Evaluation of partial k-space strategies to speed up time-domain EPR imaging

Evaluation of a deuterated triarylmethyl spin probe for in vivo R2 ∗-based EPR oximetric imaging with enhanced dynamic range

PURPOSE

In this study, we compared two triarylmethyl (TAM) spin probes, Ox071 and Ox063 for their efficacy in measuring tissue oxygen levels under hypoxic and normoxic conditions by R2 *-based EPR oximetry.

METHODS

The R2 * dependencies on the spin probe concentration and oxygen level were calibrated using deoxygenated 1, 2, 5, and 10 mM standard solutions and 2 mM solutions saturated at 0%, 2%, 5%, 10%, and 21% of oxygen. For the hypoxic model, in vivo imaging of a MIA PaCa-2 tumor implanted in the hind leg of a mouse was performed on successive days by R2 *-based EPR oximetry using either Ox071 or Ox063. For the normoxic model, renal imaging of healthy athymic mice was performed using both spin probes. The 3D images were reconstructed by single point imaging and multi-gradient technique was used to determine R2 * maps.

RESULTS

The signal intensities of Ox071 were approximately three times greater than that of Ox063 in the entire partial pressure of oxygen (pO2 ) range investigated. The histograms of the tumor pO2 images were skewed for both spin probes, and Ox071 showed more frequency counts at pO2  > 32 mm Hg. In the normoxic kidney model, there was a clear delineation between the high pO2 cortex and the low pO2 medulla regions. The histogram of high-resolution kidney oximetry image using Ox071 was nearly symmetrical and frequency counts were seen up to 55 mm Hg, which were missed in Ox063 imaging.

CONCLUSION

As an oximetric probe, Ox071 has clear advantages over Ox063 in terms of sensitivity and the pO2 dynamic range.

Hypoxia Imaging As a Guide for Hypoxia-Modulated and Hypoxia-Activated Therapy

Significance: Oxygen imaging techniques, which can probe the spatiotemporal heterogeneity of tumor oxygenation, could be of significant clinical utility in radiation treatment planning and in evaluating the effectiveness of hypoxia-activated prodrugs. To fulfill these goals, oxygen imaging techniques should be noninvasive, quantitative, and capable of serial imaging, as well as having sufficient temporal resolution to detect the dynamics of tumor oxygenation to distinguish regions of chronic and acute hypoxia. Recent Advances: No current technique meets all these requirements, although all have strengths in certain areas. The current status of positron emission tomography (PET)-based hypoxia imaging, oxygen-enhanced magnetic resonance imaging (MRI), ¹⁹F MRI, and electron paramagnetic resonance (EPR) oximetry are reviewed along with their strengths and weaknesses for planning hypoxia-guided, intensity-modulated radiation therapy and detecting treatment response for hypoxia-targeted prodrugs. Critical Issues: Spatial and temporal resolution emerges as a major concern for these areas along with specificity and quantitative response. Although multiple oxygen imaging techniques have reached the investigative stage, clinical trials to test the therapeutic effectiveness of hypoxia imaging have been limited. Future Directions: Imaging elements of the redox environment besides oxygen by EPR and hyperpolarized MRI may have a significant impact on our understanding of the basic biology of the reactive oxygen species response and may extend treatment possibilities.

Accelerated electron paramagnetic resonance imaging using partial Fourier compressed sensing reconstruction

PURPOSE

Electron paramagnetic resonance (EPR) imaging has evolved as a promising tool to provide non-invasive assessment of tissue oxygenation levels. Due to the extremely short T₂ relaxation time of electrons, single point imaging (SPI) is used in EPRI, limiting achievable spatial and temporal resolution. This presents a problem when attempting to measure changes in hypoxic state. In order to capture oxygen variation in hypoxic tissues and localize cycling hypoxia regions, an accelerated EPRI imaging method with minimal loss of information is needed.

METHODS

We present an image acceleration technique, partial Fourier compressed sensing (PFCS), that combines compressed sensing (CS) and partial Fourier reconstruction. PFCS augments the original CS equation using conjugate symmetry information for missing measurements. To further improve image quality in order to reconstruct low-resolution EPRI images, a projection onto convex sets (POCS)-based phase map and a spherical-sampling mask are used in the reconstruction process. The PFCS technique was used in phantoms and in vivo SCC7 tumor mice to evaluate image quality and accuracy in estimating O₂ concentration.

RESULTS

In both phantom and in vivo experiments, PFCS demonstrated the ability to reconstruct images more accurately with at least a 4-fold acceleration compared to traditional CS. Meanwhile, PFCS is able to better preserve the distinct spatial pattern in a phantom with a spatial resolution of 0.6mm. On phantoms containing Oxo63 solution with different oxygen concentrations, PFCS reconstructed linewidth maps that were discriminative of different O₂ concentrations. Moreover, PFCS reconstruction of partially sampled data provided a better discrimination of hypoxic and oxygenated regions in a leg tumor compared to traditional CS reconstructed images.

CONCLUSIONS

EPR images with an acceleration factor of four are feasible using PFCS with reasonable assessment of tissue oxygenation. The technique can greatly enhance EPR applications and improve our understanding cycling hypoxia. Moreover this technique can be easily extended to various MRI applications.

3D pulse EPR imaging from sparse-view projections via constrained, total variation minimization

Tumors and tumor portions with low oxygen concentrations (pO2) have been shown to be resistant to radiation therapy. As such, radiation therapy efficacy may be enhanced if delivered radiation dose is tailored based on the spatial distribution of pO2 within the tumor. A technique for accurate imaging of tumor oxygenation is critically important to guide radiation treatment that accounts for the effects of local pO2. Electron paramagnetic resonance imaging (EPRI) has been considered one of the leading methods for quantitatively imaging pO2 within tumors in vivo. However, current EPRI techniques require relatively long imaging times. Reducing the number of projection scan considerably reduce the imaging time. Conventional image reconstruction algorithms, such as filtered back projection (FBP), may produce severe artifacts in images reconstructed from sparse-view projections. This can lower the utility of these reconstructed images. In this work, an optimization based image reconstruction algorithm using constrained, total variation (TV) minimization, subject to data consistency, is developed and evaluated. The algorithm was evaluated using simulated phantom, physical phantom and pre-clinical EPRI data. The TV algorithm is compared with FBP using subjective and objective metrics. The results demonstrate the merits of the proposed reconstruction algorithm.

Comparison of pulse sequences for R1-based electron paramagnetic resonance oxygen imaging

Electron paramagnetic resonance (EPR) spin-lattice relaxation (SLR) oxygen imaging has proven to be an indispensable tool for assessing oxygen partial pressure in live animals. EPR oxygen images show remarkable oxygen accuracy when combined with high precision and spatial resolution. Developing more effective means for obtaining SLR rates is of great practical, biological and medical importance. In this work we compared different pulse EPR imaging protocols and pulse sequences to establish advantages and areas of applicability for each method. Tests were performed using phantoms containing spin probes with oxygen concentrations relevant to in vivo oxymetry. We have found that for small animal size objects the inversion recovery sequence combined with the filtered backprojection reconstruction method delivers the best accuracy and precision. For large animals, in which large radio frequency energy deposition might be critical, free induction decay and three pulse stimulated echo sequences might find better practical usage.

Accelerated 4D quantitative single point EPR imaging using model-based reconstruction

PURPOSE

Electron paramagnetic resonance imaging has surfaced as a promising noninvasive imaging modality that is capable of imaging tissue oxygenation. Due to extremely short spin-spin relaxation times, electron paramagnetic resonance imaging benefits from single-point imaging and inherently suffers from limited spatial and temporal resolution, preventing localization of small hypoxic tissues and differentiation of hypoxia dynamics, making accelerated imaging a crucial issue.

METHODS

In this study, methods for accelerated single-point imaging were developed by combining a bilateral k-space extrapolation technique with model-based reconstruction that benefits from dense sampling in the parameter domain (measurement of the T2 (*) decay of a free induction delay). In bilateral kspace extrapolation, more k-space samples are obtained in a sparsely sampled region by bilaterally extrapolating data from temporally neighboring k-spaces. To improve the accuracy of T2 (*) estimation, a principal component analysis-based method was implemented.

RESULTS

In a computer simulation and a phantom experiment, the proposed methods showed its capability for reliable T2 (*) estimation with high acceleration (8-fold, 15-fold, and 30-fold accelerations for 61×61×61, 95×95×95, and 127×127×127 matrix, respectively).

CONCLUSION

By applying bilateral k-space extrapolation and model-based reconstruction, improved scan times with higher spatial resolution can be achieved in the current single-point electron paramagnetic resonance imaging modality.

Compressed sensing of spatial electron paramagnetic resonance imaging

PURPOSE

To improve image quality and reduce data requirements for spatial electron paramagnetic resonance imaging (EPRI) by developing a novel reconstruction approach using compressed sensing (CS).

METHODS

EPRI is posed as an optimization problem, which is solved using regularized least-squares with sparsity promoting penalty terms, consisting of the l1 norms of the image itself and the total variation of the image. Pseudo-random sampling was employed to facilitate recovery of the sparse signal. The reconstruction was compared with the traditional filtered back-projection reconstruction for simulations, phantoms, isolated rat hearts, and mouse gastrointestinal (GI) tracts labeled with paramagnetic probes.

RESULTS

A combination of pseudo-random sampling and CS was able to generate high-fidelity EPR images at high acceleration rates. For three-dimensional (3D) phantom imaging, CS-based EPRI showed little visual degradation at nine-fold acceleration. In rat heart datasets, CS-based EPRI produced high quality images with eight-fold acceleration. A high resolution mouse GI tract reconstruction demonstrated a visual improvement in spatial resolution and a doubling in signal-to-noise ratio (SNR).

CONCLUSION

A novel 3D EPRI reconstruction using compressed sensing was developed and offers superior SNR and reduced artifacts from highly undersampled data.