MR‐based PET attenuation correction using a combined ultrashort echo time/multi‐echo Dixon acquisition

Abstract

†These authors contributed equally to this work. 2 Abstract Purpose: To develop a magnetic resonance (MR)-based method for estimation of continuous linear attenuation coefficients (LAC) in positron emission tomography (PET) using a physical compartmental model and ultrashort echo time (UTE)/multi-echo Dixon (mUTE) acquisitions. Methods:We propose a three-dimensional (3D) mUTE sequence to acquire signals from water, fat, and short-T2 components (e.g., bones) simultaneously in a single acquisition. The proposed mUTE sequence integrates 3D UTE with multi-echo Dixon acquisitions and uses sparse radial trajectories to accelerate imaging speed. Errors in the radial k-space trajectories are measured using a special k-space trajectory mapping sequence and corrected for image reconstruction. A physical compartmental model is used to fit the measured multi-echo MR signals to obtain fractions of water, fat and bone components for each voxel, which are then used to estimate the continuous LAC map for PET attenuation correction. Results:The performance of the proposed method was evaluated via phantom and in vivo human studies, using LACs from Computed Tomography (CT) as reference. Compared to Dixon-and atlas-based MRAC methods, the proposed method yielded PET images with higher correlation and similarity in relation to the reference. The relative absolute errors of PET activity values reconstructed by the proposed method were below 5% in all of the four lobes (frontal, temporal, parietal, occipital), cerebellum, whole white matter and gray matter regions across all subjects (n=6). Conclusions:The proposed mUTE method can generate subject-specific, continuous LAC map for PET attenuation correction in PET/MR.

Attenuation correction using deep Learning and integrated UTE/multi-echo Dixon sequence: evaluation in amyloid and tau PET imaging

“…3D mUTE sequence ( Supplementary Fig. 1 ) [ 22 ] was used to acquire Dixon and mUTE images, with the following imaging parameters: image size = 128×128×128, voxel size = 1.875×1.875×1.875 mm 3 , hard radiofrequency (RF) pulse with flip angle = 15° and pulse duration = 100 μs, repetition time (TR) = 8.0 ms, maximum readout gradient amplitude = 19.57 mT/m, maximum readout gradient slew rate = 48.9 mT/m/ms, and acquisition time = 52 s. The final reconstruction from the mUTE sequence resulted in seven images: one UTE image and six multi-echo Dixon images with corresponding echo times (TEs) of 0.07, 2.1, 2.3, 3.6, 3.7, 5.0, and 5.2 ms, respectively.…”

“…Machine learning and joint estimation methods have also been explored [ 18 – 21 ]. Recently, a UTE/multi-echo Dixon (mUTE) sequence-based method has been proposed to take full advantage of MR physics for AC [ 22 ]. The mUTE sequence integrates UTE for imaging short-T2 components (i.e., bone) and multi-echo Dixon for robust water/fat separation into a single acquisition, which enables a physical compartmental model to estimate continuous distributions of attenuation coefficients.…”

Multimodal phantoms for clinical PET/MRI

“…Animal cadavers can provide materials with similar structure and properties to the equivalent human tissues; however, these may be altered in ex vivo samples [ 11 ]. Two of these phantoms were used as part of the validation process for MRI-based attenuation correction [ 12 , 13 ]. One study described a phantom built with animal femur bone and lung lobe as a feasible solution to create tissue equivalent phantoms for PET, CT and MRI [ 14 ].…”

“…Agarose, gelatin and methyl-cellulose gels are used more widely as soft tissue surrogates in several phantoms identified for this review [ 13 , 14 , 20 – 23 ]. The ability to customise the MRI relaxation properties of gels with relative ease at manufacture by varying the concentration of gelling and contrast agents, demonstrated extensively by Gillmann et al [ 21 ], allows for flexibility in the number of tissue types represented.…”

Synthetic CT Generation of the Pelvis in Patients With Cervical Cancer: A Single Input Approach Using Generative Adversarial Network

“…The usual choice of conventional T1-, T2-, or Dixon-derived sequences as input for sCT generation algorithms derives essentially from two factors: 1) data availability, as conventional T1 and T2 sequences are the most widely used in clinical radiology [ 22 ], and Dixon sequences are used in the currently available PET/MR systems for attenuation correction [ 24 ]. 2) Dixon sequences allow the use of up to 4 channels as input, which empowers the features extraction capacity of the sCT generation methods and potentially improves the overall accuracy [ 25 ].…”