Detail publikace

Compressed sensing of perfusion MRI

Originální název

Compressed sensing of perfusion MRI

Anglický název

Compressed sensing of perfusion MRI

Jazyk

en

Originální abstrakt

Perfusion MRI is a diagnostic method in medicine. In the method a contrast agent is injected in the patient and then its concentration is observed via MRI during time. The signal captured in time from the affected area can be approximated by the lognormal distribution curve. The standard way of obtaining the measurements is very slow and does not comply with todays challenging requirements.Compressed sensing is used to acquire much less coefficients, having minimal effect on the signal reconstruction.This is based on combination of the low-rankness and sparsity assumptions on the data. Here we evaluate the model on dynamic phantom.

Anglický abstrakt

Perfusion MRI is a diagnostic method in medicine. In the method a contrast agent is injected in the patient and then its concentration is observed via MRI during time. The signal captured in time from the affected area can be approximated by the lognormal distribution curve. The standard way of obtaining the measurements is very slow and does not comply with todays challenging requirements.Compressed sensing is used to acquire much less coefficients, having minimal effect on the signal reconstruction.This is based on combination of the low-rankness and sparsity assumptions on the data. Here we evaluate the model on dynamic phantom.

Dokumenty

BibTex


@misc{BUT108301,
  author="Marie {Mangová} and Pavel {Rajmic}",
  title="Compressed sensing of perfusion MRI",
  annote="Perfusion MRI is a diagnostic method in medicine. In the method a contrast agent is injected in the patient and then its concentration is observed via MRI during time. The signal captured in time from the affected area can be approximated by the lognormal distribution curve. The standard way of obtaining the measurements is very slow and does not comply with todays challenging requirements.Compressed sensing is used to acquire much less coefficients, having minimal effect on the signal reconstruction.This is based on combination of the low-rankness and sparsity assumptions on the data. Here we evaluate the model on dynamic phantom.",
  chapter="108301",
  howpublished="online",
  year="2014",
  month="june",
  pages="1--1",
  type="presentation"
}