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Qing Zeng^1,2, Chongxue Bie^1,2, Peter C.M. van Zijl^1,2, Yuguo Li^1,2, Valentina D'souza^1,2, Michael Machado³, Kirsten Achilles Poon³, Andrew Grimm³, and Nirbhay N. Yadav^1,2
1The Russell H. Morgan Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, MD, United States, ²F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States, ³Ultragenyx Pharmaceutical Inc., Novato, CA, United States

Keywords: CEST & MT, CEST & MT, liver

Glycogen storage disease type III (GSD III) is characterized by abnormally high glycogen accumulation in liver, muscle, and heart. Treatments such as diet modification and drugs are currently being developed, but there is a lack of suitable methods for assessing disease load and possible treatment efficacy. Recently, it was shown that glycoNOE MRI can image glycogen levels in vivo and here we apply this technique to distinguish GSD III from controls in a mouse model. The results show that glycoNOE can clearly distinguish GSD III and that glycoNOE contrast exhibits a linear correlation with ex vivo quantification of glycogen levels.

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Peter van Zijl¹, Nirbhay Yadav², Sajad Mohammed Ali³, Anina Seidemo³, David Olayinka Kamson⁴, Lindsay Blair⁵, John Laterra⁶, and Linda Knutsson^7
1Radiology, F.M. Kirby Research Center, Johns Hopkins University, Kennedy Krieger Institute, Baltimore, MD, United States, ²Radiology/F.M. Kirby Research Center, Johns Hopkins University, Kennedy Krieger Institute, Baltimore, MD, United States, ³Department of Medical Radiation Physics, Lund University, Lund, Sweden, ⁴Department of Oncology, Johns Hopkins University, Baltimore, MD, United States, ⁵Neurology, Johns Hopkins University, Baltimore, MD, United States, ⁶Neurology, Johns Hopkins University, Kennedy Krieger Institute, Baltimore, MD, United States, ⁷F.M. Kirby Research Center, Radiology, Medical Radiation Physics, Kennedy Krieger Institute, Johns Hopkins University, Lund University, Baltimore, MD, United States

Keywords: CEST & MT, Cancer, Glucose

The transverse relaxation time (T₂) of water is affected by the presence of exchangeable protons that are chemically shifted with respect to the water protons. We use direct saturation (DS) MRI to dynamically measure this effect during infusion of D-glucose to assess its uptake in brain tumors.  The change in T₂ becomes apparent as a line broadening of the DS spectrum, which is acquired using a whole-brain water saturation shift reference (WASSR) acquisition. First results using 0.5g/kg D-glucose show linewidth changes on the order of a few Hertz in glioma patients, allowing separation of tumor tissue from healthy brain tissue.

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Michal Rivlin¹, Or Perlman^2,3, and Gil Navon^1
1School of Chemistry, Tel-Aviv University, Tel Aviv, Israel, ²Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel, ³Sagol School of Neuroscience, Tel-Aviv University, Tel Aviv, Israel

Keywords: CEST & MT, Brain, Glucosamine, MRI, CEST, Metabolism, Brain Disorders

The uptake of glucosamine (GlcN), a non-toxic food supplement, can be monitored by CEST MRI. While previously demonstrated in breast cancer, here we show that GlcN metabolism can be detected in the brain. Following GlcN administration in mice, the MTRasym signals were significantly elevated in the cortex, hippocampus, and striatum. A Lorentzian multi-pool fitting pointed to a significant increase in the hydroxyl, amide, and rNOE signals. An in vitro BSA study confirmed the interactions between brain compounds and GlcN shown in vivo. This study suggests that GlcN CEST has the potential to serve as a metabolic biomarker in brain disorders.

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Yuki Kanazawa¹, Tosiaki Miyati², Masafumi Harada¹, Mitsuharu Miyoshi³, Yuki Matsumoto¹, Hiroaki Hayashi², Yasuhisa Kanematsu¹, and Yasushi Takagi^1
1Tokushima University, Tokushima, Japan, ²Kanazawa University, Kanazawa, Japan, ³Global MR Applications and Workflow, GE Healthcare Japan, Hino, Japan

Keywords: CEST & MT, Atherosclerosis

We demonstrated biological metabolic activity within an atherosclerotic plaque of the carotid artery using CEST imaging. 35 patients with carotid stenosis, of which all were pathologically diagnosed with carotid endarterectomy, were evaluated. The following estimation parameters in CEST image were evaluated; bulk water, magnetization transfer, amide proton transfer (APT), and nuclear Overhauser effect (NOE). As a result, there were weak positive correlations between T₁w-signal-ratio and the above-mentioned parameters: bulk water (R = 0.37), APT (R = 0.39), and NOE (R = 0.37). Multi-parametric analysis of CEST imaging can obtain detailed information concerning the activity in an atherosclerotic plaque.

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Yannik Völzke¹, Rüdiger Stirnberg¹, Eberhard Pracht¹, Daniel Löwen¹, Vincent Gras², Nicolas Boulant², Moritz Zaiss³, and Tony Stöcker^1,4
1German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany, ²Université Paris-Saclay, Commissariat à l’Energie Atomique, CNRS, NeuroSpin, Baobab,, Gif sur Yvette, France, ³Institute of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander- Universität Erlangen-Nürnberg (FAU), Erlangen, Germany, ⁴Department of Physics and Astronomy, University of Bonn, Bonn, Germany

Keywords: CEST & MT, CEST & MT

CEST imaging benefits from the increased spectral resolution at ultra-high field. Due to the inhomogeneous RF field, a B₁⁺-correction is necessary, which requires repeated measurements. The number of required repetitions can be reduced to two by using a ptx-based saturation scheme, like MIMOSA.

We present an alternative saturation scheme that is based on PUSH saturation and universal pulses, which we name PUSHUP. Using this approach, we could reduce the inhomogeneity of the CEST saturation as effectively as MIMOSA while being more SAR efficient.

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Chuyu Liu¹, Zhensen Chen², and Xiaolei Song^1
1Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China, ²Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China

Keywords: CEST & MT, Machine Learning/Artificial Intelligence

Herein, we developed a partially separable network (PSN) for CEST acceleration. Our contributions are: 1) We found that the reconstruction error of CEST mainly exists in the spatial subspace. 2) A deep learning network based on partially separable model was developed to optimize CEST images in spatial subspace. Retrospective results suggested that our method enabled a highly accelerated CEST imaging (14X for healthy adults and 11X for brain tumor patients) with contrast maps and Z-spectrum consistent with gold standard, which could have great clinical utility.

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Chuyu Liu¹, Zhongsen Li¹, and Xiaolei Song^1
1Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China

Keywords: CEST & MT, Machine Learning/Artificial Intelligence

Here we introduced a model-based deep learning approach which enabled joint optimization of both sampling and reconstruction for CEST MRI. The main purpose is to investigate an efficient undersampling pattern for CEST acceleration. Retrospective results (4X) showed that the proposed workflow is capable of leveraging redundancy information from optimized sampling pattern, further reconstructing gold-standard-consistent contrast maps.

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Tao Zu¹, Zhechuan Dai¹, Xingwang Yong¹, Tongling Jiang¹, and Yi Zhang^1
1Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China

Keywords: CEST & MT, Parallel Imaging

The clinical use of chemical exchange saturation transfer (CEST) imaging is limited by its relatively long scan time due to the measurements of multiple frames at the same location, especially for 3D whole-brain imaging. In this study, the recently proposed reconstruction method by joint K-space and Image-space Parallel Imaging (KIPI) is utilized for prospectively accelerating 3D CEST imaging. Prospective KIPI allows an acceleration factor of up to 8-fold for acquiring source images, reducing the scan time to 5.5 min for the whole-brain quantitative CEST imaging, with 17 saturation frames and 2.9 mm isotropic resolution.

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Kianush Karimian-Jazi^1,2, Volker Sturm¹, Katharina Schregel^1,2, Jessica Hunger^1,3, Verena Turco^3,4, Noah Enbergs¹, Berin Boztepe^1,3, Manuel Fischer¹, Yannik Streibel¹, Nikolaus von Knebel-Doeberitz⁵, Andreas Korzowski⁶, Steffen Görke⁶, Florian Kroh⁶, Mark E. Ladd⁶, Heinz-Peter Schlemmer⁵, Daniel Paech^5,7, Christopher B. Rodell⁸, Michael Platten^3,4, Wolfgang Wick^2,9, Sabine Heiland¹, Martin Bendszus¹, and Michael O. Breckwoldt^1,3
1Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany, ²Clinical Cooperation Unit Neurooncology, German Cancer Consortium (DTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany, ³Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Consortium (DTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany, ⁴Department of Neurology, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany, ⁵Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany, ⁶Department of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany, ⁷Clinic for Neuroradiology, University Hospital Bonn, Bonn, Germany, ⁸School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, United States, ⁹Department of Neurology, Heidelberg University Hospital, Heidelberg, Germany

Keywords: CEST & MT, High-Field MRI, Glioma

CDNP-R848, an experimental immunotherapeutic TLR7/8 agonist, showed high treatment efficacy with a significant tumor volume reduction and led to a re-normalization of the metabolic properties (MTRex Amide, MTRex Amine and MTRex NOE) of the tumor in relation to the healthy brain, with distinct differences to vehicle treatment. The clinical relevance of CEST imaging appears to be localizing active tumor areas and a better understanding of tumor heterogeneity. In the future, we aim to better characterize the origin of the CEST contrast by correlated histological and spatial metabolic analysis.

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Ziqin Zhang^1,2, Kexin Wang^1,2, Sooyeon Park^2,3, Anna Li², Yuguo Li^2,4, Robert Weiss^4,5, and Jiadi Xu^2,4
1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States, ²F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, United States, ³Department of Neuroscience, Johns Hopkins University, Baltimore, MD, United States, ⁴Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States, ⁵Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States

Keywords: CEST & MT, CEST & MT, amide CEST, amine CEST, guanidinium CEST, creatine CEST, exchange rate, concentration, polynomial Lorentzian line-shape fitting (PLOF), high spectral resolution (HSR) CEST, two-step Bloch-McConnell (BM) fitting.

We aim to use three different approaches to estimate the exchange rate of creatine (Cr) CEST, i.e., the pure CrCEST line-shape, B1-dependent CrCEST, and the pH response with different B1 values. The pure CrCEST signal extracted using wild type and GAMT-/- mice with low Cr and PCr concentrations; the pH in the brain cells altered by hypercapnia to demonstrate the pH sensitivity of GuanCEST; a two-step Bloch-McConnell fitting implemented to quantify the exchange rates. in vivo CrCEST exchange rate was found slow (~260-350 s-1). CrCEST is the major contribution to the opposite pH-dependence of GuanCEST signal under different B1. 

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Jochen Keupp¹, Ivan E. Dimitrov^2,3, Holger Eggers¹, and Elena Vinogradov^3,4
1Philips Research, Hamburg, Germany, ²Philips Healthcare, Gainesville, FL, United States, ³Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, United States, ⁴Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, United States

Keywords: CEST & MT, Fat, Z-spectral fitting, Fat suppression pulses

Fat signal correction remains a challenge in APT/CEST-MRI for body-oncology. We show that signal background models that include direct water saturation, MT-effect, and modified fat spectra can be fitted to Z-spectra acquired with/without fat-suppression. APT/CEST signals are then extracted as the residual from the background fit (APT#). The model for fat-suppressed spectra uses a mirrored fat contribution. After B₀-correction, 5 model parameters are sufficient. This allows for the acquisition of clinically feasible protocols using less than 20 Z-spectral offsets. Fat-suppression potentially increases the precision of APT# imaging by lowering the initial fat contribution. 

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Remy Chiaffarelli^1,2, Paul Jurek³, Pedro Cruz⁴, Max Zimmermann^1,2, Carlos Geraldes⁴, Garry Kiefer³, and André Ferreira Martins^1,2
1Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, University Hospital Tuebingen, Tuebingen, Germany, ²Cluster of Excellence iFIT (EXC 2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tuebingen, Tuebingen, Germany, ³Macrocyclics, Inc., Plano, TX, United States, ⁴Coimbra Chemistry Center - Institute of Molecular Sciences (CQC-IMS), University of Coimbra, Coimbra, Portugal

Keywords: CEST & MT, CEST & MT, PARACEST, Metabolism

Lactate accumulation in the tumor microenvironment is a hallmark of cancer aggressiveness. Here, we report a method to image extracellular lactate using shiftCEST MRI and Yb- and EuPCTA Shift Reagents (SRs). In the presence of lactate, the SRs shift the lactate OH-CEST signal away from the water signal by ~14 ppm (EuPCTA) and ~95 ppm (YbPCTA), allowing for quantitative detection of extracellular lactate produced by cancer cells. In vivo studies confirmed the detection and fast renal elimination of the lactate*YbPCTA into the bladder by shiftCEST MRI. Overall, these results provide new insights into developing innovative non-invasive metabolic MRI.

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Julia Stabinska^1,2, Moritz Zaiss^3,4, Adnan Bibic¹, Farzad Sedaghat², Cristina L Sadowsky^5,6, Peter CM van Zijl^1,2, and Michael T McMahon^1,2
1F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States, ²The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States, ³High-field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany, ⁴Institute of Neuroradiology, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany, ⁵International Center for Spinal Cord Injury, Kennedy Krieger Institute, Baltimore, MD, United States, ⁶Physical Medicine and Rehabilitation, The Johns Hopkins University School of Medicine, Baltimore, MD, United States

Keywords: CEST & MT, CEST & MT

In vivo optimization of CEST iopamidol contrast in human subjects is complicated and requires multiple examinations and injections of the agent. To address this challenge, we propose application of a numerical approach that utilizes exchange rates determined under physiological conditions at 17.6T to perform kidney-like multi-pool Bloch-McConnell simulations for in silico optimization of saturation parameters for 3T applications. Our results suggest that the iopamidol-based CEST MRI is sensitive to pH in the range between 6 and 7.2 with the optimal results when short CEST saturation pulses (3x100 ms), low B1 strength (B₁~0.8 µT) and short recovery time (T_rec~T_1w) are applied.

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Huan Chen¹, Liangjie Lin², Lin Chen¹, and Zhong Chen^1
1Department of Electronic Science, Xiamen University, Xiamen, China, ²Clinical & Technical Support, Philips Healthcare, Beijing, China

Keywords: CEST & MT, Machine Learning/Artificial Intelligence

Chemical exchange saturation transfer (CEST) MRI is a versatile technique that exploits the saturation transfer between exchangeable protons and water for non-invasive detection of diluted metabolites. Although theoretically promising, the practical application of CEST MRI is still challenged by low CEST contrast and low signal-to-noise ratio (SNR) of acquired images. Here, we proposed a deep learning-based method, dubbed denoising CEST network (DCEST-Net), to fully exploit the spatiotemporal correlation prior embedded in the CEST images and restore noise-free images from their noisy observations. Results suggested that DCEST-Net can achieve better performance compared to the state-of-the-art denoising methods.

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Kexin Wang^1,2, Ran Sui^1,2, Lin Chen^3,4, Yuguo Li^2,3, and Jiadi Xu^2,3
1Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States, ²F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, United States, ³Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD, United States, ⁴Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China

Keywords: CEST & MT, Alzheimer's Disease, GuanCEST

We develop a novel two-step multi-B₁ Bloch-McConnell fitting approach for calculating the exchange rate of CEST protons in vivo, and apply it to guanidinium protons, the exchange rate of which is 70.1 ± 5.5 s^-1 with a concentration of 40.4 ± 5.2 mM in mouse brain at an optimized B₁ of 0.8 µT. Guanidinoacetate N-methyltransferase deficiency (GAMT^-/-) mice that have low creatine and phosphocreatine concentrations in brain are studied for protein guanidinium, i.e., arginineCEST (ArgCEST). The low exchange rate of ArgCEST suggests that the inverse pH dependence in GuanCEST with low B₁ is dominated by CrCEST compared to ArgCEST.

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Haoyun Su^1,2, Jianpan Huang¹, and Kannie W.Y. Chan^1,2,3,4,5
1Department of Biomedical Engineering, City University of Hong Kong, Kowloon, Hong Kong, ²Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), New Territories, Hong Kong, ³Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States, ⁴City University of Hong Kong Shenzhen Research Institute, Shenzhen, China, ⁵Tung Biomedical Sciences Centre, City University of Hong Kong, Kowloon, Hong Kong

Keywords: CEST & MT, Molecular Imaging

CEST MRI can detect mM range of endogenous and exogenous molecules, and bound small molecules such as glycogen and lactate via rNOE. Here we reported for the first time that DMSO and its structural analogs in aqueous solution had distinctive CEST peaks at the negative offsets of Z-spectrum. CEST effect of DMSO was dependent on saturation power and concentration, and less sensitive to tested temperature. Alcohols, acetone, acetonitrile, acetic acid and N,N-dimethylformamide also showed observable CEST peaks at the negative offsets. Interaction between DMSO and water molecule, such as hydrogen bonding, could contribute to the observed CEST effect.

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Xinran Chen¹, Jian Wu¹, Liangjie Lin², Zhiliang Wei³, Lin Chen¹, and Zhong Chen^1
1Department of electronic science, Xiamen University, Xiamen, China, ²Clinical & Technical Support, Philips Healthcare, Beijing, China, ³Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States

Keywords: CEST & MT, Data Processing, Denoising

Chemical exchange saturation transfer (CEST) is a powerful technique that enables non-invasive detection of endogenous metabolites in living tissues. Since the observed water signal is decreased due to the transfer of saturated spins, CEST imaging inherently suffers from low SNR, hence degrading accuracy and reproducibility. Inspired by the spatial-spectral correlation of CEST images, here we propose a Subapace denoising method with Non-Local Low-Rank constraint and Spectral-Smoothness regularization (SNLRSS) to diminish the noise, which improves the accuracy of subsequent quantitative analyses of CEST images.

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Vivian W.M. Leung¹, Joseph H.C. Lai¹, Zilin Chen¹, Jianpan Huang¹, Se Weon Park^1,2, Yang Liu^1,2, Charles Hu³, and Kannie W. Y. Chan^1,2,4,5,6
1Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China, ²Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China, ³Incando Therapeutics Pte Ltd, Singapore, Singapore, ⁴Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States, ⁵City University of Hong Kong Shenzhen Research Institute, Shenzhen, China, ⁶Tung Biomedical Sciences Centre, City University of Hong Kong, Hong Kong, China

Keywords: CEST & MT, Tumor

Glioblastoma (GBM) is hard to treat and has poor prognosis. Photodynamic therapy (PDT) is a promising treatment for GBM. Here, we detect the treatment efficacies of different PDT schemes (repeated (re-) and single (s-) PDT) on a rodent model of GBM using CEST MRI to monitor the molecular changes associated with tumor physiology and necrosis. Significant decreases in APT and rNOE signals were detected in the rePDT group as well as decreased proliferative activities in histology when compared with sPDT. This indicates that both APT and rNOE can be a reliable approach to assess PDT treatment efficacy against GBM.

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Yiqing Shen^1,2, Nhat Le^1,2, Jingpu Wu^1,3, Pengfei Guo^1,2, Jinyuan Zhou¹, Mathias Unberath², and Shanshan Jiang^1
1Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, United States, ²Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States, ³Department of Applied Mathematics and Statistics, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States

Keywords: CEST & MT, Machine Learning/Artificial Intelligence

Deep learning approaches have been widely applied to the MRI field. Among them, transformers have received increasing popularity due to their capability in handling multi modalities. Yet, transformers are hungry for large-scale data, which is expensive to collect. Here, we develop a novel memorizing transformer for small-scale multi-parametric MRI analysis. Empirically, we evaluate the proposed method on a dataset curated from 147 brain post-treatment malignant glioma cases for classifying treatment effect and tumor recurrence. The proposed memorizing transformer boosted a 5.15% improvement in the test area under the receiver-operating-characteristic curve (AUC) to the baseline transformer approaches.

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Philip S Boyd¹, Florian Kroh^1,2, Johannes Breitling¹, Vanessa L Franke^1,2, Mark E Ladd^1,2,3, Peter Bachert^1,2, Steffen Goerke¹, and Andreas Korzowski^1
1Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany, ²Faculty of Physics and Astronomy, University of Heidelberg, Heidelberg, Germany, ³Faculty of Medicine, University of Heidelberg, Heidelberg, Germany

Keywords: CEST & MT, Molecular Imaging, intracellular pH, guanidyl protons, 7 Tesla

In this study, our method for quantitative pH_i mapping using endogenous CEST-MRI was successfully transferred to examinations of the human brain at B₀=7T. Applicability in vivo was demonstrated in n=3 healthy volunteers, showing an overall median pH_i,CEST of 7.00 and 6.96 for gray and white matter, respectively. In order to proof the plausibility of the presented approach, the obtained pH_i,CEST maps were additionally validated directly in vivo via ³¹P MRSI at 7T, showing an overall median pH_i,31P of 7.02 and 7.01 for gray and white matter, respectively. Consequently, reliable CEST-based pH_i mapping is now also possible in the human brain.