Inês Machado

Inês Machado
King's College London | KCL · Department of Biomedical Engineering

Ph.D. in Biomedical Engineering
Research Associate at King's College London

About

21
Publications
2,941
Reads
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253
Citations
Introduction
I am a Research Associate with the School of Biomedical Engineering & Imaging Sciences (BMEIS) at King's College London (KCL) developing machine/deep learning algorithms for an integrated framework of medical image acquisition, reconstruction and analysis. ines.machado@kcl.ac.uk
Additional affiliations
January 2017 - December 2018
Brigham and Women's Hospital, Harvard Medical School
Position
  • PhD Student
September 2016 - December 2016
National University of Ireland
Position
  • PhD Student
January 2015 - present
Instituto Superior Técnico, Universidade de Lisboa
Position
  • PhD Student
Education
January 2015 - December 2018
MIT Portugal Program
Field of study
  • Bioengineering Systems
January 2008 - December 2013
Faculty of Sciences and Technology, New University of Lisbon
Field of study
  • Biomedical Engineering

Publications

Publications (21)
Preprint
div>Cine cardiac magnetic resonance (CMR) imaging is considered the gold standard for cardiac function evaluation. However, cine CMR acquisition is inherently slow and in recent decades considerable effort has been put into accelerating scan times without compromising image quality or the accuracy of derived results. In this paper, we present a ful...
Preprint
Full-text available
Cine cardiac magnetic resonance (CMR) imaging is considered the gold standard for cardiac function evaluation. However, cine CMR acquisition is inherently slow and in recent decades considerable effort has been put into accelerating scan times without compromising image quality or the accuracy of derived results. In this paper, we present a fully-a...
Chapter
Full-text available
Cine cardiac MRI is routinely acquired for the assessment of cardiac health, but the imaging process is slow and typically requires several breath-holds to acquire sufficient k-space profiles to ensure good image quality. Several undersampling-based reconstruction techniques have been proposed during the last decades to speed up cine cardiac MRI ac...
Chapter
Domain shift refers to the difference in the data distribution of two datasets, normally between the training set and the test set for machine learning algorithms. Domain shift is a serious problem for generalization of machine learning models and it is well-established that a domain shift between the training and test sets may cause a drastic drop...
Preprint
Full-text available
This work details a highly efficient implementation of the 3D scale-invariant feature transform (SIFT) algorithm, for the purpose of machine learning from large sets of volumetric medical image data. The primary operations of the 3D SIFT code are implemented on a graphics processing unit (GPU), including convolution, sub-sampling, and 4D peak detec...
Preprint
Full-text available
Domain shift refers to the difference in the data distribution of two datasets, normally between the training set and the test set for machine learning algorithms. Domain shift is a serious problem for generalization of machine learning models and it is well-established that a domain shift between the training and test sets may cause a drastic drop...
Preprint
Full-text available
Cine cardiac MRI is routinely acquired for the assessment of cardiac health, but the imaging process is slow and typically requires several breath-holds to acquire sufficient k-space profiles to ensure good image quality. Several undersampling-based reconstruction techniques have been proposed during the last decades to speed up cine cardiac MRI ac...
Article
Purpose Brain shift during tumor resection can progressively invalidate the accuracy of neuronavigation systems and affect neurosurgeons’ ability to achieve optimal resections. This paper compares two methods that have been presented in the literature to compensate for brain shift: a thin-plate spline deformation model and a finite element method (...
Article
Full-text available
In brain tumor surgery, the quality and safety of the procedure can be impacted by intra-operative tissue deformation, called brain shift. Brain shift can move the surgical targets and other vital structures such as blood vessels, thus invalidating the presurgical plan. Intra-operative ultrasound (iUS) is a convenient and cost-effective imaging too...
Article
Intraoperative tissue deformation, known as brain shift, decreases the benefit of using preoperative images to guide neurosurgery. Non-rigid registration of preoperative magnetic resonance (MR) to intraoperative ultrasound (iUS) has been proposed as a means to compensate for brain shift. We focus on the initial registration from MR to predurotomy i...
Preprint
Full-text available
In brain tumor surgery, the quality and safety of the procedure can be impacted by intra-operative tissue deformation, called brain shift. Brain shift can move the surgical targets and other vital structures such as blood vessels, thus invalidating the pre-surgical plan. Intra-operative ultrasound (iUS) is a convenient and cost-effective imaging to...
Conference Paper
Full-text available
Brain shift compensation attempts to model the deformation of the brain which occurs during the surgical removal of brain tumors to enable mapping of presurgical image data into patient coordinates during surgery and thus improve the accuracy and utility of neuro-navigation. We present preliminary results from clinical tumor resections that compare...
Article
Objective: Neuronavigation procedures demand high precision and accuracy. Despite this need, there are still few studies analyzing errors in such procedures. The aim of this study was to use a custom-built cranial phantom to measure target position and orientation errors in different phases of a simulated neuronavigation procedure. Methods: A cr...
Article
Background Although it is common practice to wait for an ‘embedding time’ during mechanical thrombectomy (MT) to allow strut integration of a stentriever device into an occluding thromboembolic clot, there is a scarcity of evidence demonstrating the value or optimal timing for the wide range of thrombus compositions. This work characterizes the beh...
Chapter
Full-text available
Intraoperative brain deformation reduces the effectiveness of using preoperative images for intraoperative surgical guidance. We propose an algorithm for deformable registration of intraoperative ultrasound (US) and preoperative magnetic resonance (MR) images in the context of brain tumor resection. From each image voxel, a set of multi-scale and m...
Conference Paper
A reliable Ultrasound (US)-to-US registration method to compensate for brain shift would substantially improve Image-Guided Neurological Surgery. Developing such a registration method is very challenging, due to factors such as the tumor resection, the complexity of brain pathology and the demand for fast computation. We propose a novel feature-dri...
Article
Full-text available
Purpose Matching points that are derived from features or landmarks in image data is a key step in some medical imaging applications. Since most robust point matching algorithms claim to be able to deal with outliers, users may place high confidence in the matching result and use it without further examination. However, for tasks such as feature-ba...
Article
Full-text available
Purpose: The brain undergoes significant structural change over the course of neurosurgery, including highly nonlinear deformation and resection. It can be informative to recover the spatial mapping between structures identified in preoperative surgical planning and the intraoperative state of the brain. We present a novel feature-based method for...
Article
Full-text available
A reliable Ultrasound (US)-to-US registration method to compensate for brain shift would substantially improve Image-Guided Neurological Surgery. Developing such a registration method is very challenging, due to factors such as missing correspondence in images, the complexity of brain pathology and the demand for fast computation. We propose a nove...
Conference Paper
The demand for objectivity in clinical diagnosis has been one of the greatest challenges in Biomedical Engineering. The study, development and implementation of solutions that may serve as ground truth in physical activity recognition and in medical diagnosis of chronic motor diseases is ever more imperative. This paper describes a human activity r...

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