Subject-specific four-dimensional liver motion modeling based on registration of dynamic MRI

Magnetic resonance-guided high intensity focused ultrasound treatment of the liver is a promising noninvasive technique for ablation of liver lesions. For the technique to be used in clinical practice, however, the issue of liver motion needs to be addressed. A subject-specific four-dimensional liver motion model is presented that is created based on registration of dynamically acquired magnetic resonance data. This model can be used for predicting the tumor motion trajectory for treatment planning and to indicate the tumor position for treatment guidance. The performance of the model was evaluated on a dynamic scan series that was not used to build the model. The method achieved an average Dice coefficient of 0.93 between the predicted and actual liver profiles and an average vessel misalignment of 3.0 mm. The model performed robustly, with a small variation in the results per subject. The results demonstrate the potential of the model to be used for MRI-guided treatment of liver lesions. Furthermore, the model can possibly be applied in other image-guided therapies, for instance radiotherapy of the liver

Subject-specific liver motion modeling in MRI:a feasibility study on spatiotemporal prediction

\u3cp\u3eA liver motion model based on registration of dynamic MRI data, as previously proposed by the authors, was extended with temporal prediction and respiratory signal data. The potential improvements of these extensions with respect to the original model were investigated. Additional evaluations were performed to investigate the limitations of the model regarding temporal prediction and extreme breathing motion. Data were acquired of four volunteers, with breathing instructions and a respiratory belt. The model was built from these data using spatial prediction only and using temporal forward prediction of 300 ms to 1200 ms, using the extended Kalman filter. From temporal prediction of 0 ms to 1200 ms ahead, the Dice coefficient of liver overlap decreased with 0.85%, the median liver surface distance increased with 20.6% and the vessel misalignment increased with 20%. The mean vessel misalignment was 2.9 mm for the original method, 3.42 mm for spatial prediction with a respiratory signal and 4.01 mm for prediction of 1200 ms ahead with a respiratory signal. Although the extension of the model to temporal prediction yields a decreased prediction accuracy, the results are still acceptable. The use of the breathing signal as input to the model is feasible. Sudden changes in the breathing pattern can yield large errors. However, these errors only persist during a short time interval, after which they can be corrected automatically. Therefore, this model could be useful in a clinical setting.\u3c/p\u3

Learning an MR acquisition-invariant representation using Siamese neural networks

Generalization of voxelwise classifiers is hampered by differences between MRI-scanners, e.g. different acquisition protocols and field strengths. To address this limitation, we propose a Siamese neural network (MRAI-NET) that extracts acquisition-invariant feature vectors. These can consequently be used by task-specific methods, such as voxelwise classifiers for tissue segmentation. MRAI-NET is evaluated on both simulated and real patient data. Experiments show that MRAI-NET outperforms both voxelwise classifiers trained on the source data as well as classifiers trained on the limited amount of target scanner data available

Evaluation of optimization methods for intensity-based 2D-3D registration in x-ray guided interventions

\u3cp\u3eThe advantage of 2D-3D image registration methods versus direct image-to-patient registration, is that these methods generally do not require user interaction (such as manual annotations), additional machinery or additional acquisition of 3D data. A variety of intensity-based similarity measures has been proposed and evaluated for different applications. These studies showed that the registration accuracy and capture range are influenced by the choice of similarity measure. However, the influence of the optimization method on intensity-based 2D-3D image registration has not been investigated. We have compared the registration performance of seven optimization methods in combination with three similarity measures: gradient difference, gradient correlation, and pattern intensity. Optimization methods included in this study were: regular step gradient descent, Nelder-Mead, Powell-Brent, Quasi-Newton, nonlinear conjugate gradient, simultaneous perturbation stochastic approximation, and evolution strategy. Registration experiments were performed on multiple patient data sets that were obtained during cerebral interventions. Various component combinations were evaluated on registration accuracy, capture range, and registration time. The results showed that for the same similarity measure, different registration accuracies and capture ranges were obtained when different optimization methods were used. For gradient difference, largest capture ranges were obtained with Powell-Brent and simultaneous perturbation stochastic approximation. Gradient correlation and pattern intensity had the largest capture ranges in combination with Powell-Brent, Nelder-Mead, nonlinear conjugate gradient, and Quasi-Newton. Average registration time, expressed in the number of DRRs required for convergence, was the lowest for Powell-Brent. Based on these results, we conclude that Powell-Brent is a reliable optimization method for intensity-based 2D-3D registration of x-ray images to CBCT, regardless of the similarity measure used.\u3c/p\u3

Registration of CT to pre-treatment MRI for planning of MR-HIFU ablation treatment of painful bone metastases

MR-HIFU is a new non-invasive treatment modality that can be used for palliation in patients with painful bone metastases. Since treatment strategies are mainly focused on the ablation of periosteal nerves, information on the presence and geometry of cortical bone influences the treatment strategy, both in determining the acoustic power and in avoiding safety issues related to far-field heating. Although MRI is available for imaging during treatment, CT is best used for examining the cortical bone. We present a registration method for registering CT and MR images of patients with bone metastases prior to therapy. CT and MRI data were obtained from nine patients with metastatic bone lesions at varying locations. A two-step registration approach was used, performing simultaneous rigid registration of all available MR images in the first step and an affine and deformable registration with an additional bone metric in the second step. The performance was evaluated using landmark annotation by clinical observers. An average registration error of 4.5 mm was obtained, which was comparable to the slice thickness of the data. The performance of the registration algorithm was satisfactory, even with differences in MRI acquisition parameters and for various anatomical sites. The obtained CT overlay is useful for treatment planning, as it allows an assessment of the integrity of the cortical bone. CT-MR registration is therefore recommended for HIFU treatment planning of patients with bone metastases

Robust initialization of 2D-3D image registration using the projection-slice theorem and phase correlation

Purpose: The image registration literature comprises many methods for 2D-3D registration for which accuracy has been established in a variety of applications. However, clinical application is limited by a small capture range. Initial offsets outside the capture range of a registration method will not converge to a successful registration. Previously reported capture ranges, defined as the 95% success range, are in the order of 4-11 mm mean target registration error. In this article, a relatively computationally inexpensive and robust estimation method is proposed with the objective to enlarge the capture range. Methods: The method uses the projection-slice theorem in combination with phase correlation in order to estimate the transform parameters, which provides an initialization of the subsequent registration procedure. Results: The feasibility of the method was evaluated by experiments using digitally reconstructed radiographs generated from in vivo 3D-RX data. With these experiments it was shown that the projection-slice theorem provides successful estimates of the rotational transform parameters for perspective projections and in case of translational offsets. The method was further tested on ex vivo ovine x-ray data. In 95% of the cases, the method yielded successful estimates for initial mean target registration errors up to 19.5 mm. Finally, the method was evaluated as an initialization method for an intensity-based 2D-3D registration method. The uninitialized and initialized registration experiments had success rates of 28.8% and 68.6%, respectively. Conclusions: The authors have shown that the initialization method based on the projection-slice theorem and phase correlation yields adequate initializations for existing registration methods, thereby substantially enlarging the capture range of these methods. © 2010 American Association of Physicists in Medicine

An analytical model for intravascular MR antennas

For interventional magnetic resonance imaging a need exists for intravascular MR antennas. These antennas need to be employed for the tracking of guide wires and catheters through blood vessels during surgery and/or for obtaining high resolution images of vessel walls. Such images cannot be obtained by conventional MRI operation due to the high signal to noise ratio of the signals received with conventional receiver coils. By inserting receiver coils (antennas) into the blood vessels, the SNR can be improved up to a level that obtaining high resolution images becomes feasible