[BLAUAERMEL, Alexander, BOESCKE, Tim, MEYER, Karsten, HATTENDORF, Jens, LANGE, Frank, KOSHNICHAROV, Dimo, SCHLITTER, Mirko, ZINK, Ingmar, LOEW, Richard, VOIGT, Peter, HELLRIEGEL, Ronald, FROITZHEIM, Armin] Postfach 30 02 20 70442 Stuttgart;Waldstr. 59 15366 Hoppegarten;Löberwallgraben 2 99096 Erfurt;Gustav-Adolf-Str. 15 99084 Erfurt;Friedenstrasse 7 99310 Arnstadt;Dornroeschenweg 38 99099 Erfurt;Weichselstr.6 10247 Berlin;Adalbertstr. 22 99089 Erfurt;Tungerstrasse 20 99099 Erfurt;Im Dorfe 15 99439 Vippachedelhausen;Havannaer Str. 14 99091 Erfurt;Angelikastr. 15a 01099 Dresden;Hegelstrasse 18 99423 Weimar The invention relates to a solar cell of the crystalline silicone type, comprising an anti-reflection coating (5; 4) based on silicone nitrite on the front of said solar cell, wherein, in a surface sublayer (4), the anti-reflection coating has a silicone content that is increased in relation to the stoichiometric silicone content in Si3N4 in such a way that the refraction index has a value in the range between 2.1 and 2.5.

[SCUTARU MIHAI, VOIGT INGMAR, MANSI TOMMASO, IONASEC RAZVAN, HOULE HELENE C, TATPATI ANAND VINOD, COMANICIU DORIN, GEORGESCU BOGDAN, EL ZEHIRY NOHA YOUSSRY] US;DE

[SCHLITTER MIRKO, HELLRIEGEL ROLAND, HATTENDORF JENS, BOESCKE TIM, ZINK INGMAR, KOSHNICHAROV DIMO, LANGE FRANK, VOIGT PETER, MEYER KARSTEN, FROITZHEIM ARMIN, LOEW RICHARD, BLAUAERMEL ALEXANDER] DE

[Mihai Scutaru, Ingmar Voigt, Tommaso Mansi, Razvan Ionasec, Helene C. Houle, Anand Vinod Tatpati, Dorin Comaniciu] DE Munich Anatomy, such as papillary muscle, is automatically detected (34) and/or detected in real-time. For automatic detection (34) of small anatomy, machine-learnt classification with spatial (32) and temporal (e.g., Markov) (34) constraints is used. For real-time detection, sparse machine-learnt detection (34) interleaved with optical flow tracking (38) is used.

[Hanno Heyke Homann, Ingmar Graesslin, Ulrich Katscher, Tobias Ratko Voigt, Olaf Helmut Dössel, Sebastian Alfred Seitz] DE Hamburg A magnetic resonance imaging system (300) acquires magnetic resonance data (358) from a subject (318) that may include an electrically conductive object (e.g. an implant or a medical device). The magnetic resonance imaging system includes a radio-frequency transmitter (314) for generating a radio-frequency transmit field for acquiring the magnetic resonance data using a radio-frequency antenna (310). The radio-frequency transmitter has multiple transmit channels. The radio-frequency antenna comprises multiple antenna elements (312) each adapted to connect to an antenna element. The amplitude and phase values of the RF transmit field of each of the transmit channels can be selected such that the magnetic field generated by the RF antenna is minimized at the location of the electrically conductive object, thereby reducing RF heating of the object.

[HOMANN, Hanno Heyke, GRAESSLIN, Ingmar, KATSCHER, Ulrich, VOIGT, Tobias Ratko, DÖSSEL, Olaf Helmut, SEITZ, Sebastian Alfred] High Tech Campus 5, 5656 AE Eindhoven, NL

[VOIGT, Ingmar, NICKEL, Marcel Dominik, MANSI, Tommaso, PIAT, Sebastien] Henkestraße 127, 91052 Erlangen, DE Technique for assigning a perfusion metric to dynamic contrast-enhanced, DCE, magnetic resonance, MR, images, (104) the DCE MR images obtained from a MR scanner (102) and under a free-breathing protocol is provided. As to a neural network system (100) aspect, a neural network system (100) for assigning a perfusion metric to DCE MR images, comprises an input layer (112) configured to receive at least one DCE MR image (104a) representative of a first contrast enhancement state and of a first respiratory motion state and at least one further DCE MR image (104b) representative of a second contrast enhancement state and of a second respiratory motion state. The neural network (100) further comprises an output layer (116) configured to output at least one perfusion metric based on the at least one DCE MR image (104a) and the at least one further DCE MR image (104b). The neural network system (100) with interconnections between the input layer (112) and the output layer (116) is trained by a plurality of datasets, each of the datasets comprising an instance of the at least one DCE MR image (104a) and of the at least one further DCE MR image (104b) for the input layer (114) and the at least one perfusion metric for the output layer (116). ＜img id="iaf01" file="imgaf001.tif" wi="125" he="59" img-content="drawing" img-format="tif" /＞

[VOIGT INGMAR, MANSI TOMMASO, HOULE HELENE C] US

[Teodora Marina Chitiboi, Puneet Sharma, Athira Jane Jacob, Ingmar Voigt, Mehmet Akif Gulsun] DE Erlangen For training for and performance of LGE analysis, multi-task machine-learning model is trained to output various cardiac tissue characteristics based on input of LGE MR data. The use of segmentation may be avoided or limited, resulting in a greater number of available training data samples, by using radiology clinical reports with LGE information as a source for samples. The multi-task model may be trained to output cardiac tissue characteristics using radiology clinical reports with LGE information with no segmentation or with segmentation for only a subset of the training samples. By training for multiple tasks, the accuracy of prediction for each task benefits from the information for other tasks. The trained model outputs values of characteristics for multiple tasks, such as extent of enhancement, type of enhancement, and localization of enhancement. Other tasks may be included, such as disease classification. Other inputs may be used, such as also including sensor data and/or cardiac motion.

[Peter Mountney, Markus Kaiser, Ingmar Voigt, Matthias John, Razvan Ioan Ionasec, Jan Boese, Dorin Comaniciu] US NJ Plainsboro A method and system for registering ultrasound images and physiological models to x-ray fluoroscopy images is disclosed. A fluoroscopic image and an ultrasound image, such as a Transesophageal Echocardiography (TEE) image, are received. A 2D location of an ultrasound probe is detected in the fluoroscopic image. A 3D pose of the ultrasound probe is estimated based on the detected 2D location of the ultrasound probe in the fluoroscopic image. The ultrasound image is mapped to a 3D coordinate system of a fluoroscopic image acquisition device used to acquire the fluoroscopic image based on the estimated 3D pose of the ultrasound probe. The ultrasound image can then be projected into the fluoroscopic image using a projection matrix associated with the fluoroscopic image. A patient specific physiological model can be detected in the ultrasound image and projected into the fluoroscopic image.