Evaluation of a developed MRI-guided focused ultrasound system in 7 T small animal MRI and proof-of-concept in a prostate cancer xenograft model to improve radiation therapy

Focused ultrasound (FUS) can be used to physiologically change or destroy tissue in a non-invasive way. A few commercial systems have clinical approval for the thermal ablation of solid tumors for the treatment of neurological diseases and palliative pain management of bone metastases. However, the thermal effects of FUS are known to lead to various biological effects, such as inhibition of repair of DNA damage, reduction in tumor hypoxia, and induction of apoptosis. Here, we studied radiosensitization as a combination therapy of FUS and RT in a xenograft mouse model using newly developed MRI-compatible FUS equipment. Xenograft tumor-bearing mice were produced by subcutaneous injection of the human prostate cancer cell line PC-3. Animals were treated with FUS in 7 T MRI at 4.8 W/cm2 to reach ~45 °C and held for 30 min. The temperature was controlled via fiber optics and proton resonance frequency shift (PRF) MR thermometry in parallel. In the combination group, animals were treated with FUS followed by X-ray at a single dose of 10 Gy. The effects of FUS and RT were assessed via hematoxylin-eosin (H&E) staining. Tumor proliferation was detected by the immunohistochemistry of Ki67 and apoptosis was measured by a TUNEL assay. At 40 days follow-up, the impact of RT on cancer cells was significantly improved by FUS as demonstrated by a reduction in cell nucleoli from 189 to 237 compared to RT alone. Inhibition of tumor growth by 4.6 times was observed in vivo in the FUS + RT group (85.3%) in contrast to the tumor volume of 393% in the untreated control. Our results demonstrated the feasibility of combined MRI-guided FUS and RT for the treatment of prostate cancer in a xenograft mouse model and may provide a chance for less invasive cancer therapy through radiosensitization

Unipolar MR elastography: Theory, numerical analysis and implementation

In MR elastography (MRE), zeroth moment balanced motion-encoding gradients (MEGs) are incorporated into MRI sequences to induce a phase shift proportional to the local displacement caused by external actuation. To maximize the signal-to-noise ratio (SNR), fractional encoding is employed, i.e., the MEG duration is reduced below the wave period. Here, gradients encode primarily the velocity of the motion-reducing encoding efficiency. Thus, in GRE-MRE, T2 * decay and motion sensitivity have to be balanced, imposing a lower limit on repetition times (TRs). We propose to use a single trapezoidal gradient, a "unipolar gradient", to directly encode spin displacement. Such gradients cannot be used in conventional sequences as they exhibit a large zeroth moment and dephase magnetization. By time-reversing a spoiled SSFP sequence, the spoiling gradient becomes an efficient unipolar MEG. The proposed "unipolar MRE" technique benefits from this approach in three ways: first, displacement encoding is split over multiple TRs increasing motion sensitivity; second, spoiler and MEG coincide, allowing a reduction in TR; third, motion sensitivity of a typical unipolar lobe is of an order of magnitude higher than a bipolar MEG of equal duration. In this work, motion encoding using unipolar MRE is analyzed using the extended phase graph (EPG) formalism with a periodic motion propagator. As an approximation, the two-transverse TR approximation for diffusion-weighted SSFP is extended to incorporate cyclic motion. A complex encoding efficiency metric is introduced to compare the displacement fields of unipolar and conventional GRE-MRE sequences in both magnitude and phase. The derived theoretical encoding equations are used to characterize the proposed sequence using an extensive parameter study. Unipolar MRE is validated against conventional GRE-MRE in a phantom study showing excellent agreement between measured displacement fields. In addition, unipolar MRE yields significantly increased octahedral shear strain-SNR relative to conventional GRE-MRE and allows for the recovery of high stiffness inclusions, where conventional GRE-MRE fails

Characterization and Correction of Diffusion Gradient-Induced Eddy Currents in Second-Order Motion-Compensated Echo-Planar and Spiral Cardiac DTI

Purpose Very high gradient amplitudes played out over extended time intervals as required for second-order motion-compensated cardiac DTI may violate the assumption of a linear time-invariant gradient system model. The aim of this work was to characterize diffusion gradient-related system nonlinearity and propose a correction approach for echo-planar and spiral spin-echo motion-compensated cardiac DTI. Methods Diffusion gradient-induced eddy currents of 9 diffusion directions were characterized at b values of 150 s/mm2 and 450 s/mm2 for a 1.5 Tesla system and used to correct phantom, ex vivo, and in vivo motion-compensated cardiac DTI data acquired with echo-planar and spiral trajectories. Predicted trajectories were calculated using gradient impulse response function and diffusion gradient strength- and direction-dependent zeroth- and first-order eddy current responses. A reconstruction method was implemented using the predicted -space trajectories to additionally include off-resonances and concomitant fields. Resulting images were compared to a reference reconstruction omitting diffusion gradient-induced eddy current correction. Results Diffusion gradient-induced eddy currents exhibited nonlinear effects when scaling up the gradient amplitude and could not be described by a 3D basis alone. This indicates that a gradient impulse response function does not suffice to describe diffusion gradient-induced eddy currents. Zeroth- and first-order diffusion gradient-induced eddy current effects of up to −1.7 rad and −16 to +12 rad/m, respectively, were identified. Zeroth- and first-order diffusion gradient-induced eddy current correction yielded improved image quality upon image reconstruction. Conclusion The proposed approach offers correction of diffusion gradient-induced zeroth- and first-order eddy currents, reducing image distortions to promote improvements of second-order motion-compensated spin-echo cardiac DTI.ISSN:0740-3194ISSN:1522-259

Synthetically trained convolutional neural networks for improved tensor estimation from free-breathing cardiac DTI

Cardiac diffusion tensor imaging (cDTI) provides invaluable information about the state of myocardial microstructure. For further clinical dissemination, free-breathing acquisitions are desired, which however require image registration prior to tensor estimation. Due to the varying contrast and the intrinsically low signal-to-noise ratio (SNR), registration is very challenging and thus can introduce additional errors in the tensor estimation. In the work at hand it is hypothesized, that by incorporating spatial information and physiologically plausible priors into the fitting algorithm, the robustness of diffusion tensor estimation can be improved. To this end, we present a parameterized pipeline to generate synthetic data, that captures the statistics including spatial correlations of diffusion tensors and motion of the heart. The synthetic data is used to train a residual convolutional neural network (CNN) to estimate diffusion tensors from unregistered in-vivo cDTI data. Using in-silico data, the synthetically trained CNN is demonstrated to yield increased tensor estimation accuracy and precision when compared to conventional registration followed by least squares fitting. The network outputs fewer outliers especially at the myocardial borders. In-vivo feasibility using data from five healthy subjects demonstrates the utility of the synthetically trained network. The in-vivo results predicted by the synthetically trained CNN are found to be consistent with the registered least-squares estimates while showing fewer outliers and reduced noise. Even in low SNR regimes, the network results in robust tensor estimation, enabling scan time reduction by reduced-average acquisition in-vivo. Finally, to investigate the network's capability of discriminating between healthy and lesioned tissue, the in-vivo data was artificially augmented showing preserved classification of tissue states based on diffusion metrics.ISSN:0895-611

Motion and eddy current-induced signal dephasing in in vivo cardiac DTI

ISSN:0740-3194ISSN:1522-259

Motion and eddy current–induced signal dephasing in in vivo cardiac DTI

Purpose To address motion in cardiac DWI, stimulated‐echo acquisition mode (STEAM) and second‐order motion‐compensated spin‐echo (SE) sequences have been proposed. Despite applying motion‐compensation strategies, residual motion can cause misleading signal attenuation. The purpose of this study is to estimate the motion‐induced error in both sequences by analysis of image phase. Methods Diffusion‐weighted motion‐compensated SE sequences and STEAM imaging was applied in vivo with diffusion encoding along 3 orthogonal directions. A b‐value range of 100 to 600 s/mm2 and trigger delays of 25%, 50%, and 75% of end systole and middiastole were used. Eddy‐current contributions were obtained from phantom measurements. After computation of motion‐induced phase maps, the amount of signal dephasing was computed from phase gradients, and the resulting errors in diffusion tensor parameters were calculated. Results Motion‐induced dephasing from the STEAM sequence showed less dependency on the b‐value and no dependency on the heart phase, whereas SE imaging performed best at 75% end systole followed by 50% end systole and middiastole. For a typical experimental setting, errors of 3.3%/3.0% mean diffusivity, 4.9%/4.8% fractional anisotropy, 2.9º/3.2º helix angulation, 0.8º/0.7º transverse angulation, and 9.9º/10.0º sheet angulation (SE/STEAM) were calculated. Conclusion Image phase contains valuable information regarding uncompensated motion and eddy currents in cardiac DTI. Although the trigger delay window for SE is narrower compared with the STEAM‐based approach, imaging in both systole and diastole is feasible and both sequences perform similarly if the trigger delays are selected carefully with SE

Analysis and correction of off-resonance artifacts in echo-planar cardiac diffusion tensor imaging

Purpose Cardiac diffusion tensor imaging using EPI readout is prone to image distortions in the presence of field inhomogeneities. In this work, a framework to analyze and correct image distortions in cardiac diffusion tensor imaging is presented. Methods A multi‐coil reconstruction framework was implemented to enable field map‐based off‐resonance correction. Numerical simulations were used to examine reconstruction performance for EPI phase‐encode directions blip up‐down and down‐up for different degrees of off‐resonance gradients and varying field map resolution. The impact of coil encoding was analyzed using the g‐factor and normalized RMSE. Finally, the proposed method was tested on free‐breathing in vivo cardiac diffusion tensor imaging data acquired in healthy subjects at 3 Tesla. Results Depending on the local field map gradient strength and polarity and the selected phase‐encode direction, field inhomogeneities lead to either local spatial compression or stretching with standard image reconstruction. Although spatial compression results in loss of image resolution upon field map‐based reconstruction, spatial stretching can be recovered once multiple receive coils are utilized. Multi‐coil reconstruction was found to reduce the normalized RMSE from 34.3% to 8.1% for image compression, and 33.6% to 1.8% for image stretching, with resulting average g‐factors 14.7 ± 2.9 and 1.2 ± 0.1, respectively. In vivo, multi‐coil field map‐based reconstruction yielded improved alignment of angle maps with anatomical cine data. Conclusion Multi‐coil, field map‐based image reconstruction for echo‐planar cardiac diffusion tensor imaging allows accurate image reconstruction provided that the phase‐encode direction and polarity is chosen to principally align with the direction and polarity of the prominent gradients of field inhomogeneities.ISSN:0740-3194ISSN:1522-259

Analysis and correction of off‐resonance artifacts in echo‐planar cardiac diffusion tensor imaging

Purpose Cardiac diffusion tensor imaging using EPI readout is prone to image distortions in the presence of field inhomogeneities. In this work, a framework to analyze and correct image distortions in cardiac diffusion tensor imaging is presented. Methods A multi‐coil reconstruction framework was implemented to enable field map‐based off‐resonance correction. Numerical simulations were used to examine reconstruction performance for EPI phase‐encode directions blip up‐down and down‐up for different degrees of off‐resonance gradients and varying field map resolution. The impact of coil encoding was analyzed using the g‐factor and normalized RMSE. Finally, the proposed method was tested on free‐breathing in vivo cardiac diffusion tensor imaging data acquired in healthy subjects at 3 Tesla. Results Depending on the local field map gradient strength and polarity and the selected phase‐encode direction, field inhomogeneities lead to either local spatial compression or stretching with standard image reconstruction. Although spatial compression results in loss of image resolution upon field map‐based reconstruction, spatial stretching can be recovered once multiple receive coils are utilized. Multi‐coil reconstruction was found to reduce the normalized RMSE from 34.3% to 8.1% for image compression, and 33.6% to 1.8% for image stretching, with resulting average g‐factors 14.7 ± 2.9 and 1.2 ± 0.1, respectively. In vivo, multi‐coil field map‐based reconstruction yielded improved alignment of angle maps with anatomical cine data. Conclusion Multi‐coil, field map‐based image reconstruction for echo‐planar cardiac diffusion tensor imaging allows accurate image reconstruction provided that the phase‐encode direction and polarity is chosen to principally align with the direction and polarity of the prominent gradients of field inhomogeneities