Fast, Accurate, and Robust T2 Mapping of Articular Cartilage by Neural Networks

Müller‐Franzes, Gustav; Nolte, Teresa; Ciba, Malin; Schock, Justus; Khader, Firas; Prescher, Andreas; Wilms, Lena Marie; Kühl, Christiane; Nebelung, Sven; Truhn, Daniel

doi:10.3390/diagnostics12030688

Cited by 9 publications

(8 citation statements)

References 59 publications

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“…Numerous previous studies have demonstrated the potential for deep learning in medical imaging and post-processing [36][37][38]. Among others, Zaiss et al showed the potential for deep learning in qCEST imaging [39].…”

Section: Discussionmentioning

confidence: 99%

Lorentzian-Corrected Apparent Exchange-Dependent Relaxation (LAREX) Ω-Plot Analysis—An Adaptation for qCEST in a Multi-Pool System: Comprehensive In Silico, In Situ, and In Vivo Studies

Radke

Wilms

Frenken

et al. 2022

IJMS

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Based on in silico, in situ, and in vivo studies, this study aims to develop a new method for the quantitative chemical exchange saturation transfer (qCEST) technique considering multi-pool systems. To this end, we extended the state-of-the-art apparent exchange-dependent relaxation (AREX) method with a Lorentzian correction (LAREX). We then validated this new method with in situ and in vivo experiments on human intervertebral discs (IVDs) using the Kendall-Tau correlation coefficient. In the in silico experiments, we observed significant deviations of the AREX method as a function of the underlying exchange rate (kba) and fractional concentration (fb) compared to the ground truth due to the influence of other exchange pools. In comparison to AREX, the LAREX-based Ω-plot approach yielded a substantial improvement. In the subsequent in situ and in vivo experiments on human IVDs, no correlation to the histological reference standard or Pfirrmann classification could be found for the fb (in situ: τ = −0.17 p = 0.51; in vivo: τ = 0.13 p = 0.30) and kba (in situ: τ = 0.042 p = 0.87; in vivo: τ = −0.26 p = 0.04) of Glycosaminoglycan (GAG) with AREX. In contrast, the influence of interfering pools could be corrected by LAREX, and a moderate to strong correlation was observed for the fractional concentration of GAG for both in situ (τ = −0.71 p = 0.005) and in vivo (τ = −0.49 p < 0.001) experiments. The study presented here is the first to introduce a new qCEST method that enables qCEST imaging in systems with multiple proton pools.

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Section: Discussionmentioning

confidence: 99%

Lorentzian-Corrected Apparent Exchange-Dependent Relaxation (LAREX) Ω-Plot Analysis—An Adaptation for qCEST in a Multi-Pool System: Comprehensive In Silico, In Situ, and In Vivo Studies

Radke

Wilms

Frenken

et al. 2022

IJMS

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“…Equation () describes the signal evolution during a single acquisition. In order to obtain several realizations of the same voxel, the followed model could be exploited 18–20 :

g (l) = ρ (1 - e^{- TR / T_{1}}) e^{- TE (l) / T_{2}}, l = 1, \dots, L

where repetition time TR is fixed and several acquisitions are collected varying the echo time TE. Accordingly,

g (1)

corresponds to the first acquisition that adopts

TE (1)

and

g (L)

to the last acquisition that adopts

TE (L)

.…”

Section: Mathematical Modelmentioning

confidence: 99%

“…Equation ( 1) describes the signal evolution during a single acquisition. In order to obtain several realizations of the same voxel, the followed model could be exploited [18][19][20] :…”

Section: Introductionmentioning

confidence: 99%

Intra voxel analysis in magnetic resonance imaging via deep learning

Autorino,

Franceschini,

Ambrosanio

et al. 2023

Int J Imaging Syst Tech

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Magnetic Resonance Imaging (MRI) is a useful diagnostic method for producing anatomical images of the body. It differentiates tissues thanks to the measure of their proton density and relaxation times and . Since pathological tissues often present altered , and with respect to the physiological ones, MRI is largely used for the detection of large number of conditions. In addition, MRI scan presents an effective resolution able to detect very small anatomical elements, making the imaging system well suitable in contexts like prevention and early diagnosis. Nevertheless, system resolution imposes pathological volume to be larger than the voxel dimension. Since in some pathologies and conditions it could be very helpful to find tissues even smaller than the voxel dimension, this manuscript proposes an algorithm able to analyze voxel content and detect which one presents more than one tissue in its volume (i.e., which one is heterogeneous). More in detail, a machine learning algorithm is proposed, able to highlight, in the MR image, which pixel corresponds to an heterogeneous voxel. Method shows to be promising in combining good results and near real‐time processing in both simulated and real scenario.

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“…Several groups have shown that NNs can improve robustness to image noise and are more computationally efficient than iterative fitting. One strategy, previously demonstrated for diffusion (31) and T 2 relaxometry (32) problems, is to train a one-dimensionally fully-connected neural network using a large, synthesized training dataset that simulates a forward signal model with random parameter values and Rician noise. These methods improved accuracy and reduced variability compared to the conventional NLLS method.…”

Section: Introductionmentioning

confidence: 99%

Improved Quantitative Parameter Estimation for Prostate T2 Relaxometry using Convolutional Neural Networks

Bolan

Saunders

Kay

et al. 2023

Preprint

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Purpose This work seeks to evaluate multiple methods for quantitative parameter estimation from standard T2 mapping acquisitions in the prostate. The T2 estimation performance of methods based on neural networks (NN) was quantitatively compared to that of conventional curve fitting techniques. Methods Large physics-based synthetic datasets simulating T2 mapping acquisitions were generated for training NNs and for quantitative performance comparisons. Ten combinations of different NN architectures, training strategies, and training corpora were implemented and compared with four different curve fitting strategies. All methods were compared quantitatively using synthetic data with known ground truth, and further compared on in vivo test data, with and without noise augmentation, to evaluate feasibility and noise robustness. Results In the evaluation on synthetic data, a convolutional neural network (CNN), trained in a supervised fashion using synthetic data generated from naturalistic images, showed the highest overall accuracy and precision amongst all the methods. On in vivo data, this best-performing method produced low-noise T2 maps and showed the least deterioration with increasing input noise levels. Conclusion This study showed that a CNN, trained with synthetic data in a supervised manner, may provide superior T2 estimation performance compared to conventional curve fitting, especially in low signal-to-noise regions.

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Fast, Accurate, and Robust T2 Mapping of Articular Cartilage by Neural Networks

Cited by 9 publications

References 59 publications

Lorentzian-Corrected Apparent Exchange-Dependent Relaxation (LAREX) Ω-Plot Analysis—An Adaptation for qCEST in a Multi-Pool System: Comprehensive In Silico, In Situ, and In Vivo Studies

Lorentzian-Corrected Apparent Exchange-Dependent Relaxation (LAREX) Ω-Plot Analysis—An Adaptation for qCEST in a Multi-Pool System: Comprehensive In Silico, In Situ, and In Vivo Studies

Intra voxel analysis in magnetic resonance imaging via deep learning

Improved Quantitative Parameter Estimation for Prostate T2 Relaxometry using Convolutional Neural Networks

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