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We investigated to what degree and at what rate the ultimate intrinsic (UI) signal-to-noise ratio (SNR) may be approached using finite radiofrequency detector arrays. We used full-wave electromagnetic field simulations based on dyadic Green’s functions to compare the SNR of arrays of loops surrounding a uniform sphere with the ultimate intrinsic SNR (UISNR), for increasing numbers of elements over a range of magnetic field strengths, voxel positions, sphere sizes, and acceleration factors. We evaluated the effect of coil conductor losses and the performance of a variety of distinct geometrical arrangements such as “helmet” and “open-pole” configurations in multiple imaging planes. Our results indicate that UISNR at the center is rapidly approached with encircling arrays and performance is substantially lower near the surface, where a quadrature detection configuration tailored to voxel position is optimal. Coil noise is negligible at high field, where sample noise dominates. Central SNR for practical array configurations such as the helmet is similar to that of close-packed arrangements. The observed trends can provide physical insights to improve coil design.

In high field MRI, the spatial distribution of the radiofrequency magnetic (B1) field is usually affected by the presence of the sample. For hardware design and to aid interpretation of experimental results, it is important both to anticipate and to accurately simulate the behavior of these fields. Fields generated by a radiofrequency surface coil were simulated using dyadic Green’s functions, or experimentally measured over a range of frequencies inside an object whose electrical properties were varied to illustrate a variety of transmit false(B1+false) and receive false(B1−false) field patterns. In this work, we examine how changes in polarization of the field and interference of propagating waves in an object can affect the B1 spatial distribution. Results are explained conceptually using Maxwell’s equations and intuitive illustrations. We demonstrate that the electrical conductivity alters the spatial distribution of distinct polarized components of the field, causing “twisted” transmit and receive field patterns, and asymmetries between false|B1+false| and false|B1−false|. Additionally, interference patterns due to wavelength effects are observed at high field in samples with high relative permittivity and near-zero conductivity, but are not present in lossy samples due to the attenuation of propagating EM fields. This work provides a conceptual framework for understanding B1 spatial distributions for surface coils and can provide guidance for RF engineers.

Locally advanced non-small cell lung cancer (NSCLC) patients suffer from a high local failure rate following radiotherapy. Despite many efforts to develop new dose-volume models for early detection of tumor local failure, there was no reported significant improvement in their application prospectively. Based on recent studies of biomarker proteins’ role in hypoxia and inflammation in predicting tumor response to radiotherapy, we hypothesize that combining physical and biological factors with a suitable framework could improve the overall prediction. To test this hypothesis, we propose a graphical Bayesian network framework for predicting local failure in lung cancer. The proposed approach was tested using two different datasets of locally advanced NSCLC patients treated with radiotherapy. The first dataset was collected retrospectively, which is comprised of clinical and dosimetric variables only. The second dataset was collected prospectively in which in addition to clinical and dosimetric information, blood was drawn from the patients at various time points to extract candidate biomarkers as well. Our preliminary results show that the proposed method can be used as an efficient method to develop predictive models of local failure in these patients and to interpret relationships among the different variables in the models. We also demonstrate the potential use of heterogenous physical and biological variables to improve the model prediction. With the first dataset, we achieved better performance compared with competing Bayesian-based classifiers. With the second dataset, the combined model had a slightly higher performance compared to individual physical and biological models, with the biological variables making the largest contribution. Our preliminary results highlight the potential of the proposed integrated approach for predicting post-radiotherapy local failure in NSCLC patients.

2 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2018;48:431-440.

Purpose To investigate how high‐permittivity materials (HPMs) can improve SNR when placed between MR detectors and the imaged body. Methods We used a simulation framework based on dyadic Green’s functions to calculate the electromagnetic field inside a uniform dielectric sphere at 7 Tesla, with and without a surrounding layer of HPM. SNR‐optimizing (ideal) current patterns were expressed as the sum of signal‐optimizing (signal‐only) current patterns and dark mode current patterns that minimize sample noise while contributing nothing to signal. We investigated how HPM affects the shape and amplitude of these current patterns, sample noise, and array SNR. Results Ideal and signal‐only current patterns were identical for a central voxel. HPMs introduced a phase shift into these patterns, compensating for signal propagation delay in the HPMs. For an intermediate location within the sphere, dark mode current patterns were present and illustrated the mechanisms by which HPMs can reduce sample noise. High‐amplitude signal‐only current patterns were observed for HPM configurations that shield the electromagnetic field from the sample. For coil arrays, these configurations corresponded to poor SNR in deep regions but resulted in large SNR gains near the surface due to enhanced fields in the vicinity of the HPM. For very high relative permittivity values, HPM thicknesses corresponding to even multiples of λ/4 resulted in coil SNR gains throughout the sample. Conclusion HPMs affect both signal sensitivity and sample noise. Lower amplitude signal‐only optimal currents corresponded to higher array SNR performance and could guide the design of coils integrated with HPM.

Purpose We investigated global specific absorption rate (SAR) and radiofrequency (RF) power requirements in parallel transmission as the distance between the transmit coils and the sample was increased. Methods We calculated ultimate intrinsic SAR (UISAR), which depends on object geometry and electrical properties but not on coil design, and we used it as the reference to compare the performance of various transmit arrays. We investigated the case of fixing coil size and increasing the number of coils while moving the array away from the sample, as well as the case of fixing coil number and scaling coil dimensions. We also investigated RF power requirements as a function of liftoff, and tracked local SAR distributions associated with global SAR optima. Results In all cases, the target excitation profile was achieved and global SAR (as well as associated maximum local SAR) decreased with lift-off, approaching UISAR, which was constant for all lift-offs. We observed a lift-off value that optimizes the balance between global SAR and power losses in coil conductors. We showed that, using parallel transmission, global SAR can decrease at ultra high fields for finite arrays with a sufficient number of transmit elements. Conclusion For parallel transmission, the distance between coils and object can be optimized to reduce SAR and minimize RF power requirements associated with homogeneous excitation.

In many tumors, cancer cells take up large quantities of glucose and metabolize it into lactate, even in the presence of sufficient oxygen to support oxidative metabolism. It has been hypothesized that this malignant metabolic phenotype supports cancer growth and metastasis, and that reversal of this so‐called “Warburg effect” may selectively harm cancer cells. Conversion of glucose to lactate can be reduced by ablation or inhibition of lactate dehydrogenase (LDH), the enzyme responsible for conversion of pyruvate to lactate at the endpoint of glycolysis. Recently developed inhibitors of LDH provide new opportunities to investigate the role of this metabolic pathway in cancer. Here we show that magnetic resonance spectroscopic imaging of hyperpolarized pyruvate and its metabolites in models of breast and lung cancer reveal that inhibition of LDH was readily visualized through reduction in label exchange between pyruvate and lactate, while genetic ablation of the LDH‐A isoform alone had smaller effects. During the acute phase of LDH inhibition in breast cancer, no discernible bicarbonate signal was observed and small signals from alanine were unchanged.