Review and consensus recommendations on clinical <scp>APT</scp>‐weighted imaging approaches at <scp>3T</scp>: Application to brain tumors

Zhou, Jinyuan; Zaiß, Moritz; Knutsson, Linda; Sun, Phillip Zhe; Ahn, Sung Soo; Aime, Silvio; Bachert, Peter; Blakeley, Jaishri O.; Cai, Kejia; Chappell, Michael A.; Chen, Min; Gochberg, Daniel F.; Goerke, Steffen; Heo, Hye Young; Jiang, Shanshan; Jin, Tao; Kim, Seong‐Gi; Laterra, John; Paech, Daniel; Pagel, Marty; Park, Ji Eun; Reddy, Ravinder; Sakata, Akihiko; Sartoretti–Schefer, Sabine; Sherry, A. Dean; Smith, Seth A.; Stanisz, Greg J.; Sundgren, Pia C.; Togao, Osamu; Vandsburger, Moriel; Wen, Zhibo; Wu, Yin; Zhang, Yi; Zhu, Wenzhen; Zu, Zhongliang; Zijl, Peter C.M. van

doi:10.1002/mrm.29241

Cited by 86 publications

(144 citation statements)

References 195 publications

(523 reference statements)

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“…The significantly different values between the drawn tumor ROIs (Figure 8C, Figure 9) can improve tumor segmentation and may provide more specific tumor characterization. While previous studies 51,52 have shown hyper‐intense CEST signals in tumors, which seemingly differ from the ksw and fs values obtained in this study, this discrepancy can be attributed to multiple confounding factors present in conventional CEST, as described in a recent review 53 . Specifically, in many tumors, changes to the tissue parameters occur simultaneously hyperintense CEST signals can be caused by the changing tumor water relaxation which are typically unaccounted for 54 .…”

Section: Discussioncontrasting

confidence: 77%

CEST MR fingerprinting (CEST‐MRF) for brain tumor quantification using EPI readout and deep learning reconstruction

Cohen

Tringale

et al. 2022

Magnetic Resonance in Med

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To develop a clinical CEST MR fingerprinting (CEST-MRF) method for brain tumor quantification using EPI acquisition and deep learning reconstruction. Methods: A CEST-MRF pulse sequence originally designed for animal imaging was modified to conform to hardware limits on clinical scanners while keeping scan time under 2 min. Quantitative MRF reconstruction was performed using a deep reconstruction network (DRONE) to yield the water relaxation and chemical exchange parameters. The feasibility of the six parameter DRONE reconstruction was tested in simulations using a digital brain phantom. A healthy subject was scanned with the CEST-MRF sequence, conventional MRF and CEST sequences for comparison. Reproducibility was assessed via test-retest experiments and the concordance correlation coefficient calculated for white matter and gray matter. The clinical utility of CEST-MRF was demonstrated on four patients with brain metastases in comparison to standard clinical imaging sequences. Tumors were segmented into edema, solid core, and necrotic core regions and the CEST-MRF values compared to the contra-lateral side. Results: DRONE reconstruction of the digital phantom yielded a normalized RMS error of ≤7% for all parameters. The CEST-MRF parameters were in good agreement with those from conventional MRF and CEST sequences and previous studies. The mean concordance correlation coefficient for all six parameters was 0.98 ± 0.01 in white matter and 0.98 ± 0.02 in gray matter. The CEST-MRF values in nearly all tumor regions were significantly different (P = 0.05) from each other and the contra-lateral side. Conclusion: Combination of EPI readout and deep learning reconstruction enabled fast, accurate and reproducible CEST-MRF in brain tumors. K E Y W O R D S chemical exchange rate, chemical exchange saturation transfer (CEST), deep learning, DRONE, magnetic resonance fingerprinting (MRF), pHThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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Section: Discussioncontrasting

confidence: 77%

CEST MR fingerprinting (CEST‐MRF) for brain tumor quantification using EPI readout and deep learning reconstruction

Cohen

Tringale

et al. 2022

Magnetic Resonance in Med

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“…APT signal intensity is reported as a percentage change in the bulk water signal intensity, which depends on the pulse sequence features and parameters used. According to the recent consensus ( 20 ), we summarized the MR hardware and APT imaging techniques, as shown in Table 2 . The Philips Achieva 3.0-T MR scanner was the most used device in five studies.…”

Section: Resultsmentioning

confidence: 99%

Differentiation between glioma recurrence and treatment effects using amide proton transfer imaging: A mini-Bayesian bivariate meta-analysis

et al. 2022

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BackgroundAmide proton transfer (APT) imaging as an emerging MRI approach has been used for distinguishing tumor recurrence (TR) and treatment effects (TEs) in glioma patients, but the initial results from recent studies are different.AimThe aim of this study is to systematically review and quantify the diagnostic performance of APT in assessing treatment response in patients with post-treatment gliomas.MethodsA systematic search in PubMed, EMBASE, and the Web of Science was performed to retrieve related original studies. For the single and added value of APT imaging in distinguishing TR from TEs, we calculated pooled sensitivity and specificity by using Bayesian bivariate meta-analyses.ResultsSix studies were included, five of which reported on single APT imaging parameters and four of which reported on multiparametric MRI combined with APT imaging parameters. For single APT imaging parameters, the pooled sensitivity and specificity were 0.85 (95% CI: 0.75–0.92) and 0.88 (95% CI: 0.74–0.97). For multiparametric MRI including APT, the pooled sensitivity and specificity were 0.92 (95% CI: 0.85–0.97) and 0.83 (95% CI: 0.55–0.97), respectively. In addition, in the three studies reported on both single and added value of APT imaging parameters, the combined imaging parameters further improved diagnostic performance, yielding pooled sensitivity and specificity of 0.91 (95% CI: 0.80–0.97) and 0.92 (95% CI: 0.79–0.98), respectively, but the pooled sensitivity was 0.81 (95% CI: 0.65-0.93) and specificity was 0.82 (95% CI: 0.61–0.94) for single APT imaging parameters.ConclusionAPT imaging showed high diagnostic performance in assessing treatment response in patients with post-treatment gliomas, and the addition of APT imaging to other advanced MRI techniques can improve the diagnostic accuracy for distinguishing TR from TE.

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“…Numerous research groups worldwide have confirmed that the hyperintensity on APTw images is a reliable imaging marker of malignant glioma before ( Jiang et al, 2017 , Jiang et al, 2022 , Sotirios et al, 2020 ) and after ( Liu et al, 2020 , Park et al, 2020a , Park et al, 2020b ) treatment. Notably, consensus recommendations on clinical APTw imaging approaches at 3 T for brain tumors have recently been published ( Zhou et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Learning-based analysis of amide proton transfer-weighted MRI to identify true progression in glioma patients

Guo

Unberath

Heo

et al. 2022

NeuroImage: Clinical

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Review and consensus recommendations on clinical APT‐weighted imaging approaches at 3T: Application to brain tumors

Cited by 86 publications

References 195 publications

CEST MR fingerprinting (CEST‐MRF) for brain tumor quantification using EPI readout and deep learning reconstruction

CEST MR fingerprinting (CEST‐MRF) for brain tumor quantification using EPI readout and deep learning reconstruction

Differentiation between glioma recurrence and treatment effects using amide proton transfer imaging: A mini-Bayesian bivariate meta-analysis

Learning-based analysis of amide proton transfer-weighted MRI to identify true progression in glioma patients

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