Open Access
Diagnostic and prognostic potential of multiparametric renal MRI in Kidney transplant patients
(Wiley, 2024) Echeverría Chasco, Rebeca; Martín Moreno, Paloma L.; Aramendía Vidaurreta, Verónica; García-Ruiz, Leyre; Mora-Gutiérrez, José María; Vidorreta Díaz de Cerio, Marta; Villanueva Larre, Arantxa; Cano, David; Bastarrika, Gorka; Fernández Seara, María A.; Ingeniería Eléctrica, Electrónica y de Comunicación; Institute of Smart Cities - ISC; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
Background: Multiparametric MRI provides assessment of functional and structural parameters in kidney allografts. Itoffers a non-invasive alternative to the current reference standard of kidney biopsy. Purpose: To evaluate the diagnostic and prognostic utility of MRI parameters in the assessment of allograft function in thefirst 3-months post-transplantation. Study Type: Prospective. Subjects: 32 transplant recipients (54 17 years, 20 females), divided into two groups according to estimated glomerularfiltration rate (eGFR) at 3-months post-transplantation: inferior graft function (IGF; eGFR<45 mL/min/1.73 m2,n=10) andsuperior graft function (SGF; eGFR≥45 mL/min/1.73 m2,n=22). Further categorization was based on the need for hemo-dialysis (C1) and decrease in s-creatinine (C2) at 1-week post-transplantation: delayed-graft-function (DGF:n=4 C1,n=10 C2) and early graft-function (EGF:n=28 C1,n=22 C2). Field Strength/Sequence: 3-T, pseudo-continuous arterial spin labeling, T1-mapping, and diffusion-weighted imaging. Assessment: Multiparametric MRI was evaluated at 1-week in all patients and 3-months after transplantation in 28 patients. Renalbloodflow (RBF), diffusion coefficients (ADC,ΔADC,D,ΔD,D*,flowing fractionf),T1andΔT1were calculated in cortex andmedulla. The diagnostic and prognostic value of these parameters, obtained at 3-months and 1-week post-transplantation,respectively, was evaluated in the cortex to discriminate between DGF and EGF, and between SGF and IGF. Statistical Tests: Logistic regression, receiver-operating-characteristics, area-under-the-curve (AUC), confidence intervals(CIs), analysis-of-variance,t-test, Wilcoxon-Mann–Whitney test, Fisher’s exact test, Pearson’s correlation.P-value<0.05 wasconsidered significant. Results: DGF patients exhibited significantly lower cortical RBF and f and higherD*. The diagnostic value of MRI fordetecting DGF was excellent (AUC=100%). Significant differences between patients with IGF and SGF were found inRBF,ΔT1, andΔD. Multiparametric MRI showed higher diagnostic (AUC=95.32%; CI: 88%–100%) and prognostic(AUC=97.47%, CI: 92%–100%) values for detecting IGF than eGFR (AUC=89.50%, CI: 79%–100%). Data Conclusion: Multiparametric MRI may show high diagnostic and prognostic value in transplanted patients, yieldingbetter results compared to eGFR measurements. Level of Evidence: 2. Technical Efficacy: Stage 1.
Open Access
Advancing ASL kidney image registration: a tailored pipeline with VoxelMorph
(Springer, 2025-01-31) Oyarzun Domeño, Anne; Cia Alonso, Izaskun; Echeverría Chasco, Rebeca; Fernández Seara, María A.; Martín Moreno, Paloma L.; García Fernández, Nuria; Bastarrika, Gorka; Navallas Irujo, Javier; Villanueva Larre, Arantxa; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa; Gobierno de Navarra / Nafarroako Gobernua
In clinical renal assessment, image registration plays a pivotal role, as patient movement during data acquisition can significantly impede image post-processing and the accurate estimation of hemodynamic parameters. This study introduces a deep learning-based image registration framework specifically for arterial spin labeling (ASL) imaging. ASL is a magnetic resonance imaging technique that modifies the longitudinal magnetization of blood perfusing the kidney using a series of radiofrequency pulses combined with slice-selective gradients. After tagging the arterial blood, label images are captured following a delay, allowing the tagged blood bolus to enter the renal tissue, while control images are acquired without tagging the arterial spins. Given that perfusion maps are generated at the pixel level by subtracting control images from label images and considering the relatively small signal intensity difference, precise alignment of these images is crucial to minimize motion artefacts and prevent significant errors in perfusion calculations. Moreover, due to the extended ASL acquisition times and the anatomical location of the kidneys, renal images are often susceptible to pulsation, peristalsis, and breathing motion. These motion-induced noises and other instabilities can adversely affect ASL imaging outcomes, making image registration essential. However, research on renal MRI registration, particularly with respect to learning-based techniques, remains limited, with even less focus on renal ASL. Our study proposes a learning-based image registration approach that builds upon VoxelMorph and introduces groupwise inference as a key enhancement. The dataset includes 2448 images of transplanted kidneys (TK) and 2456 images of healthy kidneys (HK). We compared the automatic image registration results with the widely recognized optimization method Elastix. The model’s performance was evaluated using the mean structural similarity index (MSSIM), normalized correlation coefficient (NCC), temporal signal-tonoise ratio (TSNR) of the samples, and the mean cortical signal (CSIM) in perfusion-weighted images, thereby extending the evaluation beyond traditional similarity-based metrics. Our method achieved superior image registration performance, with peak NCC (0.987 ± 0.006) and MSSIM (0.869 ± 0.048) values in the kidney region, significantly surpassing Elastix and the unregistered series (p\ 0.05) on TK and HK datasets. Regularization analysis showed that higher k values (1, 2) produced smoother deformation fields, while moderate k values (0.5, 0.9) balanced smoothness and detail, maintaining low non-positive Jacobian percentages (\1%) comparable to Elastix. Additionally, our method improved CSIM by 14.3% (2.304 ± 1.167) and TSNR by 13.1% (3.888 ± 2.170) in TK, and achieved up to 13.2% (CSIM) and 29.8% (TSNR) enhancements in HK, demonstrating robustness and improved signal quality across datasets and acquisition techniques.
Open Access
A deep learning image analysis method for renal perfusion estimation in pseudo-continuous arterial spin labelling MRI
(Elsevier, 2023) Oyarzun Domeño, Anne; Cia Alonso, Izaskun; Echeverría Chasco, Rebeca; Fernández Seara, María A.; Martín Moreno, Paloma L.; Bastarrika, Gorka; Navallas Irujo, Javier; Villanueva Larre, Arantxa; Ingeniería Eléctrica, Electrónica y de Comunicación; Institute of Smart Cities - ISC; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
Accurate segmentation of renal tissues is an essential step for renal perfusion estimation and postoperative assessment of the allograft. Images are usually manually labeled, which is tedious and prone to human error. We present an image analysis method for the automatic estimation of renal perfusion based on perfusion magnetic resonance imaging. Specifically, non-contrasted pseudo-continuous arterial spin labeling (PCASL) images are used for kidney transplant evaluation and perfusion estimation, as a biomarker of the status of the allograft. The proposed method uses machine/deep learning tools for the segmentation and classification of renal cortical and medullary tissues and automates the estimation of perfusion values. Data from 16 transplant patients has been used for the experiments. The automatic analysis of differentiated tissues within the kidney, such as cortex and medulla, is performed by employing the time-intensity-curves of non-contrasted T1-weighted MRI series. Specifically, using the Dice similarity coefficient as a figure of merit, results above 93%, 92% and 82% are obtained for whole kidney, cortex, and medulla, respectively. Besides, estimated cortical and medullary perfusion values are considered to be within the acceptable ranges within clinical practice.
Open Access
Multiparametric renal magnetic resonance imaging: a reproducibility study in renal allografts with stable function
(Wiley, 2023) Echeverría Chasco, Rebeca; Martín Moreno, Paloma L.; Vidorreta, Marta; Aramendía Vidaurreta, Verónica; Cano, David; Villanueva Larre, Arantxa; Bastarrika, Gorka; Fernández Seara, María A.; García Fernández, Nuria; Ingeniería Eléctrica, Electrónica y de Comunicación; Institute of Smart Cities - ISC; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
Monitoring renal allograft function after transplantation is key for the early detection of allograft impairment, which in turn can contribute to preventing the loss of the allograft. Multiparametric renal MRI (mpMRI) is a promising noninvasive technique to assess and characterize renal physiopathology; however, few studies have employed mpMRI in renal allografts with stable function (maintained function over a long time period). The purposes of the current study were to evaluate the reproducibility of mpMRI in transplant patients and to characterize normal values of the measured parameters, and to estimate the labeling efficiency of Pseudo-Continuous Arterial Spin Labeling (PCASL) in the infrarenal aorta using numerical simulations considering experimental measurements of aortic blood flow profiles. The subjects were 20 transplant patients with stable kidney function, maintained over 1 year. The MRI protocol consisted of PCASL, intravoxel incoherent motion, and T1 inversion recovery. Phase contrast was used to measure aortic blood flow. Renal blood flow (RBF), diffusion coefficient (D), pseudo-diffusion coefficient (D*), flowing fraction (f), and T1 maps were calculated and mean values were measured in the cortex and medulla. The labeling efficiency of PCASL was estimated from simulation of Bloch equations. Reproducibility was assessed with the within-subject coefficient of variation, intraclass correlation coefficient, and Bland-Altman analysis. Correlations were evaluated using the Pearson correlation coefficient. The significance level was p less than 0.05. Cortical reproducibility was very good for T1, D, and RBF, moderate for f, and low for D*, while medullary reproducibility was good for T1 and D. Significant correlations in the cortex between RBF and f (r = 0.66), RBF and eGFR (r = 0.64), and D* and eGFR (r = 0.57) were found. Normal values of the measured parameters employing the mpMRI protocol in kidney transplant patients with stable function were characterized and the results showed good reproducibility of the techniques.

Echeverría Chasco, Rebeca

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