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Objectives
To assess the image quality of T2-weighted (T2w) magnetic resonance imaging of the prostate and the visibility of prostate cancer at 7 Tesla (T).
Materials & methods
Seventeen prostate cancer patients underwent T2w imaging at 7T with only an external transmit/receive array coil. Three radiologists independently scored images for image quality, visibility of anatomical structures, and presence of artefacts. Krippendorff’s alpha and weighted kappa statistics were used to assess inter-observer agreement. Visibility of prostate cancer lesions was assessed by directly linking the T2w images to the confirmed location of prostate cancer on histopathology.
Results
T2w imaging at 7T was achievable with ‘satisfactory’ (3/5) to ‘good’ (4/5) quality. Visibility of anatomical structures was predominantly scored as ‘satisfactory’ (3/5) and ‘good’ (4/5). If artefacts were present, they were mostly motion artefacts and, to a lesser extent, aliasing artefacts and noise. Krippendorff’s analysis revealed an α = 0.44 between three readers for the overall image quality scores. Clinically significant cancer lesions in both peripheral zone and transition zone were visible at 7T.
Conclusion
T2w imaging with satisfactory to good quality can be routinely acquired, and cancer lesions were visible in patients with prostate cancer at 7T using only an external transmit/receive body array coil.
We have developed a double-tuned ¹H/¹⁹F birdcage resonator dedicated for hand and wrist imaging at 7 T to locally image non-steroidal anti-inflammatory drugs (NSAID) such as 2-{[3-(Trifluoromethyl) phenyl]amino}benzoic acid. The preliminary in vivo images acquired by the double-tuned ¹H/¹⁹F birdcage resonator demonstrate the feasibility for ¹H/¹⁹F hand- and wrist-imaging at 7 T. While the diagnostic quality of the coil needs to be assessed in patients with inflammatory rheumatoid disease, first ¹⁹F images of the NSAID are encouraging, and point towards the prospect of applying ¹⁹F-MRI to visualize and quantify the concentration of therapeutically-active compound at the sites of inflammation.
Aim of the AXON2 project (Adaptive Expert System for Object Recogniton using Neuml Networks) is the development of an object recognition system (ORS) capable of recognizing isolated 3d objects from arbitrary views. Commonly, classification is based on a single feature extracted from the original image. Here we present an architecture adapted from the Mixtures of Eaqerts algorithm which uses multiple neuml networks to integmte different features. During tmining each neural network specializes in a subset of objects or object views appropriate to the properties of the corresponding feature space. In recognition mode the system dynamically chooses the most relevant features and combines them with maximum eficiency. The remaining less relevant features arz not computed and do therefore not decelerate the-recognition process. Thus, the algorithm is well suited for ml-time applications.
With the growing interest in small distributed sensors for the “Internet of Things”, more attention is being paid to energy harvesting techologies. Reducing or eliminating the need for external power sources or batteries make devices more self-sufficient, more reliable, and reduces maintenance requirements. The Wiegand effect is a proven technology for harvesting small amounts of electrical power from mechanical motion.