Laboratorio de Procesado de Imagen

(Image Processing Lab)
E.T.S.I. Telecomunicación
Campus Miguel Delibes s/n
Universidad de Valladolid
47011 Valladolid, Spain
contact@lpi.tel.uva.es


PhD Position Available

[22 Oct 2019]

The LPI is recruiting a PhD student to carry out his/her research in the diffusion MRI field. Questions should be directed to Professor Santiago Aja-Fernandez at sanaja@tel.uva.es.

The position has been granted by the Spain Science Ministry and it is linked to the research grant entitled "Diffusion magnetic resonance imaging for precision medicine: from analysis to prediction. Application to migraine ". The PhD must be carried out in the University of Valladolid. The position is subject to an official application to the Ministry.

[Details (in Spanish)] [Overview (in Spanish)]

Preprint available: Microstructure Diffusion Scalar Measures from Reduced MRI Acquisitions

[19 Sep 2019]

We propose the so-called "Apparent Measures Using Reduced Acquisitions" (AMURA) to drastically reduce the number of samples needed for the estimation of q-space measures such as RTOP, RTAP and RTPP. AMURA avoids the calculation of the whole EAP by assuming the diffusion anisotropy is roughly independent from the radial direction.

Available to download as a preprint ar [bioRxiv]

PhD Thesis defended

[16 Sep 2019]

Last 13th September 2019 Iñali Rabanillo defended his PhD Thesis entitled "Artifact reduction in Magnetic Resonance Imaging: noise modelling in 2D/3D GRAPPA
accelerated acquisitions and motion-induced ghosting correction in multishot diffusion MRI
", getting a qualification of "Sobresaliente" and being proposed for "cum laude" distinction.

Un trabajo fin de grado realizado en el LPI consigue un 98% de acierto en el diagnóstico del TDAH

[30 Aug 2019]

El TFG realizado por Patricia Amado Caballero "Ayuda al diagnóstico del TDAH en la infancia mediante técnicas de procesado de señal y aprendizaje" ha dado lugar a un sistema que alcanza un 98% de acierto en el diagnóstico del TDAH. El TFG aplica técnicas de aprendizaje profundo (deep learning) para analizar firmas espectrales en patrones de movimiento. Para adquirir estos patrones se han utilizado pulseras de actividad que no interfieren para nada en la vida diaria del niño.

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