An information theory model for optimizing quantitative magnetic resonance imaging acquisitions.

Acquisition parameter selection is currently performed empirically for many quantitative MRI (qMRI) acquisitions. Tuning parameters for different scan times, tissues, and resolutions requires some amount of trial and error. There is an opportunity to quantitatively optimize these acquisition parameters in order to minimize variability of quantitative maps and post-processing techniques such as synthetic image generation. The objective of this work is to introduce and evaluate a quantitative method for selecting parameters that minimize image variability. An information theory framework was developed for this purpose and applied to a 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) signal model for qMRI. In this framework, mutual information is used to measure the information gained by a measurement as a function of acquisition parameters, quantifying the information content of potential acquisitions and allowing informed parameter selection. The information theory framework was tested on artificial data generated from a representative mathematical phantom, measurements acquired on a qMRI multiparametric imaging standard phantom, and in vivo measurements in a human brain. The phantom measurements showed that higher mutual information calculated by the model correlated with smaller coefficient of variation in the reconstructed parametric maps, and in vivo measurements demonstrated that information-based calibration of acquisition parameters resulted in a decrease in parametric map variability consistent with model predictions.

Accuracy of deformable image registration on magnetic resonance images in digital and physical phantoms.

PURPOSE: Accurate deformable image registration is necessary for longitudinal studies. The error associated with commercial systems has been evaluated using computed tomography (CT). Several in-house algorithms have been evaluated for use with magnetic resonance imaging (MRI), but there is still relatively little information about MRI deformable image registration. This work presents an evaluation of two deformable image registration systems, one commercial (Velocity) and one in-house (demons-based algorithm), with MRI using two different metrics to quantify the registration error. METHODS: The registration error was analyzed with synthetic MR images. These images were generated from interpatient and intrapatient variation models trained on 28 patients. Four synthetic post-treatment images were generated for each of four synthetic pretreatment images, resulting in 16 image registrations for both the T1- and T2-weighted images. The synthetic post-treatment images were registered to their corresponding synthetic pretreatment image. The registration error was calculated between the known deformation vector field and the generated deformation vector field from the image registration system. The registration error was also analyzed using a porcine phantom with ten implanted 0.35-mm diameter gold markers. The markers were visible on CT but not MRI. CT, T1-weighted MR, and T2-weighted MR images were taken in four different positions. The markers were contoured on the CT images and rigidly registered to their corresponding MR images. The MR images were deformably registered and the distance between the projected marker location and true marker location was measured as the registration error. RESULTS: The synthetic images were evaluated only on Velocity. Root mean square errors (RMSEs) of 0.76 mm in the left-right (LR) direction, 0.76 mm in the anteroposterior (AP) direction, and 0.69 mm in the superior-inferior (SI) direction were observed for the T1-weighted MR images. RMSEs of 1.1 mm in the LR direction, 0.75 mm in the AP direction, and 0.81 mm in the SI direction were observed for the T2-weighted MR images. The porcine phantom MR images, when evaluated with Velocity, had RMSEs of 1.8, 1.5, and 2.7 mm in the LR, AP, and SI directions for the T1-weighted images and 1.3, 1.2, and 1.6 mm in the LR, AP, and SI directions for the T2-weighted images. When the porcine phantom images were evaluated with the in-house demons-based algorithm, RMSEs were 1.2, 1.5, and 2.1 mm in the LR, AP, and SI directions for the T1-weighted images and 0.81, 1.1, and 1.1 mm in the LR, AP, and SI directions for the T2-weighted images. CONCLUSIONS: The MRI registration error was low for both Velocity and the in-house demons-based algorithm according to both image evaluation methods, with all RMSEs below 3 mm. This implies that both image registration systems can be used for longitudinal studies using MRI.

Use of gold nanoshells to constrain and enhance laser thermal therapy of metastatic liver tumours.

PURPOSE: To investigate the impact of intravenously injected gold nanoparticles on interstitially delivered laser induced thermal therapy (LITT) in the liver. METHODS: 3D finite element modelling, ex vivo canine liver tissue containing gold nanoparticles absorbing at 800 nm, and agar gel phantoms were used to simulate the presence of nanoparticles in the liver during LITT. Real-time magnetic resonance temperature imaging (MRTI) based on the temperature sensitivity of the proton resonance frequency shift (PRFS) was used to map the spatiotemporal distribution of heating in the experiments and validate the predictions of 3D finite element simulations of heating. RESULTS: Experimental results show good agreement with both the simulation and the ex vivo experiments. Average discrepancy between simulation and experiment was shown to be 1.6 degrees C or less with the maximum difference being 3.8 degrees C due to a small offset in laser positioning. CONCLUSION: A high nanoshell concentration in the surrounding liver parenchyma, such as that which would be expected from an intravenous injection of gold nanoshells ( approximately 120 nm) acts as both a beam stop for the laser and secondary heat source for the treatment, helping to better heat the lesions and confine the treatment to the lesion. This indicates a potential to use nanoparticles to enhance both the safety and efficacy of LITT procedures in the liver.

Optimizing heat shock protein expression induced by prostate cancer laser therapy through predictive computational models.

Thermal therapy efficacy can be diminished due to heat shock protein (HSP) induction in regions of a tumor where temperatures are insufficient to coagulate proteins. HSP expression enhances tumor cell viability and imparts resistance to chemotherapy and radiation treatments, which are generally employed in conjunction with hyperthermia. Therefore, an understanding of the thermally induced HSP expression within the targeted tumor must be incorporated into the treatment plan to optimize the thermal dose delivery and permit prediction of the overall tissue response. A treatment planning computational model capable of predicting the temperature, HSP27 and HSP70 expression, and damage fraction distributions associated with laser heating in healthy prostate tissue and tumors is presented. Measured thermally induced HSP27 and HSP70 expression kinetics and injury data for normal and cancerous prostate cells and prostate tumors are employed to create the first HSP expression predictive model and formulate an Arrhenius damage model. The correlation coefficients between measured and model predicted temperature, HSP27, and HSP70 were 0.98, 0.99, and 0.99, respectively, confirming the accuracy of the model. Utilization of the treatment planning model in the design of prostate cancer thermal therapies can enable optimization of the treatment outcome by controlling HSP expression and injury.