Magnetic Resonance Imaging and Gynecological Devices

Background: Performing magnetic resonance imaging (MRI) on women with gynecological devices is a completely accepted practice. The goal of our review is to assess how safe it is to perform MRI on women using contraceptive implants or devices. Study Design: Literature review, searching in PubMed-Medline/Ovid for the following keywords: magnetic resonance imaging, intrauterine devices, Implanon® and Essure®. Results: Though plastic devices do not represent a contraindication to the use of the technique, those including metallic components have been submitted to several tests, after which they were classified as MR Conditional (devices presenting no risks in MR-specific environments) by the Food and Drug Administration. Thus, the use of MRI can be safely advised to women with this type of device as long as the magnetic resonance equipment is ≤3.0 T. Conclusions: Presently, there is no scientific evidence that contraindicates performing MRI on women with any kind of gynecological device. Therefore, this procedure is safe as long as it is performed under previously tested conditions.

Automatic Small Bowel Tumor Diagnosis by Using Multi-Scale Wavelet-Based Analysis in Wireless Capsule Endoscopy Images

BACKGROUND: Wireless capsule endoscopy has been introduced as an innovative, non-invasive diagnostic technique for evaluation of the gastrointestinal tract, reaching places where conventional endoscopy is unable to. However, the output of this technique is an 8 hours video, whose analysis by the expert physician is very time consuming. Thus, a computer assisted diagnosis tool to help the physicians to evaluate CE exams faster and more accurately is an important technical challenge and an excellent economical opportunity. METHOD: The set of features proposed in this paper to code textural information is based on statistical modeling of second order textural measures extracted from co-occurrence matrices. To cope with both joint and marginal non-Gaussianity of second order textural measures, higher order moments are used. These statistical moments are taken from the two-dimensional color-scale feature space, where two different scales are considered. Second and higher order moments of textural measures are computed from the co-occurrence matrices computed from images synthesized by the inverse wavelet transform of the wavelet transform containing only the selected scales for the three color channels. The dimensionality of the data is reduced by using Principal Component Analysis. RESULTS: The proposed textural features are then used as the input of a classifier based on artificial neural networks. Classification performances of 93.1% specificity and 93.9% sensitivity are achieved on real data. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis systems to assist physicians in their clinical practice.