QUIBIM applies advanced algorithms to medical images to objectively measure disease or treatment-derived alterations in organs and tissues, offering additional quantitative information to the naked eye of radiologists. Technology is available for hospitals and pharma companies through QUIBIM PRECISION platform.
Our company provides state of the art solutions for early diagnosis, lesion grading-phenotyping-staging, treatment selection/follow-up, in addition to the validation against clinical endpoints.
QUIBIM offers a head to toe imaging biomarkers solution. The development of each imaging biomarker involves a controlled step-by-step process, implemented by QUIBIM and adopted in 2013 by the European Society of Radiology (ESR) guidelines, to ensure accurate and consistent high quality measures.
Number of Algorithms: Nine algorithms:
Publications and Press:
Sanz-Requena R, Martí-Bonmatí L, Pérez-Martínez R, García-Martí G. Dynamic contrast-enhanced case-control analysis in 3T MRI of prostate cancer can help to characterize tumor aggressiveness. Eur J Radiol. 2016 Nov;85(11):2119-2126. doi: 10.1016/j.ejrad.2016. 09.022. Epub 2016 Sep 28. PubMed PMID: 27776667.
Martí-Bonmatí L, Sanz-Requena R, Alberich-Bayarri A. Pharmacokinetic MR analysis of the cartilage is influenced by field strength. Eur J Radiol. 2008 Sep;67(3):448-52. doi: 10.1016/j.ejrad.2008.02.047. Epub 2008 Apr 22. PubMed PMID: 18434058.
Sanz-Requena R, Prats-Montalbán JM, Martí-Bonmatí L, Alberich-Bayarri Á, García-Martí G, Pérez R, Ferrer A. Automatic individual arterial input functions calculated from PCA outperform manual and population-averaged approaches for the pharmacokinetic modeling of DCE-MR images. J Magn Reson Imaging. 2015 Aug;42(2):477-87. doi: 10.1002/jmri.24805. Epub 2014 Nov 20. PubMed PMID: 25410482.
Sanz-Requena R, Revert-Ventura A, Martí-Bonmatí L, Alberich-Bayarri A, García-Martí G. Quantitative MR perfusion parameters related to survival time in high-grade gliomas. Eur Radiol. 2013 Dec;23(12):3456-65. doi: 10.1007/s00330-013-2967-y. Epub 2013 Jul 10. PubMed PMID: 23839170.
Delgado J, Calvillo P, Martí-Bonmatí L, Alberich-Bayarri Á, García-Castro F, González L. Cystic fibrosis imaging biomarkers: correlation with functional respiratory tests. SERAM 2017 oral communication. 2018 publication pending.
Mayorga-Ruiz I, García-Castro F, Alberich-Bayarri Á, García-Juan D, Calvillo P, Martí-Bonmatí L. Fully automated method for lung emphysema and low densities quantification from Multidetector CT images using adaptive air thresholding. European Congress of Radiology (ECR) 2017 oral communication. 2017 publication pending.
Alberich-Bayarri A, Marti-Bonmati L, Sanz-Requena R, Belloch E, Moratal D. In vivo trabecular bone morphologic and mechanical relationship using high-resolution 3T MRI. AJR Am J Roentgenol. 2008 Sep;191(3):721-6. doi: 10.2214/AJR.07.3528.
Alberich-Bayarri A, Moratal D, Ivirico JL, Rodríguez Hernández JC, Vallés-Lluch A, Martí-Bonmatí L, Estellés JM, Mano JF, Pradas MM, Ribelles JL, Salmerón-Sánchez M. Microcomputed tomography and microfinite element modeling for evaluating polymer scaffolds architecture and their mechanical properties. J Biomed Mater Res B Appl Biomater. 2009 Oct;91(1):191-202. doi: 10.1002/jbm.b.31389.
Alberich-Bayarri A, Marti-Bonmati L, Pérez MA, Sanz-Requena R, Lerma-Garrido JJ, García-Martí G, Moratal D. Assessment of 2D and 3D fractal dimension measurements of trabecular bone from high-spatial resolution magnetic resonance images at 3T. Med Phys. 2010 Sep;37(9):4930-4937. doi: 10.1118/1.3481509.
Alberich-Bayarri A, Martí-Bonmatí L, Sanz-Requena R, Sánchez-González J, Hervás Briz V, García-Martí G, Pérez MÁ. Reproducibility and accuracy in the morphometric and mechanical quantification of trabecular bone from 3 Tesla magnetic resonance images. Radiologia. 2014 Jan-Feb;56(1):27-34. doi: 10.1016/j.rx.2013.06.001.
Martí-Bonmatí L, Ramirez-Fuentes C, Alberich-Bayarri Á, Ruiz-Llorca C. State-of-the-art of bone marrow imaging in multiple myeloma. Curr Opin Oncol. 2015 Nov;27(6):540-50. doi: 10.1097/CCO.0000000000000230.
Jimenez-Pastor A, Alberich-Bayarri Á, Fos-Guarinos B, García-Castro F, García-Juan D, Glocker B, Martí-Bonmatí L. Automated CT vertebrae localization and identification by decision forests and image-based refinement. European Congress of Radiology (ECR) 2017 oral communication. 2017 publication pending.
Automatic extraction of perfusion curve descriptors from DCE-MR scans. Intensity-time perfusion curves are first converted to concentration curves. Then, several semiquantitative parameters are extracted from these curves: IAUC60: Area under the curve at 60 seconds, Maximum slope, Peak and Time-to- peak.
Zero-click automatic lung emphysema quantification with adaptive thresholding. Automatic lung segmentation through thresholding and morphological operations. Lung separation is achieved via a combination of transform distance and watershed algorithm. Vessel extraction using the Hessian matrix to detect tubular structures. Instead of using a fixed threshold for emphysema quantification, the threshold is calculated based on the Hounsfield Units (HU) of the air external to the body, achieving a much higher correlation with spirometry FEV1 (r=0.98) than with the usual -950 HU threshold1 2.
Trabecular bone feature extraction for the characterization of several bone conditions and disorders, such as osteoporosis or osteopenia. Bone Mineral Density (BMD) extracted from DEXA presents several limitations for a correct bone assessment. Using CT or a T1 MR sequence, it is possible to accurately quantify several trabecular bone characteristics 5 6 7 8 9 10: trabecular thickness and separation, bone volume, trabecular index and fractal dimension. These imaging biomarkers were collected in a comprehensive database of healthy and osteoporotic subjects. A multivariate analysis was applied to this database and a single score was derived from these biomarkers: Quality of Trabecular Structure (QTS). Thanks to this score, it is possible to easily assess the quality of the bone without the need to interpret a large number of quantitative variables.