Identification of CO2 with Magnetic Resonance Key to Understanding Production Behavior in Campo Palo Azul, Ecuador: A Case Study

2017 ◽  
Author(s):  
N. X. Ramirez ◽  
J. E. Roldan ◽  
B. Delgado ◽  
D. Cuenca
Keyword(s):  
Magnetic Resonance ◽  
Production Behavior

scholarly journals Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Markus J. Ankenbrand ◽  
Liliia Shainberg ◽  
Michael Hock ◽  
David Lohr ◽  
Laura M. Schreiber
Keyword(s):  
Neural Networks ◽  
Sensitivity Analysis ◽  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Cardiac Mri ◽  
Magnetic Resonance Images ◽  
Model Interpretation ◽  
First Case ◽  
Segmentation Models

Abstract Background Image segmentation is a common task in medical imaging e.g., for volumetry analysis in cardiac MRI. Artificial neural networks are used to automate this task with performance similar to manual operators. However, this performance is only achieved in the narrow tasks networks are trained on. Performance drops dramatically when data characteristics differ from the training set properties. Moreover, neural networks are commonly considered black boxes, because it is hard to understand how they make decisions and why they fail. Therefore, it is also hard to predict whether they will generalize and work well with new data. Here we present a generic method for segmentation model interpretation. Sensitivity analysis is an approach where model input is modified in a controlled manner and the effect of these modifications on the model output is evaluated. This method yields insights into the sensitivity of the model to these alterations and therefore to the importance of certain features on segmentation performance. Results We present an open-source Python library (misas), that facilitates the use of sensitivity analysis with arbitrary data and models. We show that this method is a suitable approach to answer practical questions regarding use and functionality of segmentation models. We demonstrate this in two case studies on cardiac magnetic resonance imaging. The first case study explores the suitability of a published network for use on a public dataset the network has not been trained on. The second case study demonstrates how sensitivity analysis can be used to evaluate the robustness of a newly trained model. Conclusions Sensitivity analysis is a useful tool for deep learning developers as well as users such as clinicians. It extends their toolbox, enabling and improving interpretability of segmentation models. Enhancing our understanding of neural networks through sensitivity analysis also assists in decision making. Although demonstrated only on cardiac magnetic resonance images this approach and software are much more broadly applicable.

Use of magnetic resonance imaging to identify the edge of a dural tear in an infant with growing skull fracture: a case study

2012 ◽  
Vol 28 (11) ◽  
pp. 1951-1954 ◽  
Author(s):  
Hideki Matsuura ◽  
Shinichi Omama ◽  
Yuki Yoshida ◽  
Shunrou Fujiwara ◽  
Takayuki Honda ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Skull Fracture ◽  
Dural Tear ◽  
Resonance Imaging ◽  
Growing Skull Fracture

Open Source Medical Devices for Innovation, Education and Global Health: Case Study of Open Source Magnetic Resonance Imaging

2018 ◽  
pp. 147-163
Author(s):  
Lukas Winter ◽  
Ruben Pellicer-Guridi ◽  
Lionel Broche ◽  
Simone A. Winkler ◽  
Henning M. Reimann ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Global Health ◽  
Open Source ◽  
Medical Devices ◽  
Resonance Imaging

Case Study of a Rare Incidence of a Leiomyosarcoma Mass Occluding the Distal Femoral Vein

2021 ◽  
pp. 875647932110440
Author(s):  
Tammy Perkins ◽  
Kelly McDonald ◽  
Douglas Clem
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Lower Extremity ◽  
Femoral Artery ◽  
Femoral Vein ◽  
Lower Leg ◽  
Resonance Imaging ◽  
Computed Tomographic ◽  
Magnetic Resonance Imaging Mri

This is a case study of a 47-year-old Caucasian male whose chief concern was left lower leg swelling for 1 month. A unilateral lower extremity venous duplex examination was performed. The results concluded that the distal femoral vein was occluded to the distal popliteal vein. Incidentally, a hypoechoic region in the distal thigh near the distal femoral artery was noted by the technologist. The patient was placed on anticoagulation and was told to return for further examination if there was no relief. Three months later, the patient continued to experience lower left leg swelling and returned for another sonogram. The hypoechoic region was seen again in the distal thigh and remained occluded. A computed tomographic arterial (CT-A) and magnetic resonance imaging (MRI) were ordered for further investigation of the hypoechoic area. The CT-A and the MRI revealed the presence of a mass in the distal thigh. The mass was biopsied and diagnosed as a leiomyosarcoma, grade 1. The mass caused the compression and occlusion of the distal femoral vein. The mass was removed, along with a portion of the distal femoral artery due to involvement of the artery within the mass. The artery was repaired with a graft.

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