Thesis etd-11092018-094823 |
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Thesis type
Tesi di laurea magistrale
URN
etd-11092018-094823
Thesis title
UNSUPERVISED ANALYSIS OF PHASE CONTRAST MRI BY MAXIMALLY STABLE EXTREMAL REGION ALGORITHM FOR PATIENT-SPECIFIC TISSUE CHARACTERIZATION
Department
INGEGNERIA DELL'INFORMAZIONE
Course of study
INGEGNERIA BIOMEDICA
Supervisors
.
relatore Prof. Positano, Vincenzo
controrelatore Prof.ssa Santarelli, Maria Filomena
correlatore Prof. Celi, Simona
tutor Ing. Sauvage, Emilie
tutor Ing. Fanni, Benigno Marco
controrelatore Prof.ssa Santarelli, Maria Filomena
correlatore Prof. Celi, Simona
tutor Ing. Sauvage, Emilie
tutor Ing. Fanni, Benigno Marco
Keywords
- IMAGE SEGMENTATION
- MRI
- MSER
- PHASE CONTRAST
- QAL METHOD
Graduation session start date
07/12/2018
Availability
Withheld
Release date
07/12/2088
Abstract (Inglese)
Abstract (Italiano)
The thesis is focused on the development of an effective methodology for patient-specific tissue characterization from Phase-Contrast (PC) MR images by flow-area loop (QAL) method.
QAL method consists in the analysis of the loop derived from the combination of flow and area time-dependent curves. The slope value obtained by the fitting of the linear portion of the loop represents the Pulse Wave Velocity (PWV). Starting from the PWV estimation, the E value can be inferred.
Both flow and area variation can be extracted from PC-MR images. While flow estimation from PC-MR is quite simple, estimation of area is challenging due to small area variations in cardiac cycle in vessels and limited spatial resolution. The thesis works aim to solve this issue by a dedicated image processing approach. To validate the proposed approach, a realistic software phantom was built basing on numeric simulations of fluid dynamic to build “synthetic” PC-MR images.
QAL method consists in the analysis of the loop derived from the combination of flow and area time-dependent curves. The slope value obtained by the fitting of the linear portion of the loop represents the Pulse Wave Velocity (PWV). Starting from the PWV estimation, the E value can be inferred.
Both flow and area variation can be extracted from PC-MR images. While flow estimation from PC-MR is quite simple, estimation of area is challenging due to small area variations in cardiac cycle in vessels and limited spatial resolution. The thesis works aim to solve this issue by a dedicated image processing approach. To validate the proposed approach, a realistic software phantom was built basing on numeric simulations of fluid dynamic to build “synthetic” PC-MR images.
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