{"paper_id":"ce9b16ed-2718-4339-8cd7-7065a149b5fa","body_text":"PAPER DRAFT \n \nTitle: Navigator-gated free-breathing joint T1-T2 mapping of the kidney \nAuthors: Pauline Calarnou1, Augustin C. Ogier 1, Christopher W. Roy1, Jean-Baptiste Ledoux1,2, Angela \nRocca3, Menno Pruijm4, Roger Hullin3, Jean-Paul-Vallée5, Jérôme Yerly1,2, Ruud B. van Heeswijk1 \n1. Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, \nSwitzerland  \n2. CIBM Center for BioMedical Imaging, Lausanne and Geneva, Switzerland \n3. Cardiology Service, Cardiovascular Department, Lausanne University Hospital and University of \nLausanne, Lausanne, Switzerland \n4. Nephrology Service, Internal Medicine Department, Lausanne University Hospital and University of \nLausanne, Lausanne, Switzerland \n5. Radiology Service, Department of Diagnostics, Geneva University Hospital and University of \nGeneva, Switzerland \nAbstract  \n \nPurpose \nTo develop and evaluate a free -breathing 2D radial joint T ₁-T₂ mapping technique for the \nkidneys at 3T, and to assess the impact of navigator gating parameters on mapping precision \nand accuracy. \nMethods \nThe PARMANav sequence (PArametric Radial M Apping with Navigator gating) was \nimplemented for renal imaging, using 25 single-shot radial gradient echo acquisitions with five \nrepeated magnetization preparations and lung -liver navigator gating  to avoid through -plane \nmotion. Virtual compressed coil and c ompressed sensing with spatial and contrast \nregularization was used for image reconstruction, followed by a model-based registration. An \nacquisition-specific joint T ₁-T₂ dictionary was generated using extended phase graph \nsimulations. T1 and T2 accuracies were quantified in a phantom study versus gold standard \nspin-echo-based sequences. T he influence of the navigator acceptance window width  \n(NAWW) and navigator rejection on T 1 and T 2 precision were  established in 10 healthy \nvolunteers and were compared to routine T1 and T2 mapping. Three patients were scanned to \ndemonstrate clinical feasibility. \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.17.25333832doi: medRxiv preprint \nNOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.\n\nResults \nIn the phantom, PARMANav T1 and T2 values showed high accuracy with the gold standard T1 \nand T2 values and were insensitive to rejected navigators (< 5% variation for  T1 and T2). As \nexpected from previous studies, in-vivo renal PARMANav T1 and T2 values were higher than \nroutine values but showed lower variability, both per subject and between subjects : in the  \ncortex PARMANav T1=1601±48ms/T2=90.8±5.0ms vs routine \nT1=1307±108ms/T2=73.3±8.0ms, while in the medulla PARMANav T 1=2044±82ms/T2 \n=90.3±5.4ms and routine T1=1560±122ms/T2=67.6±5.8ms. No T1 or T2 trend was observed for \nthe different NAWW. Feasibility was demonstrated in patients, where high-quality maps were \nobtained. \nConclusion \nPARMANav allows for precise and accurate joint T 1-T2 mapping of the kidneys without  \nrequiring breath holding. Through-plane motion artifacts were avoided with a navigator, which \ndid not impact the accuracy or precision of the resulting maps. \nIntroduction  \nIn kidney  disease, the T1 and T 2 relaxation times reflect underlying changes in tissue \ncomposition,1 while a correlation between T1 cortico-medullary differentiation (CMD) and renal \nfunction has been demonstrated in several renal diseases.2,3 T1 CMD was proven to be related \nto the degree of fibrosis, while elevated cortex T1 values is strongly associated  with poor renal \noutcome in patient with chronic kidney disease and with allograph kidneys,4 and oedema and \nT2 increases with inflammation and edema. 5 Renal T 1 mapping is typically performed with \nmodified Look-Locker inversion recovery (MOLLI),5,6 which is known to underestimate the T 1 \nvalue.7 Renal T 2 mapping is less common and is usually performed with a turbo spin -echo \nsequence, which typically overestimates the T2 value.  \nInstead of mapping a single relaxation time per acquisition, o ver the past decade, \nmultiparametric mapping techniques have enabled the simultaneous measurement of multiple \nrelaxation times in a single acquisition. These approaches have gained interest thanks to their \nability to provide more comprehensive insights into renal structure and function,5 while known \npulse sequence imperfections (e.g., slice profile, inversion efficiency)  can be incorporated in \nthe fitting model to improve accuracy.  Although such techniques have been successfully \ncommercialized for neuroimaging with MR fingerprinting,8 and a large number of variations has \nbeen studied for cardiac MRI ,9–12 its application to  renal imaging is thus far limited to the \nresearch setting, partly because they require breath holding, which is not always feasible in \nkidney disease patients. The development of free -breathing techniques would represent a n \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.17.25333832doi: medRxiv preprint \n\nimpactful step forward towards the integration of T 1 and T 2 mapping in clinical practice to \nassess kidney structure in these patients. Recent studies have demonstrated applications for \nbreath-held joint T1-T2* mapping13,T1-T2 mapping14, and free-breathing T1-T2* mapping15 of the \nkidney at 3T. Most of the abovementioned techniques require breath holding, which is not \nalways feasible in patients . Free-breathing archieved with respiratory gating, 15 relies on the \nassumption  of a constant respiratory cycle duration and used a two parameters analytical fit, \nthat doesn’t allow to model precisely the magnetization evolution. \nConversely, many recent multiparametric techniques claim to allow for free breathing through \nthe use of state-of-the-art in-plane retrospective motion correction (i.e., registration) between \nthe source images , but do not account for  through-plane motion: motion in the direction \nperpendicular to the image plane and thus invisible to a motion correction algorithm . This \nomission could be significant, potentially lead ing to perceived increased values in healthy \ntissues or normal values in a lesion.  The reliance on registration, especially non -rigid \nregistration, could also induce additional errors that propagate in final maps. To enable a free-\nbreathing acquisition that avoids such discrepancies between its source images, a lung-liver \nnavigator can  be used to  track the diaphragm position ,9 albeit at the cost of acquisition \nefficiency. The accuracy of such a technique was previously assessed in a cardiac numerical \nphantom and reported no dependencies on the number of navigator rejections.16 \nIn the current study, we aimed to demonstrate that an adaptation of the free -breathing \nnavigator-gated multi parametric mapping technique PARMANav (PArametric Radial MApping \nwith Navigator gating)16 can be used to obtain accurate and precise parametric maps of the \nkidney at 3T while avoiding through-plane motion.  \nMethods  \nPulse sequence design \nWe adapted PARMANav16 for renal imaging by implementing a free-breathing acquisition of \n25 magnetization -prepared single -shot 2D gradient -recalled echo (GRE) images , e ach \nacquired with  a continuous golden angle  trajectory (Figure 1) . To enable magnetization \nrecovery, the blocks (i.e., a magnetization preparation and a single-shot image acquisition)  \nwere repeated every 1s. To achieve joint T₁-T₂ sensitivity, five different contrast preparations \nwere repeated in series : one adiabatic inversion pulse ( a 5.12 ms hyperbolic tangent), no \npreparation, and three different T₂-preparation modules. This set was repeated five times to \nensure contrast diversity throughout the different number of skipped navigators (Figure 1 A).  \n \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.17.25333832doi: medRxiv preprint \n\n \nFigure 1.  Overview of the free -breathing radial 2D joint T 1-T2 mapping technique \nPARMANav. A) The p ulse sequence diagram  shows the first of five repeated blocks that \nconsist of five differently prepared images, and the simulated magnetization of a healthy renal \ncortex and medulla. B) Illustration of the placement of the navigator  (green) and image slice \n(yellow) as well as a trace of the navigator with the acceptance window as two parallel green \nlines. C) Example first 5 images in a healthy volunteer kidney, reconstructed using compressed \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.17.25333832doi: medRxiv preprint \n\nsensing (CS). D) The dictionary is created through EPG simulations  of the magnetization  \nacross the 25 images. E) Model-based motion correction between source and synthetic \nimages obtained with the dictionary  and the first unregistered maps.  F) The final maps are \nobtained by computing the  pixel-wise dot-product between the registered images and the \ndictionary 𝑑⃑. \nRespiratory motion was tracked using a lung-liver navigator acquired before each preparation, \nwith slice tracking enabled. To ensure that the timing of all magnetization changes was known, \nboth preparation and readout modules were skipped when navigator rejection occurred. \nAll data were acquired on a 3T clinical scanner (Magnetom Prisma  or PrismaFit, Siemens \nHealthineers, Forchheim, Germany) with nominal matrix size 192x192 (resulting in a 384x384 \nmatrix through oversampled radial gridding), 45 continuous golden-angle (68°) radial lines per \nimage (corresponding to 15% radial Nyquist sampling), pixel size=(1.56mm)2, slice thickness \n8mm, flip angle 12°, bandwidth 789Hz/pixel, repetition time TR 3.49ms, echo time TE 1.56ms, \nacquisition window duration 151ms, inversion time TI 68ms (for the image directly after the \ninversion), and T2-preparartion modules echo times 23/45/70 ms. A fixed 5s delay at the start \nof the pulse sequence allows for complete magnetization relaxation between successive \nacquisitions. For all acquisitions we used a 34-channel chest-spine coil array. \nImage and map reconstruction \nIndividual RF coil elements were combined using region-optimized virtual (ROVir) coils17,18 to \nminimize radial streaking artifacts coming from the arms and the abdominal fat. Briefly, the \n192x192-pixels central part of the  acquired image was automatically selected as the region of \ninterest, and the periphery of the image was designated as the unwanted signal  region. A \ngeneralized eigenvalue decomposition was used to identify virtual coils that maximize the \nsignal-to-interference ratio (SIR). The smallest set of virtual coils capturing ≥90% of the total \nsignal energy was retained. \nFrom these undersampled k -spaces, images were reconstructed using compressed sensing \nwith total variation regularization in the spatial dimension and local -low-rank regularization \nalong the contrast dimension:19 \n𝐱̂  =  𝐚𝐫𝐠 𝐦𝐢𝐧𝐱 ‖𝐅𝐂𝐱 − 𝐲‖𝟐\n𝟐  + 𝝀𝐒‖𝛁𝐬𝐱‖𝟏  + 𝝀𝐂 ∑ ‖𝐋𝒊𝐱‖𝒊>𝟏 ∗   (1), \nwhere 𝐱̂ is the reconstructed image, 𝐅 is the nonuniform fast Fourier operator, 𝐂 is the coil \nsensitivity, 𝐲 refers to the acquired k-space, 𝛁𝑺 is the first-order difference operator along the \nspatial dimension, 𝐋𝒊 is the operator that extracts the i-th spatial patch of size 6x6x6 pixels \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.17.25333832doi: medRxiv preprint \n\nfrom 𝐱 and forms a Casorati matrix with the contrast dimension, ‖∙‖∗ is the nuclear norm, and \n𝜆𝑆  and 𝜆𝐶  are the corresponding regularization weights along the spatial and contrast \ndimensions, respectively. Regularization parameters, 𝜆𝑆 =  0.01  and 𝜆𝐶 = 0.06  were \nempirically selected for optimal trade-off between undersampling artifact removal and image \nblurring. \n \nConsidering individual navigator rejections, a n acquisition-specific signal dictionary was \ngenerated via extended phase graph simulations in MATLAB ( version R2023b, The \nMathworks, Natick, Massachusetts, USA) across a wide range of T₁ values from 0 ms to 5000 \nms in 10 ms increments, and from 5020 ms to 6000 ms in 20 ms increments.  and T₂ values \nfrom 0 ms to 450 ms with a 5 ms increment and from 460 ms to 700 ms with a 10 ms increment, \nincorporating slice profile effects ( discretized in 50 isochromats) and inversion inefficiency \ncorrection.20 The precise acquisition timing was extracted from raw data to reflect navigator \nacceptance. The magnetization was simulated at the center of each echo, and the signal  \naveraged over each radial single-shot echo train, resulting in a 25×60 396 (T2>T1 cases being \nexcluded) matrix of simulated complex signals that formed the dictionary. \n \nA previously described model-based non-rigid image registration16 was applied to account for \nresidual in-plane motion that resulted from residual differences in the respiratory phase while \naccounting for the strong contrast variations in these images (Figure 1 E). Here, a first set off \nmaps was generated via pixel -wise dictionary matching without motion correction. Synthetic \nimages with matched contrast and averaged motion were then created and used as references \nfor non -rigid optical -flow-based registration. 21 Final maps were obtained by repeating \ndictionary matching on the motion-corrected images. \n \nPhantom study  \nThe “ISMRM/NIST” phantom22 (Premium System 130, CaliberMRI, Boulder, USA)  was \nscanned to evaluate the accuracy of PARMANav  readout echo trains were separated by \nintervals of 1s, which were sporadically extended to 2s or 3s such that navigator rejections \ncould be emulated. Clinical routine T 1 and T2 maps (pixel size=(1.4-1.9mm)2, slice thickness \n8mm) were acquired using 5(3)5 MOLLI,6 and T2-prepared (T2-prep) bSSFP T2 mapping,23,24 \nrespectively. It was compared against the values obtained with  gold-standard inversion -\nrecovery spin -echo (for T 1 relaxation) and spin -echo (for T 2 relaxation) techniques. The \ndifferent sequences parameters are reported in Table 1.  \n \n \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.17.25333832doi: medRxiv preprint \n\n \n \n \n \nTable 1. Parameters of the sequences used for T1 and T2 mapping. The same parameters \nwere used in the phantom and in-vivo scans. \n PARMANav MOLLI T2-prep bSSFP \nResolution 1.56 x 1.56 mm2 1.4 x 1.4 mm2 1.9 x 1.9 mm2 \nMatrix size 192 x 192 256 x 141 192 x 117 \nSlice thickness 8 mm \nFlip angle 12 ° 12 ° 60 ° \nBandwidth 789 Hz/pixel 977 Hz/pixel 930 Hz/pixel \nTR 3.49 ms   \nTE 1.56 ms 1.18 ms 1.04 \nPreparation \npulse \nInversion, 3 T2-prep Inversion 3 T2-prep \nTrajectory Golden-angle radial Cartesian \nAcceleration Radial 6.7x GRAPPA 2x \nFree-breathing Yes, with a navigator Breath-held (13 s) Breath-held (9 s) \n \nHealthy volunteer study  \nEthics approval was obtained from the Ethics Committee of the Canton of Vaud (CER-VD) of \nSwitzerland under reference numbers 2021-00697 and 2022-00934.  All participants provided \nwritten informed consent to participate prior to enrollment.  \n \nPARMANav maps of N=10 healthy volunteers (31.8±9.1 y, 4F) were acquired with four different \nnavigator acceptance window widths (NAWWs of ±4mm, ±8mm, ±16mmm, and ±32mm) of the \nlung-liver navigator in a randomized order to study the impact of through -plane motion.  \nVolunteers were instructed to breath normally.  Like in the phantom study, c linical routine  \nbreath-held T1 and T2 maps were acquired using MOLLI and T2-prepared bSSFP T2 mapping, \nrespectively.  \nThe T1 and T2 value of the visible cortex and medulla as well as the CMD  ratio (as the ratio \nT1cortex/T1medulla) were determined for each map in each volunteer by manually segmenting \nregions of interest (ROIs) in both kidneys on the T 1 map. Values from the two kidneys were \naveraged. The coefficient of variation (CoV) was calculated as the regional standard deviation \ndivided by the mean relaxation time. The acquisition time was recorded for the four NAWWs. \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.17.25333832doi: medRxiv preprint \n\n \nPatient study \nTo preliminarily evaluate its clinical feasibility, PARMANav was acquired in three patients \n(65.7±15.0,1F) as part of an ongoing study on heart failure.25 Two patients had chronic kidney \ndisease (CKD), one with heart failure with preserved ejection fraction  (HFpEF) and one with \nheart failure with reduced ejection fraction, while one patient had HFpEF but no CKD. In each \npatient, one map was acquired with a NAWW of ±8mm. No routine mapping techniques were \nacquired due to the constraints of the total MRI protocol duration. Anatomical reference images \n(T2-weighted HASTE) were acquired at the same location for two of the three patients, and at \na slightly different orientation for the last patient. \nStatistics \nAgreement between the measured and gold-standard values in the phantom was evaluated \nusing the slope and coefficient of determination (R²) from linear regression analysis for both \nPARMANav with and without skipped navigator, and with the routine techniques. Bland-\nAltman analysis was performed to quantify biases and limits of agreement, and a paired t-\ntest was conducted to evaluate statistical significance.  \nIn the healthy volunteers, the T1 and T2 values of the cortex and medulla were extracted for \nthe four NAWWs and the two routines methods. A Shapiro-Wilk test was performed to \nassess normality. Their differences, as well as the time of acquisition differences, were \ntested with a repeated-measures analysis of variance (RM-ANOVA) with post-hoc Tukey \nanalysis. The regional T1-T2 values and CoV means and standard deviations (SDs) across \nthe healthy volunteers were reported. Bland-Altman analysis was performed to quantify \nbiases and limits of agreement. \nResults  \nPhantom study \n \nIn the phantom, PARMANav showed high agreement with the gold standard, both with and \nwithout rejected navigators (Nskipped =23) (Figure 2): the slope of the correlation was closer \nto identity than the routine technique for T1 (1.05 and 1.00 for without and with skipped \nheartbeat, respectively vs 0.76 for MOLLI), while there was a larger difference for T2 (1.10 \nand 1.21 vs 0.60). R² was above 0.99 for both the PARMANav scans and MOLLI, and \nslightly different for the T2 mapping methods (>0.99 for the two PARMANav vs 0.94 for T2-\nprep bSSFP). As with previous studies,16 PARMANav with and without skipped navigators \ndid not significantly differ for T2 (P>0.1). A significant difference was reported for T1 \n(P=0.005), with a small average relative difference (6.6%). \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.17.25333832doi: medRxiv preprint \n\n \nFigure 2. Agreement of PARMANav T 1 and T 2 maps in the ISMRM-NIST phantom \ncompared to spin-echo reference values in case of 23 skipped navigator. A) T1 map of \nthe phantom obtained with PARMANav. B) T2 map of the reference phantom obtained with \nPARMANav. C) Linear regression of PARMANav T1 values with and without rejected navigator \nand the clinical routine 5(3) 5 MOLLI versus gold -standard IR-SE. D) Linear regressions of \nPARMANav T 2 values with and without rejected navigator and the clinical routine T 2-prep \nbSSFP in the phantom in the clinically relevant range versus gold -standard SE. E) T1 Bland-\nAltman plot of PARMANav with and without rejected navigator and MOLLI versus gold -\nstandard IR-SE. The bias and confidence bounds are reported in the legend . PARMANav \npresented a larger bias when adding a large number  of skips (N=23). F) Same as E) for T 2 \nwith T2-prep bSSFP as the clinical routine and SE as the gold-standard.  \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.17.25333832doi: medRxiv preprint \n\nPrecision in Healthy Volunteers  \nIn all 10 healthy volunteers, visually sharp and precise maps of the kidney were obtained with \nPARMANav (Figure 3). PARMANav T1-T2 values and CMD were significantly different from \nroutine techniques, which are known to underestimate T1 and overestimate T2,26 but there were \nno significant differences as a function of the differ ent NAWWs (Figure 4). The cortex and \nmedulla T1 values presented a larger spread for NAWW=±4 mm than for NAWW=±8 mm with \nmore outliers (SD4mm = 117 ms and SD8mm = 48 ms for the cortex). NAWW=±4 mm also resulted \nin a significantly longer acquisition time  (p<0.02 for NAWW=±4 mm versus all the other \nNAWWs)(Figure 5). Given these results  and the need for balance between low navigator \nacceptance (at small NAWW) and tolerated through-plane motion (at large NAWW s), \nNAWW=±8 mm mapping was chosen as best compromise for further analysis.  \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.17.25333832doi: medRxiv preprint \n\n \nFigure 3. PARMAN av T1 and T 2 maps for the different NAWWs compared to the \nroutine techniques. The NAWW did not significantly change the relaxation times in the \nresulting PARMANav T1 or T 2 maps of the cortex, with the exception of the poles (red \narrowheads), where partial volume effect lower ed the T 1 relaxation time. However, the \nshape and the T1 values of the medulla varied with the NAWW (orange arrowhead), likely \ndue to through-plane motion. The PARMANav T1 and T2 values were consistently higher \nthan those obtained with the routine techniques. \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.17.25333832doi: medRxiv preprint \n\n \nFigure 4. Impact of the navigator acceptance window width (NAWW) . in 10 healthy \nvolunteers. A-C) T1 values the cortex and in the medulla and the corresponding CMD ratio for \nthe four NAWWs compared to MOLLI, which is known to underestimate T1 value, especially at \nthese higher values . D, E) T2 values in the cortex and in the medulla for the four NAWW \ncompared to T2-prepared bSSFP.  \n \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.17.25333832doi: medRxiv preprint \n\n \nFigure 5. PARMANav a cquisition time for  the different NAWWs. Acquisition time  was \nlonger for smaller NAWW as expect ed. For NAWW=±16 and 32 mm, the time of acquisition \nwas almost identical,  due to no rejection for both NAWWs . The difference was significant \nbetween all the time of acquisition (p<0.02) except between NAWW=±16 and 32 mm (p=0.79) \nPARMANav T1-T2 values were higher than routine techniques (Table 2). Smaller inter-subject \nSD was reported for all relaxation times.  \nTable 2. Cortex and medulla T1 and T2 mean and standard deviation in ms across the 10 \nhealthy volunteers, as well as the coefficient of variation (CoV). \n PARMANav NAWW=±8 mm Routine \n Mean ± SD CoV Mean ± SD CoV \nCortex T1  1601 ± 48 ms 4.1 ± 0.7 % 1307 ± 108 ms 5.3 ± 2.0 % \nMedulla T1 2044 ± 83 ms 4.0 ± 0.7 % 1434 ± 171 ms 5.4 ± 1.8 % \nCortex T2 90.8 ± 5.0 ms 6.3 ± 2.4 % 73.3 ± 8.0 ms 4.3 ± 1.3 % \nMedulla T2 90.3 ± 5.4 ms 7.4 ± 3.4 % 67.6 ± 5.8 ms 7.2 ± 5.4 % \n \n \nThe Bland Altman analysis (Figure 6) demonstrated a bias against the reference techniques, \nwhich is consistent with the phantom results and segmental averages.  \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.17.25333832doi: medRxiv preprint \n\n \nFigure 6. Bland -Altman analyses of the agreement between PARMAN av with \nNAWW=±8mm and the reference techniques. A) Cortex T1 values. B) Cortex T2 values. C) \nMedulla T1 values. D) Medulla T2 values. \n \nFeasibility in Patients \n \nPARMANav was successfully acquired in the three patients and resulted in sharp and \nartefact-free maps (Figure 7). A cyst, also visible on the localizer image could be clearly \nobserved on the maps for Patient 1 – the free liquid interior results in very high T1 and T2 \nrelaxation times. The individual structures in the kidney are more difficult to differentiate in all \nthree patients, while they had a CMD ratio close to one (CMD ratio = 0.80,0.87 and 0.89 for \npatient 1,2 and 3, respectively) as expected.2,3 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.17.25333832doi: medRxiv preprint \n\n \nFigure 7. Parametric PARMANav maps obtained in patients. Patient 1 had chronic kidney \ndisease (CKD) and heart failure with preserved ejection fraction (HFpEF) as well as a cyst (red \narrowhead), Patients 2 had chronic kidney disease  and heart failure with reduced ejection \nfraction, and Patient 3 had HFpEF without CKD. A T2 HASTE anatomical image is provided for \nanatomical reference.  \nDiscussion  \nIn this work, a free -breathing 2D technique was characterized for joint T 1–T2 mapping of the \nkidneys at 3T. Previous numerical simulations have shown that the estimated T1 and T2 values \nwere not impacted by the number of rejected navigators16. The mapping phantom accuracy \nobtained in this study further support this finding.  \nPARMANav resulted in precise  free-breathing joint T 1-T2 maps of the kidney  in the healthy \nvolunteers. It was successfully acquired in all healthy volunteers with all tested NAWWs, which \nresulted in similar mapped relaxation times.  In patients, free -breathing joint T1-T2 maps \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.17.25333832doi: medRxiv preprint \n\nresulted in artefact -free maps of high quality  with the added advantage that the T1 and T2 \nmaps are intrinsically registered. \n \nThe Bland -Altman study in the healthy volunteers showed a bias compared to routine \ntechniques, which was relatively high for the medulla T 1 values, but agreed well  with the \nphantom study.  T1 and T2 values were systematically higher for PARMANav, both in the cortex \nand medulla, with large limit of agreement.  \nThe higher T1 SD for NAWW=± 4 mm might indicate that a higher number of navigator \nrejections impacts the T1 precision. To mitigate this effect, a variable flip angle could be \nintroduced for the acquisition, which could also be used to calculate a B1 map.11 Although the \nimpact of the number and timing of rejected navigators (e.g., immediately following the \ninversion pulse) assessed in a mapping phantom would be valuable, the current experimental \nsetup did not permit such precise control. Future studies using a motion phantom may enable \nthese investigations and facilitate evaluation of accuracy in the presence of motion. \n \nNo consistent relationship was observed between NAWW and T1-T2 precision or accuracy,  \nalthough this is likely due to avoiding clear artefactual borders during the segmentation process \n(as illustrated in Figure 3). This suggests that NAWW selection may be guided primarily by the \ntrade-off between scan duration and through -plane motion tolerance. However, the shape  of \nthe cortex and medulla varied with the larger NAWW s, likely due to partial volume effect s or \nlimitations in the registration algorithm under conditions of increased motion.  The increased \nnumber of outliers observed with NAWW=±4mm motivated the selection of an ±8 mm NAWW \nfor subsequent experiments. Additional parameters, such as the flip angle and the number of \nradial lines per source image  could be experimentally optimized and potentially reduced in \nfuture studies. \nSeveral breath-held magnetic resonance fingerprinting (MRF) techniques have been proposed \nfor renal mapping, all relying on Bloch equation simulations. Chen et al.⁸ developed an \nabdominal fingerprinting technique for simultaneous T ₁ and T₂ mapping with B ₁ correction. \nCompared to this method, which used spiral sampling and Bloch-based dictionary simulation, \nPARMANav yielded higher T ₁ and T₂ values in both the cortex and medulla. More recently, \nHermann et al.⁹ introduced a breath -held MRF sequence for T ₁ and T₂ mapping across four \nslices. Their reported T₁ values, while still slightly lower, were more closely aligned with those \nobtained using PARMANav (T1 = 1456 ms and 1921 ms in cortex and medulla, respectively, \nvs. 1601 ms and 2044 ms with PARMANav). MacAsk ill et al.¹⁰ also presented a breath -held \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.17.25333832doi: medRxiv preprint \n\nkidney MRF technique for T ₁ and T ₂ mapping with B ₁ correction, again reporting shorter \nrelaxation times than those measured using PARMANav. A free -breathing method was \nproposed by Ding et al.15 for T1 and T2* mapping, using respiratory triggering via a respiratory \nbelt. The mapping was based on an analytical equation, which does not allow to model the \nimperfection of the acquisition (e.g., RF profile, inversion efficiency) and assumes a constant \nrespiratory cycle duration.  \nThe images acquired in patients were sharp and artefact-free. These results are encouraging \nand suggest that combined T1-T2 free breathing mapping with NAWW is possible in CKD \npatients, whether or not will they suffer from associated heart fail ure. As a next step, \nPARMANav should be compared to standard techniques. Future studies should also include \na larger number of CKD patients, with different associated comorbidities known to lead to \ndisturbances in breathing such as underlying lung disease or morbid obesity.  \nThis study has several limitations. As mentioned, the number of included patients was small. \nBesides, dictionary-based multiparametric mapping is limited by discretization and \ncomputational demands: coarse grids introduce  small errors, while finer grids increase \ngeneration and matching time. Other limitations of the proposed technique include challenges \nin sequence optimizations, as navigator rejections may have a greater impact on contrast \nvariability than the parameter being optimized. Due to the complexity of kidney anatomy, only \nrelatively small ROIs can typically be manually segmented. Although a second ROI was drawn \non the second kidney to mitigate those effect, automated segmentation of the entire cortex and \nmedulla could provide more representative and robust measurements .27 Finally, further \ndevelopments might include semi-automated segmentation, an extension to T2* mapping28 to \nmore sensitively assess oxygenation, and diffusion modules to further characterize fibrosis. \nConclusion \n \nWe demonstrated that the proposed navigator -gated 2D radial GRE sequence PARMANav \nenables accurate and precise simultaneous T₁-T₂ mapping of the kidneys during free breathing \nat 3T. The navigator acceptance window width (NAWW) had minimal impact on the accuracy, \nalthough very narrow or wide windows introduced more outliers, which led to map degradation, \npotentialy due to residual motion and registration errors. Based on these findings, an NAWW \nof ±8 mm was selected as a trade-off between scan efficiency and motion robustness.  \n \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.17.25333832doi: medRxiv preprint \n\nAcknowledgement  \nThis study was funded by the Swiss National Science Foundation (SNSF) under grant number \nCRSII5_202276. \nReferences \n1.  Dekkers IA, de Boer A, Sharma K, et al. Consensus-based technical recommendations for clinical \ntranslation of renal T1 and T2 mapping MRI. MAGMA. 2020;33(1):163-176. \ndoi:10.1007/s10334-019-00797-5 \n2.  Marotti M, Hricak H, Terrier F, McAninch JW, Thuroff JW. MR in renal disease: importance of \ncortical-medullary distinction. Magn Reson Med. 1987;5(2):160-172. \ndoi:10.1002/mrm.1910050207 \n3.  Hricak H, Terrier F, Demas BE. Renal allografts: evaluation by MR imaging. 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Investigating and Reducing the Effects of Confounding Factors \nfor Robust T1 and T2 Mapping with Cardiac MR Fingerprinting. Magn Reson Imaging. \n2018;53:40-51. doi:10.1016/j.mri.2018.06.018 \n21.  Horn BKP, Schunck BG. Determining optical flow. Artificial Intelligence. 1981;17(1):185-203. \ndoi:10.1016/0004-3702(81)90024-2 \n22.  Stupic KF, Ainslie M, Boss MA, et al. A standard system phantom for magnetic resonance \nimaging. Magnetic Resonance in Medicine. 2021;86(3):1194-1211. doi:10.1002/mrm.28779 \n23.  Giri S, Chung YC, Merchant A, et al. T2 quantification for improved detection of myocardial \nedema. J Cardiovasc Magn Reson. 2009;11:56. doi:1532-429X-11-56 [pii] 10.1186/1532-429X-\n11-56 \n24.  van Heeswijk RB, Feliciano H, Bongard C, et al. Free-Breathing 3 T Magnetic Resonance T2-\nMapping of the Heart. JACC: Cardiovascular Imaging. 2012;5(12):1231-1239. \ndoi:10.1016/j.jcmg.2012.06.010 \n25.  Meyer P, Rocca A, Banus J, et al. Characterizing subtypes of heart failure with preserved \nejection fraction: the HeartMagic prospective observational study. April \n2025:2025.04.10.25325567. doi:10.1101/2025.04.10.25325567 \n26.  Messroghli DR, Moon JC, Ferreira VM, et al. Clinical recommendations for cardiovascular \nmagnetic resonance mapping of T1, T2, T2* and extracellular volume: A consensus statement \nby the Society for Cardiovascular Magnetic Resonance (SCMR) endorsed by the European \nAssociation for Cardiovascular Imaging (EACVI). J Cardiovasc Magn Reson. 2017;19(1):75. \ndoi:10.1186/s12968-017-0389-8 \n27.  Aslam I, Aamir F, Kassai M, et al. Validation of automatically measured T1 map cortico-\nmedullary difference (ΔT1) for eGFR and fibrosis assessment in allograft kidneys. PLOS ONE. \n2023;18(2):e0277277. doi:10.1371/journal.pone.0277277 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.17.25333832doi: medRxiv preprint \n\n28.  Li LP, Milani B, Pruijm M, et al. Renal BOLD MRI in patients with chronic kidney disease: \ncomparison of the semi-automated twelve layer concentric objects (TLCO) and manual ROI \nmethods. Magn Reson Mater Phy. 2020;33(1):113-120. doi:10.1007/s10334-019-00808-5 \n \n  \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.17.25333832doi: medRxiv preprint \n\nFigure Captions \nFigure 1. Overview of the free-breathing radial 2D joint T1-T2 mapping technique \nPARMANav. A) The pulse sequence diagram shows the first of five repeated blocks that \nconsist of five differently prepared images, and the simulated magnetization of a healthy \nrenal cortex and medulla. B) Illustration of the placement of the navigator (green) and image \nslice (yellow) as well as a trace of the navigator with the acceptance window as two parallel \ngreen lines. C) Example first 5 images in a healthy volunteer kidney, reconstructed using \ncompressed sensing (CS). D) The dictionary is created through EPG simulations of the \nmagnetization across the 25 images. E) Model-based motion correction between source and \nsynthetic images obtained with the dictionary and the first unregistered maps. F) The final \nmaps are obtained by computing the pixel-wise dot-product between the registered images \nand the dictionary d. \nFigure 2. Agreement of PARMANav T1 and T2 maps in the ISMRM-NIST phantom compared \nto spin-echo reference values in case of 23 skipped navigator. A) T1 map of the phantom \nobtained with PARMANav. B) T2 map of the reference phantom obtained with PARMANav. C) \nLinear regression of PARMANav T1 values with and without rejected navigator and the clinical \nroutine 5(3)5 MOLLI versus gold -standard IR-SE. D) Linear regressions of PARMANav T 2 \nvalues with and without rejected navigator and the clinical routine T 2-prep bSSFP in the \nphantom in the clinically relevant range versus gold -standard SE. E) T1 Bland-Altman plot of \nPARMANav with and without rejected navigator and MOLLI versus gold-standard IR-SE. The \nbias and confidence bounds are reported in the legend . PARMANav presented a larger bias \nwhen adding a large number of skips (N=23). F) Same as E) for T2 with T2-prep bSSFP as the \nclinical routine and SE as the gold-standard.  \nFigure 3. PARMANav T1 and T2 maps for the different NAWWs compared to the routine \ntechniques. The NAWW did not significantly change the relaxation times in the resulting \nPARMANav T1 or T2 maps of the cortex, with the exception of the poles (red arrowheads), \nwhere partial volume effect lowered the T1 relaxation time. However, the shape and the T1 \nvalues of the medulla varie d with the NAWW (orange arrowhead), likely due to through -\nplane motion. The PARMAN av T1 and T 2 values were consistently higher than those \nobtained with the routine techniques. \nFigure 4. Impact of the navigator acceptance window width (NAWW). in 10 healthy volunteers. \nA-C) T1 values the cortex and in the medulla and the corresponding CMD  ratio for the four \nNAWWs compared to MOLLI, which is known to underestimate T 1 value, especially at these \nhigher values. D, E) T2 values in the cortex and in the medulla for the four NAWW compared \nto T2-prepared bSSFP.  \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.17.25333832doi: medRxiv preprint \n\nFigure 5. PARMANav acquisition time for the different NAWWs. Acquisition time was longer \nfor smaller NAWW as expect ed. For NAWW=±16 and 32 mm, the time of acquisition was \nalmost identical, due to no rejection for both NAWWs. The difference was significant between \nall the time of acquisition (p<0.02) except between NAWW=±16 and 32 mm (p=0.79) \nFigure 6. Bland-Altman analyses of the agreement between PARMANav with NAWW=±8mm \nand the reference techniques. A) Cortex T1 values. B) Cortex T2 values. C) Medulla T1 values. \nD) Medulla T2 values. \nFigure 7. Parametric PARMANav maps obtained in patients . Patient 1 had chronic kidney \ndisease (CKD) and heart failure with preserved ejection fraction (HFpEF) as well as a cyst (red \narrowhead), Patients 2 had chronic kidney disease  and heart failure with reduced ejection \nfraction, and Patient 3 had HFpEF without CKD. A T2 HASTE anatomical image is provided for \nanatomical reference. \n \n \n \n \nTables Captions \n \nTable 1. Parameters of the sequences used for T 1 and T 2 mapping. The same parameters \nwere used in the phantom and in-vivo scans. \n \nTable 2. Cortex and medulla T 1 and T 2 mean and standard deviation  in ms across the 10 \nhealthy volunteers, as well as the coefficient of variation (CoV). \n \n \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.17.25333832doi: medRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}