Fully integrated 3D high‐resolution multicontrast abdominal PET‐MR with high scan efficiency

The combination of positron emission tomography (PET) with magnetic resonance (MR) imaging opens the way to more accurate diagnosis and improved patient management. At present, the data acquired by PET and MR scanners are essentially processed separately, and the search for ways to improve accuracy of the tomographic reconstruction via synergy of the two imaging techniques is an active area of research. The aim of the collaborative computational project on PET and MR (CCP-PETMR), supported by the UK engineering and physical sciences research council (EPSRC), is to accelerate research in synergistic PET-MR image reconstruction by providing an open access software platform for efficient implementation and validation of novel reconstruction algorithms.

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“…Example images of cardiac patient data acquired on Siemens mMR reconstructed with SIRF. Data from[7].…”

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confidence: 99%

SIRF: Synergistic Image Reconstruction Framework

Ovtchinnikov

Brown

Kolbitsch

et al. 2020

Computer Physics Communications

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“…In a recent clinical PET/MRI study, a development of respiratory-resolved μ -maps was proposed using a T1-weighted diagnostic scan without requiring an additional breath-hold MRAC scan (Kolbitsch et al ., 2018), although the scan duration (4 m 30 s) was still long. Our MRAC PM-CIRCUS can also support reconstruction of respiratory-resolved (or 4D) MRAC images at multiple respiratory phases (Figure 10).…”

Section: Discussionmentioning

confidence: 99%

“…MRAC PM approaches have been developed to address this problem and consist of two separate MR data acquisitions (Kolbitsch et al ., 2018; Grimm et al ., 2013; Buerger et al ., 2012): First, motion-free μ -maps were acquired at a predetermined respiratory phase (e.g., breath-hold end-expiration or end-inspiration); and second, the μ -maps were deformed to a target phase by motion models derived from separately acquired motion-resolved MR images. In a simulated PET study, an Ultrashort Echo Time (UTE) data was acquired to derive μ -maps and multiple 3D MR volumes were dynamically acquired to derive motion models (Buerger et al ., 2012), but this approach required long data acquisitions (11–16 m for UTE and additional time for multiple 3D MR volumetric imaging) and computationally expensive registration and respiratory modeling (3–4 m).…”

Section: Introductionmentioning

confidence: 99%

Developing an efficient phase-matched attenuation correction method for quiescent period PET in abdominal PET/MRI

Yang

Liu

Menini³

et al. 2018

Phys. Med. Biol.

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Respiratory motion causes misalignments between positron emission tomography (PET) and magnetic resonance (MR)-derived attenuation maps (µ-maps) in addition to artifacts on both PET and MR images in simultaneous PET/MRI for organs such as liver that can experience motion of several centimeters. To address this problem, we developed an efficient MR-based attenuation correction (MRAC) method to generate phase-matched µ-maps for quiescent period PET (PET) in abdominal PET/MRI. MRAC data was acquired with CIRcular Cartesian UnderSampling (CIRCUS) sampling during 100 s in free-breathing as an accelerated data acquisition strategy for phase-matched MRAC (MRAC). For comparison, MRAC data with raster (Default) k-space sampling was also acquired during 100 s in free-breathing (MRAC), and used to evaluate MRAC as well as un-matched MRAC (MRAC) that was un-gated. We purposefully oversampled the MRAC data to ensure we had enough information to capture all respiratory phases to make this comparison as robust as possible. The proposed MRAC was evaluated in 17 patients with Ga-DOTA-TOC PET/MRI exams, suspected of having neuroendocrine tumors or liver metastases. Effects of CIRCUS sampling for accelerating a data acquisition were evaluated by simulating the data acquisition time retrospectively in increments of 5 s. Effects of MRAC on PET were evaluated using uptake differences in the liver lesions (n = 35), compared to PET with MRAC and MRAC. A Wilcoxon signed-rank test was performed to compare lesion uptakes between the MRAC methods. MRAC showed higher image quality compared to MRAC for the same acquisition times, demonstrating that a data acquisition time of 30 s was reasonable to achieve phase-matched µ-maps. Lesion update differences between MRAC (30 s) versus MRAC (reference, 100 s) were 0.1% ± 1.4% (range of -2.7% to 3.2%) and not significant (P > .05); while, the differences between MRAC versus MRAC were 0.6% ± 11.4% with a large variation (range of -37% to 20%) and significant (P < .05). In conclusion, we demonstrated that a data acquisition of 30 s achieved phase-matched µ-maps when using specialized CIRCUS data sampling and phase-matched µ-maps improved PET quantification significantly.

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“…A non-commercial prototype version of MR-based free-breathing motion correction of PET data is available for the Biograph mMR PET/MR system (Siemens Healthcare, Erlangen, Germany). Thus, encouraged by the result of technical studies such as in [32][33][34], the aim of this study was to evaluate the impact of respiratory motion correction in PET/MR of the thorax on the obtained image quality to facilitate and potentially improve the clinical diagnosis. The motion correction method was tested in a collective of 20 patients with oncologic findings in the lung.…”

Section: Introductionmentioning

confidence: 99%

Impact of respiratory motion correction on lesion visibility and quantification in thoracic PET/MR imaging

et al. 2020

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The impact of a method for MR-based respiratory motion correction of PET data on lesion visibility and quantification in patients with oncologic findings in the lung was evaluated. Twenty patients with one or more lesions in the lung were included. Hybrid imaging was performed on an integrated PET/MR system using 18 F-FDG as radiotracer. The standard thoracic imaging protocol was extended by a free-breathing self-gated acquisition of MR data for motion modelling. PET data was acquired simultaneously in list-mode for 5-10 mins. One experienced radiologist and one experienced nuclear medicine specialist evaluated and compared the post-processed data in consensus regarding lesion visibility (scores 1-4, 4 being best), image noise levels (scores 1-3, 3 being lowest noise), SUVmean and SUVmax. Motion-corrected (MoCo) images were additionally compared with gated images. Nonmotion-corrected free-breathing data served as standard of reference in this study. Motion correction generally improved lesion visibility (3.19 ± 0.63) and noise ratings (2.95 ± 0.22) compared to uncorrected (2.81 ± 0.66 and 2.95 ± 0.22, respectively) or gated PET data (2.47 ± 0.93 and 1.30 ± 0.47, respectively). Furthermore, SUVs (mean and max) were compared for all methods to estimate their respective impact on the quantification. Deviations of SUVmax were smallest between the uncorrected and the MoCo lesion data (average increase of 9.1% of MoCo SUVs), while SUVmean agreed best for gated and MoCo reconstructions (MoCo SUVs increased by 1.2%). The studied method for MR-based respiratory motion correction of PET data combines increased lesion sharpness and improved lesion activity quantification with high signal-to-noise ratio in a clinical setting. In particular, the detection of small lesions in moving organs such as the lung and liver may thus be facilitated. These advantages justify the extension of the PET/MR imaging protocol by 5-10 minutes for motion correction.

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Fully integrated 3D high‐resolution multicontrast abdominal PET‐MR with high scan efficiency

Abstract: The proposed method provides motion-compensated 3D high-quality MR and PET images in a comprehensive and highly efficient examination. Magn Reson Med 79:900-911, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Cited by 16 publications

References 50 publications

SIRF: Synergistic Image Reconstruction Framework

SIRF: Synergistic Image Reconstruction Framework

Developing an efficient phase-matched attenuation correction method for quiescent period PET in abdominal PET/MRI

Impact of respiratory motion correction on lesion visibility and quantification in thoracic PET/MR imaging

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