MRI's transverse relaxation time (T2) is sensitive to tissues' composition and pathological state. While variations in T2 values can be used as clinical biomarkers, it is challenging to quantify this parameter in vivo due to the complexity of the MRI signal model, differences in protocol implementations, and hardware imperfections. Herein, we provide a detailed analysis of the echo modulation curve (EMC) platform, offering accurate and reproducible mapping of T2 values, from 2D multi‐slice multi‐echo spin‐echo (MESE) protocols. Computer simulations of the full Bloch equations are used to generate an advanced signal model, which accounts for stimulated echoes and transmit field (B1+) inhomogeneities. In addition to quantifying T2 values, the EMC platform also provides proton density (PD) maps, and fat‐water fraction maps. The algorithm's accuracy, reproducibility, and insensitivity to T1 values are validated on a phantom constructed by the National Institute of Standards and Technology and on in vivo human brains. EMC‐derived T2 maps show excellent agreement with ground truth values for both in vitro and in vivo models. Quantitative values are accurate and stable across scan settings and for the physiological range of T2 values, while showing robustness to main field (B0) inhomogeneities, to variations in T1 relaxation time, and to magnetization transfer. Extension of the algorithm to two‐component fitting yields accurate fat and water T2 maps along with their relative fractions, similar to a reference three‐point Dixon technique. Overall, the EMC platform allows to generate accurate and stable T2 maps, with a full brain coverage using a standard MESE protocol and at feasible scan times. The utility of EMC‐based T2 maps was demonstrated on several clinical applications, showing robustness to variations in other magnetic properties. The algorithm is available online as a full stand‐alone package, including an intuitive graphical user interface.

Purpose: Multi-echo spin-echo (MESE) protocol is the most effective tool for mapping T 2 relaxation in vivo. Still, MESE extensive use of radiofrequency pulses causes magnetization transfer (MT)-related bias of the water signal, instigated by the presence of macromolecules (MMP). Here, we analyze the effects of MT on MESE signal, alongside their impact on quantitative T 2 measurements. Methods: Study used 3 models: in vitro urea phantom, ex vivo horse brain, and in vivo human brain. MT ratio (MTR) was measured between single-SE and MESE protocols under different scan settings including varying echo train lengths, number of slices, and inter-slice gap. MTR and T 2 values were extracted for each model and protocol. Results: MT interactions biased MESE signals, and in certain settings, the corresponding T 2 values. T 2 underestimation of up to 4.3% was found versus single-SE values in vitro and up to 13.8% ex vivo, correlating with the MMP content. T 2 bias originated from intra-slice saturation of the MMP, rather than from indirect saturation in multi-slice acquisitions. MT-related signal attenuation was caused by slice crosstalk and/or partial T 1 recovery, whereas smaller contribution was caused by MMP interactions. Inter-slice gap had a similar effect on in vivo MTR (21.2%), in comparison to increasing the number of slices (18.9%). Conclusions: MT influences MESE protocols either by uniformly attenuating the entire echo train or by cumulatively attenuating the signal along the train. Although both processes depend on scan settings and MMP content, only the latter will cause underestimation of T 2 . K E Y W O R D S magnetization transfer (MT), MRI signal models, multi spin-echo, quantitative MRI, T 2 mapping, T 2 relaxation 146 | RADUNSKY et Al.

Background Magnetic resonance imaging (MRI) diagnosis is usually performed by analyzing contrast‐weighted images, where pathology is detected once it reached a certain visual threshold. Computer‐aided diagnosis (CAD) has been proposed as a way for achieving higher sensitivity to early pathology. Purpose To compare conventional (i.e., visual) MRI assessment of artificially generated multiple sclerosis (MS) lesions in the brain's white matter to CAD based on a deep neural network. Study Type Prospective. Population A total of 25 neuroradiologists (15 males, age 39 ± 9, 9 ± 9.8 years of experience) independently assessed all synthetic lesions. Field Strength/Sequence A 3.0 T, T2‐weighted multi‐echo spin‐echo (MESE) sequence. Assessment MS lesions of varying severity levels were artificially generated in healthy volunteer MRI scans by manipulating T2 values. Radiologists and a neural network were tasked with detecting these lesions in a series of 48 MR images. Sixteen images presented healthy anatomy and the rest contained a single lesion at eight increasing severity levels (6%, 9%, 12%, 15%, 18%, 21%, 25%, and 30% elevation in T2). True positive (TP) rates, false positive (FP) rates, and odds ratios (ORs) were compared between radiological diagnosis and CAD across the range lesion severity levels. Statistical Tests Diagnostic performance of the two approaches was compared using z‐tests on TP rates, FP rates, and the logarithm of ORs across severity levels. A P‐value <0.05 was considered statistically significant. Results ORs of identifying pathology were significantly higher for CAD vis‐à‐vis visual inspection for all lesions' severity levels. For a 6% change in T2 value (lowest severity), radiologists' TP and FP rates were not significantly different (P = 0.12), while the corresponding CAD results remained statistically significant. Data Conclusion CAD is capable of detecting the presence or absence of more subtle lesions with greater precision than the representative group of 25 radiologists chosen in this study. Level of Evidence 1 Technical Efficacy Stage 3

High-resolution mapping of magnetic resonance imaging (MRI)'s transverse relaxation time (T 2 ) can benefit many clinical applications by offering improved anatomic details, enhancing the ability to probe tissues' microarchitecture, and facilitating the identification of early pathology. Increasing spatial resolutions, however, decreases data's signal-to-noise ratio (SNR), particularly at clinical scan times. This impairs imaging quality, and the accuracy of subsequent radiological interpretation. Recently, principal component analysis (PCA) was employed for denoising diffusion-weighted MR images and was shown to be effective for improving parameter estimation in multiexponential relaxometry. This study combines the Marchenko-Pastur PCA (MP-PCA) signal model with the echo modulation curve (EMC) algorithm for denoising multiecho spin-echo (MESE) MRI data and improving the precision of EMC-generated single T 2 relaxation maps. The denoising technique was validated on simulations, phantom scans, and in vivo brain and knee data. MESE scans were performed on a 3-T Siemens scanner. The acquired images were denoised using the MP-PCA algorithm and were then provided as input for the EMC T 2 -fitting algorithm. Quantitative analysis of the denoising quality included comparing the standard deviation and coefficient of variation of T 2 values, along with gold standard SNR estimation of the phantom scans. The presented denoising technique shows an increase in T 2 maps' precision and SNR, while successfully preserving the morphological features of the tissue. Employing MP-PCA denoising as a preprocessing step decreases the noise-related variability of T 2 maps produced by the EMC algorithm and thus increases their precision. The proposed method can be useful for a wide range of clinical applications by facilitating earlier detection of pathologies and improving the accuracy of patients' follow-up.

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