Browsing by Author "Arrieta, Cristobal"

Now showing 1 - 6 of 6
  • Thumbnail Image
    Item
    Automatic quantification of fat infiltration in paraspinal muscles using T2-weighted images: An OsiriX application
    (ELSEVIER SCI LTD, 2020) Arrieta, Cristobal; Urrutia, Julio; Besa, Pablo; Montalba, Cristian; Lafont, Nelson; Andia, Marcelo E.; Uribe, Sergio
    Fat infiltration of paraspinal muscles has been related with low back pain and quantified using T2w MR images and manual segmentation techniques. This methodology is time consuming and has low reproducibility. Moreover, the accuracy of T2w images to quantify fat has not been validated. This paper presents the development and validation of an OsiriX application to semi-automatically segment infiltrated fat on T2w images. This software was also utilized to validate the quantification of muscle fat infiltration with T2w images, considering Dixon fat images assessments as a gold standard.
  • Thumbnail Image
    Item
    Impact of Respiratory Gating on Hemodynamic Parameters from 4D Flow MRI
    (2022) Denecken, Esteban ; Sotelo, Julio ; Arrieta, Cristobal ; Andia, Marcelo E. ; Uribe, Sergio
    The hemodynamic parameters from 4D flow datasets have shown promising diagnostic value in different cardiovascular pathologies. However, the behavior of these parameters can be affected when the 4D flow data are corrupted by respiratory motion. The purpose of this work was to perform a quantitative comparison between hemodynamic parameters computed from 4D flow cardiac MRI both with and without respiratory self-gating. We considered 4D flow MRI data from 15 healthy volunteers (10 men and 5 women, 30.40 +/- 6.23 years of age) that were acquired at 3T. Using a semiautomatic segmentation process of the aorta, we obtained the hemodynamic parameters from the 4D flow MRI, with and without respiratory self-gating. A statistical analysis, using the Wilcoxon signed-rank test and Bland-Altman, was performed to compare the hemodynamic parameters from both acquisitions. We found that the calculations of the hemodynamic parameters from 4D flow data that were acquired without respiratory self-gating showed underestimated values in the aortic arch, and the descending and diaphragmatic aorta. We also found a significant variability of the hemodynamic parameters in the ascending aorta of healthy volunteers when comparing both methods. The 4D flow MRI requires respiratory compensation to provide reliable calculations of hemodynamic parameters.
  • Thumbnail Image
    Item
    Is a single-level measurement of paraspinal muscle fat infiltration and cross-sectional area representative of the entire lumbar spine?
    (2018) Urrutia Escobar, Julio Octavio; Besa, Pablo; Lobos, Daniel; Andía Kohnenkampf, Marcelo Edgardo; Arrieta, Cristobal; Uribe Arancibia, Sergio A.
  • No Thumbnail Available
    Item
    Liver PDFF estimation using a multi-decoder water-fat separation neural network with a reduced number of echoes
    (2023) Meneses, Juan Pablo; Arrieta, Cristobal; della Maggiora, Gabriel; Besa, Cecilia; Urbina, Jesus; Arrese, Marco; Gana, Juan Cristobal; Galgani, Jose E.; Tejos, Cristian; Uribe, Sergio
    ObjectiveTo accurately estimate liver PDFF from chemical shift-encoded (CSE) MRI using a deep learning (DL)-based Multi-Decoder Water-Fat separation Network (MDWF-Net), that operates over complex-valued CSE-MR images with only 3 echoes.MethodsThe proposed MDWF-Net and a U-Net model were independently trained using the first 3 echoes of MRI data from 134 subjects, acquired with conventional 6-echoes abdomen protocol at 1.5 T. Resulting models were then evaluated using unseen CSE-MR images obtained from 14 subjects that were acquired with a 3-echoes CSE-MR pulse sequence with a shorter duration compared to the standard protocol. Resulting PDFF maps were qualitatively assessed by two radiologists, and quantitatively assessed at two corresponding liver ROIs, using Bland Altman and regression analysis for mean values, and ANOVA testing for standard deviation (STD) (significance level: .05). A 6-echo graph cut was considered ground truth.ResultsAssessment of radiologists demonstrated that, unlike U-Net, MDWF-Net had a similar quality to the ground truth, despite it considered half of the information. Regarding PDFF mean values at ROIs, MDWF-Net showed a better agreement with ground truth (regression slope = 0.94, R-2 = 0.97) than U-Net (regression slope = 0.86, R-2 = 0.93). Moreover, ANOVA post hoc analysis of STDs showed a statistical difference between graph cuts and U-Net (p < .05), unlike MDWF-Net (p = .53).ConclusionMDWF-Net showed a liver PDFF accuracy comparable to the reference graph cut method, using only 3 echoes and thus allowing a reduction in the acquisition times.
  • Thumbnail Image
    Item
    Lumbar paraspinal muscle fat infiltration is independently associated with sex, age, and inter-vertebral disc degeneration in symptomatic patients
    (2018) Urrutia Escobar, Julio Octavio; Besa, Pablo; Lobos, Daniel; Campos Daziano, Mauricio Andrés; Arrieta, Cristobal; Andía Kohnenkampf, Marcelo Edgardo; Uribe Arancibia, Sergio A.
  • No Thumbnail Available
    Item
    Unbiased and reproducible liver MRI-PDFF estimation using a scan protocol-informed deep learning method
    (2024) Meneses, Juan P.; Qadir, Ayyaz; Surendran, Nirusha; Arrieta, Cristobal; Tejos, Cristian; Andia, Marcelo E.; Chen, Zhaolin; Uribe, Sergio
    ObjectiveTo estimate proton density fat fraction (PDFF) from chemical shift encoded (CSE) MR images using a deep learning (DL)-based method that is precise and robust to different MR scanners and acquisition echo times (TEs).MethodsVariable echo times neural network (VET-Net) is a two-stage framework that first estimates nonlinear variables of the CSE-MR signal model, to posteriorly estimate water/fat signal components using the least-squares method. VET-Net incorporates a vector with TEs as an auxiliary input, therefore enabling PDFF calculation with any TE setting. A single-site liver CSE-MRI dataset (188 subjects, 4146 axial slices) was considered, which was split into training (150 subjects), validation (18), and testing (20) subsets. Testing subjects were scanned using several protocols with different TEs, which we then used to measure the PDFF reproducibility coefficient (RDC) at two regions of interest (ROIs): the right posterior and left hepatic lobes. An open-source multi-site and multi-vendor fat-water phantom dataset was also used for PDFF bias assessment.ResultsVET-Net showed RDCs of 1.71% and 1.04% on the right posterior and left hepatic lobes, respectively, across different TEs, which was comparable to a reference graph cuts-based method (RDCs = 1.71% and 0.86%). VET-Net also showed a smaller PDFF bias (-0.55%) than graph cuts (0.93%) when tested on a multi-site phantom dataset. Reproducibility (1.94% and 1.59%) and bias (-2.04%) were negatively affected when the auxiliary TE input was not considered.ConclusionVET-Net provided unbiased and precise PDFF estimations using CSE-MR images from different hardware vendors and different TEs, outperforming conventional DL approaches.Key PointsQuestionReproducibility of liver PDFF DL-based approaches on different scan protocols or manufacturers is not validated.FindingsVET-Net showed a PDFF bias of -0.55% on a multi-site phantom dataset, and RDCs of 1.71% and 1.04% at two liver ROIs.Clinical relevanceVET-Net provides efficient, in terms of scan and processing times, and unbiased PDFF estimations across different MR scanners and scan protocols, and therefore it can be leveraged to expand the use of MRI-based liver fat quantification to assess hepatic steatosis.Key PointsQuestionReproducibility of liver PDFF DL-based approaches on different scan protocols or manufacturers is not validated.FindingsVET-Net showed a PDFF bias of -0.55% on a multi-site phantom dataset, and RDCs of 1.71% and 1.04% at two liver ROIs.Clinical relevanceVET-Net provides efficient, in terms of scan and processing times, and unbiased PDFF estimations across different MR scanners and scan protocols, and therefore it can be leveraged to expand the use of MRI-based liver fat quantification to assess hepatic steatosis.Key PointsQuestionReproducibility of liver PDFF DL-based approaches on different scan protocols or manufacturers is not validated.FindingsVET-Net showed a PDFF bias of -0.55% on a multi-site phantom dataset, and RDCs of 1.71% and 1.04% at two liver ROIs.Clinical relevanceVET-Net provides efficient, in terms of scan and processing times, and unbiased PDFF estimations across different MR scanners and scan protocols, and therefore it can be leveraged to expand the use of MRI-based liver fat quantification to assess hepatic steatosis.