Figure 2: Representative slices of the 3D templates for R₁, MPF, ihMTR, ihMTsat with different T_1D-filtering along with plp-GFP fluorescence microscopy images at -3.2, -0.7 and +0.7 mm from bregma. Brain structures in WM (IC – internal capsule, CC – medial corpus callosum, ON – optical tract), Grey Matter (CTX – cerebral cortex, HP – hippocampus) and mixed WM/GM structures (TH – thalamus, CP – caudoputamen) where quantitative analyses were performed are indicated on the T₂w/plp-GFP images.

Figure 3: Correlations of MR metrics and plp-GFP signal. a) Pearson correlation coefficient matrix for all techniques. b) Linear regressions of normalized R₁, MPF, ihMTR^0.8 and ihMTsat^0.8 with GFP. c) Linear regression of MPF with ihMTsat^CM, ihMTsat^0.8, ihMTsat^1.6 and ihMTsat^3.2. Shaded areas correspond to confidence curves for line fits with an α-level of 0.1.

Establishing the iron relaxivity framework in vitro. (a) R2* and R1 vary with the concentration of iron-binding proteins (x-axis), with their type (transferrin, ferritin) and with the molecular environment (liposomes, saline, BSA) (different colors). Data points are phantom samples’ medians. Symbols represent water fractions. (b) The dependency of R1 on R2* for different molecular compounds and environments of iron. R2* and R1 of samples with varying water and protein concentrations were binned, data points are the bins’ median. The R1-R2* slopes are different for each iron form.

Fully-constrained model predicts the fractions of iron-binding proteins in the in vivo human brain. The measured R1-R2* slope in each brain area was modeled (Eq. 1) as a weighted sum of the R1-R2* slopes of transferrin (Tf) and ferritin (Fer) (estimated in liposomal phantoms). The Tf and Fer fractions sum to one (Eq. 2). Rearranging the model (Eq. 3) allows to predict the transferrin fraction (Tf/(Tf+Fer), y-axis) for younger (<64) and older subjects in 11 brain regions. There are no free parameters. In the x-axis is the Tf fraction measured post-mortem^5,6,9,10. MAE=mean absolute error.

Figure 3) Simulation results of the parameter variation of t_SL and f_SL. The resulting NEMO-amplitude a is shown as a heat map (a). In (b) and (c) profiles for t_SL and f_SL are shown. In (d) the corresponding sinusoidal fits can be seen for varying phases ϕ. The optimal sequence parameters are t_SL=82.4ms and f_SL=10.625Hz and thus deviate significantly from the parameters chosen in [3] (125ms, 10Hz). In the simulation, typical relaxation times for gray matter T_1ρ=78ms [3] and T_2ρ=1.5*T1ρ were considered.

Figure 5) Results of the parameter variation. In (a) f_SL was varied (constant t_SL=100ms). Similar to the simulation, the NEMO-amplitude shows a peak at f_SL≈10Hz. In (b) t_SL was varied (constant f_SL=10Hz). The emergence of local maxima can be seen. However, the behavior of RSS(t_SL) is remarkable. In the case ≈25ms, ≈75ms and ≈125ms, image artifacts lead to great difficulties in the sinusoidal fit. A reliable determination of the NEMO-amplitude was therefore not possible in this range.

Figure 2. The relative changes in T₁ and the corresponding membrane potentials. The dotted black lines in each plot represent the normal condition. (a): Relative T₁ changes and the membrane potentials when those are altered by [K⁺]. (b): Relative T₁ changes and the membrane potentials when those are altered by [Ba²⁺]. (c): Relative T₁ changes in (a) and (b) are illustrated on the same plot using the abscissa of membrane potentials.

Figure 3. The relative changes in T₂ and the corresponding membrane potentials. The dotted black lines in each plot represent the normal condition. (a): Relative T₂ changes and the membrane potentials when those are altered by [K⁺]. (b): Relative T₂ changes and the membrane potentials when those are altered by [Ba²⁺]. (c): Relative T₂ changes in (a) and (b) are illustrated on the same plot using the abscissa of membrane potentials.

Figure 2. Temporospatial imaging of neuronal activity propagation. (a) Illustration of DIANA-fMRI experiment with a coronal slice containing both thalamus and S1BF activated by electrical stimulation applied to mouse whisker pad. (b) DIANA time series from thalamus, contralateral S1BF (n = 10). Dotted line indicates the stimulation onset. (c) Time series of t-value maps for 30 ms after stimulation with 5 ms temporal resolution in one representative mouse (paired t-test, p < 0.05, cluster size > 5 voxels). Dashed circular markers highlight the DIANA response areas. Error bar: S.E.M.

Figure 3. In vivo electrophysiological validation of neuronal activity. (a) Illustration of simultaneous electrophysiological recording in vivo both thalamus and contralateral S1BF. (b) Representative MUA signals and single-unit spikes acquired in thalamus (top) and contralateral S1BF (bottom). (c) Post-stimulation time histogram of the responsive single units over time plotted with DIANA signals in thalamus (top, 7 units from n = 7) and contralateral S1BF (bottom, 16 units from n = 7). Dotted lines indicate the stimulation onset. Shaded area: S.E.M.

Summary of the findings, illustrated by comparing the FA with the T1-T2 injury image.

APP density (% area) from 132 tissue regions, consisting of 4 APP-positive regions from each TAI case (total of 32, blue dots), 4 to 6 normal-appearing WM regions from all cases (total of 56, red dots), and 4 cortical GM regions from all cases (total of 44, yellow dots), and the corresponding MR parameter correlations. Individual data points represent the mean ROI value from each post-mortem tissue sample. Scatterplots of the mean (with 95% confidence interval error bars) % area APP and all MR parameters.

Figure 3. In-vivo rat brain experimental results. The rats were under deep anesthetic. Four regions of interest (ROI) were chosen on each rat brain for analysis, which are marked on SE-EPI images.

Figure 4. In-vivo rat brain experimental results. The rat was recovering from deep anesthetic during acquisition. (a) Reference ADC maps from SE-EPI. (b) Reference T₂ maps from SE-EPI. (c) Temporal ADC maps from DT₂M-OLED. (d) Temporal T₂ maps from DT₂M-OLED.

Figure-3 Accelerated FLAIR T1 and FLAIR T2 images (at 2x and 3x) are shown for a test case. For this MR examination, all three contrasts were acquired on a 1.5T GE Signa Creator MR system. The FLAIR (accelerated scans) and T2FSE (reference scan) were obtained at same in-plane resolution but different slice thickness and slice spacing (FLAIR T2 being thicker). The proposed method is designed to deal with such acquisitions. All the accelerated reconstructions have high SSIM values showing robust and high quality reconstruction

Figure-2 The training workflow is shown here. The network is trained on both reference image and image obtained from the grafted k-space. It predicts the grafting artifact which is then removed from the accelerated image to obtain a clean image

Figure.3 Examples of MULTIPLEX’s calculated images of the same data.

Figure.1 Exemplary design of the MULTIPLEX sequence, which directly generates from k-space 2(N₁+N₂) sets of images.

Figure 2: R2* maps from different views obtained from GRAPPAx2 and compressed sensing reconstructions with 1.6 mm and 0.8 mm isotropic resolutions.

Figure 5: Comparison of sagittal R2* maps obtained from GRAPPAx2 and CS acquisition with 1.6 mm and 0.8 mm resolution.

Figure 2. Voxel-wise randomized clustering comparison between NAWM patients and WM controls in A) qT1, B,C) QSM and D) MWF and susceptibility, respectively.

Figure 3. Vertex-wise comparison between NAGM patients and GM controls in A) qT1, B,C) QSM and D) MWF, respectively.

Figure 2. Representative T1/T2/T2*/QSM mapping at 3 slice locations using MR Multitasking and the corresponding reference protocols on a healthy volunteer.

Figure 4. Results from a 23-year-old female volunteer including qualitative images and quantitative data. The first row shows quantitative maps and the second and third rows show the synthesized weighted images. SWI and tSWI were minimum intensity projection (mIP) results with an effective slab thickness of 16 mm. χ₁=0, χ₂=450ppb was adopted for tSWI²³.

Figure 3 – Co-registered T₁ and T₂ maps, fat fraction and B₀ map for one healthy subject acquired with 3-point Dixon GRE read out. Coronal, sagittal and transversal views are shown. T₁=704±30ms, T₂ = 58±3ms and fat fraction = 3.3±1.7% were measured within a ROI in the liver. Acquisition parameters included FA=8deg, isotropic resolution of 2mm³, FOV=320x320x168mm³, coronal orientation, 14 echoes for iNAV acquisition, acquisition window of 200ms, bandwidth=801Hz/pixel, T2prep=50ms, TI=120ms and total scan time of ~6min.

Figure 1 – Four interleaved volumes are acquired with variable density Cartesian trajectory, three-point Dixon GRE readout. 2D-iNAVs are acquired to correct for translational motion and respiratory binning. Water images are generated with Dixon water/fat separation and used to obtain the signal evolution of each voxel. T₁ and T₂ maps are obtained by matching the acquired signal evolution to the EPG simulated dictionary. Fat fraction and M₀ map are obtained from water/fat separation algorithm.

Figure 1. The symmetric cynomolgus macaque brain templates in coronal, sagittal, and axial view. A) Templates of the originally acquired weighted data sets (from top to bottom) include T1-weighted, MT-weighted, ME-GRE mean across echo times (mGRE), and T2-weighted template. B) Parametric map templates (from top to bottom) incorporate QSM, R2*, and MT saturation and apparent T1 maps.

Figure 2. Cynomolgus macaque brain structural parcellation derived from the openly available rhesus macaque brain templates. Emanated ROI labels are from the Cortical Hierarchy Atlas of the Rhesus Macaque (CHARM), D99, Subcortical Atlas of the Rhesus Macaque (SARM), and NeuroMaps atlas.

Figure 1 A, Sequence diagram of SIMPLE, in which three alternate T2-prep pulses of 25, 50, and 0 ms and IR pulses, incorporated with 3D golden-angle radial acquisition, are adopted. B, Simulated signal evolutions of the vessel wall and blood within three continuous shots with different T2-prep pulses. And the illustration of view sharing and sliding window reconstruction of the generated multi-contrast images from SIMPLE, including MRA, T1w, T2w and PDw images. C, Scan parameters of SIMPLE and conventional multi-contrast MR imaging.

Figure 2 An example of the generated MRA as well as axial MRA, T1w, T2w, and PDw images by SIMPLE from one patient with atherosclerotic plaques, with comparison to the conventional sequences. Coronal maximum intensity projection (MIP) of MRA demonstrated clear depictions of bilateral carotid arteries. IPH (red arrow) showed hyperintensity on both SIMPLE-T1w image and T1w TSE image. Blue arrows indicated the hypointensity of calcification.

Figure 3. The Mixed Model. A) H&E histology; (B) R_1PC map; (C) ADC map; (D) T1W_PC; (E) MTR map; (F) DCE_AUC map from a slice through the brain of a C57BL/6 mouse irradiated hemispherically with a GK into which GL261 tumor cells were orthotopically implanted 4 weeks post irradiation. Images and histology were collected 18 days post tumor implantation. Regions of pathology (tumor or RN) appear hyperintense on R_1PC map, ADC map, T1W_PC, and DCE_AUC map, while only the tumor is hypointense on the MTR map. The orange-colored oval guides the eye to the whole lesion region, yellow to tumor, cyan to RN.

Figure 4: Box plot of MRI parameter means of lesion (tumor/RN) in the mixed, tumor alone and RN alone models. A. R₁; B.R_1PC; C. R₂; D. ADC; E. MTR; F. DCE_AUC. Lesion ROIs were identified by histology. Differences in values of R₁, R₂, ADC, MTR, DCE_AUC, for tumor vs. RN in the mixed model are highly statistically significant. Tumor in tumor alone (n=9) and RN in RN alone (n=9), differences in values of R₁, R_1PC, R₂, ADC, MTR are statistically significant. *p < 0.05, **p < 0.01, ****p<0.0001.

Fig 3. Images show MRI measurements in vivo for BDL and CCl4 model of liver fibrosis. Representative axial images of (a) native T1 maps, (b) T1 ρ maps and (c) T2 maps for each group (Metavir stages F0-F4). The images (d-f) show the corresponding data for all rats in BDL and CCl4 model. One-way ANOVA followed by Scheffe post hoc test was performed. *P < 0.05, **P < 0.01, ***P < 0.001.

Fig 1. Images show liver specimens characterization of changes in fibrosis (Metavir stages F0-F4) in BDL and CCl4 model. Representative images of H&E-stained (top row) and Sirius red-stained (bottom row) liver tissue from each experimental group. H&E, hematoxylin and eosin, 200×original magnification; Sirius red stain, 200×original magnification. CCl4, carbon tetrachloride; BDL, bile duct ligation

Figure 1: The schematic diagram of the SE based Multi-contrast pulse sequence. g_d’s are the magnitudes of the diffusion encoding gradients. I^± and T_C are the amplitude and the duration of the injected current, respectively. The current pulse is injected after 90° RF pulse until the beginning of g_d.

Figure 5: The reconstructed ECDR (η) and conductivity tensor distributions of the imaging phantom using the vertical and horizontal current injections. (a) η (b) c_xx (c) c_yy (d) c_zz. The mean values of the reconstructed anisotropic conductivity for the left and right inhomogeneities are: c_xx,Left = 0.33 S/m, c_yy,Left = 0.28 S/m, c_zz,Left = 0.31 S/m and c_xx,Right = 0.30 S/m, c_yy,Right = 0.32 S/m, c_zz,Right = 0.35 S/m.

Sampling for both databases, varying inversion time (TI), delay time (TD), (θ,φ), b-value, and b_∆ parameterising the ”shape” of the diffusion-encoding b-tensor¹⁶ (represented by the ellipsoids, with corresponding encoding waveforms along each axis). Left: images are scaled per volume except in the TD/TE dimension. Right: number of directions for each parameter setting.

Mean-squared error across voxels, K is the maximum number of selected measurements. Top: distributions for varying K and for each subject left out of the training and feature selection. Bottom: MSE map for subject 1. K= 50 shows a larger MSE in frontal, occipital, and deep gray matter structures.

Fig. 1: a) Supervised methods learn a source-to-target mapping using costly datasets of fully-sampled images of the source- and target-contrasts. b) The proposed semi-supervised model can be trained to synthesize fully-sampled target images using only undersampled ground-truth acquisitions of the target-contrast and allows recovery from undersampled sources to further reduce data requirements. c) The proposed method involves a selective loss function measuring the error only on the acquired k-space coefficients in terms of k-space, image-domain L1 and adversarial losses.

Fig. 2: Representative results from the ssGAN and fsGAN models are displayed for the synthesis tasks in the IXI dataset with fully-sampled source images (R_source=1): a) T₁-weighted image synthesis and b) T₂-weighted image synthesis. Synthesized target-contrast images are displayed for ssGAN-2 (R_target=2), ssGAN-3 (R_target=3), ssGAN-4 (R_target=4), and fsGAN together with fully-sampled reference images.

Figure 1. a) IIR-bSSFP acquisition. A nonselective inversion pulse is applied followed by a fixed number of segmented bSSFP acquisitions (interval of Tinv). b) With a continuous data acquisition, a series of 3D images are reconstructed at different TIs. c) Simulated signal evolutions of different brain tissues with assumed T1/T2 values and scan parameters (Tinv=3s, flip angle=30 degrees, TR=4ms).

Figure 3. Whole brain multi-contrast MRI acquired in a single scan of 6 minutes. The imaging sequence has inherent motion robustness to small amounts of motion, but any excess motion would cause image quality degradation without motion compensation (right block, arrows highlighting motion artifacts).

Fig. 1. (a) The architecture of the proposed multi-contrast denoising method using residual U-Net by combining U-Net and ResNet. (b) Residual blocks consisting of two convolutional layers with a ReLU activation in between. (c) Strided conv2D block consisting of a convolutional layer and ReLU activation. (d) Transposed conv2D block consisting of a transposed convolutional layer and ReLU activation.

Fig. 3. The denoising results for images with a higher noise level. The same level of noise (σ=25) was added to form noisy images. The proposed method remained similar performance, while BM3D smoothed the image details even more if attempt to reduce the noise to the same level as the proposed method.

Fig.1 Scheme of the MIXTURE (Multi-Interleaved X-prepared tse with inTUitive RElaxometry).

(a) T2-mapping was performed using T2-prepared 3D segmented turbo spin- echo (TSE) with variable refocusing pulse trains. (b) Two images with different TE (TE = 0 and 50ms) were acquired with interleaved acquisition. To obtain the compatible contrasts with routine TSE images, TSE shot#1 did not apply any pre-pulses (as “PDW) and shot#2 applied both SPAIR and T2pre (as “fat-suppressed T2W”).

Fig.4 Clinical images with low back pain using MIXTURE.

(A) MIXTURE, Multiple Interleaved X-prepared TSE with Universal Relaxation-mapping Extensions,(B) Conventional 2D TSE

Fig. 1 (a) The proposed MRI parameter mapping method consists of two neural networks module and a pixel-wise curve fitting process. (b) The reconstruction module is based on deep ADMM-Net for reconstruction of weighted images from under-sampled k-space data. (c) The generative module is a densely connected neural network to generate the corresponding weighted images from the reconstructed image pairs under the supervision of corresponding weighted images.

Fig. 5 The ROI analysis of test $$$T_{1\rho}$$$ knee data (R=1) and error map of test $$$T_{1\rho}$$$ knee data (R=1); Proposed: Mapping with 2 acquired fully sampled images and 3 synthetic images (Equivalent R = 2.5); Two contrast images: Mapping with 2 acquired fully sampled images only (Equivalent R = 2.5).

Fig.1. (a) A schematic pulse sequence diagram of SCETA (Serial Connection of Echo Train acquisition) to acquire T1w, T2w and FLAIR using the hybrid acquisition of FSE and GRE. (b) Echo signal of white matter, gray matter and CSF builds three imaging contrasts in a TR.

Fig. 2. A comparsion result with conventional 3D scan of T2w, FLAIR and T1w. SCETA reduces scan time by a factor of approximately 2.4 while maintaining similar image appearance to conventional scan.

Figure 1. Pulse sequence diagram showing the sampling strategy, which includes the time-multiplexed multislice scheme, SPRING TSE in/out variant data acquisition, and driven-inversion RF pulses. The boxes with stripes show slices for the T2-weighted acquisition, while the open boxes show slices for the FLAIR acquisition. The time interval between boxes with the same number is set to TI. A SPRING TSE in/out variant sampling scheme is used in each box for data acquisition, while a 180°-90° RF pair is applied at the end of the echo train for boxes with stripes to achieve driven inversion.

Figure 3. Comparison of in-vivo images acquired using standard Cartesian TSE for T2-weighted images, Cartesian FLAIR for fluid-attenuated images, and the proposed method for both the T2-weighted and fluid-attenuated images. Both the T2-weighted images (top panel) and FLAIR images (bottom panel) from the proposed method show image quality and contrast similar to the conventional images.

Figure 1. The juxtaposition of a human subject’s T1–QSM fusion image (TQ-SILiCON map) in comparison with the respective T1 weighted image (A, left) and QSM (B, left). TQ-SILiCON maps were generated with the weights of 0.318 and -0.790 for T1w and QSM respectively. 1 – thalamus, 2 – external globus pallidus, 3 – internal globus pallidus, 4 –substantia nigra, 5 – red nucleus, 6 – white matter, 7 – internal capsule, and 8 – pulvinar nucleus.

Figure 2. Illustration of a macaque’s T1-QSM fusion image (TQ-SILiCON map) in comparison with the respective T1 weighted image (A, left) and QSM (B, left). The emphasized areas on the TQ-SILiCON map are parts of the subcortical gray matter nuclei: 1 – thalamus, 2 – external globus pallidus, 3 – internal globus pallidus, 4 – substantia nigra, 5 – red nucleus, and 6 – white matter.

Figure 1 - Overall study design of inducing myocardial infarction in Yorkshire pigs and imaging 3 days post MI before sacrificing animal to perform histological validaiton.

Figure 3 – Polar AHA plots of example animal with corresponding segment based bar graphs. With a visible increased segment 2 and 3 (2036±132 [a.u]) in the LGE plot indicating the infarct and segment 5 as remote segment (SI of 1844±48 [a.u.]). The T1 in those segments was non-significantly increase to 1260±56ms but the remote segment also showed a high T1 value of 1275±42ms. The infarct T2 was 48±4ms, which was a significant increase compared to the remote 35±2ms. The ADC values was also increased 1933±95.25x10-6mm2/s compared to 1839±123x10-6mm2/s in the remote, but was not significant.

Fig. 3. Qualitative results of different methods. The blue and red edge lines indicate the stroke lesion regions annotated by radiologists based on DWI and FLAIR, and the green lines indicate the predictions of different methods.

Fig.2. Flow chart of the proposed method. M represents the moving image, F represents the fixed image, and M^-1_D represents the prediction of the inverse transformation. We impose MSE constraints on M^-1_D and M_A to ensure topological deformation.

T1-weighted angiographic imaging with ferumoxytol at 64mT showing enhancement of dural venous sinuses and jugular veins, distal cervical and intracranial internal carotid arteries (ICA) as well as middle cerebral arteries (MCA, arrows).

Contrast enhancement with ferumoxytol on standard sequences optimized for brain tissue contrasts at 64 mT. On T1 images, ferumoxutol modestly increases signal in the superior sagittal sinus (arrows) and other venous structures. On T2 and T2-FLAIR images ferumoxytol markedly decreases signal of intrinsically hyperintense veins, shown as enhancement on inverse difference images (i.e. pre-minus-post).

Figure 4: Results of the single-subject analyses overlaid in MNI space. At both 480° and 500° power levels focal activation in the visual cortex can be observed. At 0° MT power, apparent activation can be observed in the sagittal sinus, most likely due to blood volume changes within the sinus.

Figure 3: Temporal SNR achieved for the three different power levels. The reduction of signal due to MT leads to a notable fall in tSNR.

Figure 5. Comparison of the images acquired with the proposed protocol, MT-GRE, and MT-TSE. When calculated for the ROIs in b), the contrast of the sandwich-fsNM is superior to both MT-GRE and MT-TSE imaging in terms of CR and CNR. SNR of sandwich-fsNM is lower than MT-GRE but higher than MT-TSE. MT-GRE images suffer from flow artifacts.

Figure 4. Simulated and experimental CR between SN and CC with respect to a) D_flowsat and b) number of FSs (N_flowsat). The CR increases as D_flowsat decreases and as N_flowsat increases, both in simulations and experiments. The effect size of decreasing D_flowsat is relatively smaller than that of increasing N_flowsat. c) In the experimental data, CNR shows similar trends to CR whereas SNR decreases as D_flowsat decreases and as d) N_flowsat increases.

Figure 1 – (a-e) EPR spectra and the fitting of GP sample at different temperatures. Inserts represent the spectrum region around g = 2.0. f) The organic radical peak after subtraction of other fitted peaks.

Figure 2 – Second integral and linewidth as a function of temperature for each peak (Fe_hs, Cu, broad peak at g=2.0 and the organic radical) and each sample (SN, GP, LC). For the Fe_hs, Cu and organic radical peaks fitting was performed using Curie-Weiss Law. For the broad peak Vav-Vleck paramagnetism for S=5/2 and S=1/2 with Curie-Weiss law was used for fitting.

Figure 2: Scan without RF heating results for the macaque (run 1). (a) Mean ΔT maps and (b) corresponding SD maps computed from voxel-wise detrended signals. ΔT maps were computed from images reconstructed with nominal trajectories (upper row) and first order spherical harmonics field correction (lower row). ΔT computed from phase images corrected with rotation regressors around LR, AP and HF axis are also displayed. (c,d) Rotation parameters and (e,f) time evolution of ΔT computed from nominal and FS-corrected images, respectively, in the voxel indicated by the white cross in (a).

Figure 1: (a) Temperature variation measured in vitro with the fiber optical probe and from nominal and field sensor (FS) corrected MRT images at probe location over the 20 minutes scan at approximately 0% of the maximal SAR. (b) Temperature variation measured during the heating scan (5 min pre-heating at 0% SAR + 20 min heating at maximal SAR + 5 min post-heating at 0% SAR) by the fiber optical probe and from nominal and FS-corrected MRT images at probe location, in field disturbed conditions.

Figure 5: MHD flow velocity distributions of the homogeneous experimental phantom: x-component with current injection parameters (a) I = 10 mA, TC = 10 ms, (b) I = 5 mA, TC = 10 ms, and (c) I = 10 mA, TC = 5 ms; y-component with current injection parameters (d) I = 10 mA, TC = 10 ms, (e) I = 5 mA, TC = 10 ms, and (f) I = 10 mA, TC = 5 ms; and z-component with current injection parameters (g) I = 10 mA, TC = 10 ms, (h) I = 5 mA, TC = 10 ms, and (i) I = 10 mA, TC = 5 ms.

Figure 4: MHD flow velocity distributions of the homogeneous simulation model: x-component with current injection parameters (a) I = 10 mA, T_C = 10 ms, (b) I = 5 mA, T_C = 10 ms, and (c) I = 10 mA, T_C = 5 ms; y-component with current injection parameters (d) I = 10 mA, T_C = 10 ms, (e) I = 5 mA, T_C = 10 ms, and (f) I = 10 mA, T_C = 5 ms; and z-component with current injection parameters (g) I = 10 mA, T_C = 10 ms, (h) I = 5 mA, T_C = 10 ms, and (i) I = 10 mA, T_C = 5 ms.

Figure 3: MHD flow velocity distributions of the experimental phantom with vertical current injection. Using the conventional flow-encoding set ($$$\mathbf{{G_f^k}}$$$): (a) v_x, (b) v_y, and (c) v_z; and using the proposed flow-encoding set ($$$\mathbf{{G_f^k}^*}$$$): (d) v_x, (e) v_y, and (f) v_z. The $$$RMSE(\%)$$$ values in each direction: $$$RMSE_x=2.17\%$$$, $$$RMSE_y=2.80\%$$$, and $$$RMSE_z=1.66\%$$$.

Figure 5: Diffusion tensor images obtained from simultaneous DTI and MHD imaging. $$$\overline{\overline{D}}$$$ distributions obtained without current injection: (a) d_xx, (b) d_yy, and (c) d_zz; with vertical current injection: (d) d_xx, (e) d_yy, and (f) d_zz; and with horizontal current injection: (g) d_xx, (h) d_yy, and (i) d_zz. $$$\overline{\overline{D}}$$$ images obtained with current injections have $$$\sqrt2$$$ SNR advantage.

Figure 1. a) Simulated basis spectra with expected in vivo lineshape at equal concentrations for glutamate, glutamine, lactate, glucose, and their mono- and double- deuterated counterparts. b) Synthetic normal brain spectrum with main metabolite peaks labeled vs. the expected brain spectrum after supplementation of deuterated glucose with effect size taken from Ref. 3 and c) difference between the two, amplified by a factor of 15 for better visualization.

Figure 2. Comparison of simulated and measured phantom spectra of [6,6’-²H₂]-Glc and [6,6’-¹H₂]-Glc at high resolution to verify the appropriateness of the simulations and thus the applicability of the precision estimation based on CRB calculations in synthetic spectra with in vivo conditions.

Figure 1. T2-weighted images (left), T2 maps in 5 different orientations with respect to B0 (middle), and calculated T2 relaxation anisotropy maps (right) for representative tissue samples: A) Articular cartilage (bovine patella); B) Tendon (bovine ACL); C) Brain tissue (mouse, right hemisphere, coronal view); D) Spinal cord and muscle (mouse, sagittal view); E) Cardiac muscle (mouse, coronal view); F) Kidney (mouse, coronal view). The color scales are adjusted separately for each sample.

Figure 2. Relaxation anisotropy maps of different parameters, T1, CW-T1ρ (500 Hz), adiabatic T1ρ, RAFF2, and T2 in representative tissue samples: A) Articular cartilage (bovine patella); B) Tendon (bovine ACL); C) Brain tissue (mouse, right hemisphere, coronal view); D) Spinal cord and muscle (mouse, sagittal view); E) Cardiac muscle (mouse, coronal view); F) Kidney (mouse, coronal view). The color scales are adjusted separately for each sample.

Figure 4: 4a. spatial maps of temperature rise calculated from MRT measurements after drift correction. The images in the top row were acquired with no RF heating, while the images on the two bottom rows were acquired with the same RF shim. 4b. Time series of temperature change in the indicated voxels for all 3 measurements.

Figure 3: 3a and 3b. Spatial maps of temporal standard temperature deviation the upper leg during a MRT scan where no RF heating is applied, without and with cardiac triggering. 3c and 3d. Time series of temperature deviations in indicated voxels (3a and 3b). No drift correction was applied yet during this short time interval.

Figure 4: In-vivo scans of patient one. Image acquired with a low flip angle of 5° (A) and the corresponding VNC image (B). Image acquired with a high flip angle of 26° (C) and the corresponding VNC image (D). T1weighted image acquired after CA injection (E), true non-contrast enhanced image (F).

Figure 5: In-vivo scans of patient two. Image acquired with a low flip angle of 5° (A) and the corresponding VNC image (B). Image acquired with a high flip angle of 26° (C) and the corresponding VNC image (D). T1weighted image acquired after CA injection (E), true non-contrast enhanced image (F).

Simulated magnetic field distribution (in Hz) and CPMG signals for (a) grid, (b) random, and (c) clustered nanoparticle configurations, with the same number of particles. The apparent T₂ values were 0.2, 0.4 and 0.7s for (a), (b) and (c), respectively.

T₂ relaxation times of different concentration iron nanoparticles in water. The red points are simulation results and the solid black points are experimentally acquired. The simulated bulk T₂ of pure water was 2 s. 128 CPMG data points were acquired with 32000 diffusing water molecules.

Figure 3: σ_T in the brain with ETL 49, TE 81, 3.8s/frame: σ_T values of less than 2.5°C are overlaid on average magnitude image (8 out of 12 sagittal slices). σ_T is less than 1°C in most of the brain, demonstrating excellent precision. Average σ_T of 0.7 ± 0.2°C was observed in the ROI indicated on the figure. This protocol offers a higher frame rate compared with that with ETL 33. The slight reduction in σ_T, as compared with the protocol with ETL 33, can be attributed to the longer TE.

Figure 5: σ_T in the prostate with TR 100ms, TE 45ms, 7.2 s/frame. σ_T values of less than 2.5°C are overlaid on average magnitude image (8 out of 12 axial slices; cropped to show details). Average σ_T of 1.4±0.3°C was observed in the ROI indicated on the figure, demonstrating good precision.

Fig. 1: The diagrams of water and fat phasors of MR data in Cartesian coordinates. In (a), the add-up phasor represents the actual phasor obtained from image, where the orange and blue arrows are phasors for fat and water protons, respectively. (b,c) show the water phase rotation around point 'O' caused by temperature changes and depict two scenarios of underestimating (φ₂ < φ₁) and overestimating (φ₂ > φ₁), respectively. A circle fitting can be applied here to determine the circle center 'O', which represents the fat phasor AO in (b) and (c), for getting the accurate water phase changes.

Fig. 3: Results of in vitro phantom experiments. In (a), the red pentagon and filled circle indicate the phantom region and the selected voxel for the plots of temperature evolution over time in (b). The orange, blue and red lines display the temperature changes obtained by the conventional method, the proposed method and the optical fiber. Approximately 33% of temperature error caused by 15% fat in phantom was observed and corrected in (b).

Figure 2: T1 calculation bias for standard VFA method, SR-VFA method, and T2* corrected SR-VFA method. TR = 10 ms, T1 baseline = 350 ms, TE/T2* = 0.2;. SR-VFA line is equivalent with the bolded lines in Figure 1.

Figure 1: (left) – T1 calculation bias vs Z ratio with TE/T2* = 0.2; (right) – T1 calculation bias vs TE/T2* with Z ratio = 0.5; For both plots – TR = 10 ms, T1 baseline = 350 ms, α = 6.4°, β = 21.2°. Bolded lines are equivalent.

Figure 5: Quantification of MR thermometry accuracy in all patients and in the selected patients. The red dotted line represents the thresholds for acceptance.

Figure 3: Temperature distributions acquired during treatment from the representative patient, where the time corresponds to the minutes from the time that the RF power was applied. The slice presented correspond to the middle slice (yellow line from Figure 1).

Figure 4. T1 (ms) as a function of temperature (°C) for the 14 consecutive sonications in experiment 2. Mean +/- standard error over 3x3 voxels is shown for heating (red) and cooling (blue), and lines indicate linear fits. The title for each sub-plot states the slope of the heating and cooling part, respectively. The last 4 sonications delivered increasing amounts of dose, from 41 to 1*10⁸ CEM43.

Figure 3. Temperature (°C) and T1 (ms) as a function of time for experiment 2. The first five sonications used 29 eW, the next five used 52 eW, and the last four used 66 eW, all for 19.6 s with 39.2 s cooling in-between consecutive sonications.

The results of image analysis of VBM and VBA in GM are shown.The cross-sectional image is a coronal image.In VBA analysis, from left to right, AD/FA/RD in DTI analysis, and ODI/FICVF in NODDI analysis are presented.For each image parameter, if group injected with FUS is significantly higher(p<0.05) with respect to group injected with saline (upper) before injection FUS (lower), it is represented as red, and if group injected with FUS is significantly lower(p<0.05), it is represented as blue.

The results of image analysis of VBM and VBA in WM are shown.The cross-sectional image is a coronal image.In VBA analysis, from left to right, AD/FA/RD in DTI analysis, and ODI/FICVF in NODDI analysis are presented.For each image parameter, if group injected with FUS is significantly higher(p<0.05) with respect to group injected with saline (upper) before injection FUS (lower), it is represented as red, and if group injected with FUS is significantly lower(p<0.05), it is represented as blue.

AATD=Alpha-one antitrypsin deficiency; AATD-1= Ex-smoker AATD; ADC = apparent diffusion coefficient; VDP = ventilation defect percentage; Lm = mean linear intercept estimate.

Figure 4 shows five representative ADC^He/ADC^Xe maps for the AATD-1 subject.

Figure 1: Pulse sequence diagram for metabolite-specific 2D EPI imaging of [1-13C]pyruvate and [1-13C]lactate at 4.7T. Lactate-only excitation pulse is shown.

Figure 3: Metabolite images of [1-13C]pyruvate (1st column) and [1-13C]lactate (2nd column) in the liver and kidney of a rat following injection of [1-13C]pyruvate. Images in the first row were acquired using metabolite-specific EPI (Figure 1) while images in the second row were acquired using a standard CSI sequence. All images were obtained by summing signal over all time points. White lines are acquisition grids for each image. Images are shown with a 2x interpolation factor.

Figure 5: Resulting parameter maps of the thigh from the in vivo experiment: a) ω-3 fatty acid fraction, b) PDFF, c) T2*, d) ndb, e) nmidb and f) CL. A ROI analysis (see ROI #1 in b)) yielded ω-3: 25.4±7.5%, ndb: 2.41±0.40, nmidb: 0.67±0.26, CL: 18.41±0.54, T2*: 30.7±6.9ms. Low PDFF regions (e.g. muscle tissue) were masked out in a) and e)-f).

Figure 2: Obtained parameter maps from the phantom experiment and correlation plots for the ω-3 fraction, ndb, nmidb and CL. ROI placement is indicated in red color in b) the PDFF map. Fig.3 shows the correlation of the ROI measurements with GC-MS calibrated reference values.