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Equipe ARAMIS
Institut du Cerveau et de la Moelle épinière
Hôpital Pitié-Salpêtrière
47 Boulevard de l’Hôpital
Paris
75013
France
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Miscellaneous Information

NinonResearch Interests

Image synthesis, atlas-based segmentation, attenuation correction, PET/MR, radiotherapy planning


Previous Education
I first studied in France where I obtained an Engineering degree from a graduate school in electronic engineering and computer science. I then moved to the UK and obtained an MSc in Biomedical Engineering from Imperial College London in 2012. After graduating, I joined TIG where I completed my PhD in 2016. After spending a year as a research associate in the lab, I decided to move back to France and I am currently a postodoctoral researcher in the ARAMIS lab (Paris).

 

PhD Thesis: Image Synthesis for the Attenuation Correction and Analysis of PET/MR Data

While magnetic resonance imaging (MRI) provides high-resolution anatomical information, positron emission tomography (PET) provides functional information. Combined PET/MR scanners are expected to offer a new range of clinical applications but efforts are still necessary to mitigate some limitations of this promising technology. One of the factors limiting the use of PET/MR scanners, especially in the case of neurology studies, is the imperfect attenuation correction, leading to a strong bias of the PET activity. Exploiting the simultaneous acquisition of both modalities, I explored a new family of methods to synthesise X-ray computed tomography (CT) images from MR images. The synthetic images are generated through a multi-atlas information propagation scheme, locally matching the MRI-derived patient's morphology to a database of MR/CT image pairs, using a local image similarity measure. The proposed algorithm provides a significant improvement in PET reconstruction accuracy when compared with the current correction, allowing an unbiased analysis of the PET images. You can try the method online.

A similar image synthesis scheme was then used to better identify abnormalities in cerebral glucose metabolism measured by 18F-fluorodeoxyglucose (FDG) PET. This framework consists of creating a subject-specific healthy PET model based on the propagation of morphologically-matched PET images, and comparing the subject's PET image to the model via a Z-score. By accounting for inter-subject morphological differences, the proposed method reduces the variance of the normal population used for comparison in the Z-score, thus increasing the sensitivity.

To demonstrate that the applicability of the proposed CT synthesis method is not limited to PET/MR attenuation correction, I redesigned the synthesis process to derive tissue attenuation properties from MR images in the head & neck and pelvic regions to facilitate MR-based radiotherapy treatment planning.

 

Result CT synthesisFrom left to right: The acquired T1-weighted MRI, CT and FDG PET, and the synthetic CT and PET generated by our proposed CT synthesis method.

 

 

MR-based Radiotherapy Treatment Planning

The aim of radiotherapy treatment planning (RTP) is to deliver an optimal dose of radiation over the target area while sparing the normal tissues. RTP first requires contouring the target and organs at risk (OARs). Once these volumes have been defined, the optimal dose distribution for treating the tumour is determined according to the attenuation properties of the different tissues. Most radiotherapy treatments are planned using an X-ray computed tomography (CT) scan of the patient. The acquisition of a CT is fast and the tissue attenuation coefficients can easily be derived from the CT intensity values in Hounsfield unit (HU). However, CT images have low soft tissue contrast, which can lead to large variations when delineating the organs, particularly when located in the brain, head & neck, or pelvic regions. Magnetic resonance (MR) imaging is often preferred over CT as a structural imaging modality, mainly for its excellent soft tissue contrast. Although increasingly used in clinical practice, the role of MR in RTP is currently limited by the fact that it does not readily provide electron density information, hampering the calculation of dose distributions. My work consists of trying to tackle the problem of RTP from MR images by developing a multi-atlas propagation framework to jointly delineate the OARs and estimate the tissue attenuation properties.

 

 

Representative publications

Burgos N, Cardoso MJ, Thielemans K, Modat M, Pedemonte S, Dickson J, Barnes A, Ahmed R, Mahoney CJ, Schott JM, Duncan JS, Atkinson D, Arridge SR, Hutton BF and Ourselin S: Attenuation Correction Synthesis for Hybrid PET-MR Scanners: Application to Brain Studies, IEEE Transactions on Medical Imaging, 33(12): 2332–2341, 2014

Burgos N, Cardoso MJ, Mendelson AF, Schott JM, Atkinson D, Arridge SR, Hutton BF, Ourselin S: Subject-specific Models for the Analysis of Pathological FDG PET Data. MICCAI 2015, LNCS 9350, pp. 651-658, 2015

Burgos N, Guerreiro F, McClelland J, Presles B, Modat M, Nill S, Dearnaley D, deSouza N, Oelfke U, Knopf AC, Ourselin S, Cardoso MJ: Iterative Framework for the Joint Segmentation and CT Synthesis of MR Images: Application to MRI-only Radiotherapy Treatment Planning. An invited paper in the Special Issues of Physics in Medicine and Biology on Recent Progress in Applications of Computing to Radiotherapy (in press)

 

Publications

  • Journal papers
  1. Burgos N, Guerreiro F, McClelland J, Presles B, Modat M, Nill S, Dearnaley D, deSouza N, Oelfke U, Knopf AC, Ourselin S, and Cardoso MJ: Iterative Framework for the Joint Segmentation and CT Synthesis of MR Images: Application to MRI-only Radiotherapy Treatment Planning. An invited paper in the Special Issues of Physics in Medicine and Biology on Recent Progress in Applications of Computing to Radiotherapy (in press)
  2. Guerreiro* F, Burgos* N, Dunlop A, Wong K, Petkar I, Nutting C, Harrington K, Bhide S, Newbold K, Dearnaley D, deSouza NM, Morgan VA, McClelland J, Nill S, Cardoso MJ, Ourselin S, Oelfke U, and Knopf AC: Evaluation of a multi-atlas CT synthesis approach for MRI-only radiotherapy treatment planning. Physica Medica: European Journal of Medical Physics, 35:7–17, 2017 (*: joint first authorship)
  3. Lane C, Parker T, Cash DM, Macpherson K, Donnachie E, Murray-Smith H, Barnes A, Barker S, Beasley D, Bras J, Brown D, Burgos N, Byford M, Cardoso MJ, Carvalho A, Collins J, De Vita E, Dickson J, Epie N, Espak M, Henley SMD, Hoskote C, Hutel M, Klimova J, Malone I, Markiewicz P, Melbourne A, Modat M, Schrag A, Shah S, Sharma NS, Sudre C, Thomas D, Wong A, Zhang Hui, Hardy J, Zetterberg H, Ourselin S, Crutch SJ, Kuh D, Richards M, Fox NC, and Schott JM: Study Protocol: Insight 46—a neuroscience sub-study of the MRC National Survey of Health and Development. BMC Neurology (in press)
  4. Ladefoged CN, Law I, Anazodo U, St. Lawrence K, Izquierdo-Garcia D, Catana C, Burgos N, Cardoso MJ, Hutton BF, Ourselin S, Merida I, Costes N, Hammers A, Benoit D, Holm S, Juttukonda M, An H, Cabello J, Lukas M, Nekolla S, Ziegler S, Fenchel M, Jakoby B, Casey M, Benzinger T, Højgaard L, Hansen AE, and Andersen FL: A multi-centre evaluation of eleven clinically feasible brain PET/MRI attenuation correction techniques using a large cohort of patients. NeuroImage, 147: 346–359, 2017
  5. Jiao J, Bousse A, Thielemans K, Burgos N, Weston P, Markiewicz P, Schott J, Atkinson D, Arridge SR, Hutton BF and Ourselin S: Direct parametric reconstruction with joint motion estimation/correction for dynamic brain PET data. Medical Imaging, IEEE Transactions on, 36(1): 203–2013, 2017
  6. Sekine T, Burgos N, Warnock G, Huellner MW, Buck A, ter Voert EGW, Cardoso MJ, Hutton BF, Ourselin S, Veit-Haibach P and Delso G: Multi atlas-based attenuation correction for brain FDG-PET imaging using a TOF-PET/MR scanner—comparison with clinical single atlas- and CT-based attenuation correction. Journal of Nuclear Medicine, 57(8): 1258–1264, 2016
  7. Burgos N, Cardoso MJ, Thielemans K, Modat M, Dickson J, Schott JM, Atkinson D, Arridge SR, Hutton BF and Ourselin S: Multi-contrast Attenuation Map Synthesis for PET/MR Scanners: Assessment on FDG and Florbetapir PET Tracers. European Journal of Nuclear Medicine and Molecular Imaging, 42(9): 1447-1458, 2015
  8. Zuluaga* MA, Burgos* N, Taylor AM, Ourselin S: Voxelwise Atlas Rating for Computer Assisted Diagnosis: Application to Congenital Heart Diseases of the Great Arteries. Medical Image Analysis, 2015. doi:10.1016/j.media.2015.09.001
  9. Weston PSJ, Paterson RW, Modat M, Burgos N, Cardoso MJ, Magdalinou N, Lehmann M, Dickson J, Barnes A, Bomanji JB, Kayani I, Cash DM, Ourselin S, Toombs J, Lunn MP, Mummery CJ, Warren JD, Rossor MN, Fox NC, Zetterberg H, Schott JM: Using florbetapir PET to explore CSF cut-points and grey zones in small sample sizes. Alzheimer’s & Dementia: Diagnosis and Disease Monitoring, 1(4): 440-446, 2015
  10. Kochan M, Daga P, Burgos N, White M, Cardoso MJ, Mancini L, Winston GP, McEvoy AW, Thornton J, Yousry T, Duncan JS, Stoyanov D, Ourselin S: Simulated Field Maps for Susceptibility Artefact Correction in Interventional MRI. International Journal of Computer Assisted Radiology and Surgery, 2015. doi:10.1007/s11548-015-1253-7
  11. Burgos N, Cardoso MJ, Thielemans K, Modat M, Pedemonte S, Dickson J, Barnes A, Ahmed R, Mahoney CJ, Schott JM, Duncan JS, Atkinson D, Arridge SR, Hutton BF and Ourselin S: Attenuation Correction Synthesis for Hybrid PET-MR Scanners: Application to Brain Studies, IEEE Transactions on Medical Imaging, 33(12): 2332–2341, 2014

 

  • Conferences with full-length peer-reviewed proceedings
  1. Burgos N, Guerreiro F, McClelland J, Nill S, Dearnaley D, deSouza N, Oelfke U, Knopf AC, Ourselin S, Cardoso MJ: Joint Segmentation and CT Synthesis for MRI-only Radiotherapy Treatment Planning. MICCAI 2016
  2. Ladefoged CN, Law I, Anazodo U, St. Lawrence K, Izquierdo-Garcia D, Catana C, Burgos N, Cardoso MJ, Hutton BF, Ourselin S, Merida I, Costes N, Hammers A, Benoit D, Holm S, Juttukonda M, An H, Cabello J, Lukas M, Nekolla S, Ziegler S, Fenchel M, Jakoby B, Casey M, Benzinger T, Højgaard L, Hansen AE, and Andersen FL: Multi-center evaluation of eleven PET/MRI brain attenuation correction methods. IEEE NSS-MIC, 2016
  3. Burgos N, Cardoso MJ, Mendelson AF, Schott JM, Atkinson D, Arridge SR, Hutton BF, Ourselin S: Subject-specific Models for the Analysis of Pathological FDG PET Data. MICCAI 2015, LNCS 9350, pp. 651-658, 2015
  4. Burgos N, Cardoso MJ, Guerreiro F, Veiga C, Modat M, McClelland J, Knopf A-C, Punwani S, Atkinson D, Arridge SR, Hutton BF and Ourselin S: Robust CT Synthesis for Radiotherapy Planning: Application to the Head & Neck region. MICCAI 2015, LNCS 9350, pp. 476-484, 2015
  5. Zuluaga* MA, Burgos* N, Taylor AM, Ourselin S: Multi-atlas Synthesis for Computer Assisted Diagnosis: Application to Cardiovascular Diseases, Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on, 2015
  6. Jiao J, Markiewicz P, Burgos N, Atkinson, D, Hutton BF, Arridge SR, Ourselin S: Detail-Preserving PET Reconstruction with Sparse Image Representation and Anatomical Priors. Information Processing in Medical Imaging. LNCS 9123 pp. 540–551. 2015
  7. Burgos N, Thielemans K, Cardoso MJ, Markiewicz P, Jiao J, Dickson J, Duncan JS, Atkinson D, Arridge SR, Hutton BF and Ourselin S: Effect of Scatter Correction When Comparing Attenuation Maps: Application to Brain PET/MR, IEEE NSS-MIC, 2014
  8. Jiao J, Bousse A, Thielemans K, Markiewicz P, Burgos N, Atkinson D, Arridge SR, Hutton BF and Ourselin S: Joint Parametric Reconstruction and Motion Correction Framework for Dynamic PET Data. MICCAI 2014
  9. Kochan M, Daga P, Burgos N, White M, Cardoso MJ, Mancini L, Winston GP, McEvoy AW, Thornton J, Yousry T, Duncan JS, Stoyanov D, Ourselin S: Simulated Field Maps: Toward Improved Susceptibility Artefact Correction in Interventional MRI. IPCAI 2014
  10. Burgos N, Cardoso MJ, Modat M, Pedemonte S, Dickson J, Barnes A, Duncan JS, Atkinson D, Arridge SR, Hutton BF and Ourselin S: Attenuation Correction Synthesis for Hybrid PET-MR Scanners. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, pp. 147–154, 2013

 

  • Conferences abstracts
  1. Burgos N, Cardoso MJ, Guerreiro, F, McClelland J, Knopf AC and Ourselin S: Simultaneous Organ-at-Risk Segmentation and CT Synthesis in the Pelvic Region for MRI-only Radiotherapy Treatment Planning. ICCR, 2016
  2. Burgos N, Cardoso MJ, Guerreiro, F, McClelland J, Knopf AC, Punwani S and Ourselin S: CT Synthesis in the Head & Neck and Pelvic Regions for Radiotherapy Treatment Planning. Workshop on MRI Guided Radiotherapy, 2016
  3. Sekine T, Burgos N, Warnock G, Huellner MW, Buck A, ter Voert E, Cardoso MJ, Hutton BF, Ourselin S, Veit-Haibach P and Delso G: Multi atlas-based attenuation correction for brain FDG-PET imaging using a TOF-PET/MR scanner—comparison with clinical single atlas- and CT-based attenuation correction. ISMRM, 2016
  4. Prados F, Cardoso MJ, Burgos N, Wheeler-Kingshott C and Ourselin S: NiftyWeb: web based platform for image processing on the cloud, ISMRM, 2016
  5. Ladefoged CN, Law I, Anazodo U, Izquierdo-Garcia D, Burgos N, Merida I, Benoit D, Juttukonda M, Cabello J, Fenchel M, Jakoby B, Højgaard L, Hansen AE, and Andersen FL: A multi-method, multi-center study of PET/MRI brain attenuation correction on a large cohort of [18F]-FDG patients: ready for clinical implementation. RSNA, 2016
  6. Burgos N, Cardoso MJ, Modat M, Punwani S, Atkinson D, Arridge SR, Hutton BF and Ourselin, S: CT Synthesis in the Head & Neck Region for PET/MR Attenuation Correction: an Iterative Multi-atlas Approach. EJNMMI Physics, 2(Suppl 1):A31, 2015
  7. Guerreiro, F, McClelland J, Burgos N, Cardoso MJ, Dunlop A, Wong K, Nill S, Oelfke U, Knopf AC: Evaluation of different approaches to obtain synthetic CT images for a MRI-only radiotherapy workflow. MRinRT, 2015
  8. Dickson JC, Erlandsson K, Lehmann M, Modat M, Burgos N, Groves A, Schott J: Partial Volume Correction of Amyvid and FDG PET data using the discrete iterative Yang technique. EJNMMI, 42, S69-S69, 2015
  9. Mota A, Cuplov V, Schott JM, Hutton BF, Thielemans K, Drobnjak I, Dickson J, Bert J, Burgos N, Cardoso MJ, Modat M, Ourselin S and Erlandsson K: Establishment of an open database of realistic simulated data for evaluation of partial volume correction techniques in brain PET/MR. EJNMMI Physics, 2015
  10. Burgos N, Cardoso MJ, Thielemans K, Duncan JS, Atkinson D, Arridge SR, Hutton BF and Ourselin, S: Attenuation Correction Synthesis for Hybrid PET-MR Scanners: Validation for Brain Study Applications. EJNMMI Physics, 1(Suppl 1):A52, 2014
  11. Markiewicz P, Thielemans K, Burgos N, Manber R, Jiao J, Barnes A, Atkinson D, Arridge SR, Hutton BF and Ourselin S: Image reconstruction of mMR PET data using the open source software STIR. EJNMMI Physics, 2014