Kunte SC, Unterrainer LM, Kunz WG, Winkelmann M, Lindner S, Jurkschat K, Wängler C, Wängler B, Schirrmacher R, Bartenstein P, Belka C, Schichor C, Albert NL, Unterrrainer M (2024) Lenticulostriatal ischemia shows relevant SSTR-expression on PET/CT imaging using the novel SSTR-targeting peptide [18F]SiTATE. Clinical Nuclear Medicine, 49(12), e689–e690. DOI:: 10.1097/RLU.0000000000005482 Abstract A 64-year-old woman with meningioma
... weiter lesenKunte SC, Wenter V, Toms J, Lindner S, Unterrainer M, Eilsberger F, Jurkschat K, Wängler C, Wängler B, Schirrmacher R, Tiling MW, Sheikh GT, Mehrens D, Brendel M, Rübenthaler J, Auernhammer CJ, Spitzweg C, Unterrainer LM, Holzgreve A (2025) PET/CT imaging of differentiated and medullary thyroid carcinoma using the novel SSTR-targeting peptide [18F]SiTATE – first
... weiter lesenProchiner M, Judmann B, Ruder A, Wängler B, Schirrmacher R, Wängler C* (2024) Ultrasound-assisted solid phase affibody synthesis on the example of ZEGFR:1907 – superior to the conventional protocol? Pharmaceuticals, 17(10), 1280. DOI: 10.3390/ph17101280. Abstract Background: Affibody molecules represent a class of highly specific binders of particular interest for the development of highly affine target-specific radiopharmaceuticals.
... weiter lesenStrittmatter A, Zöllner FG. Multistep Networks for Deformable Multimodal Medical Image Registration. IEEE Access, 2024, 12, pp.82676-82692. DOI: 10.1109/ACCESS.2024.3412216 Publisher: IEEE Abstract: We proposed neural networks for deformable multimodal medical image registration that use multiple steps and varying resolutions. The networks were trained jointly in an unsupervised manner with Mutual Information and Gradient L2 loss.
... weiter lesenVellala A, Mogler C, Haag F, Tollens F, Rudolf H, Pietsch F, Wängler C, Wängler B, Schoenberg SO, Froelich MF, Hertel A. Comparing quantitative image parameters between animal and clinical CT-scanners: a translational phantom study analysis. Front Med (Lausanne). 2024 Jun 5;11:1407235. doi: 10.3389/fmed.2024.1407235. Abstract Purpose: This study compares phantom-based variability of extracted radiomics features
... weiter lesenC. D. Wörnle, R. Lutz, L. Kuntz, J. Stallkamp, M. Siegfarth, Heidelberg University, Mannheim (DE) [ISCAS-LE-59-V].A rapid calibration pipeline for robotic percutaneous interventions – how a prototype learned to be accurate.CARS 2024—Computer Assisted Radiology and Surgery. Proceedings of the 38th International Congress and Exhibition. Barcelona, Spain, June 18–21, 2024. doi: 10.1007/s11548-024-03128-9
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Anish Raj, Fabian Tollens, Anna Caroli, Dominik Nörenberg, Frank G. Zöllner. Automated prognosis of renal function decline in ADPKD patients using deep learning. Zeitschrift für Medizinische Physik,. Volume 34, Issue 2, 2024, Pages 330-342. doi:10.1016/j.zemedi.2023.08.001 Abstract An accurate prognosis of renal function decline in Autosomal Dominant Polycystic Kidney Disease (ADPKD) is crucial for early intervention.
... weiter lesenEbner R, Lohse A, Fabritius MP, Rübenthaler J, Wängler C, Wängler B, Schirrmacher R, Völter F, Schmid HP, Unterrainer LM, Öcal O, Hinterberger A, Spitzweg C, Auernhammer CJ, Geyer T, Ricke J, Bartenstein P, Holzgreve A, Grawe F. Validation of the standardization framework SSTR-RADS 1.0 for neuroendocrine tumors using the novel SSTR‑targeting peptide [18F]SiTATE. Eur
... weiter lesenSparks H, Rink JS, Ramakrishnan A, Sung K, Ni J, Lu DSK, Raman SS, Lee EW, Chiang J. Characterization of Microwave Generator Energy and Ablation Volumes following Transarterial Embolization in an In Vivo Porcine Liver Model. J Vasc Interv Radiol. 2024 May 14:S1051-0443(24)00339-7. doi: 10.1016/j.jvir.2024.05.005. Abstract Purpose: To characterize the relationship between ablation zone volume
... weiter lesenStrittmatter A, Schad LR, Zöllner FG. Deep Learning-Based Affine Medical Image Registration for Multimodal Minimal-Invasive Image-Guided Interventions – A Comparative Study on Generalizability. Z Med Phys, 2024, 34 (2), pp.291-317. https://doi.org/10.1016/j.zemedi.2023.05.003 Abstract Multimodal image registration is applied in medical image analysis as it allows the integration of complementary data from multiple imaging modalities. In recent
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