Dataset Information

Rapid mono and biexponential 3D-T_1? mapping of knee cartilage using variational networks.

ABSTRACT: In this study we use undersampled MRI acquisition methods to obtain accelerated 3D mono and biexponential spin-lattice relaxation time in the rotating frame (T_1?) mapping of knee cartilage, reducing the usual long scan time. We compare the accelerated T_1? maps obtained by deep learning-based variational network (VN) and compressed sensing (CS). Both methods were compared with spatial (S) and spatio-temporal (ST) filters. Complex-valued fitting was used for T_1? parameters estimation. We tested with seven in vivo and six synthetic datasets, with acceleration factors (AF) from 2 to 10. Median normalized absolute deviation (MNAD), analysis of variance (ANOVA), and coefficient of variation (CV) were used for analysis. The methods CS-ST, VN-S, and VN-ST performed well for accelerating monoexponential T_1? mapping, with MNAD around 5% for AF?=?2, which increases almost linearly with the AF to an MNAD of 13% for AF?=?8, with all methods. For biexponential mapping, the VN-ST was the best method starting with MNAD of 7.4% for AF?=?2 and reaching MNAD of 13.1% for AF?=?8. The VN was able to produce 3D-T_1? mapping of knee cartilage with lower error than CS. The best results were obtained by VN-ST, improving CS-ST method by nearly 7.5%.

SUBMITTER: Zibetti MVW

PROVIDER: S-EPMC7645759 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Rapid mono and biexponential 3D-T1ρ mapping of knee cartilage using variational networks.

Zibetti Marcelo V W MVW Johnson Patricia M PM Sharafi Azadeh A Hammernik Kerstin K Knoll Florian F Regatte Ravinder R RR

Scientific reports 20201105 1

In this study we use undersampled MRI acquisition methods to obtain accelerated 3D mono and biexponential spin-lattice relaxation time in the rotating frame (T1ρ) mapping of knee cartilage, reducing the usual long scan time. We compare the accelerated T1ρ maps obtained by deep learning-based variational network (VN) and compressed sensing (CS). Both methods were compared with spatial (S) and spatio-temporal (ST) filters. Complex-valued fitting was used for T1ρ pa ...[more]

PMID: 33154515

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Project description:BackgroundIn addition to the articular cartilage, osteoarthritis (OA) affects several other tissues such as tendons, ligaments, and subchondral bone. T1ρ relaxation study of these short T2 tissues may provide a more comprehensive evaluation of OA.PurposeTo develop a 3D spin-lattice relaxation in the rotating frame (T1ρ ) prepared zero echo time (ZTE)-based pointwise encoding time reduction with radial acquisition (3D-T1ρ -PETRA) sequence for relaxation mapping of semisolid short-T2 tissues on a clinical 3 T scanner.Study typeProspective.PopulationPhantom, two bovine whole knee joint and Achilles tendon specimens, 10 healthy volunteers with no known inflammation, trauma or pain in the knee or ankle.Field strength/sequenceA customized PETRA sequence to acquire fat-suppressed 3D T1ρ -weighted images tissues with semisolid short T2 / T2* relaxation times in the knee and ankle joints at 3 T.AssessmentMono- and biexponential T1ρ relaxation components were assessed in the patellar tendon (PT), anterior cruciate ligament (ACL), posterior cruciate ligament (PCL), and Achilles tendon (AT).Statistical testsKruskal-Wallis with post-hoc Dunn's test for multiple pairwise comparisons.ResultsPhantom and ex vivo studies showed the feasibility of T1ρ relaxation mapping using the proposed 3D-T1ρ -PETRA sequence. The in vivo study demonstrated an averaged mono-T1ρ relaxation of (median [IQR]) 15.9 [14.5] msec, 23.6 [9.4] msec, 17.4 [7.4] msec, and 5.8 [10.2] msec in the PT, ACL, PCL, and AT, respectively. The bicomponent analysis showed the short and long components (with their relative fractions) of 0.65 [1.0] msec (46.9 [15.3]%) and 37.3 [18.4] msec (53.1 [15.3]%) for PT, 1.7 [2.1] msec (42.5 [12.5]%) and 43.7 [17.8] msec (57.5 [12.5]%) for ACL, and 1.2 [1.9] msec (42.6 [14.0]%) and 27.7 [14.7] msec (57.3 [14.0]%) for PCL and 0.4 [0.02] msec (58.8 [13.3]%/) and 31.3 [10.8] msec (41.2 [13.3]%) for AT. Statistically significant (P ≤ 0.05) differences were observed in the mono- and biexponential relaxation between several regions.Data conclusionThe 3D-T1ρ -PETRA sequence allows volumetric, isotropic (0.78 × 0.78 × 0.78 mm), biexponential T1ρ assessment with corresponding fractions of the tissues with semisolid short T2 / T2* .Level of evidence2 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019;50:1207-1218.

Project description:BackgroundFast and accurate T1ρ mapping in myocardium is still a major challenge, particularly in small animal models. The complex sequence design owing to electrocardiogram and respiratory gating leads to quantification errors in in vivo experiments, due to variations of the T1ρ relaxation pathway. In this study, we present an improved quantification method for T1ρ using a newly derived formalism of a T1ρ* relaxation pathway.MethodsThe new signal equation was derived by solving a recursion problem for spin-lock prepared fast gradient echo readouts. Based on Bloch simulations, we compared quantification errors using the common monoexponential model and our corrected model. The method was validated in phantom experiments and tested in vivo for myocardial T1ρ mapping in mice. Here, the impact of the breath dependent spin recovery time Trec on the quantification results was examined in detail.ResultsSimulations indicate that a correction is necessary, since systematically underestimated values are measured under in vivo conditions. In the phantom study, the mean quantification error could be reduced from - 7.4% to - 0.97%. In vivo, a correlation of uncorrected T1ρ with the respiratory cycle was observed. Using the newly derived correction method, this correlation was significantly reduced from r = 0.708 (p < 0.001) to r = 0.204 and the standard deviation of left ventricular T1ρ values in different animals was reduced by at least 39%.ConclusionThe suggested quantification formalism enables fast and precise myocardial T1ρ quantification for small animals during free breathing and can improve the comparability of study results. Our new technique offers a reasonable tool for assessing myocardial diseases, since pathologies that cause a change in heart or breathing rates do not lead to systematic misinterpretations. Besides, the derived signal equation can be used for sequence optimization or for subsequent correction of prior study results.

Project description:Background3D-T1ρ mapping is useful to quantify various neurologic disorders, but data are currently time-consuming to acquire.PurposeTo compare the performance of five compressed sensing (CS) algorithms-spatiotemporal finite differences (STFD), exponential dictionary (EXP), 3D-wavelet transform (WAV), low-rank (LOW) and low-rank plus sparse model with spatial finite differences (L + S SFD)-for 3D-T1ρ mapping of the human brain with acceleration factors (AFs) of 2, 5, and 10.Study typeRetrospective.SubjectsEight healthy volunteers underwent T1ρ imaging of the whole brain.Field strength/sequenceThe sequence was fully sampled 3D Cartesian ultrafast gradient echo sequence with a customized T1ρ preparation module on a clinical 3T scanner.AssessmentThe fully sampled data was undersampled by factors of 2, 5, and 10 and reconstructed with the five CS algorithms. Image reconstruction quality was evaluated and compared to the SENSE reconstruction of the fully sampled data (reference) and T1ρ estimation errors were assessed as a function of AF.Statistical testsNormalized root mean squared errors (nRMSE) and median normalized absolute deviation (MNAD) errors were calculated to compare image reconstruction errors and T1ρ estimation errors, respectively. Linear regression plots, Bland-Altman plots, and Pearson correlation coefficients (CC) are shown.ResultsFor image reconstruction quality, at AF = 2, EXP transforms had the lowest mRMSE (1.56%). At higher AF values, STFD performed better, with the smallest errors (3.16% at AF = 5, 4.32% at AF = 10). For whole-brain quantitative T1ρ mapping, at AF = 2, EXP performed best (MNAD error = 1.62%). At higher AF values (AF = 5, 10), the STFD technique had the least errors (2.96% at AF = 5, 4.24% at AF = 10) and the smallest variance from the reference T1ρ estimates.Data conclusionThis study demonstrates the use of different CS algorithms that may be useful in reducing the scan time required to perform volumetric T1ρ mapping of the brain.Level of evidence2.Technical efficacy stage1.

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Rapid mono and biexponential 3D-T_1? mapping of knee cartilage using variational networks.

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Rapid mono and biexponential 3D-T<sub>1ρ</sub> mapping of knee cartilage using variational networks.

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Dataset Information

Rapid mono and biexponential 3D-T1? mapping of knee cartilage using variational networks.

Publications

Rapid mono and biexponential 3D-T<sub>1ρ</sub> mapping of knee cartilage using variational networks.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

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Rapid mono and biexponential 3D-T_1? mapping of knee cartilage using variational networks.