Gamma Source Location Learning from Synthetic Multi-pinhole Collimator Data-Reference-Cited by-同舟云学术

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Gamma Source Location Learning from Synthetic Multi-pinhole Collimator Data

Published:2019 Issue: Volume: Page:205-214
ISSN:0302-9743
Container-title:Machine Learning for Medical Image Reconstruction
language:
Short-container-title:

Author:

von Niederhäusern Peter A.,Seppi Carlo,Pezold Simon,Nicolas Guillaume,Gkoumas Spyridon,Haerle Stephan K.,Cattin Philippe C.

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-33843-5_19

Reference6 articles.

1. Borbón-Arce, M., et al.: An innovative multimodality approach for sentinel node mapping and biopsy in head and neck malignancies. Revista Espanola de Medicina Nuclear e Imagen Molecular 33(5), 274–279 (2014)

2. Wendler, T., et al.: First demonstration of 3-D lymphatic mapping in breast cancer using freehand SPECT. Eur. J. Nucl. Med. Mol. Imaging 37(8), 1452–1461 (2010)

3. Lecture Notes in Computer Science;C Seppi,2017

4. Nahum, U., Seppi, C., von Niederhäusern, P.A., Pezold, S., Haerle, S.K., Cattin, P.C.: Sentinel lymph node fingerprinting. Phys. Med. Biol. 64(11), 16 (2019). Article: 115028

5. Mansour, H.: Beyond l1-norm minimization for sparse signal recovery. In: IEEE Statistical Signal Processing Workshop, SSP 2012, pp. 337–340 (2012)

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1. Augmented reality for sentinel lymph node biopsy;International Journal of Computer Assisted Radiology and Surgery;2023-09-25

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