Indication-based national diagnostic reference levels for paediatric CT: a new approach with proposed values.

Indication-based national diagnostic reference levels (DRLs) for a few most common paediatric computed tomography (CT) examinations are proposed. Patient dose data (CTDI vol and dose length product) were collected for over 1000 patients in 4 university hospitals with best experiences in paediatric CT. Four indications for chest CT and two for abdomen (abdomen + pelvis), chest + abdomen and head CT were considered. The DRLs for the body examinations are proposed as exponential DRL-curves, where CTDI vol and dose length product are presented as a function of patient weight. The same DRL curve applies to all the indications studied. The basic 75 % level curve is supplemented by 50 % level curve to enable considerations on varying levels of technology. For head CT, DRLs are proposed for a few age groups (1, 1-5, 5-10 and 10-15 y), separately for routine CT and CT for ventricular size. The proposed DRLs are generally lower than the few published DRLs in other countries.

Effect of EEG electrode density on dipole localization accuracy using two realistically shaped skull resistivity models.

The effect of number of EEG electrodes on the dipole localization was studied by comparing the results obtained using the 10-20 and 10-10 electrode systems. Two anatomically detailed models with resistivity values of 177.6 omega m and 67.0 omega m for the skull were applied. Simulated potential values generated by current dipoles were applied to different combinations of the volume conductors and electrode systems. High and low resistivity models differed slightly in favour of the lower skull resistivity model when dipole localization was based on noiseless data. The localization errors were approximately three times larger using low resistivity model for generating the potentials, but applying high resistivity model for the inverse solution. The difference between the two electrode systems was minor in favour of the 10-10 electrode system when simulated, noiseless potentials were used. In the presence of noise the dipole localization algorithm operated more accurately using the denser electrode system. In conclusion, increasing the number of recording electrodes seems to improve the localization accuracy in the presence of noise. The absolute skull resistivity value also affects the accuracy, but using an incorrect value in modelling calculations seems to be the most serious source of error.

Accuracy of two dipolar inverse algorithms applying reciprocity for forward calculation.

Two inverse algorithms were applied for solving the EEG inverse problem assuming a single dipole as a source model. For increasing the efficiency of the forward computations the lead field approach based on the reciprocity theorem was applied. This method provides a procedure to calculate the computationally heavy forward problem by a single solution for each EEG lead. A realistically shaped volume conductor model with five major tissue compartments was employed to obtain the lead fields of the standard 10-20 EEG electrode system and the scalp potentials generated by simulated dipole sources. A least-squares method and a probability-based method were compared in their performance to reproduce the dipole source based on the reciprocal forward solution. The dipole localization errors were 0 to 9 mm and 2 to 22 mm without and with added noise in the simulated data, respectively. The two different inverse algorithms operated mainly very similarly. The lead field method appeared applicable for the solution of the inverse problem and especially useful when a number of sources, e.g., multiple EEG time instances, must be solved.

A software implementation for detailed volume conductor modelling in electrophysiology using finite difference method.

There is an evolving need for new information available by employing patient tailored anatomically accurate computer models of the electrical properties of the human body. Because construction of a computer model can be difficult and laborious to perform sufficiently well, devised models have varied greatly in the level of anatomical accuracy incorporated in them. This has restricted the validity of conducted simulations. In the present study, a versatile software package was developed to transform anatomic voxel data into accurate finite difference method volume conductor models conveniently and in a short time. The package includes components for model construction, simulation, visualisation and detailed analysis of simulation output based on volume conductor theory. Due to the methods developed, models can comprise more anatomical details than the prior computer models. Several models have been constructed, for example, a highly detailed 3-D anatomically accurate computer model of the human thorax as a volume conductor utilising the US National Library of Medicine's (NLM) Visible Human Man (VHM) digital anatomy data. Based on the validation runs the developed software package is readily applicable in analysis of a wide range of bioelectric field problems.

Effects of tissue resistivities on electroencephalogram sensitivity distribution.

The effects of tissue resistivities on EEG amplitudes were studied using an anatomically accurate computer model based on the finite difference method (FDM) and lead field analysis covering the whole brain area with 180,000 nodes. Five tissue types and three lead fields were considered for analysis. The changes in sensitivity distribution are directly comparable to changes in the potential distribution on the scalp. The results indicate that a 10% decrease in any tissue resistivity caused 3.0-4.1% differences in the sensitivity distributions of the selected EEG leads. The applied 10% decrease in the resistivity values covers only a fraction of the range of variation of 50% to 100% reported in the literature. The use of a 55% decreased skull resistivity value or a commonly applied three-compartment model increased the differences to 28% and 33%, respectively. In conclusion, both a realistic anatomy and accurate resistivity data are important in EEG head models.

Segmentation of T1 MR scans for reconstruction of resistive head models.

This paper describes a segmentation method primarily developed for reconstructing resistive head models for electroencephalographic modelling purposes. The method was implemented by combining several image processing techniques, such as amplitude segmentation, region growing, and image fusion. Also a graphical user interface was developed to enable semiautomatic approach to the segmentation process. This method was developed especially for segmentation of the brain and skull from T1-weighted magnetic resonance images, but can also be applied in any segmentation procedure. The entire project was implemented successfully in a PC-based computer running the Unix/NeXTstep operating system.

Validation of a detailed computer model for the electric fields in the brain.

A computer model has been designed for the calculation of the electrical fields in the head, based on the finite difference method. This method has not previously been applied for head modelling. The model was validated by using three concentric spheres and comparing it with an analytic model. Three levels of accuracy were tested. The forward solutions show that the finite difference algorithm works correctly and, by selecting the size of the volume elements properly, accurate results are obtained. The model will be applied to accurate and realistic geometries of the human head obtained from magnetic resonance images.