Figure 1: Typical S0 (a, b, c), CrCEST (d, e, f), T1 (g, h, i) and T2 maps (k,l,m) for WT (a, d, g, k), Tau (b, e, h, l), and APP (c, f, i, m) mice. The CrCEST maps (RCr) of Tau and APP mice showed clear reduction compared to that of WT mice, while the T1 and T2 maps were closely resembled among the three types of mice. The typical ROIs used to extract regional values are indicated in (a).

Figure 2. The CrCEST values (RCr) represented by Cr apparent relaxation rate for cortex (a ), thalamus (b), and corpus callosum (c) regions in WT (green square), Tau (red circle) and APP (blue diamond) mice. The T1 values for cortex (d), thalamus (e), and corpus callosum regions (f) in WT (green square), Tau (red circle) and APP (blue diamond) mice. The T2 values for cortex (g), thalamus (h), and corpus callosum regions (i) in WT (green square), Tau (red circle) and APP (blue diamond) mice.

Figure 4: Summary of main results overlaid with axonal projection atlas of the mouse brain. Grey arrows and dots represent axonal projections and regions respectively. Colored dots represent affected regions according to our results. Colored arrows represent axonal projections between affected regions and can be associated with white matter alteration.

Figure 1: Variation maps between Ki140 and WT mice. a) Volume variation maps, green regions show atrophy in Ki140. b) GluCEST variation maps, blue regions show decreased level in Ki140. c) MT variation maps, violet regions show decreased level in Ki140. * p < 0.05, ** p<0.01.

Figure 2. Anatomical and DTI analysis of the mouse lumbar spinal cord. (A) Coronal view showing the enlarged lumbar region. Blue circles indicate the positions for L1, L3 and L6. (B) High-resolution PD axial images. (C) GM/WM segmentation using SCT. (D) Affine registration of ROIs into DTI maps using b0. DTI parameters: number diffusion gradient directions = 9; b value = 1200 s/mm² ; TR/TE = 800/18 ms; Resolution = 78 x 78 µm; Slice thickness = 0.5 mm; NSA = 1.

Figure 1. MRS of the lumbar spinal cord. High-resolution PD was used to locate enlarged lumbar spinal cord GM area. The dimension of MRS voxel is 2.8 x 1.6 x 0.9 mm as Z, X & Y directions as shown on A, B and C (coronal, sagittal and axial). PRESS was used with the following parameters: TR/TE = 2500/14 ms; number of signal averages (NSA) = 128; RF pulse band width = 8400Hz.

Fig 2. Dynamic T1 map of in vivo MetHb induction at baseline(t=0), t=2, 8, 30 mins after SN treatment and typical ROI selections for mean T1 measurement.

Fig 3. In vivo T1 changes in vascular structures were plotted as a function of time after SN injection. Plots at baseline, and different time delay (4, 8, 12, 14, 16, 20, 26 and 30 minutes) from SN injection from four rabbit studies.

Figure 4. Drug release, longitudinally CEST monitoring, cytotoxicities and swelling of the MGLH (n=3). (A) MTX and Gem release profiles by UV-vis measurements. (B) the longitudinal CEST tracking of drug release and hydrogel matrix using contrast at 2.4 ppm (drugs) and -3.6 ppm (hydrogel matrix) on day 0, 1, 2, 3, and 7, respectively. (C) the relative cell viabilities treated with these hydrogels, using PBS treated group (100%) for normalization. (D) the swelling tests by gravimetric measurement. P > 0.05, NS; P < 0.05，*; P<0.01，**; P<0.001，***; P<0.0001，****.

Figure 3. CEST contrast and rheology of MGLH (n=3). The Z-spectra and corresponding CEST contrast (A), and maps (B) under 1.0 μT. The frequency sweep measurements of MGLH hydrogel (C).

Figure 1. (A.) Dual tuned ¹H/¹⁹F torso coil (MRI Tools, Berlin, Germany), with (B.) agar spacer/loading phantoms, and (C.) 1.5 ml PFPE phantoms (white) and 2 ml ¹⁹F labeled canine NK cell phantoms (pink). PFPE phantoms were fixed to upper coil array elements under a single 4L agar loading phantom approximately 2 cm from coil surface to simulate typical in vivo osteosarcoma placement, and (D.) loaded in MRI with additional agar phantoms (4L, 8L) to create spacing approximate to canine body.

Figure 3. (A.) Coronal image of 2 cm thick 4L agar spacer and pelleted NK cell phantoms. (B.) Typical ¹⁹F-image obtained from the cartesian 3D fast spin echo sequence of the NK cell phantoms, with (C.) ¹⁹F-MRI overlay (red) of ¹H-image depicting the presence of ¹⁹F-signal within the tips of the pelleted NK cell phantoms.

Liver Uptake of MnTriCP-PhOEt at 3 Tesla. a) T₁-weighted images of a representative mouse before and up to 60 minutes post intraperitoneal injection of 0.025 mmol/kg MnTriCP-PhOEt. b) In vivo signal intensity of various organs over time of a representative mouse. c) Average in vivo signal enhancement of liver (n=3). d) Ex vivo nuclear magnetic relaxation dispersion (NMRD) profiles of various tissues 44 minutes post injection. e) Differences in spin-lattice relaxation rates of various tissues between mice injected 0.025 mmol/kg MnTriCP-PhOEt or saline, 44 minutes post injection.

MnTriCP-PhOEt Structure and Synthesis. a) Molecular structure of MnTriCP-PhOEt. b) 3-dimensional geometry of MnTriCP-PhOEt. q, hydration number. c) Synthesis and yield of MnTriCP-PhOEt. Yields shown in brackets.

Figure 1: Example of MR images illustrating the placentomes from a single sheep at mid and late gestation.

Figure 2: Boxplots summarising results over all singleton pregnancies at mid and late gestation. Each plot shows: the median (red line), the 25th and 75th percentile (purple box) and individual means of each sheep (pink circles).

Figure 1: Principle of how image contrast of moving cells with different velocities is simulated by creating synthetic k space.

Figure 3: Image sections of an overlay of a T2*-weighted image obtained by the time-lapse MRI protocol and the contrast simulation for different cell motion velocities, showing a real (red arrowhead) and a simulated (blue arrowhead) cell in the brain cortex. Simulations used a (a) cartesian and (b) radial sampling scheme.

Microglial isolation protocol followed by metabolite extraction for NMR. Brains were removed from healthy rats, minced with a razor blade and homogenized before straining and centrifugation. Layers of 70% and 50% Percoll solution and PBS were added. Centrifugation separated microglia from the other CNS elements. Microglia was added to ice-cold methanol, chloroform and water. The metabolite layer was then lyophilized before resuspension in 200μl D₂O and acquisition of NMR spectra at 800MHz. Created in BioRender.com

(A) Spectral fitting of six microglia-specific metabolites shown on spectrum from 3 rats combined, using Chenomx software: PE = o-phosphoethanolamine, PCr = creatine phosphate, Asp = apartate, Glu = glutamate, Tau = taurine (B) Total concentration of the six microglia-specific metabolites in all brain samples, expressed in mM. These graphs show that the levels of metabolites are linearly increasing with cell number, with good reproducibility. (C) Concentration of the six microglia specific metabolites expressed as mM/cell.

Figure 1 Microfluidic preparation of alginate microbeads incorporated with Iopamiro or Iohexol-loaded liposome. (a) Microfluidics design for preparation of microbeads. 1, 2, 3 were inlets for water phase, oil phase and oil/acid phase, respectively, and 4 was outlet. (b) Schematic of liposomal hydrogel beads, i.e. alginate microbeads containing liposomes loaded with CT contrast agent (CT-lipobead). Prepared lipobeads(c). Chemical structure of Iohexol (d) and Iopamiro (e), and protons that can be detected by CEST are in red circles.

Figure 2 Power optimization and pH dependency for CT-lipobeads showing that CEST contrast at 4.2ppm reach the highest value at 1.6uT and 2.0uT for both beads and capability to distinguish HM from NM. CEST contrast for Iopamiro liposome beads and Iohexol liposome beads at different B1 at neutral pH(a). CEST contrast at 4.2ppm in saline with different pH at 1.6 uT(b). CEST contrast at 4.2ppm in Iopamiro lipobeads in HM, NM and FM at 1.6 uT (c) and corresponded CEST map(d). pH of HM, NM and FM are 6.8, 7.1 and 7.2, respectively. HM: Hypoxic medium. NM: Normoxic medium. FM: Fresh medium

Figure 1: A) Plot of the modulus of SSFP’s signal vs. frequency offset under the four phase-cyclings, shifting the “banding“ pattern as indicated for TR=5.7ms. Also shown are the frequencies expected for deuterated lactate, glucose and water (green lines) at the utilized field. B) ²H MRI acquisition of bSSFP images with multiple phase-cyclings on a three-metabolite phantom. C) The images can be analyzed as a sum of complex signals (shown in A) arising from each metabolite, based on the phase-cycling; the contribution of each metabolite is then determined by matrix inversion.

Figure 5: LC-SSFP of deuterated water and glucose in a pancreatic cancer tumor, 30 minutes after injection of deuterated glucose (i.e, before the generation of substantial ²H-lactate). A) Magnitude/phase signal evolutions of water (top) and glucose (bottom), comparing measured values to predicted values. The TR was 5.7ms, and carrier offset 4.3ppm (between glucose and water). B) Metabolites separated using LC-SSFP’s theoretical weights. T=tumor. Left: Raw ²H images. Right: ²H images overlaid on ¹H MRI anatomy.

Figure 4: Compilation of cortical and medullary results of perfusion as well as T1 and T2 relaxation time for all experimental groups (survivors, non-survivors, healthy sham group) and statistical evaluation using the Mann-Whitney U-test(*: p<0.05, **:p<0.01).

Figure 3:Cortical and medullary perfusion as well as T1 and T2 values in correlation with the severity score based on data from all experimental groups (3 different diets in both the PCI- and sham-group).

Figure 2. Representative Z-spectra for the GuanCEST experiments (a) pre and (b) post-CO2 inhalation for the cortex region. The PCr peak is observable and is indicated. Representative Z-spectrum of amideCEST experiment (c) pre and (d) post-CO2 inhalation and CrCEST experiment (e) pre and (f) post-CO2 inhalation for the cortex region. Solid lines are the fitted background using the PLOF method. (g) Scatter plots showing the CEST contrast difference for CrCEST, amideCEST and GuanCEST experiments obtained by PLOF (n= 5) and the corresponding contrast changes (h).

Figure 1: (a) The typical Z-spectra of egg white that titrated to 6.0, 6.5, 7 and 7.5 pH. The spectrum was recorded using CW-CEST with 1μT and 3s saturation at 37°C by an air heater. The CEST peaks from amide, amine, guanidinium, amideNOE are indicated. The simulated DS spectrum for egg white with pH 7.5 is also plotted.

Fig. 2 MRI measured at 4.7T scanner using ¹H/³¹P RF surface coil. Phantoms (c_P=100mmol/L; V=1.4ml in H₂O) positioning is visible on ¹H MR image (RARE sequence; ST=1min; coronal plane) with probes containing a phosphoester (P=O) group on the left and a phosphorothioate (P=S) group on the right with water reference between them (A). Next, ³¹P MRI (CSI sequence; ST=3h) of polymers (B: P=O; C: P=S) are presented. In the last image, overlapped ¹H/³¹P MR images are shown (D); ³¹P signal is highlighted by green colour. SNR resulting from ³¹P MR images is 5.4 for P=O polymer and 13.6 for P=S polymer.

Fig.1 ³¹P MR spectra measured at 4.7T scanner using ¹H/³¹P RF surface coil. Single-pulse sequence (TR=200ms; ST=3h) was used for the measurement and spectra from both p(TMPC) and p(MPC) polymers (c_P=100mmol/L; V=1.4ml in H₂O) were obtained during one measurement. Peak on the left with slightly lower amplitude represents a polymer with a phosphoester group (P=O) with SNR=163.7; peak on the right represents a polymer with a phosphorothioate group (P=S) with SNR=195.1. The chemical shift between the peaks is 56.07ppm.

Fig1. (A) The synthetic scheme of zwitterionic MCP contrast agent PAA-Gd. (B) Viability of HUVE cells treated by PAA-Gd and Gd-DTPA. (C) Signal intensity map of T1 weighted MR images (TE= 10 ms, TR= 300 ms) of aqueous solution of PAA-Gd in 200 μL Eppendorf tubes according to different Gd³⁺ concentration from 0 to 1mM. (D) Linear regression fitting curve (solid line) of calculated R1 value (‘■’ points) was performed.

Fig2. The image obtained immediately (A) and 12 minutes (B) after intravenous injection of PAA-Gd. DCE images of pre-injection (C), post-injection (D) and the final status of mouse after 30 minutes (E) on the kidney plane. A long distance transportation trace is depicted and emphasized with white arrow in (F) on centric section.

Figure 4. (A) Accuracy in image derived Gd estimation versus ICPMS measurement. The average error for T_1M method was 14.3%. The average error for T_1T method was 17%. (B) [Gd] from MRI measurement using T_1M method and T_1T method (C) vs. ICP-MS measured [Gd]. (B) has a correlation coefficient of 0.84. (C) has a correlation coefficient of 0.85. The gray dash lines are unity lines.

Figure 2. Example of signal intensity to [Gd] conversion using the top 10 most enhanced vessels from IAUC selection. (A) Normalized vessel signal enhancement curves. (B) Signal enhancement curves converted to [Gd] using T_1M. (C) Signal enhancement curves converted to [Gd] from T_1T. (D) All signal enhancement curves overlaid with a dashed box over the tail end. (E) A zoomed-in image for the tail end of the signal enhancement curves. The concentrations at last time point (610 s) were used to cross validation with ICP-MS method.

Figure 1: A picture of the (A) 3 Tesla clinical MRI (MR750, General Electric) and dual-tuned (¹H/¹⁹F) surface coil (MR Solutions) used for ¹⁹F imaging, and (B) preclinical Momentum^TM MPI scanner (Magnetic Insight Inc.). (C) The relationship between MPI signal and iron mass is determined for MPI signal calibration by imaging known amounts of ferucarbotran with 3.0 T/m gradients.

Figure 4: MPI detection of ferucarbotran-labeled MSC. (A) 2D MPI of individual MSC pellets containing various cell numbers (M = 10⁶, k = 10³). As few as 2000 MSC was detected in 6 of 9 replicates, however with low SNR (2.51). The detection limit of 4000 MSC is reliable and quantifiable (9/9 replicates, SNR = 3.77). (B) 2D MPI signal (and the associated iron content) significantly increases with number of MSC. (D) In 3D (35 projections), 2000 cells can be reliably detected (9/9 replicates, SNR = 6.42) and as few as 1000 cells were detected in 2 of 9 replicates (SNR = 4.69, signal volume 116mm³).

Figure 1. Multi-task learning structure. The input images are on the left; the output count of lesions is on the bottom right; the output map is on the top right: distance map for point annotation dataset and segmentation map for full segmentation dataset. After each convolutional layer, a rectified linear unit activation is applied. In the regression network, after the last convolutional layer, a global max-pooling layer is added to connect the following fully connected layers.

Figure 4. Representative detection results. In the prediction image (right column): green circle for true positive, red circle for false positive and orange circle for false negative.

Fig. 3 Representative MRI images of axial T2-w and T1-w CE images showing different lesion morphologies. (A) T2 lesion without corresponding enhancement and a low T2 signal ratio (SR: 1.54). (B) Ring-enhancing lesion with a T2 SR of 1.78. (C) T2 lesion with a high T2 signal ratio (SR: 1.93) and clear contrast-enhancement. (D) Nodularenhancing lesions (n=33) showed significantly higher T2 signal ratios than nonenhancing (n=185, ****p<0.0001) and ring-shaped enhancing lesions (n=5 ****p<0.0001). NASC: normal appearing spinal cord.

Fig. 2 T2-w and T1-w contrast enhanced images (A, B). Size of the T2 lesion weakly correlates with the size of the contrast enhancement (C). Pearson correlation analysis, r2 = 0.17, **p < 0.01, n = 44 lesions).

Liquid chromatography-high-resolution mass spectrometry (LC-HMRS) chromatograms of gadobutrol samples exposed to various doses of radiation. The entire profile is displayed in (A) where 5 peaks are visible. Peak P3 was identified as gadobutrol, which is enlarged and quantified in (B).

Liquid chromatography-high-resolution mass spectrometry (LC-HMRS) chromatograms of gadobenate dimeglumine samples exposed to various doses of radiation. The entire profile is displayed in (A) where 5 peaks are visible. Peak P2 was identified as gadobenate dimeglumine, which is enlarged and quantified in (B).

Figure 1. Contrast enhancement of the infundibular recess. The infundibular recess is hyperintense on axial (A, arrow) and midsagittal reformatted (B, arrow) MR cisternography (MRC). It shows contrast enhancement on axial (C, arrow) and midsagittal reformatted (D, arrow) post-contrast HT2-FLAIR.

Figure 2. Chronological changes in contrast enhancement in the infundibular recess. The infundibular recess (IR) is hyperintense on MR cisternography (MRC) (A, arrow). On HT2-FLAIR, compared with a pre-contrast image (B), a post-contrast image (C, arrow) of IR shows stronger enhancement. A 4-h delayed post-contrast image (D, arrow) shows weaker enhancement. We fuse MRC and each HT2-FLAIR into one image (E, F, and G), and enhancement on post-contrast HT2-FLAIR (F) corresponds to IR hyperintensity on MRC.

Figure 2. Retrospective undersampling analysis on a fully-sampled mouse brain. A. T₁ maps of fully sampled (R_xy=1, R_z=1) mouse brain and prospectively undersampled data (R_xy=8, R_z=1; R_xy=8, R_z=3; R_xy=16, R_z=1; R_xy=16, R_z=3) of three central axial views. B. T₂ maps of fully sampled and undersampled results.

Figure 1. Retrospective undersampling analysis of fully-sampled phantom data. A. Images of the 120th frame image of a central axial view reconstructed from fully sampled data (R_xy=1, R_z=1) and undersampled data (R_xy=8, R_z=1; R_xy=8, R_z=3; R_xy=16, R_z=1; R_xy=16, R_z=3). B. Plots of fingerprints vs. dictionary matching results for the pixel highlighted in A. C. T₁ maps of fully sampled and undersampled results. D. T₂ maps of fully sampled and undersampled results. E. Normalized RMSE (NRMSE) of T₁ and T₂ estimations with different undersampling factors.

Representative T2-weighted high resolution image overlayed with manually drawn ROIs, T2 map, T1 map, FA, TR and the template MR image.

The T2, T1, FA, and TR ROIs values in four brain regions are shown on the panel. The analysis of the derived results showing elevated trace values in all selected regions in HA conditioned mice which was reversed by PLX treatment.

Three representative spectra from magnetization saturation transfer (MST) experiment in swine heart to measure myocardial 31P reaction rates. Control spectrum (middle) without saturation pules to measure $$$ M_{o,PCr} $$$ , and $$$ M_{o,γATP} $$$. Single resonance (γ-ATP) saturated spectrum (left) to quantify $$$ M_{s,PCr} $$$. Dual resonance(PCr and Pi) saturated spectrum (right) to quantify $$$ M_{s,γATP} $$$ .

(a) BISTRO based saturation transfer pulse sequence. Tsat is the saturation time (b) Simulationof single saturation (green) and dual saturation (blue) BISTRO RF pulse at various power levels overlaid over spectrum from heart demonstrating saturation of γ-ATP (single saturation) and PCr and Pi (dual saturation) frequencies.

Figure 4: Continuous tyGA acquisition with simultaneous administration of contrast agent. a) and c) show image ROI and k-space intensities over the whole acquisition, b) shows the image intensities around the timepoint of administration. d) shows example sliding-window images and e) and f) correspond to dual gated reconstructions before and after contrast administration. Row-wise differences indicate different respiration stages, column-wise differences show the influence of the cardiac phase.

Figure 3: Self-gated reconstructions of an exemplary cardiac (SAx) and lung (coronal) acquisition. Due to the properties of the tyGA trajectory, artefact levels for respiratory ungated cardiac imaging is very low. However, differences to respiratory gated imaging can not only be seen in the vessels, but also in the heart itself. Cardiac gating seems mandatory for lung imaging, as shape and position of vessels clearly change with the heartbeat and could influence functional measurements.

Figure 1. Representative images and diffusion MRI maps. (a) T2w structural image for volumetric analysis. b-d: Parametric maps extracted from diffusion MRI after modelling the signal to the diffusion kurtosis model: FA: fractional anisotropy (b), MD: mean diffusivity (c), and MK: mean kurtosis (d). e,f: Parametric maps extracted using the NODDI model: OD: orientation dispersion (e) and ICVF: intracellular volume fraction (f). Scale bar: 5 mm.

Figure 3. Effect of genotype in brain volume in TDP-M323K mice. (a) When compared directly to WT mice, TDP-M323K mice showed smaller volumes in almost all regions of the brain. (b) When volumes relative to the whole brain size were compared, TDP-M323K mice showed atrophy mainly in the white matter, while regions in the cortex and hippocampus had relatively increased volume. Most of the spatial patterns in t-statistics maps are bilaterally symmetric. Atrophic regions in TDP-M323K mice are show in blue and hypertrophic regions are show in red/yellow. False discovery rate of 5%.

Fig. 1. The 256 FID MRS data acquisition protocol in the acute stress experiment using 9.4T Bruker equipment.

Fig. 2. Non-parametric sign test about correlation coefficient value both of two experimental groups. The orange box is satisfied with the statistically significant sign test p < 0.05. The Control group has not significant correlation coefficient between all metabolites. However, acute stress group have significant between Gln-GSH, Glu-NAA, GSH-NAA.

Figure 1: Example slices of the in vivo T₂-weighted images with segmentation masks for the cerebellum (A) and corpus callosum (B) overlaid in ITK-Snap (http://www.itksnap.org).

Figure 2: Boxplots showing the full range, median and average (+) values for brain volumes in neonates (A), young adults (B), aged adults (C) and regional volumes of corpus callosum (D) and cerebellum (E). Significance levels are indicated by: ns = P > 0.05, * = P ≤ 0.05, **= P ≤ 0.01, ***= P ≤ 0.001, ****= P ≤ 0.0001.

Figure 2. Representative ¹H MR spectra from three groups Left. Wild Type, Mid. Pank1,2 neuronal KO, and Right. Pank1,2 neuronal KO mouse treated with BBP-671

Figure 3. Metabolite to total creatine ratios of glutamate+glutamine (Glx), γ-amino butyric acid, N-acetyl aspartate, and lactate

Figure 2: Multi-gradient echo (MGE) imaging to separate the contribution of [²H₇]glucose and HDO to DMRI image of the rat brain. Image (d) represents the deuterium image of HDO-only and (e) is the overlaid images of (a) proton (grey) and (d) deuterium (green) images due to HDO-only in the rat brain. Image (f) represents the deuterium image from the contribution of [2H7]glucose-only and image (g) is the overlaid image of (a) proton and (f) deuterium [²H₇]glucose-only images.

Figure 3: Schematic representation of the production of deuterated lactate, glutamate/glutamine (glx), and HDO from [²H₇]glucose during glycolysis and the TCA cycle. Deuterium (²H) loss has been shown in the form of ²HOH, and NAD²H. Small and large red filled circles represent 1 and 2 deuterium atoms respectively, black filled, and empty circles represent hydrogen atoms and quaternary carbons, respectively.

Figure 3: MPI of mouse brains injected with 2x10⁵ Synomag-D labeled 231BR cells at day 0, imaged with a FOV of 6 cm x 4 cm.

Figure 4: Representative MRI with a FOV of 1.5 cm x 1.5 cm of mouse brains injected with 2.5 x 10⁵ 231BR cells labeled with MPIO at Day 0. Discrete signal voids representing iron labeled cancer cells appear throughout the brain (white arrows).

Figure 1: The SBE system matrix, $$$\mathbf{L}$$$, colour-coded to indicate source of terms: Red-shaded entries are associated with RF excitation; Yellow-shaded entries are off-resonance terms; Green-shaded terms are associated with residual quadrupolar oscillation; Blue-shaded terms are associated with the fluctuating quadrupolar interaction.

Figure 3: (a) Shape of the WURST inversion pulse, and the evolution of bulk magnetisation in (b) saline, (c) xanthan.

Figure 1: Schematics of homonuclear BIRD (HB) filter. After an initial adiabatic 90⁰ hard pulse and two adiabatic 180⁰ pulses, α-ATP spins acquire a phase of 90⁰ with respect to uncoupled spins at t=TE_j =1/2J and end up aligned with y’ (t=TE_j). At t_90-x a non-selective 90⁰ block pulse flips spins with J-coupling constants different from that of α-ATP about the y’-axis back towards the z-axis. Spins remaining spins in the x’-y’ plane are then dephased by G_Spoil. Zero quantum coherences (ZQC) are removed by co-adding signals acquired with a variable delay T_d.

Figure 3: Comparison between NAD⁺/H fitting results from (A) FID and (B) HB filtered spectra. NAD⁺/H resonances are clearly more separated from α-ATP in HB filtered spectra than in the FID.

Figure 2. Comparison between (A) CBF, (B) OEF and (C) CMRO₂ estimates from DBFM (PET-only technique) and OxFlow (n = 8). No significant difference was observed for all three measurements. The dashed and solid lines represent the identify and regression lines, respectively. A significant anesthetics-induced reduction in (D) WB CBF (27.3 ± 7.0 mL/100g/min) was accompanied by an increase in (E) WB OEF (0.38 ± 0.12), resulting in a significant decrease in (F) CMRO₂ (1.15 ± 0.33 mLO2/100g/min).

Figure 1. Magnitude and phase images from the slices used to estimate (A)-(B) WB CBF and (C)-(D) S_vO₂. The regions-of-interest (red dashed) were transferred from the magnitude to the phase image (in white). Images are from one representative animal.

Figure 1. Averaged T1 map (n=7) of the rhesus macaque brain before and after EBOV exposure: Compared to pre-exposure (top row), the post-exposure T1 map (bottom row) showed noticeable increases in multiple regions, including cerebellum, occipital lobes, deep gray matter, and frontal cortices (white arrows and dotted circles).

Figure 2. A voxel-based statistical comparison of T1 of the rhesus macaque brain before and after EBOV exposure: There were significant increases in T1 values in various brain regions, in particular in the cerebellum and occipital lobes. Statistical significance was considered at corrected p < 0.001 (one-tailed paired t-test, false discovery rate correction). The colorbar shows a T-score range.

Figure 1: Coronal slices showing FA, AVF, MVF, and g-ratio parameter maps in WT and M1M3M5KO mice. Selected slices in the top two rows permit visualization of the genu of the corpus callosum and the anterior commissure white matter tract. The bottom two rows display the internal capsule and the splenium of the corpus callosum in the mouse brain. The difference myelin volume fraction signal between WT and M1M3M5KO is evident based on red arrows overlaid on the mouse brain images.

Associations between myelin volume fraction, derived from BSS rPCA-based multi-component T₂* analysis, and relative myelin basic protein mRNA levels in three major white matter tracts of the mouse brain. Mbp mRNA levels were derived using qRT-PCR as described in Bagheri et al.¹ In anterior commissure, splenium of the corpus callosum and internal capsule white matter, MVF scales linearly with relative Mbp mRNA. This supports the conjecture that Mbp mRNA level is a critical determinant of MRI-sensitive myelin bilayer properties.

Figure 1 Statistical parametric maps of the FA changes between pre- and post-impact Significantly increases in FA at bilateral thalamic borders (green arrows) were observed post-impact as compared with pre-impact. The dark blue indicated the location of bilateral TRN. These maps were further masked by the threshold of group-averaged FA > 0.15.

Figure 3. Longitudinal changes in DTI metrics after impact (A) Bilateral segmented ROIs showing the areas of thalamus (red),cortex (yellow), TRN(blue) and WM(green) (B) Longitudinal follow-up during the first 35 days shows that FA increased in thalamus, WM and TRN at day 7 as well as during the follow-up. Contrary to FA, MD, AD and RD decreased with time except for AD in TRN. (thalamus comparison with baseline[before impact]: # P <0.01; cortex comparison with baseline: * P <0.01; WM comparison with baseline: ǂ P <0.05; TRN comparison with baseline: + P <0.05 ; data presented as mean ± sd)

Figure 1. CEST signals of 3TC. (A) Chemical structure of 3TC. The hydroxyl group is in red circle, and the amino group in blue circle. (B) MTR plots of 3TC in PBS at 37^oC. MTR increases at 2 ppm with the 3TC concentration. (C) MTR@2ppm increases linearly with 3TC concentration (R² = 0.95). (D) Pixel-by-pixel color maps of 3TC samples.

Figure 3. (A) Lorentzian functions of CEST signals of amino protons on thalamus from a mouse scanned before 3TC administration (baseline), 6 hours after 1st drug administration and after 5 days of daily administration. (B) CEST signal integrals of 3TC on thalamus. (C) Color maps of CEST signal integrals of 3TC superimposed on anatomical images of the mouse brain.

Figure-2: A densely connected Encoder-Decoder network of CNNs (top and middle), enhanced by spatial-attention and channel-attention layers (bottom), for computing ADC maps from undersampled complex image-space data.

Figure-1 : Representative diffusion-weighted images and ADC map. The tumor is indicated by the red arrow.

Figure 2. Longitudinal follow-up magnetic resonance imaging (MRI) measurements of liver. Intravoxel incoherent motion (IVIM)-derived parameters (D, D*, PF), diffusion kurtosis imaging (DKI)-derived parameters (MD, MK), and the blood oxygen level-dependent (BOLD)-derived parameter (R2*) of the liver parenchyma all showed regular changes from days 0 to 21 after surgery, and finally returned to baseline (bs)

Figure 1. Longitudinal changes of hepatocyte Ki - 67 indices (A) and sizes (B) from baseline (bs) to 21 days (d21) after 70% hepatectomy in rats

Figure 4:

A) oPLS scores plot (LV1 – LV2) using the resistance as scaled prior knowledge (y-table).

B) Description of the PLS model showing the measured compared to the predicted resistance values.

Figure 3:

PCA scores plot (PC1 – PC2) of all samples using the color and form coding introduced in Fig.1.

Figure 1: Representative short-axis cine FLAH images obtained at end-systolic stage and end-diastolic stage in wild-type (WT) and transgenic (TG) rats (resolution 0.3x0.3x1mm3).

Figure 5: (A) Typical cardiac localized ³¹P MRS spectrum obtained in control rat; (B) Ratio phoshocreatine/ATPβ of wild type (WT, n=11) and transgenic (TG, n=12) rats for a duration of 35min prior and after dobutamine injection (10µg/kg). Mean±SEM and Groups were compared by nonparametric t test Mann-Whitney.

Figure 4. Statistics of FA values of 86 ROIs in the cortex, white matter, and subcortical nucleus between SD and WT groups. The t values were obtained by unpaired t-test of FA maps from two groups.

Figure 3. Comparison of connectivity strength between social deprivation and wild type groups. The r values of 86 ROIs were compared between SD and WT. The two charts showed ROIs with significant differences. The red arrows indicated that the FA values in the temporal lobe and nucleus caudatus of socially deprived were significantly reduced compared with WT.

Figure 3. Violin plots of median FA, OD and ICVF values within 7 ROIs for male mice. *: p-value ≤0.05, FDR-corrected (5%).

Figure 1. Illustration of the dMRI pipeline. All 64 volumes are corrected for Gibbs ringing⁶. The data was first corrected for susceptibility-induced off-resonance field distortions and movement using topup⁷ and eddy⁸. The diffusion tensor model (FSL DTIFIT) and the NODDI¹² model (FSL cuDIMOT¹³ implementation; d||=5.6x10-4, diso=1x10-3mm²/s, dot compartment included) were fit to the data to produce FA, MD, OD, ICVF and isotropic volume fraction (ISOVF) maps.

FIGURE 2

PCA applied to all samples and buckets from 1D projected spectra (259 x 39) (A). Excerpts from the PCA plot in (A) for better visibility of the subclasses, i.e. samples without carriers (B), samples with KP as carrier (C) and samples with PVP as carrier (D).

FIGURE 5

(A) HR-MAS ¹H-diffusion-edited NMR spectra (1D-DOSY) of lysed HeLa cell suspensions in PBS incubated with different concentrations of Ce4 without carrier or encapsulated into KP or PVP. Shown is the spectral region where the choline containing resonances appear. The GPC resonance exhibits upfield shifts;

(B) Chemical shift of the GPC-choline resonance as function of condition (Ce4 concentration and carrier)

Figure 5: Results obtained in oxford with the array 16-channel coil. On the right, ATP concentration map overlaid on the heart localizer for better visualization. On the left, an example of fitting using AMARES algorithm (OXSA toolbox, MatLab). In the figure A there is a fitted spectrum. In figure B individual peaks are depicted. Residuals are shown in figure C and in the figure D the initial values for non-linear fit are depicted.

Figure 4: Results obtained in Cambridge with the dipole array coil from Tesla DC. In A), the placement of the csi grid is shown. In B), an example of fitting using AMARES algorithm (OXSA toolbox, MatLab) from the highlighted pixel. In the figure, there is a fitted spectrum, individual peaks are depicted. In C), The ATP concentration map is overlaid on the heart localizer for better visualization.

Figure 5. ¹H T2-TurboRARE image (a) ¹H- (b) and ²H-PRESS spectra (c). The phantom consists of six 5 mm NMR tubes with ETX-D4,4’ (0, 9, 18, 27, 36 and 45 mM) in aqueous 100 mM PBS buffer. Red boxes represent the PRESS voxels: 4mm*4mm*4mm. ¹H-PRESS parameters are: TE=16.5ms, TR=2000ms, NS=1200, acquisition time AQ=467ms, experiment time ET=40min, spectral bandwidth SW=4386 Hz. ²H-PRESS parameters are TE=14.2ms, TR=300ms, NS=8000, AQ=247ms, SW=8196Hz, ET=40min. Exponential line broadening was 4 Hz. Spectra were zero-filled to achieved nominal spectral resolution per point of 1 Hz.

Figure 4. ETX-D4,4’ ²H-PRESS signal as a function of TE (a, TR = 200 ms), TR (b, TE = 14.2 ms) and ²H-PRESS signal divided by TR0.5 (c). The later reveals the most efficient for SNR value of TR with the fixed scanning time. This value is predicted to be close to 1.25*T1 = 300 ms. Lines are fitting of experimentally measured integrals of ETX-D4,4’, fitting equations are given on the plots.

Figure 1: Schematic for co-registration of high-resolution 3D MRH with cytometric property maps derived from 2D H&E histology slides. Demonstration of the four phases for correlative MR studies: (1) Registration of MR to H&E slides; (2) Implementation of a multi-step algorithm for nuclear segmentation over entire histology slides; (3) Measurement of segmented nuclei and generation of quantitative cytometric feature maps; (4) Correlative studies of tumor MR signal and cytometric features.

Figure 4: Relationships between MR and pathology features. Shown is an in vivo T2* sarcoma image (A). A map of variance in Delaunay triangle area (V_Dta) shows variable tumor cell organization (B). Correlation of V_Dta and T2* shows non-zero relationships in both ex vivo T2* (C; p < 0.0001, R² = 0.27) and in vivo T2* (D; p = 0.0004, R² = 0.18). R² values reflect small pilot size (n=8).

PET images of a tumor bearing mouse (A,C) and a sham mouse (B,D) with coronal (A,B) and corresponding axial slices (C,D). Corresponding fused PET-MR images (E-H).

Bioluminescent images of 5 orthotopic ID8-Defb29-VEGF tumor bearing mice