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Medical image acquisition devices provide a vast amount of anatomical and functional information, which facilitate and improve diagnosis and patient treatment, especially when supported by modern quantitative image analysis methods. However, modality specific image artifacts, such as the phenomena of intensity inhomogeneity in magnetic resonance images (MRI), are still prominent and can adversely affect quantitative image analysis. In this paper, numerous methods that have been developed to reduce or eliminate intensity inhomogeneities in MRI are reviewed. First, the methods are classified according to the inhomogeneity correction strategy. Next, different qualitative and quantitative evaluation approaches are reviewed. Third, 60 relevant publications are categorized according to several features and analyzed so as to reveal major trends, popularity, evaluation strategies and applications. Finally, key evaluation issues and future development of the inhomogeneity correction field, supported by the results of the analysis, are discussed

A Review of Methods for Correction of Intensity Inhomogeneity in MRI

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Image segmentation plays a crucial role in many medical imaging applications by automating or facilitating the delineation of anatomical structures and other regions of interest. We present herein a critical appraisal of the current status of semi-automated and automated methods for the segmentation of anatomical medical images. Current segmentation approaches are reviewed with an emphasis placed on revealing the advantages and disadvantages of these methods for medical imaging applications. The use of image segmentation in different imaging modalities is also described along with the difficulties encountered in each modality. We conclude with a discussion on the future of image segmentation methods in biomedical research.

A Survey of Current Methods in Medical Image Segmentation

We measured the deformation of the dura and brain surfaces between the time of imaging and the start of surgical resection for 21 patients. All patients underwent intraoperative functional mapping, allowing us to measure brain surface motion at two times that were separated by nearly an hour after opening the dura but before resection. The positions of the dura and brain surfaces were recorded and transformed to the coordinate space of a preoperative MR image using the Acustar neurosurgical navigation system. The mean displacements of the dura and the first and second brain surfaces were 1.2, 4.4, and 5.6 mm, respectively, with corresponding mean volume reductions under the craniotomy of 6, 22, and 29 ml. The maximum displacement was greater than 10 mm in approximately one-third of the patients for the first brain surface measurement and one-half of the patients for the second. In all cases the direction of brain shift corresponds to a “sinking” of the brain intraoperatively, compared with its preoperative position. We observed two patterns of the brain surface deformation field depending on the inclination of the craniotomy with respect to gravity. Separate measurements of brain deformation within the closed cranium caused by changes in patient head orientation with respect to gravity suggested that less than 1 mm of the brain shift recorded intraoperatively could have resulted from the change in patient orientation between the time of imaging and the time of surgery. These results suggest that intraoperative brain deformation is an important source of error that needs to be considered when using neurosurgical navigation systems.

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Measurement of Intraoperative Brain Surface Deformation Under a Craniotomy

The analytical anisotropic algorithm (AAA) was implemented in the Eclipse (Varian Medical Systems) treatment planning system to replace the single pencil beam (SPB) algorithm for the calculation of dose distributions for photon beams. AAA was developed to improve the dose calculation accuracy, especially in heterogeneous media. The total dose deposition is calculated as the superposition of the dose deposited by two photon sources (primary and secondary) and by an electron contamination source. The photon dose is calculated as a three-dimensional convolution of Monte-Carlo precalculated scatter kernels, scaled according to the electron density matrix. For the configuration of AAA, an optimization algorithm determines the parameters characterizing the multiple source model by optimizing the agreement between the calculated and measured depth dose curves and profiles for the basic beam data. We have combined the acceptance tests obtained in three different departments for 6, 15, and 18 MV photon beams. The accuracy of AAA was tested for different field sizes (symmetric and asymmetric) for open fields, wedged fields, and static and dynamic multileaf collimation fields. Depth dose behavior at different source-to-phantom distances was investigated. Measurements were performed on homogeneous, water equivalent phantoms, on simple phantoms containing cork inhomogeneities, and on the thorax of an anthropomorphic phantom. Comparisons were made among measurements, AAA, and SPB calculations. The optimization procedure for the configuration of the algorithm was successful in reproducing the basic beam data with an overall accuracy of 3%, 1 mm in the build-up region, and 1%, 1 mm elsewhere. Testing of the algorithm in more clinical setups showed comparable results for depth dose curves, profiles, and monitor units of symmetric open and wedged beams below dmax. The electron contamination model was found to be suboptimal to model the dose around dmax, especially for physical wedges at smaller source to phantom distances. For the asymmetric field verification, absolute dose difference of up to 4% were observed for the most extreme asymmetries. Compared to the SPB, the penumbra modeling is considerably improved (1%, 1 mm). At the interface between solid water and cork, profiles show a better agreement with AAA. Depth dose curves in the cork are substantially better with AAA than with SPB. Improvements are more pronounced for 18 MV than for 6 MV. Point dose measurements in the thoracic phantom are mostly within 5%. In general, we can conclude that, compared to SPB, AAA improves the accuracy of dose calculations. Particular progress was made with respect to the penumbra and low dose regions. In heterogeneous materials, improvements are substantial and more pronounced for high (18 MV) than for low (6 MV) energies.

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Testing of the analytical anisotropic algorithm for photon dose calculation.

✓ The development of a unique neurosurgical navigator is described and a preliminary series of seven cases of intracerebral lesions approached with the assistance of this neuronavigation system under ultrasound control is presented. The clinical series included five low-grade astrocytomas, one chronic intracerebral hematoma, and one porencephalic cyst. Management procedures included biopsy in all cases, drainage of the hematoma, and endoscopy and fenestration for the cyst. The features of the neuronavigation system are interactive reconstructions of preoperative computerized tomography and magnetic resonance imaging data, corresponding intraoperative ultrasound images, versatility of the interchangeable end-effector instruments, graphic presentation of instruments on the reconstructed images, and voice control of the system. The principle of a common axis in the reconstructed images served to align the navigational pointer, biopsy guide, endoscope guide, ultrasound transducer, and surgical microscope to t...

Ultrasound-controlled neuronavigator-guided brain surgery.

In this work, a novel three-dimensional superposition algorithm for photon dose calculation is presented. The dose calculation is performed as a superposition of pencil beams, which are modified based on tissue electron densities. The pencil beams have been derived from Monte Carlo simulations, and are separated into lateral and depth-directed components. The lateral component is modeled using exponential functions, which allows accurate modeling of lateral scatter in heterogeneous tissues. The depth-directed component represents the total energy deposited on each plane, which is spread out using the lateral scatter functions. Finally, convolution in the depth direction is applied to account for tissue interface effects. The method can be used with the previously introduced multiple-source model for clinical settings. The method was compared against Monte Carlo simulations in several phantoms including lung- and bone-type heterogeneities. Comparisons were made for several field sizes for 6 and 18 MV energies. The deviations were generally within (2%, 2 mm) of the field central axis dmax. Significantly larger deviations (up to 8%) were found only for the smallest field in the lung slab phantom for 18 MV. The presented method was found to be accurate in a wide range of conditions making it suitable for clinical planning purposes.

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A 3D pencil-beam-based superposition algorithm for photon dose calculation in heterogeneous media

We propose a spiking neural network model that encodes information in the relative timing of individual neuron spikes and performs classification using the first output neuron to spike. This temporal coding scheme allows the supervised training of the network with backpropagation, using locally exact derivatives of the postsynaptic with respect to presynaptic spike times. The network uses a biologically-inspired alpha synaptic transfer function and trainable synchronisation pulses as temporal references. We successfully train the network on the MNIST dataset encoded in time. Our spiking neural network outperforms comparable spiking models and achieves similar accuracy to a fully connected conventional network. During training, our network displays a speed-accuracy trade-off, with either slow and highly-accurate or very fast but less accurate classification. The results demonstrate the computational power of spiking networks with biological characteristics that encode information in the timing of individual neurons. Our code is publicly available.

Temporal Coding in Spiking Neural Networks with Alpha Synaptic Function

Accurate modelling of the radiation output of a medical linear accelerator is important for radiotherapy treatment planning. The major challenge is the adjustment of the model to a specific treatment unit. One approach is to use a multiple-source model containing a set of physical parameters. In this work, the parameters were derived from standard beam data measurements using optimization methods. The source model used includes sub-sources for bremsstrahlung radiation from the target, extra-focal photon radiation and electron contamination. The cost function includes a gamma error measure between measurements and current dose calculations. The procedure was applied to six beam data sets (6 MV to 23 MV) measured with accelerators from three vendors, but the results focus primarily on Varian accelerators. The obtained average gamma error (1%, 1 mm) between dose calculations and measurements used in optimization was smaller than 0.7 for each studied treatment beam and field size, and a minimum of 83% of measurement points passed the γ < 1 criterion. For experiments made at different SSDs and for asymmetric fields, the average gamma errors were smaller than 1.1. For irregularly shaped MLC apertures, the differences in point doses were smaller than 1.0%. This work demonstrates that the source model parameters can be automatically derived from simple measurements using optimization methods. The developed procedure is applicable to a wide range of accelerators, and has an acceptable accuracy and processing time.

/pdf/determination-of-parameters-for-a-multiple-source-model-of-2arjhdmzfk.pdf

Jyrki Alakuijala

Papers

Testing of the analytical anisotropic algorithm for photon dose calculation.

Ultrasound-controlled neuronavigator-guided brain surgery.

A 3D pencil-beam-based superposition algorithm for photon dose calculation in heterogeneous media

Temporal Coding in Spiking Neural Networks with Alpha Synaptic Function

Determination of parameters for a multiple-source model of megavoltage photon beams using optimization methods