Feasibility of ultrashort echo time quantitative susceptibility mapping with a 3D cones trajectory in the human brain

Abstract

PurposeQuantitative susceptibility mapping (QSM) has surfaced as a promising non-invasive quantitative biomarker that provides information about tissue composition and microenvironment. Recently, ultrashort echo time quantitative susceptibility mapping (UTE-QSM) has been investigated to achieve QSM of short T2 tissues. As the feasibility of UTE-QSM has not been demonstrated in the brain, the goal of this study was to develop a UTE-QSM with an efficient 3D cones trajectory and validate it in the human brain.Materials and methodsAn ultrashort echo time (UTE) cones sequence was implemented in a 3T clinical MRI scanner. Six images were acquired within a single acquisition, including UTE and gradient recalled echo (GRE) images. To achieve QSM, a morphology-enabled dipole inversion (MEDI) algorithm was incorporated, which utilizes both magnitude and phase images. Three fresh cadaveric human brains were scanned using the 3D cones trajectory with eight stretching factors (SFs) ranging from 1.0 to 1.7. In addition, five healthy volunteers were recruited and underwent UTE-QSM to demonstrate the feasibility in vivo. The acquired data were processed with the MEDI-QSM pipeline.ResultsThe susceptibility maps estimated by UTE-QSM showed reliable tissue contrast. In the ex vivo experiment, high correlations were found between the baseline (SF of 1.0) and SFs from 1.1 to 1.7 with Pearson’s correlations of 0.9983, 0.9968, 0.9959, 0.9960, 0.9954, 0.9943, and 0.9879, respectively (all p-values < 0.05). In the in vivo experiment, the measured QSM values in cortical gray matter, juxtacortical white matter, corpus callosum, caudate, and putamen were 25.4 ± 4.0, −21.8 ± 3.2, −22.6 ± 10.0, 77.5 ± 18.8, and 53.8 ± 7.1 ppb, consistent with the values reported in the literature.ConclusionUltrashort echo time quantitative susceptibility mapping enables direct estimation of the magnetic susceptibility in the brain with a dramatically reduced total scan time by use of a stretched 3D cones trajectory. This technique provides a new biomarker for susceptibility mapping in the in vivo brain.

Evaluation of gadolinium deposition in cortical bone using three‐dimensional ultrashort echo time quantitative susceptibility mapping: A preliminary study

“…The minimum TE was 0.032 ms because of the radiofrequency (RF) coil transmit/receive mode switching time, during which data cannot be acquired. Figure 1B shows the 3D Cones k‐space trajectory, which allows efficient encoding of a 3D k‐space 22,28,29 …”

“…Figure 1B shows the 3D Cones k-space trajectory, which allows efficient encoding of a 3D k-space. 22,28,29 The following imaging parameters were used for UTE-Cones imaging: flip angle = 15 ; repetition time (TR) = 15 ms; TE = 0.032, 0.2, 0.4, 0.8, and 1.2 ms; readout bandwidth = 125 kHz, field of view = 60 Â 60 Â 50 mm 3 ; acquisition matrix = 160 Â 160 Â 100; total scan time = 45 min (i.e., 9 min per scan; five scans to achieve five TEs). Note that images were acquired at five different TEs to achieve information regarding the phase evolution in free induction decay, which are the essential data for QSM processing.…”

A new 3D phase unwrapping method by region partitioning and local polynomial modeling in abdominal quantitative susceptibility mapping

“…While the magnitude is typically used, the phase is often disregarded in clinical diagnosis ( Chavez et al, 2002 ). However, accurately recovering the underlying true phase is crucial for various clinical applications, such as water-fat separation ( Ma, 2008 ), susceptibility weighted imaging ( Rauscher et al, 2005 ; Sehgal et al, 2005 ), human brain phase imaging ( Liu et al, 2011 ; Robinson et al, 2017 ), and quantitative susceptibility mapping ( Wang and Liu, 2015 ; Langkammer et al, 2018 ; Lu et al, 2018 ; Jerban et al, 2019 ; Zhang et al, 2019 ; Yan et al, 2021 ; Jang et al, 2022 ; Chen et al, 2023 ). The phase is typically calculated using the arctangent function and falls within the range of (−π, π) radians ( Cusack and Papadakis, 2002 ; Jang et al, 2019 ).…”

3D shape reconstruction with a multiple-constraint estimation approach

“…As an important component of computer vision, 3D shape reconstruction has been widely used in many applications (Li et al, 2016(Li et al, , 2018Adamkiewicz et al, 2022;Chiang et al, 2022;Fombona-Pascual et al, 2022;Jang et al, 2022;Lu et al, 2022;Nian et al, 2022a,b;Wang et al, 2022;Wen et al, 2022). Among the various 3D shape reconstruction methods, non-rigid structure from motion (NRSFM) offers a technique to simultaneously recover the 3D structures and motions of an object, by using the 2D landmarks in a series of images (Graßhof and Brandt, 2022;Kumar and Van Gool, 2022;Song et al, 2022).…”