring departments, e.g., vascular surgery), providers (interventional radiologists, nurses, technicians, and staff of the angiography suite), and specialists of the hospital’s controlling department.ResultsA generally binding and standardized process map was created, describing the entire procedure for a patient in whom the radiological intervention of PTA or stent treatment is contemplated from admission to the department of vascular surgery until discharge after successful treatment. This visualization tool assists in better understanding (especially given natural staff fluctuation over time) and efficiently organizing PTA and stent procedures.ConclusionProcess mapping can be applied for streamlining workflow in healthcare, especially in interdisciplinary settings. By defining exactly what a business entity does, who is responsible, to what standard a process should be completed, and how the success can be assessed, this technique can be used to eliminate waste and inefficiencies from the workplace while providing high-quality goods and services easily, quickly, and inexpensively.Main Messages• Process mapping can be used in a university hospital’s interventional radiology department.• Process mapping can describe the patient’s entire process from admission to PTA/stent placement until discharge.• Process mapping can be used in interdisciplinary teams (e.g., referrers, providers, and controlling specialists).• Process mapping can be used in order to more efficiently organize PTA and stent placement procedures.• Process mapping can assist in better understanding and efficiently organizing procedures in standardized fashion.
Temperature-based death time estimation is based either on simple phenomenological models of corpse cooling or on detailed physical heat transfer models. The latter are much more complex but allow a higher accuracy of death time estimation, as in principle, all relevant cooling mechanisms can be taken into account.Here, a complete workflow for finite element-based cooling simulation is presented. The following steps are demonstrated on a CT phantom: Computer tomography (CT) scan Segmentation of the CT images for thermodynamically relevant features of individual geometries and compilation in a geometric computer-aided design (CAD) model Conversion of the segmentation result into a finite element (FE) simulation model Computation of the model cooling curve (MOD) Calculation of the cooling time (CTE) For the first time in FE-based cooling time estimation, the steps from the CT image over segmentation to FE model generation are performed semi-automatically. The cooling time calculation results are compared to cooling measurements performed on the phantoms under controlled conditions. In this context, the method is validated using a CT phantom. Some of the phantoms' thermodynamic material parameters had to be determined via independent experiments.Moreover, the impact of geometry and material parameter uncertainties on the estimated cooling time is investigated by a sensitivity analysis.
ObjectivesTo apply the economic terminology of lean manufacturing and the Toyota Production System to the procurement of vascular stents in interventional radiology.MethodsThe economic- and process-driven terminology of lean manufacturing and the Toyota Production System is first presented, including information and product flow as well as value stream mapping (VSM), and then applied to an interdisciplinary setting of physicians, nurses and technicians from different medical departments to identify wastes in the process of endovascular stent procurement in interventional radiology.ResultsUsing the so-called seven wastes approach of the Toyota Production System (waste of overproducing, waiting, transport, processing, inventory, motion and waste of defects and spoilage) as well as further waste characteristics (gross waste, process and method waste, and micro waste), wastes in the process of endovascular stent procurement in interventional radiology were identified and eliminated to create an overall smoother process from the procurement as well as from the medical perspective.ConclusionEconomic terminology of lean manufacturing and the Toyota Production System, especially VSM, can be used to visualise and better understand processes in the procurement of vascular stents in interventional radiology from an economic point of view.
ZUSAMMENFASSUNGZiel In der forensischen Odontologie ist der Vergleich von Ante-mortem und Post-mortem Panorama-Röntgenauf-nahmen (PR) eine zuverlässige Methode zur Personenidentifizierung. Das Ziel ist die automatische Identifizierung von unbekannten Personen mithilfe eines Vergleichs von Antemortem und Post-mortem PR unter Anwendung der Computer Vision. This document was downloaded for personal use only. Unauthorized distribution is strictly prohibited. Material und Methoden Materials and MethodsThe study includes 43 467 PRs from 24 545 patients (46 % females/54 % males). All PRs were filtered and evaluated with Matlab R2014b including the toolboxes image processing and computer vision system. The matching process used the SURF feature to find the corresponding points between two PRs (unknown person and database entry) out of the whole database.Results From 40 randomly selected persons, 34 persons (85 %) could be reliably identified by corresponding PR matching points between an already existing scan in the database and the most recent PR. The systematic matching yielded a maximum of 259 points for a successful identification between two different PRs of the same person and a maximum of 12 corresponding matching points for other nonidentical persons in the database. Hence 12 matching points are the threshold for reliable assignment.Conclusion Operating with an automatic PR system and computer vision could be a successful and reliable tool for identification purposes. The applied method distinguishes itself by virtue of its fast and reliable identification of persons by PR. This Identification method is suitable even if dental characteristics were removed or added in the past. The system seems to be robust for large amounts of data.
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