Strategic steps for advanced molecular imaging with magnetic resonance-based diagnostic modalities.

With the rapidly-expanding sophistication in our understanding of cancer cell biology, molecular imaging offers a critical bridge to oncology. Molecular imaging through magnetic resonance spectroscopy (MRS) can provide information about many metabolites at the same time. Since MRS entails no ionizing radiation, repeated monitoring, including screening can be performed. However, MRS via the fast Fourier transform (FFT) has poor resolution and signal-to-noise ratio (SNR). Moreover, subjective and non-unique (ambiguous) fittings of FFT spectra cannot provide reliable quantification of clinical usefulness. In sharp contrast, objective and unique (unambiguous) signal processing by the fast Padé transform (FPT) can increase resolution and retrieve the true quantitative metabolic information. To illustrate, we apply the FPT to in vitro MRS data as encoded from malignant ovarian cyst fluid and perform detailed analysis. This problem area is particularly in need of timely diagnostics by more advanced modalities, such as high-resolution MRS, since conventional methods usually detect ovarian cancers at late stages with poor prognosis, whereas at an early stage the prognosis is excellent. The reliability and robustness of the FPT is assessed for time signals contaminated with varying noise levels. In the presence of higher background noise, all physical metabolites were unequivocally identified and their concentrations precisely extracted, using small fractions of the total signal length. Via the "signal-noise separation" concept alongside the "stability test", all non-physical information was binned, such that fully denoised spectra were generated. These results imply that a reformulation of data acquisition is needed, as guided by the FPT in MRS, since a small number of short transient time signals can provide high resolution and good SNR. This would enhance the diagnostic accuracy of MRS and shorten examination times, thereby improving efficiency and cost-effectiveness of this high throughput cancer diagnostic modality. Such advantages could be particularly important for more effective ovarian cancer detection, as well as more broadly for improved diagnostics and treatment within oncology.

Improving the therapeutic ratio in stereotactic radiosurgery: optimizing treatment protocols based on kinetics of repair of sublethal radiation damage.

Sublethal damage after radiation exposure may become lethal or be repaired according to repair kinetics. This is a well-established concept in conventional radiotherapy. It also plays an important role in single-dose stereotactic radiotherapy treatments, often called stereotactic radiosurgery, when duration of treatment is extended due to source decay or treatment planning protocol. The purpose of this study is to look into the radiobiological characteristics of normal brain tissue and treatment protocols and find a way to optimize the time course of these protocols. The general problem is nonlinear and can be solved numerically. For numerical optimization of the time course of radiation protocol, a biexponential repair model with slow and fast components was considered. With the clinically imposed constraints of a fixed total dose and total treatment time, three parameters for each fraction (dose-rate, fraction duration, time of each fraction) were simultaneously optimized. A biological optimization can be performed by maximizing the therapeutic difference between tumor control probability and normal tissue complication probability. Specifically, for gamma knife radiosurgery, this approach can be implemented for normal brain tissue or tumor voxels separately in a treatment plan. Differences in repair kinetics of normal tissue and tumors can be used to find clinically optimized protocols. Thus, in addition to considering the physical dose in tumor and normal tissue, we also account for repair of sublethal damage in both these tissues.

A comparative analysis of radiobiological models for cell surviving fractions at high doses.

For many years the linear-quadratic (LQ) model has been widely used to describe the effects of total dose and dose per fraction at low-to-intermediate doses in conventional fractionated radiotherapy. Recent advances in stereotactic radiosurgery (SRS) and stereotactic radiotherapy (SRT) have increased the interest in finding a reliable cell survival model, which will be accurate at high doses, as well. Different models have been proposed for improving descriptions of high dose survival responses, such as the Universal Survival Curve (USC), the Kavanagh-Newman (KN) and several generalizations of the LQ model, e.g. the Linear-Quadratic-Linear (LQL) model and the Pade Linear Quadratic (PLQ) model. The purpose of the present study is to compare a number of models in order to find the best option(s) which could successfully be used as a fractionation correction method in SRT. In this work, six independent experimental data sets were used: CHOAA8 (Chinese hamster fibroblast), H460 (non-small cell lung cancer, NSLC), NCI-H841 (small cell lung cancer, SCLC), CP3 and DU145 (human prostate carcinoma cell lines) and U1690 (SCLC). By detailed comparisons with these measurements, the performance of nine different radiobiological models was examined for the entire dose range, including high doses beyond the shoulder of the survival curves. Using the computed and measured cell surviving fractions, comparison of the goodness-of-fit for all the models was performed by means of the reduced χ (2)-test with a 95% confidence interval. The obtained results indicate that models with dose-independent final slopes and extrapolation numbers generally represent better choices for SRT. This is especially important at high doses where the final slope and extrapolation numbers are presently found to play a major role. The PLQ, USC and LQL models have the least number of shortcomings at all doses. The extrapolation numbers and final slopes of these models do not depend on dose. Their asymptotes for the cell surviving fractions are exponentials at low as well as high doses, and this is in agreement with the behaviour of the corresponding experimental data. This is an important improvement over the LQ model which predicts a Gaussian at high doses. Overall and for the highlighted reasons, it was concluded that the PLQ, USC and LQL models are theoretically well-founded. They could prove useful compared to the other proposed radiobiological models in clinical applications for obtaining uniformly accurate cell surviving fractions encountered in stereotactic high-dose radiotherapy as well as at medium and low doses.

An analytical model for light ion pencil beam dose distributions: multiple scattering of primary and secondary ions.

An analytical algorithm based on the generalized Fermi-Eyges theory, amended for multiple Coulomb scattering and energy loss straggling, is used for calculation of the dose distribution of light ion beams in water. Pencil beam energy deposition distributions are derived for light ions by weighting a Monte Carlo (MC) calculated planar integral dose distribution with analytically calculated multiple scattering and range straggling distributions. The planar integral dose distributions are calculated using the MC code SHIELD-HIT07, in which multiple scattering and energy loss straggling processes are excluded. The contribution from nuclear reactions is included in the MC calculations. Multiple scattering processes are calculated separately for primary and secondary ions and parameters of the initial angular and radial spreads, and the covariance of these are derived by a least-square parameterization of the SHIELD-HIT07 data. The results from this analytical algorithm are compared to pencil beam dose distributions obtained from SHIELD-HIT07, where all processes are included, as well as to experimental data. The presented analytical approach allows for the accurate calculation of the spatial energy deposition distributions of ions of atomic numbers Z = 1 - 8.