Faculty Bibliography

This study investigates the fibril nanostructure of fresh celery samples by modeling the anisotropic behavior of the transverse relaxation time (T₂) in nuclear magnetic resonance (NMR). Experimental results are interpreted within the framework of a previously developed theory, which was successfully used to model the nanostructures of several biological tissues as a set of water filled nanocavities, hence explaining the anisotropy the T₂ relaxation time in vivo. An important feature of this theory is to determine the degree of orientational ordering of the nanocavities, their characteristic volume, and their average direction with respect to the macroscopic sample. Results exhibit good agreement between theory and experimental data, which are, moreover, supported by optical microscopic resolution. The quantitative NMR approach presented herein can be potentially used to determine the internal ordering of biological tissues noninvasively.

PURPOSE/OBJECTIVE:relaxation maps. METHODS:values, along with gold standard SNR estimation of the phantom scans. RESULTS:maps' precision and SNR, while successfully preserving the morphological features of the tissue. CONCLUSIONS: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.

Loading on the joints during running may have a deleterious effect on post-partial meniscectomy knee cartilage, leading to osteoarthritis. Utilizing T2-mapping measurements before and after running may enable the observation of changes in the articular cartilage of the postmeniscectomy knees compared with healthy knees. After medial partial meniscectomy, 12 volunteers underwent magnetic resonance imaging (MRI) of the both knees, before and immediately after 30 minutes of running. Quantitative assessment of articular cartilage was performed using a T2-mapping technique. In the medial compartment of the operated knees, significantly lower T2 values were found in anterior tibial plateau (pre- vs. postrun: 33.85 vs. 30.45 ms; p = 0.003) and central tibial plateau (33.33 vs. 30.63 ms; p = 0.007). Similar differences were found in lateral regions of central femur (post- vs. prerun: 35.86 vs. 40.35 ms; p = 0.015), posterior femur (34.89 vs. 37.73 ms; p = 0.001), and anterior tibia (24.66 vs. 28.70 ms, p = 0.0004). In lateral compartment, postrun values were significantly lower in operated compared with healthy knees, in central femur (34.89 vs. 37.59 ms; p = 0.043), posterior femoral (36.88 vs. 39.36 ms; p = 0.017), anterior tibia (24.66 vs. 30.20 ms; p = 0.009), and posterior tibia (28.84 vs. 33.17 ms; p = 0.006). No statistical difference was found while comparing postrun to prerun healthy knees. Lower T2 values were found in operated knees after 30 minutes of running. These changes were seen in medial and lateral compartments. We suspect that running may subject the articular cartilage to excessive loads in the post-partial meniscectomy knee, loads that in healthy knee do not cause any changes.

PURPOSE/OBJECTIVE:motifs in white matter segments before deconvolving the local signal at each voxel. METHODS:motifs without additional prior assumptions regarding the number of microscopic components. The end results of this process are voxel-wise myelin water fraction maps. RESULTS:phantoms, exhibiting excellent agreement between calculated myelin water fraction and ground truth. Proof-of-concept in vivo validation is done by calculating myelin water fraction maps for white matter segments of the human brain. Interscan stability of myelin water fraction values was also estimated, showing good correlation between scans. CONCLUSION/CONCLUSIONS:space. This new approach can improve myelin water imaging and the investigation of microstructural compartmentation in general.

Background Quantitative T₂-relaxation-based contrast maps have shown to be highly beneficial for clinical diagnosis and follow-up. The generation of quantitative maps, however, is impaired by long acquisition times, and time-consuming post-processing schemes. The EMC platform is a dictionary-based technique, which involves simulating theoretical signal curves for different physical and experimental values, followed by matching the experimentally acquired signals to the set simulated ones. Purpose Although the EMC technique has shown to produce accurate T₂ maps, it involves computationally intensive post-processing procedures. In this work we present an approach for accelerating the reconstruction of T₂ relaxation maps. Methods This work presents two alternative post-processing approaches for accelerating the reconstruction of EMC-based T₂ relaxation maps. These are (a) Dictionary compression using principal component analysis (PCA) and (b) gradient-descent search algorithm. Additional acceleration was achieved by finding the optimal MATLAB C++ compiler. The utility of the two suggested approaches was examined by calculating the relative error, produced by each technique. Results Gradient descent method was in perfect agreement with the ground truth exhaustive search matching process. PCA based acceleration produced root mean square error (RMSE) of up to 4% compared to exhaustive matching process. Overall acceleration of x16 was achieved using gradient descent in addition to x7 acceleration by choosing the optimal MATLAB C++ compiler. Conclusions Postprocessing of EMC-based T₂ relaxation maps can be accelerated without impairing the accuracy of the ensuing T₂ values.

BACKGROUND:Current registration methods for diffusion-MRI (dMRI) data mostly focus on white matter (WM) areas. Recently, dMRI has been employed for the characterization of gray matter (GM) microstructure, emphasizing the need for registration methods that consider all tissue types. PURPOSE/OBJECTIVE:To develop a dMRI registration method based on GM, WM, and cerebrospinal fluid (CSF) tissue probability maps (TPMs). STUDY TYPE/METHODS:Retrospective longitudinal study. POPULATION/METHODS:Thirty-two healthy participants were scanned twice (legacy data), divided into a training-set (n = 16) and a test-set (n = 16), and 35 randomly-selected participants from the Human Connectome Project. FIELD STRENGTH/SEQUENCE/UNASSIGNED:3.0T, diffusion-weighted spin-echo echo-planar sequence; T1-weighted spoiled gradient-recalled echo (SPGR) sequence. ASSESSMENT/RESULTS:A joint segmentation-registration approach was implemented: Diffusion tensor imaging (DTI) maps were classified into TPMs using machine-learning approaches. The resulting GM, WM, and CSF probability maps were employed as features for image alignment. Validation was performed on the test dataset and the HCP dataset. Registration performance was compared with current mainstream registration tools. STATISTICAL TESTS/UNASSIGNED:Classifiers used for segmentation were evaluated using leave-one-out cross-validation and scored using Dice-index. Registration success was evaluated by voxel-wise variance, normalized cross-correlation of registered DTI maps, intra- and inter-subject similarity of the registered TPMs, and region-based intra-subject similarity using an anatomical atlas. One-way ANOVAs were performed to compare between our method and other registration tools. RESULTS:The proposed method outperformed mainstream registration tools as indicated by lower voxel-wise variance of registered DTI maps (SD decrease of 10%) and higher similarity between registered TPMs within and across participants, for all tissue types (Dice increase of 0.1-0.2; P < 0.05). DATA CONCLUSION/UNASSIGNED:A joint segmentation-registration approach based on diffusion-driven TPMs provides a more accurate registration of dMRI data, outperforming other registration tools. Our method offers a "translation" of diffusion data into structural information in the form of TPMs, allowing to directly align diffusion and structural images. LEVEL OF EVIDENCE/METHODS:1 Technical Efficacy Stage: 1.

OBJECTIVE/UNASSIGNED:MRI (magnetic resonance imaging). DESIGN/UNASSIGNED:test. RESULTS/UNASSIGNED:< 0.001). The lateral regions did not show any significant changes. CONCLUSIONS/UNASSIGNED:Running leads to microstructural changes in the articular cartilage in several weight-bearing areas of the medial compartment, both in the femoral and the tibial cartilage.

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.