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Deep learning for ultrasound data-rate reduction
| dc.contributor | Thiran, Jean-Philippe |
| dc.contributor | Ruiz Hidalgo, Javier |
| dc.contributor.author | Sainz Lorenzo, Yeray |
| dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions |
| dc.date.accessioned | 2019-05-16T07:03:31Z |
| dc.date.available | 2019-05-16T07:03:31Z |
| dc.date.issued | 2019-02-19 |
| dc.identifier.uri | http://hdl.handle.net/2117/133019 |
| dc.description | Ultrasound (US) is a widely used medical imaging modality mostly because of its non-invasive and real-time characteristics. Recent advances in US imaging (e.g. ultrafast imaging, 3D imaging, elastography, functional imaging etc.) gave rise to a crucial challenge: dealing with the huge amount of data that has to be transferred and processed in real-time. To address this problem, the LTS5 is focusing on two main aspects: 1) Maximizing the image quality for a given amount of data using advanced image reconstruction methods 2) Minimizing the data-rate to reach a given image q |
| dc.description.abstract | US devices generate a set of signals that are carried from a transducer probe to a computer for further processing in order to obtain images. Those signals are transmitted between both ends through a set of cables, making up a high capacity data transmission channel. In order to achieve a portable US device, it will be required to transfer the data through a much lower capacity channel. To reduce the data - rate, deep/convolutional neural networks are used for this purpose in this master thesis, showing that it is possible to reduce remarkably the data rates generated by those devices while keeping a high quality in the final reconstructed ultrasound images. |
| dc.language.iso | eng |
| dc.publisher | Universitat Politècnica de Catalunya |
| dc.rights | S'autoritza la difusió de l'obra mitjançant la llicència Creative Commons o similar 'Reconeixement-NoComercial- SenseObraDerivada' |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
| dc.subject | Àrees temàtiques de la UPC::Enginyeria de la telecomunicació |
| dc.subject.lcsh | Neural networks (Computer science) |
| dc.subject.lcsh | Data compression (Telecommunication) |
| dc.subject.lcsh | Machine learning |
| dc.subject.other | Deep learning |
| dc.subject.other | Ultrasound |
| dc.subject.other | Deep Neural Networks |
| dc.title | Deep learning for ultrasound data-rate reduction |
| dc.type | Master thesis |
| dc.subject.lemac | Xarxes neuronals (Informàtica) |
| dc.subject.lemac | Dades -- Compressió (Telecomunicació) |
| dc.subject.lemac | Aprenentatge automàtic |
| dc.identifier.slug | ETSETB-230.137914 |
| dc.rights.access | Open Access |
| dc.date.updated | 2019-03-29T06:51:05Z |
| dc.audience.educationlevel | Màster |
| dc.audience.mediator | Escola Tècnica Superior d'Enginyeria de Telecomunicació de Barcelona |
| dc.audience.degree | MÀSTER UNIVERSITARI EN ENGINYERIA DE TELECOMUNICACIÓ (Pla 2013) |
