Absolute quantification of brain deuterium metabolic imaging in healthy volunteers and glioblastoma patients at 7T

Absolute quantification of brain deuterium metabolic imaging in healthy volunteers and glioblastoma patients at 7T | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Absolute quantification of brain deuterium metabolic imaging in healthy volunteers and glioblastoma patients at 7T Jabrane Karkouri , Masha Novoselova , Sarah Miller , Minghao Zhang , Alexandra Constantinou , Carina Graf , Daniel Atkinson , Brandon Tramm , Scott Schillak , Richard Mair , Kevin Brindle , View ORCID Profile Christopher T. Rodgers doi: https://doi.org/10.1101/2025.10.08.25337571 Jabrane Karkouri 1 Wolfson Brain Imaging Centre, University of Cambridge , Cambridge, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: jabrane.karkouri{at}gmail.com Masha Novoselova 1 Wolfson Brain Imaging Centre, University of Cambridge , Cambridge, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sarah Miller 1 Wolfson Brain Imaging Centre, University of Cambridge , Cambridge, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site Minghao Zhang 1 Wolfson Brain Imaging Centre, University of Cambridge , Cambridge, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alexandra Constantinou 1 Wolfson Brain Imaging Centre, University of Cambridge , Cambridge, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site Carina Graf 1 Wolfson Brain Imaging Centre, University of Cambridge , Cambridge, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site Daniel Atkinson 1 Wolfson Brain Imaging Centre, University of Cambridge , Cambridge, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site Brandon Tramm 2 Virtumed, LLC , Minneapolis, MN, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site Scott Schillak 2 Virtumed, LLC , Minneapolis, MN, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site Richard Mair 3 Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre , Cambridge, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kevin Brindle 3 Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre , Cambridge, United Kingdom 4 Department of Biochemistry, University of Cambridge , Cambridge, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christopher T. Rodgers 1 Wolfson Brain Imaging Centre, University of Cambridge , Cambridge, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Christopher T. Rodgers Abstract Full Text Info/History Metrics Preview PDF ABSTRACT Purpose We present a method for absolute quantification of deuterated metabolites in vivo at 7T. We describe acquisition protocols and an analysis pipeline compatible with 7T Terra MRIs, and apply these using a 2 H/ 1 H receive array in healthy volunteers and glioblastoma patients. Methods B 1 + /B 1 − maps from multiple CSI scans in a uniform phantom were used to derive calibrated coil weights for absolute quantification, validated in phantoms with known deuterated compounds. 2 H MRSI was performed in twelve healthy volunteers (two post-[6,6- 2 H 2 ]-glucose) and five glioblastoma patients (all post-[6,6- 2 H 2 ]-glucose). Spectra were fitted with OXSA, and Glx/Lac compared between tumor and normal-appearing brain using linear mixed-effects models. Results Measured B 1 + maps were across the whole phantom. Natural abundance deuterium in water was 8.96 ± 0.7 mmol/L. Absolute maps of HDO, Glc, Glx, and Lac were acquired following [6,6- 2 H 2 ]glucose. Rate maps showed higher Lac production in tumors (2.3 μmol/L/min, SE = 0.87) compared with normal-appearing regions (1.0 μmol/L/min, SE = 0.36; p < 0.01) and healthy brain (0.5 μmol/L/min, SE = 0.17; p < 0.01). Glx production was lower in tumors (3.8 μmol/L/min, SE = 0.44) relative to normal-appearing regions (6.0 μmol/L/min, SE = 0.36; p < 0.001) and healthy brain (9.2 μmol/L/min, SE = 0.61; p < 0.001). Conclusion We demonstrate robust absolute quantification for human 7T DMI. Glioblastomas showed elevated Lac and reduced Glx labeling relative to normal brain, with inter-patient heterogeneity consistent with an existence of different metabolic subtypes. 1 Introduction Altered brain energy metabolism is a hallmark of prevalent diseases, including neurodegenerative disorders and cancer( 1 - 7 ). Insufficient cellular energy impairs neuronal function, leading to cell death during ageing and as a result of neurodegenerative disease. Therapies aimed at restoring brain energy metabolism—such as oral supplementation with NAD + or exogenous ketones, or intermittent fasting—are actively being investigated to treat Parkinson’s disease and other disorders( 8 - 11 ). Tumors, by contrast, often display fundamentally different metabolism, with a preference for glycolysis( 7 , 12 - 15 ). Non-invasive characterization of such metabolic changes could enable personalized treatment selection( 16 ). Alternatively, tumor metabolism itself is an attractive therapeutic target; recent reviews highlight glycolysis, glutaminolysis, and mitochondrial pathways as key precision oncology targets ( 13 , 17 , 18 ). Glioblastoma, the most aggressive brain cancer, is metabolically heterogeneous. Spatial transcriptomics and mass spectrometry imaging of 13 C-glucose–infused patients reveal glycolytic and oxidative subtypes( 19 - 22 ). These can coexist at the centimeter scale, suggesting they may be resolvable by clinical metabolic imaging. Because the glycolytic subtype is radiation-resistant and carries a poorer prognosis, imaging could inform prognosis and treatment selection. Clinical trials of energy-modifying therapies likewise require robust, non-invasive metabolic endpoints to permit trials in economically-viable cohorts. Magnetic resonance spectroscopy (MRS) and spectroscopic imaging (MRSI) provide powerful in vivo tools to probe brain metabolism( 23 - 26 ). A particularly promising emerging MRSI approach, called deuterium metabolic imaging (DMI), uses deuterium-labelled substrates to track metabolic fluxes in real time. DMI was introduced in 2018 at 11.7T( 27 ) and has subsequently been applied in vivo by several groups( 28 - 48 ). DMI allows mapping of the partitioning of tissue glucose metabolism between glycolysis and mitochondrial oxidation. 2 H-MRSI follows metabolism of [6,6- 2 H 2 ]-glucose into glutamate/glutamine (Glx) via the TCA cycle and into lactate (Lac) via glycolysis, as pioneered by de Feyter and de Graaf( 27 , 49 - 52 ). This provides unique insights into metabolic reprogramming and may serve as a radiation-free alternative to 18 FDG-PET, as shown recently in Alzheimer’s disease( 53 ). Preclinical work supports DMI’s potential for monitoring treatment response( 16 , 54 - 58 ), for grading tumours( 59 - 61 ) and to determine subtypes of glioblastoma xenografts by quantifying [6,6- 2 H 2 ]-glucose partitioning between Glx and Lac( 62 ). Ultra-high-field MRI improves both SNR and also spectral separation (between HDO, Glc, Glx, and Lac), as demonstrated by studies at 9.4T( 63 ) and 7T( 64 - 71 ) compared to 3T( 72 - 76 ). Here, we focus on quantifying deuterium metabolites at 7T in healthy volunteers and glioblastoma patients. We adapted a method for absolute quantification (finding metabolite concentrations in mol/L) that was originally developed by Purvis et al .( 77 ) for liver 7T 31 P-MRS. We validated it in phantoms and in healthy volunteers without administration of a deuterated tracer. Then, we applied the method to quantify the temporal dynamics of HDO, Glc, Glx, and Lac concentrations after oral [6,6- 2 H 2 ]glucose. We compared healthy tissue in volunteers with tumor-containing and normal-appearing regions in patients to reveal inter-patient and intra-tumor metabolic heterogeneity. 2 Theory 2.1 Quantitation framework The key requirements for quantitative MRSI with a receive array are: (i) combining signals from array elements appropriately for quantitation, (ii) fitting spectral peaks, and (iii) correcting for metabolite and reference phantom relaxation. We adapt “Method 1: Phantom Replacement” from Purvis et al . which was originally developed for 7T 31 P-MRS of the human liver( 77 ). This relies on acquiring data from a conductivity-matched phantom of known concentration and T 1 with a matched protocol for calibration. 2.2 B 1 + mapping The Phantom Replacement method requires knowledge of the coil’s B 1 + and B 1 − fields. Following Purvis et al .( 77 ), we measured these using Variable Flip Angle (VFA) B 1 + mapping on a uniform phantom( 78 ). Multiple CSI scans were acquired with N V =9 RF excitation voltages ( V1,V2 , ⃛ ). For each voxel, all the single-element spectra were concatenated to form a joint spectrum: of size N c × ( N V N s ), where N c is the number of receive elements and N s is the number of points in each spectrum. The noise covariance Ξ was estimated from a no-RF prescan. Whitened Singular Value Decomposition (WSVD) was then applied in each voxel to compute N c × 1 coil weight vectors w ( r ) jointly for all the CSI scans, and maximum likelihood combined spectra S j ( r ). The combined spectrum with maximum signal was fitted in AMARES( 79 ) to obtain a model spectrum S M ( r ). Using linear least squares (“\” in MATLAB), complex single-element amplitudes A k,j ( r ) = S k,j ( r )\ S M ( r ) and combined amplitudes A j ( r ) = S j ( r ) \ S M ( r ) were derived for each element k and voltage j . Using the known T R (1000ms) and phantom T 1,P (355ms), voxel-wise B 1 + maps were obtained by nonlinear least-squares fitting (lsqcurvefit, MATLAB) to: where is the transmit field strength per volt in units of µT/V RMS , and accounts for the deuterium gyromagnetic ratio and the RF pulse shape f ( t ) integrated over the pulse duration T P , T R is the repetition time, T 1, P the phantom P 2 H T 1 and k( r ) is a scaling factor representing both coil receive sensitivity and the phantom concentration( 80 ). 2.3 B 1 - mapping According to the Roemer signal model( 81 , 82 ), the signal in the k th element depends on the sample magnetisation and that element’s receive sensitivity . The magnetisation depends on the 2 H concentration (known in the phantom), and the pulse sequence. We include in “receive sensitivity” all fixed factors (cable losses, preamplifier/channel gains), which remain identical for reference and main scans. The overall phase of was defined by assuming the spectrum at the voltage giving maximum signal was real and positive. This was solved by linear least squares (“\” in MATLAB). Effectively, the scaling k ( r ) from the mapping is distributed across receive elements, with relative phases captured since is complex. 2.4 Uniform sensitivity coil combination The joint WSVD weights w k at position r are mathematically equivalent to Roemer’s uniform sensitivity weights (Eq. 27,( 81 )): Roemer weights capture the complex scaling from spatially varying coil sensitivity, whereas WSVD— being data-driven—assumes an arbitrary overall sensitivity. For quantitative analysis, we apply the reference-phantom-derived weights to combine per-element spectra from the main scan (e.g. an in vivo scan to be calibrated). This assumes that and noise covariance Ξ are similar between the reference and main scans, which holds if conductivity and coil loading are comparable. The combined spectra are then fitted with AMARES, giving metabolite amplitudes for the main scan and for the reference phantom. Following Purvis et al . (Eq. 10), the metabolite concentration is: where F m and F p are saturation-correction factors for metabolite and the reference phantom, which has concentration [ P ], and N c is the number of receive elements. For a spoiled CSI sequence: where T 1,m is the T 1 of metabolite m (or phantom compound). 3 Methods 3.1 Hardware All experiments were performed using a Magnetom Terra 7T MRI (Siemens, Erlangen, Germany) with a dual-tuned 2 H/ 1 H head coil (Virtumed LLC, Minneapolis, MN). Figure 1d shows a photograph of the coil, which comprises a 15-element Transverse Electromagnetic mode (TEM) resonator for 2 H transmit (one rung is left out to allow visual stimulus), 16x 2 H receive loops, and 4x 1 H transmit/receive loops driven in quadrature. Download figure Open in new tab Figure 1: Custom 3D-printed head-shaped phantoms for calibration and validation. a) Photograph of the phantom housing. b) Reference phantom filled with deuterium oxide and water. c) Multi-compartment phantom containing two table tennis balls containing aqueous solutions of Lac and Fum, with the remaining space filled with deuterium oxide and water. d) Photograph of the dual-tuned 7 T 2 H/ 1 H head coil used for this study. e) B1+ map with the reference phantom (b) plotted for the mid-transverse slice illustrated in (a). 3.2 Reference phantom We 3D-printed a custom head-shaped phantom ( Figure 1a ) based on the design by Shajan et al.( 83 ), to which we added a threaded bottle neck and cap, and a support for the inner compartments (table tennis balls). It was printed from polylactic acid (80% infill, Bambu 3D printer, Shenzhen, China) and waterproofed with four coats of red rubber spray (PlastiDip UK Limited, Petersfield, UK). The phantom was filled with 4.5 L ultrapure water (Electrolube, Ashby de la Zouch, UK), 51 g deuterium oxide (yielding an HDO concentration of 1.14 mol/L), 0.25 g Dotarem (Guerbet, Princeton, USA), 20.93 g sodium chloride, and 8.4 g sodium benzoate. Chemicals were obtained from Sigma-Aldrich and used as supplied. Conductivity was estimated at 1.0 S/m( 84 ), comparable to tissue values( 85 , 86 ), where grey matter is 0.47 S/m, white matter 0.27 S/m and cerebrospinal fluid 2.04 S/m. 3.3 Quantitation protocol The scans need to implement this method are: 1. Main MRSI scan ( in vivo or phantom) to be quantified. 2. Reference phantom B 1 + maps to fix w k ( r ) in Eq. ( 4 ) and ( r ) in Eq. ( 3 ) . 3. Phantom T 1 determination. 4. Reference scan with matched protocol to determine F P ( r ) and ŝ P ( r ) in Eq. ( 5 ) . Data from steps 2-3 can be reused to quantify many MRSI scans providing their protocols match, including having identical position and orientation for their MRSI matrices. 3.4 B 1 + and B 1 - maps Reference phantom B 1 + and B 1 + maps were obtained by acquiring nine 3D CSI scans with 4ms rectangular excitation pulses having peak voltages ranging from 25V to 225V in 25V steps, 1 s TR, 16x16x8 matrix, 5 kHz spectral bandwidth, 1024 points, and acquisition weighting with 1 average at k=0. The total acquisition time was 1 hr 30 min. 3.5 Validation of absolute quantification The reference phantom ( Figure 1a-b ) was scanned as part of the “Reference scan” with T R = 250 ms, 16×16×8 matrix, 1 ms block excitation pulse at 210 V, 5 kHz spectral bandwidth over 1024 points, 6 averages, and acquisition weighting, giving a total time of 5 min. A second scan (i.e. the “main scan” for the purpose of this test) was performed on another day with identical parameters and grid position. A second multi-compartment phantom was built using the same head-shaped shell but with two table-tennis balls glued inside ( Figure 1c ). Each contained 50 mL ultrapure water and 0.23 g sodium chloride. One was supplemented with [2,3- 2 H 2 ]fumaric acid (CLM-1529; 0.99 g; MW 118.1 g/mol; 0.25 mol/L; deuterium concentration 0.5 mol/L) and the other with sodium L-[3,3,3- 2 H 2 ]lactate (DLM-9071; 1 g; MW 115.03 g/mol; 0.26 mol/L; deuterium concentration 0.78 mol/L). Both were obtained from CK Isotopes Limited (Leicester, United Kingdom). The bulk head volume contained 9 g D 2 O and water, giving an HDO concentration of 0.2 mol/L. To minimise voxel partial-volume effects, we used a 20×20×16 matrix while keeping other parameters identical, for a total acquisition time of 29 min 12 s. A matching calibration scan of the reference phantom was also acquired at this resolution. Following Purvis et al.( 77 ), B 1 maps were interpolated to this grid using MATLAB’s imresize3 . m (bilinear interpolation). 3.5.1 T 1 measurements Separate measurements were performed to determine the T 1 values for each metabolite in each phantom, i.e. HDO in the reference phantom, and HDO, Lac, and Fum in the multi-compartment phantom. The reference phantom was scanned alone, each table-tennis ball was scanned individually, and finally the complete multi-compartment phantom was scanned after assembly. The T 1 values of each metabolite were measured using non-localized inversion recovery scans and fitted to a 3-parameter model. The inversion-recovery acquisitions had inversion times that were incremented from 25 ms to 8000 ms, 10 s TR, 1ms rectangular excitation pulses and 2ms rectangular inversion pulses both set to 150 V rms . 3.6 In vivo data acquisition 3.6.1 Participants Twelve healthy volunteers (6 females, 6 males, 29–58 years) and five glioblastoma patients (2 females, 3 males, 53–67 years) gave written consent. Ethics approvals were obtained from the University of Cambridge HBREC (HBREC.2021.08) and the London Camden & King’s Cross REC (21/PR/0828). 3.6.2 Study protocol Ten healthy volunteers were scanned using natural-abundance HDO only. The remaining two volunteers and all patients fasted ≥4 hours before receiving a glucose drink (0.75 g/kg body weight, max 60 g) of 6,6’-[ 2 H 2 ]glucose in ≈200 mL water prior to the 7T scan. Patients also underwent a 3T clinical scan during the same visit. 3.6.3 7T MRI protocol Participants were scanned lying head-first supine, with lights switched off at the main circuit breaker to reduce noise at the 2 H frequency ( Figure SI1 ). MRI acquisition began with 1 H gradient-echo scouts (TE = 3.6 ms, TR = 8.6 ms, FOV = 400 mm 2 , 1.56 × 1.56 mm 2 in-plane, 5 mm slice, 17° flip angle) followed by T1-weighted images (TE = 1.8 ms, TR = 1 s, FOV = 280 × 245 × 192 mm 3 , 1.09 × 1.09 mm 2 in-plane, 1 mm slice, 5° flip angle). B 0 shimming was performed with the vendor’s “GRE-Brain” tool, and post-shim maps were acquired with dual-echo GRE (TE = 1.02/3.06 ms, TR = 18 ms, FOV = 256 × 256 × 256 mm 3 , 4 mm in-plane, 4 mm slice, 7° flip angle). 2 H MRSI data were collected in blocks by repeating a UTE-CSI( 87 , 88 ) sequence with 6.9 mL nominal resolution (16 × 16 × 8 matrix, FOV = 220 × 200 × 320 mm 3 , TR = 250 ms, 1 ms rectangular pulse at 210 V RMS , 5 kHz bandwidth), giving 4min59s per measurement. The CSI grid and parameters matched those of the phantom scans and were fixed across all participants. 3.6.4 3T acquisition protocol The five glioblastoma patients also underwent 3T imaging (Prisma, Siemens, Erlangen, Germany) using a standard brain protocol( 89 , 90 ) including MPRAGE, T 2 SPACE, T 2 -weighted turbo spin echo, and diffusion-weighted EPI, all following Gadovist contrast administration (Bayer New Zealand Limited, New Zealand). 3.7 Post-processing Anatomical 3T images were co-registered to the 7T GRE scan using an affine transformation with 12-degrees of freedom and mutual information (imregtform, MATLAB). Data were processed in MATLAB R2022b with an updated OXSA toolbox supporting Siemens 7T Terra scanners, incorporating AMARES( 91 ). For 2 H MRSI, analysis proceeded as follows (summarised in Figure 2 ): Download figure Open in new tab Figure 2: 7T DMI protocol (top) and 2 H MRSI processing steps (bottom). All participants fasted at least for 4h prior to the scan, which included the uptake of a deuterated glucose drink, anatomical localization and a series of 3D CSI scans over time. The data were then exported for further analysis. A metabolite mask was generated from the HDO peak amplitude and its CRLB values before amplitudes were corrected for partial saturation. Raw k -space data (Siemens “TWIX” format) were Fourier transformed into image and frequency space. For each voxel: Multi-channel signals were combined using Roemer weights calibrated on the reference phantom (see Section 2 above for details). The last timepoint was quantified using prior knowledge of 4 Lorentzian peaks. The prior knowledge was updated in memory to fix those linewidths before fitting data from all other timepoints. Amplitudes were corrected for partial saturation using literature T 1 values (Lac: 297 ms; Glc: 66 ms; Glx: 149 ms; HDO: 450 ms)( 28 , 44 , 92 ). 3.7.1 Quality Control and Thresholding Cramér-Rao Lower Bounds (CRLB) were computed to estimate uncertainty. Mask #1 excluded voxels with [HDO]30%. Mask #2 was taken first from mask #1 and also excluded voxels with Lac/(Lac + Glx) concentration ratio CRLB > 30%. 3.7.2 ROI analysis To compare metabolism between healthy controls, tumor regions, and contralateral normal-appearing brain, three ROIs were defined In patients, separate ROIs were outlined based on 3T images: tumor (ROI tumor ) and contralateral normal-appearing white matter (ROI NAWM ). In glucose-ingesting healthy controls, a similar size ROI placed in the center of the brain was used (ROI HV ).Voxel-wise data within each ROI were averaged to yield an ROI mean value per region, per patient, per time point. These ROI-mean data were used for all statistical analyses (see Figure SI2 ). 3.8 Time series analysis Following the approach by Li et al .( 93 ) and Niess et al .( 41 ), to test whether there were statistically significant changes in metabolite concentrations over time, we fitted Lac and Glx concentrations to a linear model( 41 , 71 ): This produces a summary of the rate of increase of Lac, i.e. , and Glx, i.e. , for the whole scan. Maps of the product selectivity toward lactate were then investigated with mask #2 (section 2.7.1). 3.9 Statistical analysis 3.9.1 Regional influence on Glx and Lac temporal dynamics To assess whether tumor or normal-appearing regions had different Glx and Lac kinetics, we used two complementary statistical approaches. First, we performed paired comparisons using the non-parametric Wilcoxon Signed-Rank Test( 94 ), matching each tumor value with the corresponding value in normal-appearing brain from the same patient at the same time point. Second, we applied stepwise linear modelling (MATLAB “stepwiselm.m”) with the Akaike Information Criterion (AIC) to identify predictors of Glx or Lac concentrations. Candidate predictors included Patient ID, tissue type (tumor vs normal-appearing), and nuisance variables (age, gender, time). The best-fitting models were then refined using linear mixed-effects modelling (MATLAB “fitlme.m”) to account for repeated measurements within patients (Model 1)( 95 ). 3.9.2 Inter-Patient Metabolic Heterogeneity in Tumour Regions To examine inter-patient metabolic variability, analysis was restricted to tumor regions and two linear mixed-effects models were fitted. Model 2: This model assumes a fixed slope (d[m]/dt) across patients, with age, gender, and PatientID contributing variable intercepts. And Model 3: where time is included as a random slope by patient, allowing each patient to have their own trajectory of metabolic change. Likelihood ratio tests( 94 ) were used to assess whether including random slopes significantly improved model fit. 4 Results 4.1 Phantom Experiments 4.1.1 B 1 + maps Figure 1a-b shows the reference phantom used for the acquisition of the B 1 maps and for the validation of the absolute quantification methods. Figure 1e shows a mid-transverse slice from the B 1 + map. This shows a strong transmit field close to the edges of the phantom and a B 1 + dropout in the right-anterior region. The average B 1 + for the mid-transverse slice shown in Figure 1e is (mean ± SD, with range: ). Across the whole phantom it is 0.82 ± 0.19 (range: 0.14–1.26 ). 4.1.2 Validation of the absolute quantification method For the reference phantom, a T 1 HDO of 697 ms was measured. For the multi-compartment phantom, T 1 Lac = 251 ms, T 1 Fum = 314 ms and T 1 HDO = 567 ms. Results validating the absolute quantification method are shown in Figure 3 . For the calibration scan, the non-calibrated HDO amplitude map is shown in Figure 3a , with saturation-corrected and calibrated maps in Figures 3b–c . As expected, calibration yielded a uniform concentration matching exactly the phantom value. For the repeat scan, the calibrated [HDO] map (as per Equation 5 , Figure 3d ) gave an average concentration of 1.00 ± 0.05 mol/L (range 0.87–1.25 mol/L), a 12% error relative to the true value (1.14 mol/L). Download figure Open in new tab Figure 3: Phantom validation of absolute quantification concentration measurements. (a-d) reference phantom containing 1.14 mol/L HDO. a) Raw HDO signal in the calibration scan without calibration for absolute quantification. b) Corrected HDO signal for partial saturation in the calibration scan using Equation ( 5 ). c) quantitative HDO map after calibration for absolute quantification in the calibration scan. d) Quantitative HDO map after calibration for absolute quantification using a CSI scan acquired on a different date (repeat scan). Phantom validation of concentration measurements on the (e-h) multi-compartment phantom containing 0.2 M HDO deuterons, 0.78 M Lac deuterons (in left ball) and, 0.5M Fum deuterons (in right ball). [HDO], [Lac] and [Fum] metabolite maps are shown in e), f) and g), respectively after calibration using Equation ( 5 ). h) Distribution of the [HDO] values within the reference phantom, and of the [HDO], [Lac] and [Fum] within each whole 3D compartment of the multi-compartment (MC) phantom. In the multi-compartment phantom ( Figures 3e–g ), average concentrations were: [HDO] 0.30 ± 0.07 mol/L (range 0.14–0.50 mol/L, 33% error), [Lac] 0.67 ± 0.18 mol/L (range 0.47–0.89 mol/L, 14% error), and [Fum] 0.30 ± 0.06 mol/L (range 0.25–0.39 mol/L, 40% error). Figure 3h shows the distribution of [HDO], [Lac], and [Fum] within each compartment of each phantom. 4.2 In vivo DMI 4.2.1 Natural Abundance scans in Healthy Volunteers Sample HDO maps from natural abundance scans in one healthy volunteer are shown in Figure 4 . Mean Linewidths across volunteers varied from 11 to 26Hz in the brain. The strong signal peaks in posterior brain due to high receive sensitivity as seen in Figure 4c are corrected yielding a much more biologically plausible concentration distribution ( Figure 4d ). Download figure Open in new tab Figure 4: Natural HDO abundance scan in a healthy volunteer. a) Position of the transverse slice. b) Linewidth (FWHM) map, illustrating increased linewidth in the frontal lobe near the sinuses. c) Raw HDO map (i.e. not calibrated, prior to absolute quantification). d) Calibrated map showing absolute [HDO]. Figure 5 shows natural [HDO] maps for all volunteers, with violin plots depicting the distribution of [HDO] across the brain. The average HDO concentration in the brain in the 10 healthy volunteers was 8.96 ± 0.7 mmol/L (range 7.5–10.1 mmol/L). Download figure Open in new tab Figure 5: [HDO] maps for all healthy volunteers scanned with natural abundance (1 st row). Distribution of the [HDO] values within the whole 3D brain (2 nd row) shown as violin plots. 4.2.2 Imaging glucose metabolism in healthy controls 2 H images from a healthy volunteer after oral [6,6- 2 H 2 ]-glucose are shown in Figure 6 . Maps of HDO ( Figure 6a ), Glc ( Figure 6b ), Glx ( Figure 6c ), and Lac ( Figure 6d ) over time are presented. Signals from HDO, Glc, and Glx increased, with higher HDO in the brain center. As in prior DMI studies, a peripheral “Lac” ring is visible, which is said to be an artefact from subcutaneous lipids( 96 ) . Download figure Open in new tab Figure 6: Evolution of metabolite maps evolution following oral [6,6- 2 H 2 ]-glucose administration to a healthy volunteer. HDO, Glc, Glx and Lac maps are shown in a), b), c) and d), respectively after application of the whole brain mask #1. 4.2.3 Imaging glucose metabolism in patients with glioblastoma Images from glioblastoma patients are presented in Figures 7 - 10 . Example spectra from a tumor region and a control region are shown in Figure 7b-i . Deuterated metabolite time courses for the respective voxels are shown in Figure 7j-m . Increasing HDO, Glc and Glx concentrations were observed following the administration of labelled glucose. A higher Lac concentration uptake in the tumor can be observed in Figure 7m and an opposite trend can be seen for the Glx concentration ( Figure 7l ). Download figure Open in new tab Figure 7: 2 H-MRS data from a glioblastoma patient. Spectra for 2 different time points from the highlighted voxels in control (green, light-blue) and tumour (red, pink) regions. Spectra from 25 min (b-e) and 71 min (f-i)) are shown from the highlighted voxels in a), overlaid on an anatomical 3T image. Time courses for each metabolite are shown in (i-m) following Glc administration. Linear fits according to Equation ( 7 ) are shown in l) and m). Figure 8 shows the Glx concentration maps (row 1) derived from the last 3 time points for one healthy volunteer and the five glioblastoma patients. Also shown is the rate of increase in the product selectivity towards Lac ratio (row 2). A lower Glx signal and a higher ratio was observed in the tumor when compared to normal appearing brain tissue. Glc and Lac concentration maps are also shown in Figure SI3 . Figure SI4 illustrates the benefits of applying an additional mask (mask #2), which excludes voxels with CRLB values of the ratio greater than 30%. Download figure Open in new tab Figure 8: Maps of [Glx] (mmol/L) (top row, based on mask ) and the S Lac ratio (bottom row) for the two healthy volunteers and the 5 glioblastoma patients (based on mask #2). Tumor regions are circled in light yellow. These findings are further supported by averaged voxel data from healthy volunteers (ROI HV ), and glioblastoma patients in control (ROI NAWM ) and tumor (ROI Tumor ) regions ( Figure 9a–b ). Download figure Open in new tab Figure 9: Comparison of Glx and Lac production in healthy volunteers and glioblastoma patients based the different ROis. (a) Glx and (b) Lac concentrations. Each data point represents an individual measurement per region (ROI), per time point (section 3.7.2) from a volunteer or patient, and all data within each group/region were fitted with a linear model (healthy volunteers, ROI HV = green; glioblastoma normal-appearing white matter, ROI NAWM = blue; tumor region, ROI Tumor = red). Linear fits are also shown for each region. Rates of Glx (c), Lac (d) and S Lac (e) production in each region (* = significant difference, p<0.01, ** = highly significant, p<0.001). For Glx, the fitted rate of increase (d[Glx]/dt) differed significantly between tissue types. Tumor regions showed lower production rates (3.8 μmol/L/min, SE = 0.44; 95% CI: 2.88-4.66) compared with NAWM (6.0 μmol/L/min, SE = 0.36; 95% CI: 5.29-6.75; p < 0.001) and healthy volunteers (9.2 μmol/L/min, SE = 0.61; 95% CI: 7.86-10.53; p < 0.001). For Lac, the fitted rate of increase (d[Lac]/dt) was significantly higher in tumors (2.3 μmol/L/min, SE = 0.87; 95% CI: 0.50-4.02) compared with NAWM (1.0 μmol/L/min, SE = 0.36; 95% CI: 0.28-1.74; p < 0.01) and healthy volunteers (0.5 μmol/L/min, SE = 0.17; 95% CI: 0.17-0.91; p < 0.01). Overall, Glx production rates decrease from healthy tissue to NAWM and further to tumors ( Figure 9c ), whereas Lac production shows the inverse pattern ( Figure 9d ). The ratio similarly increases in tumor tissue ( Figure 9e ). 4.2.4 Metabolic heterogeneity Pairwise comparisons of tumor (ROI Tumor ) and normal-appearing regions (ROI NAWM ) revealed significant differences in both Glx and Lac (Glx: p<0.001, z = 4.6, r = 0.7; Lac: p<0.001, z = -4.4, r = - 0.67). Mean regional differences were -0.06 mmol/L for Glx and +0.08 mmol/L for Lac. The best models selected by a stepwise linear modelling approach were: with Region emerging as a significant predictor for both metabolites. For Glx, Region was associated with lower concentrations in tumors (p<0.001, β = -0.06 ± 0.01 mmol/L, 95% CI: -0.07 to -0.04; R 2 = 0.876). For Lac, tumors showed higher concentrations (p<0.001, β = 0.08 ± 0.01 mmol/L, 95% CI: 0.05–0.10; R 2 = 0.759) ( Figures SI5 – SI6 , Fig. 10k–l ). Download figure Open in new tab Figure 10: Metabolic heterogeneity in glioblastoma. [Glx] (a, c, e, g, i) and [Lac] (b, d, f, h, j) maps in all glioblastoma patients at the last time point. Boxplots showing concentrations of (k) Glx and (l) Lac for all 5 patients in NAWM and tumor regions. Black lines connect group means. For Glx, linear mixed modelling showed a significant effect of Region (β = –0.056, SE = 0.009, 95% CI [–0.073, –0.039], p < 0.001). For Lac, concentrations were significantly higher in tumor regions (β = 0.077, SE = 0.011, 95% CI [0.056, 0.098], p < 0.001). Each data point used for the boxplots corresponds to the average concentration within all voxels of the ROI (tumor or NAWM) for a given patient at a given time point, i.e. one data point per region per patient per time point. A linear mixed-effects model (Model 1, Eq. 10) further confirmed Region as a significant determinant, with random intercepts and slopes by patient (Glx: F( 1 , 84 )=49.5, p<0.001, β=-0.06; Lac: F( 1 , 84 )=53.8, p<0.001, β=+0.08). Figures 10a–j illustrate spatial heterogeneity in three representative patients. Across all patients at the last time point, mean tumor concentrations were 0.5 ± 0.2 mmol/L for [Glx] (0.02–0.9 mmol/L range) and 0.5 ± 0.2 mmol/L for [Lac] (0.1–1.2 mmol/L range). Likelihood ratio tests indicated that adding time as a random effect significantly improved model fit over random-intercept-only models (Model 2 vs Model3, Eq. 8 vs. Eq. 9 ). For Glx, random slopes yielded a better fit (p<0.01, LR = 12.8), and for Lac, a similar improvement was observed (p<0.01, LR = 4.6). Accordingly, linear mixed-effects models with random intercepts and slopes for time (Model 3, Eq. 9 ) were fitted separately for Glx and Lac using tumor-only data. These models included time as a fixed effect while capturing inter-patient variability in both baseline levels and temporal trajectories. For Glx, there was significant variability in intercepts (SD = 0.061, 95% CI: 0.026–0.145) and slopes (SD = 0.00098, 95% CI: 0.00046–0.00208). A strong negative intercept–slope correlation (r = –0.996) indicated that patients with higher baseline Glx tended to show slower—or even negative— temporal changes. For Lac, variability was also evident in intercepts (SD = 0.077) and slopes (SD = 0.00069), though confidence intervals could not be reliably estimated due to limited patient numbers. Nevertheless, the model suggests meaningful differences in lactate dynamics across patients. Together, these results confirm that there can be significantly different metabolic trajectories within tumor regions between individual glioblastoma patients, which is expected given the underlying biology of these tumors. 5 Discussion 5.1 Performance of the coil array We demonstrated the feasibility of using a novel TEM head array coil for brain 2 H-MRSI at 7T. B 1 + maps acquired on a head-shaped phantom showed acceptable coverage across the brain. The modest anterior-right B 1 + drop-out did not impede absolute quantification analyses. Transmit efficiency was (range in the mid-transverse slice; , range across the whole phantom), comparable to values from previous 7T DMI studies (around ). Higher transmit efficiencies have been reported in other DMI setups, such as around at 7T( 39 , 93 ) and at 9.4T( 98 ). A key advantage of this design is its tolerance of a “missing rung,” yielding an open geometry that improves patient comfort and will enable future visual-stimulus fMRS studies. It also enables interleaved 1 H/ 2 H acquisitions( 45 , 99 , 100 ). This provides the opportunity to implement motion correction, frequency shot-to-shot correction, and interleaved B 0 shimming( 101 , 102 ). The main limitation is B 1 + inhomogeneity in frontal regions, particularly left–right asymmetry. 5.2 Natural abundance HDO in healthy volunteers The absolute concentration of HDO measured in the brain of 10 volunteers (8.96 ± 0.7 mmol/L) was lower than has been reported previously (10.12 to 17.8 mmol/L( 34 , 93 , 100 , 103 )). However, to our knowledge, ours is the first study to report a directly calculated natural abundance HDO concentration in the human brain in vivo . Previous studies instead estimated brain HDO concentration based on the expected fraction of deuterated water in water (0.0115%( 104 ) to 0.015%( 105 )) based on 2 H natural abundance, combining this with assumed brain water fractions (ranging from 75% to 100%). Yet the fraction of deuterated water in water can vary depending on the environment( 105 , 106 ) and it is well established that the water content in brain tissue varies between approximately 70–80% in gray and white matter( 107 , 108 ). Based on these values, the natural HDO concentration in the brain is estimated to range between 8.9 mmol/L (assuming 0.0115% deuterated water and 70% brain water fraction) and 13.3 mmol/L (assuming 0.015% deuterated water and 80% brain water fraction). The mean value measured in this study falls within this range. Absolute quantification of metabolites provides direct concentration values, unlike metabolite ratios, which rely on reference metabolites that may themselves vary depending on the biochemical state of the tissue. This is particularly important in heterogeneous diseases like glioblastoma, where reference metabolites can be altered by tumor metabolism, edema, or treatment( 62 , 109 ) as we demonstrated by the changes in “normal-appearing” tissue in Figure 8 . Moreover, absolute quantification avoids reliance on fixed assumptions, for example an assumption that the natural abundance HDO concentration is the same in all patients. Such assumptions fail to account for inter-subject variability due to differences in hydration status, pathology, or metabolic state. 5.3 In vivo glucose metabolism A linear model was fitted to the Glx and Lac concentration time courses, consistent with approaches used in other studies( 40 , 93 ). We chose this simpler model because Glx and Lac concentrations did not reach a steady state; however, more sophisticated kinetic models are available for fitting concentration dynamics over time( 110 ). The increase in HDO concentration observed in the center of the brain could be due to accumulation of HDO produced from Glc metabolism in cerebrospinal fluid (CSF has 99% water, which is higher than in the white and grey matters). A UTE-CSI sequence( 111 , 112 ) was used here that had been optimized for DMI acquisition. However, newer sequences such as SSFP( 113 ) or EPSI( 114 ) may improve the signal-to-noise ratio and therefore the metabolic information attainable. The spatial resolution reported here falls between those reported in recent in vivo brain DMI studies at 7T (2–3 cm 3 ) and 9.4T (1–2 cm 3 )(31,32,39,96,97,115). 5.4 Assessment of tumor metabolism In 4 of 5 glioblastoma patients, tumors showed an increased S Lac ratio when compared to normal appearing brain regions, indicating increased glycolytic metabolism, as has been observed in preclinical( 110 ) and clinical studies( 92 ). Rates of Lac production varied between patients, consistent with preclinical studies showing the presence of different metabolic subtypes of glioblastoma with differences in glycolytic and oxidative activities( 92 , 110 ) and clinical studies showing that the tumors of glioblastoma patients can show differences in the proportion of these different metabolic subtypes( 62 ). Similar inter-patient variation has also been observed in hyperpolarized [1- 13 C]-pyruvate studies, where tumor lactate labelling differed markedly( 116 ). The mean [Glx] increase rate in healthy volunteers is lower than the rate of 21 μmol/L/min reported in a recent study( 40 ), but is comparable to other preclinical studies( 62 , 110 ). Prior studies have suggested that [Glx] declines (5 – 15 % per decade) with aging( 117 - 119 ), which could partially contribute to the difference in concentrations seen between healthy volunteers (24-28 years old) and glioblastoma patients (> 50 years old). Intra-tumoral variations in [Glx] and [Lac] were also visible in all representative patients. Elevated lactate or high pyruvate to lactate conversion has been observed in hypoxic, poorly perfused regions (tumor rim vs core)( 116 ), while [Glx] changes are often found at invasive margins, with the direction of change depending on tumour subtype, local neuron–astrocyte metabolism and partial-volume effects( 120 - 122 ). Moreover, intratumoral [Glx] heterogeneity appears to be associated with more aggressive tumor behavior, as seen in a recent study( 122 ). Heterogeneous glucose turnover has been observed previously where some tumor regions have higher glycolytic flux while others rely more on oxidative metabolism (OXPHOS)( 110 , 123 ). For this reason, achieving higher spatial resolution in DMI would be valuable, and might be facilitated by advanced analysis methods such as deep learning, low-rank approaches, or model-based reconstruction techniques( 124 , 125 ). Most glioblastoma studies have focused on [Lac] or [Lac]/[X] ratios. But these ratios are difficult to interpret when both numerator and denominator approach zero, often yielding misleading values. Our use of absolute quantification enables robust detection of [Glx] differences, highlighting [Glx] as a potentially more reliable biomarker of tumor heterogeneity in DMI. Future work should emphasize quantitative [Glx] analysis and consider full kinetic modelling to better capture fluxes, although this lies beyond the scope of the present study. These metabolic heterogeneities in between tumours and patients could be due to the tumour microenvironment (TME), cellular composition, genetics or be driven by therapy effects( 20 , 22 , 126 ). The ability to assess these metabolic phenotypes non-invasively in vivo with DMI could improve the precision of targeted therapies( 62 , 127 ). A limitation of our study is the small sample size, nevertheless the observation of intra- and inter-tumor heterogeneity warrants larger-scale studies. 6 Conclusion We present step-by-step a method for absolute quantification of 2 H MRS images, which we validated using a TEM head array coil on phantoms and healthy volunteers. Studies in glioblastoma patients revealed both inter- and intra-tumor metabolic heterogeneity. We anticipate that this new method will enhance our understanding of brain metabolism and tumor behavior, as well as aid in optimizing drug therapies and monitoring treatment response. Data availability The code is hosted on GitLab and can be made available upon request to the authors. Data is available from the corresponding author subject to a data transfer agreement as mandated by our ethical approvals and hospital policies. 7 Supplementary information Download figure Open in new tab Figure SI1: Effects of the lights on the environment noise captured with the coil’s receive channels. Difference in noise level between lights ON, lights OFF and circuit breaker turned OFF completely in the 7T “technical room”. Download figure Open in new tab Figure SI2: Illustration of the different ROIs that have been chosen for further statistical analysis on a) a glioblastoma patient (ROITumor and ROINAWM) and b) a healthy volunteer (ROIHV). Download figure Open in new tab Figure SI3: Maps of [Glc] (mmol/L) (top row) and [Lac] (bottom row) for the two healthy volunteers and the 5 glioblastoma patients based on mask #1 averaged across the last three time points. Download figure Open in new tab Figure SI4: Maps of S Lac ratio (top row) for two healthy volunteers and five glioblastoma patients, based on mask #1 (i.e., without excluding voxels with CRLB values of the ratio >30%). The bottom row shows the corresponding CRLB maps for the same subjects. Note that the % CRLB is highest in areas with low metabolic rate -- i.e. low d[Lac]/dt and low d[Glx]/dt. Download figure Open in new tab Figure SI5: Matlab code and output from the stepwise linear modelling of [Glx] using the Akaike Information Criterion (AIC). Download figure Open in new tab Figure SI6: Matlab code and output from the stepwise linear modelling of [Lac] using the Akaike Information Criterion (AIC). Acknowledgements This work was supported by the EU Horizon Europe project “MITI” #101058229. This research was supported by the NIHR Cambridge Biomedical Research Centre (NIHR203312) and the NIHR Applied Research Collaboration East of England. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. Minghao Zhang was funded by the Medical Research Council (MR N013433-1) and the Cambridge Trust. Masha Novoselova was funded by the Commonwealth Scholarship Commission and the Cambridge Trust. Carina Graf was supported by the Cambridge Trust, the W.D. Armstrong Fund and the European Union’s H2020 research and innovation program under grant agreement [801075]. We thank the WBIC radiographers for their assistance with the GBM patient scans. We thank Prof Carpenter and Prof Williams for reviewing our coil safety documentation. We thank the Department of Radiology and Cambridge University Hospitals NHS Trust Pharmacy for assistance storing the 6,6’-[ 2 H 2 ]-glucose appropriately and for defining the procedure for administration. Abbreviations RF RadioFrequency FID Free Induction Decay CSI Chemical Shift Imaging DMI Deuterium Metabolic Imaging WSVD Whitened Singular Value Decomposition AMARES Advanced Method for Accurate, Robust and Efficient Spectral fitting AIC Akaike Information Criterion TEM Transverse electromagnetic Glx Glutamine/glutamate Glc Glucose Lac Lactate Fum Fumarate HDO semi heavy water SAR Specific Absorption rate FWHM Frequency Width to Half Maximum References 1. ↵ Cunnane SC , Trushina E , Morland C , Prigione A , Casadesus G , Andrews ZB , Beal MF , Bergersen LH , Brinton RD , de la Monte S , Eckert A , Harvey J , Jeggo R , Jhamandas JH , Kann O , la Cour CM , Martin WF , Mithieux G , Moreira PI , Murphy MP , Nave KA , Nuriel T , Oliet SHR , Saudou F , Mattson MP , Swerdlow RH , Millan MJ . Brain energy rescue: an emerging therapeutic concept for neurodegenerative disorders of ageing . Nat Rev Drug Discov 2020 ; 19 ( 9 ): 609 – 633 . OpenUrl CrossRef PubMed 2. Blaszczyk JW . Energy Metabolism Decline in the Aging Brain-Pathogenesis of Neurodegenerative Disorders . Metabolites 2020 ; 10 ( 11 ). 3. ↵ Yang S , Park JH , Lu HC . Axonal energy metabolism, and the effects in aging and neurodegenerative diseases . Molecular Neurodegeneration 2023 ; 18 ( 1 ). 4. ↵ Beard E , Lengacher S , Dias S , Magistretti PJ , Finsterwald C. Astrocytes as Key Regulators of Brain Energy Metabolism: New Therapeutic Perspectives . Front Physiol 2021 ; 12 : 825816 . OpenUrl PubMed 5. ↵ Rae CD , Baur JA , Borges K , Dienel G , Diaz-Garcia CM , Douglass SR , Drew K , Duarte JMN , Duran J , Kann O , Kristian T , Lee-Liu D , Lindquist BE , McNay EC , Robinson MB , Rothman DL , Rowlands BD , Ryan TA , Scafidi J , Scafidi S , Shuttleworth CW , Swanson RA , Uruk G , Vardjan N , Zorec R , McKenna MC . Brain energy metabolism: A roadmap for future research . J Neurochem 2024 ; 168 ( 5 ): 910 – 954 . OpenUrl CrossRef PubMed 6. Zhao JY , Ma XM , Gao PX , Han XQ , Zhao PX , Xie F , Liu MY . Advancing glioblastoma treatment by targeting metabolism . Neoplasia 2024 ; 51 . 7. ↵ Tufail M , Jiang CH , Li N. Altered metabolism in cancer: insights into energy pathways and therapeutic targets . Mol Cancer 2024 ; 23 ( 1 ): 203 . OpenUrl CrossRef PubMed 8. ↵ Ye YC , Chai SF , Li XR , Wu MN , Cai HY , Wang ZJ . Intermittent fasting and Alzheimer’s disease-Targeting ketone bodies as a potential strategy for brain energy rescue . Metab Brain Dis 2024 ; 39 ( 1 ): 129 – 146 . OpenUrl PubMed 9. Brakedal B , Dolle C , Riemer F , Ma Y , Nido GS , Skeie GO , Craven AR , Schwarzlmuller T , Brekke N , Diab J , Sverkeli L , Skjeie V , Varhaug K , Tysnes OB , Peng S , Haugarvoll K , Ziegler M , Gruner R , Eidelberg D , Tzoulis C. The NADPARK study: A randomized phase I trial of nicotinamide riboside supplementation in Parkinson’s disease . Cell Metab 2022 ; 34 ( 3 ): 396 – 407 e396 . OpenUrl CrossRef PubMed 10. Jensen NJ , Wodschow HZ , Nilsson M , Rungby J. Effects of Ketone Bodies on Brain Metabolism and Function in Neurodegenerative Diseases . Int J Mol Sci 2020 ; 21 ( 22 ). 11. ↵ Seyfried TN . Ketone strong: emerging evidence for a therapeutic role of ketone bodies in neurological and neurodegenerative diseases . J Lipid Res 2014 ; 55 ( 9 ): 1815 – 1817 . OpenUrl FREE Full Text 12. ↵ Asano H , Elhelaly AE , Hyodo F , Iwasaki R , Noda Y , Kato H , Ichihashi K , Tomita H , Murata M , Mori T , Matsuo M. Deuterium Magnetic Resonance Imaging Using Deuterated Water-Induced 2H-Tissue Labeling Allows Monitoring Cancer Treatment at Clinical Field Strength . Clin Cancer Res 2023 ; 29 ( 24 ): 5173 – 5182 . OpenUrl PubMed 13. ↵ Stine ZE , Schug ZT , Salvino JM , Dang CV . Targeting cancer metabolism in the era of precision oncology . Nat Rev Drug Discov 2022 ; 21 ( 2 ): 141 – 162 . OpenUrl CrossRef PubMed 14. Montrazi ET , Bao Q , Martinho RP , Peters DC , Harris T , Sasson K , Agemy L , Scherz A , Frydman L. Deuterium imaging of the Warburg effect at sub-millimolar concentrations by joint processing of the kinetic and spectral dimensions . NMR Biomed 2023 ; 36 ( 11 ): e4995 . OpenUrl PubMed 15. ↵ Corbin Z , Prado I , Fulbright R , Rothman D , de Graaf R , De Feyter H. NIMG-15 . DEUTERIUM METABOLIC IMAGING (DMI) MEASURES THE WARBURG EFFECT IN BRAIN TUMORS. Neuro-b Oncology 2019 ; 21 (Supplement_6): vi164 – vi164 . OpenUrl 16. ↵ Low J , Cao J , Hesse F , Wright A , Tsyben A , Alshamleh I , Mair R , Brindle K. Deuterium Metabolic Imaging Differentiates Glioblastoma Metabolic Subtypes and Detects Early Response to Chemoradiotherapy . Cancer Res 2024 ; 84 ( 12 ): 1996 – 2008 . OpenUrl PubMed 17. ↵ Xiao Y , Yu TJ , Xu Y , Ding R , Wang YP , Jiang YZ , Shao ZM . Emerging therapies in cancer metabolism . Cell Metab 2023 ; 35 ( 8 ): 1283 – 1303 . OpenUrl CrossRef PubMed 18. ↵ Luengo A , Gui DY , Vander Heiden MG . Targeting Metabolism for Cancer Therapy . Cell Chem Biol 2017 ; 24 ( 9 ): 1161 – 1180 . OpenUrl PubMed 19. ↵ Fares J , Wan Y , Mair R , Price SJ . Molecular diversity in isocitrate dehydrogenase-wild-type glioblastoma . Brain Commun 2024 ; 6 ( 2 ): fcae108 . OpenUrl 20. ↵ Hoang-Minh LB , Siebzehnrubl FA , Yang C , Suzuki-Hatano S , Dajac K , Loche T , Andrews N , Schmoll Massari M , Patel J , Amin K , Vuong A , Jimenez-Pascual A , Kubilis P , Garrett TJ , Moneypenny C , Pacak CA , Huang J , Sayour EJ , Mitchell DA , Sarkisian MR , Reynolds BA , Deleyrolle LP . Infiltrative and drug-resistant slow-cycling cells support metabolic heterogeneity in glioblastoma . EMBO J 2018 ; 37 ( 23 ). 21. Wood J , Smith SJ , Castellanos-Uribe M , Lourdusamy A , May ST , Barrett DA , Grundy RG , Kim DH , Rahman R. Metabolomic characterisation of the glioblastoma invasive margin reveals a region-specific signature . Heliyon 2025 ; 11 ( 1 ): e41309 . OpenUrl 22. ↵ Badr CE , Silver DJ , Siebzehnrubl FA , Deleyrolle LP . Metabolic heterogeneity and adaptability in brain tumors . Cell Mol Life Sci 2020 ; 77 ( 24 ): 5101 – 5119 . OpenUrl CrossRef PubMed 23. ↵ Zhu XH , Chen W. In vivo X-Nuclear MRS Imaging Methods for Quantitative Assessment of Neuroenergetic Biomarkers in Studying Brain Function and Aging . Front Aging Neurosci 2018 ; 10 : 394 . OpenUrl PubMed 24. Zhu X-H , Lu M , Lee B-Y , Ugurbil K , Chen W. In vivo NAD assay reveals the intracellular NAD contents and redox state in healthy human brain and their age dependences . Proceedings of the National Academy of Sciences 2015 ; 112 ( 9 ): 2876 – 2881 . OpenUrl Abstract / FREE Full Text 25. Maudsley AA , Andronesi OC , Barker PB , Bizzi A , Bogner W , Henning A , Nelson SJ , Posse S , Shungu DC , Soher BJ . Advanced magnetic resonance spectroscopic neuroimaging: Experts’ consensus recommendations . NMR Biomed 2021 ; 34 ( 5 ): e4309 . OpenUrl PubMed 26. ↵ Posse S , Otazo R , Dager SR , Alger J. MR spectroscopic imaging: principles and recent advances . J Magn Reson Imaging 2013 ; 37 ( 6 ): 1301 – 1325 . OpenUrl CrossRef PubMed 27. ↵ De Feyter H , Behar K , Corbin Z , Fulbright R , Brown P , McIntyre S , Nixon T , Rothman D , de Graaf R. Deuterium metabolic imaging (DMI) for MRI-based 3D mapping of metabolism in vivo . Sci Adv 2018 ; 4 ( 8 ). 28. ↵ Pan F , Liu X , Wan J , Guo Y , Sun P , Zhang X , Wang J , Bao Q , Yang L. Advances and prospects in deuterium metabolic imaging (DMI): a systematic review of in vivo studies . Eur Radiol Exp 2024 ; 8 ( 1 ): 65 . OpenUrl PubMed 29. Frese S , Strasser B , Hingerl L , Montrazi E , Frydman L , Motyka S , Bader V , Duguid A , Osburg A , Krssak M , Lanzenberger R , Scherer T , Bogner W , Niess F. Balanced steady state free precession enables high-resolution dynamic 3D Deuterium Metabolic Imaging of the human brain at 7T . medRxiv 2025 . 30. Bogh N , Aastrup M , Mortensen JK , Gottrup H , Blicher JU , Borghammer P , Kristensen MH , Hansen ESS , Vaeggemose M , Laustsen C. Comparison of Deuterium Metabolic Imaging with FDG PET in Alzheimer Disease . Radiology 2025 ; 315 ( 1 ): e241808 . OpenUrl PubMed 31. Bader V , Strasser B , Bogner W , Hingerl L , Frese S , Duguid A , Osburg A , Clarke WT , Motyka S , Krssak M , Trattnig S , Scherer T , Lanzenberger R , Niess F. Concentric Ring Trajectory Sampling With k-Space Reordering Enables Assessment of Tissue-Specific T(1) and T(2) Relaxation for (2)H-Labeled Substrates in the Human Brain at 7 T . NMR Biomed 2025 ; 38 ( 2 ): e5311 . OpenUrl PubMed 32. Ahmadian N , Konig MM , Otto S , Tesselaar K , van Eijsden P , Gosselink M , Gursan A , Klomp DW , Prompers JJ , Wiegers EC . Human Brain Deuterium Metabolic Imaging at 7 T: Impact of Different [6,6’-(2)H(2)]Glucose Doses . J Magn Reson Imaging 2025 ; 61 ( 3 ): 1170 – 1178 . OpenUrl CrossRef PubMed 33. Payne K , Zhao Y , Bhosale AA , Zhang X. Dual-Tuned Coaxial-Transmission-Line RF Coils for Hyperpolarized (13)C and Deuterium (2)H Metabolic MRS Imaging at Ultrahigh Fields . IEEE Trans Biomed Eng 2024 ; 71 ( 5 ): 1521 – 1530 . OpenUrl PubMed 34. ↵ Niess F , Strasser B , Hingerl L , Bader V , Frese S , Clarke WT , Duguid A , Niess E , Motyka S , Krssak M , Trattnig S , Scherer T , Lanzenberger R , Bogner W. Whole-brain deuterium metabolic imaging via concentric ring trajectory readout enables assessment of regional variations in neuronal glucose metabolism . Hum Brain Mapp 2024 ; 45 ( 6 ): e26686 . OpenUrl CrossRef PubMed 35. Khan AS , Peterson KA , Vittay OI , McLean MA , Kaggie JD , O’Brien JT , Rowe JB , Gallagher FA , Matys T. Deuterium Metabolic Imaging of Alzheimer Disease at 3-T Magnetic Field Strength: A Pilot Case-Control Study . Radiology 2024 ; 312 ( 1 ): e232407 . OpenUrl CrossRef PubMed 36. de Graaf RA , Liu Y , Corbin ZA , De Feyter HM . Lipid removal in deuterium metabolic imaging (DMI) using spatial prior knowledge . Magn Reson (Gott) 2024 ; 5 ( 1 ): 21 – 31 . OpenUrl PubMed 37. Bogh N , Vaeggemose M , Schulte RF , Hansen ESS , Laustsen C. Repeatability of deuterium metabolic imaging of healthy volunteers at 3 T . Eur Radiol Exp 2024 ; 8 ( 1 ): 44 . OpenUrl PubMed 38. Adamson PM , Datta K , Watkins R , Recht LD , Hurd RE , Spielman DM . Deuterium metabolic imaging for 3D mapping of glucose metabolism in humans with central nervous system lesions at 3T . Magn Reson Med 2024 ; 91 ( 1 ): 39 – 50 . OpenUrl CrossRef PubMed 39. ↵ Seres Roig E , De Feyter HM , Nixon TW , Ruhm L , Nikulin AV , Scheffler K , Avdievich NI , Henning A , de Graaf RA . Deuterium metabolic imaging of the human brain in vivo at 7 T . Magn Reson Med 2023 ; 89 ( 1 ): 29 – 39 . OpenUrl CrossRef PubMed 40. ↵ Niess F , Strasser B , Hingerl L , Niess E , Motyka S , Hangel G , Krssak M , Gruber S , Spurny-Dworak B , Trattnig S , Scherer T , Lanzenberger R , Bogner W. Reproducibility of 3D MRSI for imaging human brain glucose metabolism using direct ((2)H) and indirect ((1)H) detection of deuterium labeled compounds at 7T and clinical 3T . Neuroimage 2023 ; 277 : 120250 . OpenUrl PubMed 41. ↵ Niess F , Hingerl L , Strasser B , Bednarik P , Goranovic D , Niess E , Hangel G , Krssak M , Spurny-Dworak B , Scherer T , Lanzenberger R , Bogner W. Noninvasive 3-Dimensional 1 H-Magnetic Resonance Spectroscopic Imaging of Human Brain Glucose and Neurotransmitter Metabolism Using Deuterium Labeling at 3T : Feasibility and Interscanner Reproducibility . Invest Radiol 2023 ; 58 ( 6 ): 431 – 437 . OpenUrl PubMed 42. Nam KM , Gursan A , Bhogal AA , Wijnen JP , Klomp DWJ , Prompers JJ , Hendriks AD . Deuterium echo-planar spectroscopic imaging (EPSI) in the human liver in vivo at 7 T . Magn Reson Med 2023 ; 90 ( 3 ): 863 – 874 . OpenUrl PubMed 43. Gursan A , Hendriks AD , Welting D , de Jong PA , Klomp DWJ , Prompers JJ . Deuterium body array for the simultaneous measurement of hepatic and renal glucose metabolism and gastric emptying with dynamic 3D deuterium metabolic imaging at 7 T . NMR Biomed 2023 ; 36 ( 8 ): e4926 . OpenUrl CrossRef PubMed 44. ↵ Cocking D , Damion RA , Franks H , Jaconelli M , Wilkinson D , Brook M , Auer DP , Bowtell R. Deuterium brain imaging at 7T during D(2) O dosing . Magn Reson Med 2023 ; 89 ( 4 ): 1514 – 1521 . OpenUrl PubMed 45. ↵ Liu Y , De Feyter HM , Fulbright RK , McIntyre S , Nixon TW , de Graaf RA . Interleaved fluid-attenuated inversion recovery (FLAIR) MRI and deuterium metabolic imaging (DMI) on human brain in vivo . Magn Reson Med 2022 ; 88 ( 1 ): 28 – 37 . OpenUrl CrossRef PubMed 46. Kaggie JD , Khan AS , Matys T , Schulte RF , Locke MJ , Grimmer A , Frary A , Menih IH , Latimer E , Graves MJ , McLean MA , Gallagher FA . Deuterium metabolic imaging and hyperpolarized (13)C-MRI of the normal human brain at clinical field strength reveals differential cerebral metabolism . Neuroimage 2022 ; 257 : 119284 . OpenUrl CrossRef PubMed 47. De Feyter HM , de Graaf RA . Deuterium metabolic imaging - Back to the future . J Magn Reson 2021 ; 326 : 106932 . OpenUrl CrossRef PubMed 48. ↵ de Graaf RA , Hendriks AD , Klomp DWJ , Kumaragamage C , Welting D , Arteaga de Castro CS , Brown PB , McIntyre S , Nixon TW , Prompers JJ , De Feyter HM . On the magnetic field dependence of deuterium metabolic imaging . NMR Biomed 2020 ; 33 ( 3 ): e4235 . OpenUrl PubMed 49. ↵ Corbin Z , Prado I , Fulbright R , Rothman D , de Graaf R , De Feyter H. DEUTERIUM METABOLIC IMAGING (DMI) MEASURES THE WARBURG EFFECT IN BRAIN TUMORS . Neuro-Oncology 2019 ; 21 : 164 – 164 . OpenUrl 50. De Feyter H , de Graaf R. Deuterium metabolic imaging - Back to the future . JOURNAL OF MAGNETIC RESONANCE 2021 ; 326 . 51. Straathof M , Meerwaldt A , De Feyter H , de Graaf R , Dijkhuizen R. Deuterium Metabolic Imaging of the Healthy and Diseased Brain . Neuroscience 2021 ; 474 : 94 – 99 . OpenUrl CrossRef PubMed 52. ↵ Low J , Wright A , Hesse F , Cao J , Brindle K. Metabolic imaging with deuterium labeled substrates . PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2023 ; 134 : 39 – 51 . OpenUrl PubMed 53. ↵ Bogh N , Aastrup M , Mortensen J , Gottrup H , Blicher J , Borghammer P , Kristensen M , Hansen E , Vaeggemose M , Laustsen C. Comparison of Deuterium Metabolic Imaging with FDG PET in Alzheimer Disease . RADIOLOGY 2025 ; 315 ( 1 ). 54. ↵ Tan J , Djayakarsana D , Wang H , Chan R , Bailey C , Lau A. Deuterium MRS of early treatment-induced changes in tumour lactate in vitro . NMR IN BIOMEDICINE 2021 ; 34 ( 12 ). 55. Hesse F , Somai V , Kreis F , Bulat F , Wright A , Brindle K. Monitoring tumor cell death in murine tumor models using deuterium magnetic resonance spectroscopy and spectroscopic imaging . PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2021 ; 118 ( 12 ). 56. Veltien A , van Asten J , Ravichandran N , de Graaf R , De Feyter H , Oosterwijk E , Heerschap A. Simultaneous Recording of the Uptake and Conversion of Glucose and Choline in Tumors by Deuterium Metabolic Imaging . CANCERS 2021 ; 13 ( 16 ). 57. Ge X , Song K , Engelbach J , Yuan L , Gao F , Dahiya S , Rich K , Ackerman J , Garbow J. Distinguishing Tumor Admixed in a Radiation Necrosis (RN) Background: 1H and 2H MR With a Novel Mouse Brain-Tumor/RN Model . FRONTIERS IN ONCOLOGY 2022 ; 12 . 58. ↵ Asano H , Elhelaly A , Hyodo F , Iwasaki R , Noda Y , Kato H , Ichihashi K , Tomita H , Murata M , Mori T , Matsuo M. Deuterium Magnetic Resonance Imaging Using Deuterated Water-Induced 2H-Tissue Labeling Allows Monitoring Cancer Treatment at Clinical Field Strength . Clin Cancer Res 2023 ; 29 ( 24 ): 5173 – 5182 . OpenUrl PubMed 59. ↵ Simoes R , Henriques R , Cardoso B , Fernandes F , Carvalho T , Shemesh N. Glucose fluxes in glycolytic and oxidative pathways detected in vivo by deuterium magnetic resonance spectroscopy reflect proliferation in mouse glioblastoma . Neuroimage-Clin 2022 ; 33 . 60. Taglang C , Batsios G , Mukherjee J , Tran M , Gillespie A , Hong D , Ronen S , Luchman H , Pieper R , Viswanath P. Deuterium magnetic resonance spectroscopy enables noninvasive metabolic imaging of tumor burden and response to therapy in low-grade gliomas . Neuro-Oncology 2022 ; 24 ( 7 ): 1101 – 1112 . OpenUrl CrossRef PubMed 61. ↵ Ip K , Thomas M , Behar K , de Graaf R , De Feyter H. Mapping of exogenous choline uptake and metabolism in rat glioblastoma using deuterium metabolic imaging (DMI) . FRONTIERS IN CELLULAR NEUROSCIENCE 2023 ; 17 . 62. ↵ Low JCM , Cao J , Hesse F , Wright AJ , Tsyben A , Alshamleh I , Mair R , Brindle KM . Deuterium Metabolic Imaging Differentiates Glioblastoma Metabolic Subtypes and Detects Early Response to Chemoradiotherapy . Cancer Res 2024 ; 84 ( 12 ): 1996 – 2008 . OpenUrl PubMed 63. ↵ Ruhm L , Avdievich N , Ziegs T , Nagel A , De Feyter H , de Graaf R , Henning A. Deuterium metabolic imaging in the human brain at 9.4 Tesla with high spatial and temporal resolution . NEUROIMAGE 2021 ; 244 . 64. ↵ de Graaf R , Hendriks A , Klomp D , Kumaragamage C , Welting D , de Castro C , Brown P , McIntyre S , Nixon T , Prompers J , De Feyter H. On the magnetic field dependence of deuterium metabolic imaging . NMR IN BIOMEDICINE 2020 ; 33 ( 3 ). 65. Bednarik P , Goranovic D , Svatkova A , Niess F , Hingerl L , Strasser B , Deelchand D , Spurny-Dworak B , Krssak M , Trattnig S , Hangel G , Scherer T , Lanzenberger R , Bogner W. 1H magnetic resonance spectroscopic imaging of deuterated glucose and of neurotransmitter metabolism at 7 T in the human brain . NATURE BIOMEDICAL ENGINEERING 2023 ; 7 ( 8 ): 1001 – 1013 . OpenUrl PubMed 66. Niess F , Strasser B , Hingerl L , Niess E , Motyka S , Hangel G , Krssak M , Gruber S , Spurny-Dworak B , Trattnig S , Scherer T , Lanzenberger R , Bogner W. Reproducibility of 3D MRSI for imaging human brain glucose metabolism using direct ( 2 H) and indirect ( 1 H) detection of deuterium labeled compounds at 7T and clinical 3T* . NEUROIMAGE 2023 ; 277 . 67. Roig E , De Feyter H , Nixon T , Ruhm L , Nikulin A , Scheffler K , Avdievich N , Henning A , de Graaf R. Deuterium metabolic imaging of the human brain in vivo at 7 T . MAGNETIC RESONANCE IN MEDICINE 2023 ; 89 ( 1 ): 29 – 39 . OpenUrl CrossRef PubMed 68. Ahmadian N , Konig M , Otto S , Tesselaar K , van Eijsden P , Gosselink M , Gursan A , Klomp D , Prompers J , Wiegers E. Human Brain Deuterium Metabolic Imaging at 7 T: Impact of Different [6,6′-2H2]Glucose Doses . JOURNAL OF MAGNETIC RESONANCE IMAGING 2025 ; 61 ( 3 ): 1170 – 1178 . OpenUrl CrossRef PubMed 69. Bader V , Strasser B , Bogner W , Hingerl L , Frese S , Duguid A , Osburg A , Clarke W , Motyka S , Krssak M , Trattnig S , Scherer T , Lanzenberger R , Niess F. Concentric Ring Trajectory Sampling With k-Space Reordering Enables Assessment of Tissue-Specific T1 and T2 Relaxation for 2H-Labeled Substrates in the Human Brain at 7 T . NMR IN BIOMEDICINE 2025 ; 38 ( 2 ). 70. Gursan A , Kahraman-Agir B , Gosselink M , Welting D , Froeling M , Hoogduin H , Wiegers E , Prompers J , Klomp D. Development of a Double Tuned 2H/31P Whole-Body Birdcage Transmit Coil for 2H and 31P MR Applications From Head to Toe at 7 T . NMR IN BIOMEDICINE 2025 ; 38 ( 3 ). 71. ↵ Li X , Zhu X , Li Y , Wang T , Zhang G , Wiesner H , Liang Z , Chen W. Quantitative mapping of key glucose metabolic rates in the human brain using dynamic deuterium magnetic resonance spectroscopic imaging . PNAS NEXUS 2025 ; 4 ( 3 ). 72. ↵ Kaggie J , Khan A , Matys T , Schulte R , Locke M , Grimmer A , Frary A , Menih I , Latimer E , Graves M , McLean M , Gallagher F. Deuterium metabolic imaging and hyperpolarized 13C-MRI of the normal human brain at clinical field strength reveals differential cerebral metabolism . NEUROIMAGE 2022 ; 257 . 73. Niess F , Hingerl L , Strasser B , Bednarik P , Goranovic D , Niess E , Hangel G , Krssak M , Spurny-Dworak B , Scherer T , Lanzenberger R , Bogner W. Noninvasive 3-Dimensional 1H-Magnetic Resonance Spectroscopic Imaging of Human Brain Glucose and Neurotransmitter Metabolism Using Deuterium Labeling at 3T Feasibility and Interscanner Reproducibility . INVESTIGATIVE RADIOLOGY 2023 ; 58 ( 6 ): 431 – 437 . OpenUrl PubMed 74. Adamson P , Datta K , Watkins R , Recht L , Hurd R , Spielman D. Deuterium metabolic imaging for 3D mapping of glucose metabolism in humans with central nervous system lesions at 3T . MAGNETIC RESONANCE IN MEDICINE 2024 ; 91 ( 1 ): 39 – 50 . OpenUrl CrossRef PubMed 75. Bogh N , Vaeggemose M , Schulte R , Hansen E , Laustsen C. Repeatability of deuterium metabolic imaging of healthy volunteers at 3 T . EUROPEAN RADIOLOGY EXPERIMENTAL 2024 ; 8 ( 1 ). 76. ↵ Khan A , Peterson K , Vittay O , Mclean M , Kaggie J , O’Brien J , Rowe J , Gallagher F , Matys T. Deuterium Metabolic Imaging of Alzheimer Disease at 3-T Magnetic Field Strength: A Pilot Case-ControlStudy . RADIOLOGY 2024 ; 312 ( 1 ). 77. ↵ Purvis LAB , Valkovic L , Robson MD , Rodgers CT . Feasibility of absolute quantification for (31) P MRS at 7 T . Magn Reson Med 2019 ; 82 ( 1 ): 49 – 61 . OpenUrl PubMed 78. ↵ Insko EK , Bolinger L. Mapping of the Radiofrequency Field . J Magn Reson Ser A 1993 ; 103 ( 1 ): 82 – 85 . OpenUrl 79. ↵ Vanhamme L , van den Boogaart A , Van Huffel S. Improved method for accurate and efficient quantification of MRS data with use of prior knowledge . J Magn Reson 1997 ; 129 ( 1 ): 35 – 43 . OpenUrl CrossRef PubMed Web of Science 80. ↵ Brown RW , Cheng Y-CN , Haacke EM , Thompson MR , Venkatesan R. Magnetic resonance imaging : physical principles and sequence design . Hoboken, New Jersey : John Wiley & Sons, Inc .; 2014 . xxxii, 944 pages p. 81. ↵ Roemer PB , Edelstein WA , Hayes CE , Souza SP , Mueller OM . The NMR phased array . Magn Reson Med 1990 ; 16 ( 2 ): 192 – 225 . OpenUrl CrossRef PubMed Web of Science 82. ↵ Rodgers CT , Robson MD . Receive array magnetic resonance spectroscopy: Whitened singular value decomposition (WSVD) gives optimal Bayesian solution . Magn Reson Med 2010 ; 63 ( 4 ): 881 – 891 . OpenUrl CrossRef PubMed 83. ↵ Shajan G , Kozlov M , Hoffmann J , Turner R , Scheffler K , Pohmann R. A 16-channel dual-row transmit array in combination with a 31-element receive array for human brain imaging at 9.4 T . Magn Reson Med 2014 ; 71 ( 2 ): 870 – 879 . OpenUrl CrossRef PubMed 84. ↵ Bennett D. NaCl doping and the conductivity of agar phantoms . Mat Sci Eng C-Mater 2011 ; 31 ( 2 ): 494 – 498 . OpenUrl 85. ↵ Cole KS , Cole RH . Dispersion and Absorption in Dielectrics I. Alternating Current Characteristics . The Journal of Chemical Physics 1941 ; 9 ( 4 ): 341 – 351 . OpenUrl CrossRef 86. ↵ Gabriel C , King’s College London. Department of P . Compilation of the dielectric properties of body tissues at RF and microwave frequencies : King’s College , London, Department of Physics ; 1996 . 87. ↵ Rodgers CT , Clarke WT , Snyder C , Vaughan JT , Neubauer S , Robson MD . Human cardiac 31P magnetic resonance spectroscopy at 7 Tesla . Magn Reson Med 2014 ; 72 ( 2 ): 304 – 315 . OpenUrl CrossRef PubMed 88. ↵ Tyler DJ , Hudsmith LE , Clarke K , Neubauer S , Robson MD . A comparison of cardiac (31)P MRS at 1.5 and 3 T . NMR Biomed 2008 ; 21 ( 8 ): 793 – 798 . OpenUrl PubMed 89. ↵ Kaufmann TJ , Smits M , Boxerman J , Huang R , Barboriak DP , Weller M , Chung C , Tsien C , Brown PD , Shankar L , Galanis E , Gerstner E , van den Bent MJ , Burns TC , Parney IF , Dunn G , Brastianos PK , Lin NU , Wen PY , Ellingson BM . Consensus recommendations for a standardized brain tumor imaging protocol for clinical trials in brain metastases . Neuro Oncol 2020 ; 22 ( 6 ): 757 – 772 . OpenUrl CrossRef PubMed 90. ↵ National Institute for H, Care E . Brain tumours (primary) and brain metastases in adults . London, England : National Institute for Health and Care Excellence ; 2018 . 91. ↵ Purvis LAB , Clarke WT , Biasiolli L , Valkovic L , Robson MD , Rodgers CT . OXSA: An open-source magnetic resonance spectroscopy analysis toolbox in MATLAB . PLoS One 2017 ; 12 ( 9 ): e0185356 . OpenUrl CrossRef PubMed 92. ↵ De Feyter HM , Behar KL , Corbin ZA , Fulbright RK , Brown PB , McIntyre S , Nixon TW , Rothman DL , de Graaf RA . Deuterium metabolic imaging (DMI) for MRI-based 3D mapping of metabolism in vivo . Sci Adv 2018 ; 4 ( 8 ): eaat7314 . OpenUrl FREE Full Text 93. ↵ Li X , Zhu XH , Li Y , Wang T , Zhang G , Wiesner HM , Liang ZP , Chen W. Quantitative mapping of key glucose metabolic rates in the human brain using dynamic deuterium magnetic resonance spectroscopic imaging . PNAS Nexus 2025 ; 4 ( 3 ): pgaf072 . OpenUrl 94. ↵ Wilcoxon F. Individual comparisons of grouped data by ranking methods . J Econ Entomol 1946 ; 39 : 269 . OpenUrl CrossRef PubMed 95. ↵ West BT , Welch KB , Gałecki AT , Gillespie BW . Linear mixed models : a practical guide using statistical software . Boca Raton : CRC Press ; 2022 . 96. ↵ Ruhm L , Avdievich N , Ziegs T , Nagel AM , De Feyter HM , de Graaf RA , Henning A. Deuterium metabolic imaging in the human brain at 9.4 Tesla with high spatial and temporal resolution . Neuroimage 2021 ; 244 : 118639 . OpenUrl CrossRef PubMed 97. Gursan A , Kahraman-Agir B , Gosselink M , Welting D , Froeling M , Hoogduin H , Wiegers EC , Prompers JJ , Klomp DWJ . Development of a Double Tuned (2)H/(31)P Whole-Body Birdcage Transmit Coil for (2)H and (31)P MR Applications From Head to Toe at 7 T . NMR Biomed 2025 ; 38 ( 3 ): e5325 . OpenUrl PubMed 98. ↵ Zhang Y , Gao Y , Fang K , Ye J , Ruan Y , Yang X , Zhang Y , Thompson G , Chen G , Zhang X. Proton/Deuterium Magnetic Resonance Imaging of Rodents at 9.4T Using Birdcage Coils . Bioelectromagnetics 2022 ; 43 ( 1 ): 40 – 46 . OpenUrl PubMed 99. ↵ Karkouri J , Rodgers CT . Sequence building block for magnetic resonance spectroscopy on Siemens VE-series scanners . NMR Biomed 2024 ; 37 ( 10 ): e5165 . OpenUrl PubMed 100. ↵ Liu Y , De Feyter HM , Corbin ZA , Fulbright RK , McIntyre S , Nixon TW , de Graaf RA . Parallel Detection of Multicontrast MRI and Deuterium Metabolic Imaging for Time-efficient Characterization of Neurologic Diseases . Radiology 2025 ; 315 ( 1 ): e241597 . OpenUrl CrossRef PubMed 101. ↵ Liu Y , De Feyter H , Fulbright R , McIntyre S , Nixon T , de Graaf R. Interleaved fluid-attenuated inversion recovery (FLAIR) MRI and deuterium metabolic imaging (DMI) on human brain in vivo . MAGNETIC RESONANCE IN MEDICINE 2022 ; 88 ( 1 ): 28 – 37 . OpenUrl CrossRef PubMed 102. ↵ Liu Y , De Feyter H , Corbin Z , Fulbright R , McIntyre S , Nixon T , de Graaf R. Parallel Detection of Multicontrast MRI and Deuterium Metabolic Imaging for Time-efficient Characterization of Neurologic Diseases . RADIOLOGY 2025 ; 315 ( 1 ). 103. ↵ Nam KM , Gursan A , Lee NG , Klomp DWJ , Wijnen JP , Prompers JJ , Hendriks AD , Bhogal AA . 3D deuterium metabolic imaging (DMI) of the human liver at 7 T using low-rank and subspace model-based reconstruction . Magn Reson Med 2025 ; 93 ( 5 ): 1860 – 1873 . OpenUrl PubMed 104. ↵ Harris RK , Becker ED , Cabral de Menezes SM , Goodfellow R , Granger P. NMR Nomenclature: Nuclear Spin Properties and Conventions for Chemical Shifts . IUPAC Recommendations 2001. Solid State Nucl Magn Reson 2002 ; 22 ( 4 ): 458 – 483 . OpenUrl PubMed 105. ↵ Geochemical S. Geochimica et cosmochimica acta . Oxford; United Kingdom : Pergamon Press . 106. ↵ Gleason JD , Friedman I. Deuterium: natural variations used as a biological tracer . Science 1970 ; 169 ( 3950 ): 1085 – 1086 . OpenUrl Abstract / FREE Full Text 107. ↵ Christiansen P , Toft PB , Gideon P , Danielsen ER , Ring P , Henriksen O. MR-visible water content in human brain: a proton MRS study . Magn Reson Imaging 1994 ; 12 ( 8 ): 1237 – 1244 . OpenUrl CrossRef PubMed Web of Science 108. ↵ Oros-Peusquens AM , Loucao R , Abbas Z , Gras V , Zimmermann M , Shah NJ . A Single-Scan, Rapid Whole-Brain Protocol for Quantitative Water Content Mapping With Neurobiological Implications . Front Neurol 2019 ; 10 : 1333 . OpenUrl PubMed 109. ↵ Ricci R , Bacci A , Tugnoli V , Battaglia S , Maffei M , Agati R , Leonardi M. Metabolic findings on 3T 1H-MR spectroscopy in peritumoral brain edema . AJNR Am J Neuroradiol 2007 ; 28 ( 7 ): 1287 – 1291 . OpenUrl CrossRef PubMed Web of Science 110. ↵ Simoes RV , Henriques RN , Olesen JL , Cardoso BM , Fernandes FF , Monteiro MAV , Jespersen SN , Carvalho T , Shemesh N. Deuterium metabolic imaging phenotypes mouse glioblastoma heterogeneity through glucose turnover kinetics . Elife 2025 ; 13 . 111. ↵ Karkouri J , Watson W , Forner R , Weir-McCall JR , Horn T , Hill M , Hoole S , Klomp D , Rodgers CT . Regionally resolved cardiac metabolism using a dipole-loop array coil for 7 T (31)P-MRSI . Magn Reson Med 2025 ; 94 ( 2 ): 480 – 496 . OpenUrl PubMed 112. ↵ Tyler DJ , Emmanuel Y , Cochlin LE , Hudsmith LE , Holloway CJ , Neubauer S , Clarke K , Robson MD . Reproducibility of 31P cardiac magnetic resonance spectroscopy at 3 T . NMR Biomed 2009 ; 22 ( 4 ): 405 – 413 . OpenUrl CrossRef PubMed 113. ↵ Montrazi E , Bao Q , Martinho R , Peters D , Harris T , Sasson K , Agemy L , Scherz A , Frydman L. Deuterium imaging of the Warburg effect at sub-millimolar concentrations by joint processing of the kinetic and spectral dimensions . NMR IN BIOMEDICINE 2023 ; 36 ( 11 ). 114. ↵ Nam K , Gursan A , Bhogal A , Wijnen J , Klomp D , Prompers J , Hendriks A. Deuterium echo-planar spectroscopic imaging (EPSI) in the human liver in vivo at 7 T . MAGNETIC RESONANCE IN MEDICINE 2023 ; 90 ( 3 ): 863 – 874 . OpenUrl PubMed 115. Niess F , Strasser B , Hingerl L , Bader V , Frese S , Clarke W , Duguid A , Niess E , Motyka S , Krssák M , Trattnig S , Scherer T , Lanzenberger R , Bogner W. Whole-brain deuterium metabolic imaging via concentric ring trajectory readout enables assessment of regional variations in neuronal glucose metabolism . Hum Brain Mapp 2024 ; 45 ( 6 ). 116. ↵ Zaccagna F , McLean MA , Grist JT , Kaggie J , Mair R , Riemer F , Woitek R , Gill AB , Deen S , Daniels CJ , Ursprung S , Schulte RF , Allinson K , Chhabra A , Laurent MC , Locke M , Frary A , Hilborne S , Patterson I , Carmo BD , Slough R , Wilkinson I , Basu B , Wason J , Gillard JH , Matys T , Watts C , Price SJ , Santarius T , Graves MJ , Jefferies S , Brindle KM , Gallagher FA . Imaging Glioblastoma Metabolism by Using Hyperpolarized [1-(13)C]Pyruvate Demonstrates Heterogeneity in Lactate Labeling: A Proof of Principle Study . Radiol Imaging Cancer 2022 ; 4 ( 4 ): e210076 . OpenUrl 117. ↵ Ortuno M , Fortea A , Valli I , Munoz-Moreno E , Borras R , Martinez-Serrano I , Masias Bruns M , Camprodon-Boadas P , Vilajosana E , Fortea L , Radua J , de la Serna E , Baeza I , Castro-Fornieles J , Sugranyes G. The effect of age and sex on brain metabolites: from childhood to adulthood . Neuroimage 2025 ; 317 : 121353 . OpenUrl PubMed 118. Revie L , Metzler-Baddeley C. Age-related fornix decline predicts conservative response strategy-based slowing in perceptual decision-making . Aging Brain 2024 ; 5 : 100106 . OpenUrl CrossRef PubMed 119. ↵ Nikolova S , Stark SM , Stark CEL . 3T hippocampal glutamate-glutamine complex reflects verbal memory decline in aging . Neurobiol Aging 2017 ; 54 : 103 – 111 . OpenUrl PubMed 120. ↵ de Ruiter Swain J , Michalopoulou E , Noch EK , Lukey MJ , Van Aelst L. Metabolic partitioning in the brain and its hijacking by glioblastoma . Genes Dev 2023 ; 37 ( 15-16 ): 681 – 702 . OpenUrl Abstract / FREE Full Text 121. Ekici S , Nye JA , Neill SG , Allen JW , Shu HK , Fleischer CC . Glutamine Imaging: A New Avenue for Glioma Management . AJNR Am J Neuroradiol 2022 ; 43 ( 1 ): 11 – 18 . OpenUrl Abstract / FREE Full Text 122. ↵ Hangel G , Lazen P , Cadrien C , Chambers S , Furtner J , Hingerl L , Strasser B , Kiesel B , Mischkulnig M , Preusser M , Roetzer-Pejrimovsky T , Wohrer A , Bogner W , Rossler K , Trattnig S , Widhalm G. High-Resolution Mapping of Tumor and Peritumoral Glutamate and Glutamine in Gliomas Using 7-T MRSI . Radiol Imaging Cancer 2025 ; 7 ( 5 ): e240494 . OpenUrl PubMed 123. ↵ Autry AW , Vaziri S , LaFontaine M , Gordon JW , Chen HY , Kim Y , Villanueva-Meyer JE , Molinaro A , Clarke JL , Oberheim Bush NA , Xu D , Lupo JM , Larson PEZ , Vigneron DB , Chang SM , Li Y. Multi-parametric hyperpolarized (13)C/(1)H imaging reveals Warburg-related metabolic dysfunction and associated regional heterogeneity in high-grade human gliomas . Neuroimage Clin 2023 ; 39 : 103501 . OpenUrl PubMed 124. ↵ Nam K , Gursan A , Lee N , Klomp D , Wijnen J , Prompers J , Hendriks A , Bhogal A. 3D deuterium metabolic imaging (DMI) of the human liver at 7 T using low-rank and subspace model-based reconstruction . MAGNETIC RESONANCE IN MEDICINE 2025 ; 93 ( 5 ): 1860 – 1873 . OpenUrl PubMed 125. ↵ Iqbal Z , Nguyen D , Hangel G , Motyka S , Bogner W , Jiang S. Super-Resolution (1)H Magnetic Resonance Spectroscopic Imaging Utilizing Deep Learning . Front Oncol 2019 ; 9 : 1010 . OpenUrl PubMed 126. ↵ Tataranu LG , Turliuc S , Kamel A , Rizea RE , Dricu A , Staicu GA , Baloi SC , Rodriguez SMB , Manole AIM . Glioblastoma Tumor Microenvironment: An Important Modulator for Tumoral Progression and Therapy Resistance . Curr Issues Mol Biol 2024 ; 46 ( 9 ): 9881 – 9894 . OpenUrl PubMed 127. ↵ Lemarie A , Lubrano V , Delmas C , Lusque A , Cerapio JP , Perrier M , Siegfried A , Arnauduc F , Nicaise Y , Dahan P , Filleron T , Mounier M , Toulas C , Cohen-Jonathan Moyal E. The STEMRI trial: Magnetic resonance spectroscopy imaging can define tumor areas enriched in glioblastoma stem-like cells . Sci Adv 2023 ; 9 ( 44 ): eadi0114 . OpenUrl PubMed View the discussion thread. Back to top Previous Next Posted October 09, 2025. Download PDF Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Absolute quantification of brain deuterium metabolic imaging in healthy volunteers and glioblastoma patients at 7T Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Absolute quantification of brain deuterium metabolic imaging in healthy volunteers and glioblastoma patients at 7T Jabrane Karkouri , Masha Novoselova , Sarah Miller , Minghao Zhang , Alexandra Constantinou , Carina Graf , Daniel Atkinson , Brandon Tramm , Scott Schillak , Richard Mair , Kevin Brindle , Christopher T. Rodgers medRxiv 2025.10.08.25337571; doi: https://doi.org/10.1101/2025.10.08.25337571 Share This Article: Copy Citation Tools Absolute quantification of brain deuterium metabolic imaging in healthy volunteers and glioblastoma patients at 7T Jabrane Karkouri , Masha Novoselova , Sarah Miller , Minghao Zhang , Alexandra Constantinou , Carina Graf , Daniel Atkinson , Brandon Tramm , Scott Schillak , Richard Mair , Kevin Brindle , Christopher T. Rodgers medRxiv 2025.10.08.25337571; doi: https://doi.org/10.1101/2025.10.08.25337571 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Radiology and Imaging Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (297) Cardiovascular Medicine (4421) Dentistry and Oral Medicine (443) Dermatology (382) Emergency Medicine (606) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1507) Epidemiology (15212) Forensic Medicine (30) Gastroenterology (1121) Genetic and Genomic Medicine (6581) Geriatric Medicine (667) Health Economics (996) Health Informatics (4520) Health Policy (1366) Health Systems and Quality Improvement (1611) Hematology (539) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15906) Intensive Care and Critical Care Medicine (1103) Medical Education (620) Medical Ethics (144) Nephrology (667) Neurology (6580) Nursing (345) Nutrition (998) Obstetrics and Gynecology (1141) Occupational and Environmental Health (956) Oncology (3324) Ophthalmology (970) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (663) Pediatrics (1689) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5433) Public and Global Health (9212) Radiology and Imaging (2193) Rehabilitation Medicine and Physical Therapy (1368) Respiratory Medicine (1194) Rheumatology (593) Sexual and Reproductive Health (709) Sports Medicine (529) Surgery (709) Toxicology (99) Transplantation (288) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9ff5b0616e3358f4',t:'MTc3OTM4ODYxMA=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();