Sequence optimization to reduce velocity offsets in cardiovascular magnetic resonance volume flow quantification--a multi-vendor study.

PURPOSE: Eddy current induced velocity offsets are of concern for accuracy in cardiovascular magnetic resonance (CMR) volume flow quantification. However, currently known theoretical aspects of eddy current behavior have not led to effective guidelines for the optimization of flow quantification sequences. This study is aimed at identifying correlations between protocol parameters and the resulting velocity error in clinical CMR flow measurements in a multi-vendor study. METHODS: Nine 1.5T scanners of three different types/vendors were studied. Measurements were performed on a large stationary phantom. Starting from a clinical breath-hold flow protocol, several protocol parameters were varied. Acquisitions were made in three clinically relevant orientations. Additionally, a time delay between the bipolar gradient and read-out, asymmetric versus symmetric velocity encoding, and gradient amplitude and slew rate were studied in adapted sequences as exploratory measurements beyond the protocol. Image analysis determined the worst-case offset for a typical great-vessel flow measurement. RESULTS: The results showed a great variation in offset behavior among scanners (standard deviation among samples of 0.3, 0.4, and 0.9 cm/s for the three different scanner types), even for small changes in the protocol. Considering the absolute values, none of the tested protocol settings consistently reduced the velocity offsets below the critical level of 0.6 cm/s neither for all three orientations nor for all three scanner types. Using multilevel linear model analysis, oblique aortic and pulmonary slices showed systematic higher offsets than the transverse aortic slices (oblique aortic 0.6 cm/s, and pulmonary 1.8 cm/s higher than transverse aortic). The exploratory measurements beyond the protocol yielded some new leads for further sequence development towards reduction of velocity offsets; however those protocols were not always compatible with the time-constraints of breath-hold imaging and flow-related artefacts. CONCLUSIONS: This study showed that with current systems there was no generic protocol which resulted into acceptable flow offset values. Protocol optimization would have to be performed on a per scanner and per protocol basis. Proper optimization might make accurate (transverse) aortic flow quantification possible for most scanners. Pulmonary flow quantification would still need further (offline) correction.

3D velocity quantification in the heart: improvements by 3D PC-SSFP.

PURPOSE: To test whether a 3D imaging sequence with phase contrast (PC) velocity encoding based on steady-state free precession (SSFP) improves 3D velocity quantification in the heart compared to the currently available gradient echo (GE) approach. MATERIALS AND METHODS: The 3D PC-SSFP sequence with 1D velocity encoding was compared at the mitral valve in 12 healthy subjects with 3D PC-GE at 1.5T. Velocity measurements, velocity-to-noise-ratio efficiency (VNR(eff)), intra- and interobserver variability of area and velocity measurements, contrast-to-noise-ratio (CNR), and artifact sensitivity were evaluated in both long- and short-axis orientation. RESULTS: Descending aorta mean and peak velocities correlated well (r(2) = 0.79 and 0.93) between 3D PC-SSFP and 3D PC-GE. At the mitral valve, mean velocity correlation was moderate (r(2) = 0.70 short axis, 0.56 long axis) and peak velocity showed good correlation (r(2) = 0.94 short axis, 0.81 long axis). In some cases VNR(eff) was higher, in others lesser, depending on slab orientation and cardiac phase. Intra- and interobserver variability was generally better for 3D PC-SSFP. CNR improved significantly, especially at end systole. Artifact levels did not increase. CONCLUSION: 3D SSFP velocity quantification was successfully tested in the heart. Blood-myocardium contrast improved significantly, resulting in more reproducible velocity measurements for 3D PC-SSFP at 1.5T.

Extrinsic multiecho phase-contrast SSFP: evaluation on cardiac output measurements.

Multiecho phase-contrast steady-state free precession (PC-SSFP) is a recently introduced sequence for flow quantification. In this multiecho approach, a phase reference and a velocity-encoded readout were acquired at different echo times after a single excitation. In this study, the sequence is validated in vitro for stationary flow. Subsequently, the sequence was evaluated on cardiac output measurements in vivo for through-plane flow in comparison to regular single gradient echo velocity quantification [phase-contrast spoiled gradient echo (PC-GE)]. In vitro results agreed with regular flow meters (RMS 0.1 cm/s). Cardiac output measurements with multiecho PC-SSFP on 10 healthy subjects gave on average the same results as the standard PC-GE. However, the limits of repeatability of PC-SSFP were significantly larger than those of PC-GE (2 l/min and 0.5 l/min, respectively, P=.001). The multiecho approach introduced some specific problems in vivo. The difference in echo times made the velocity maps sensitive for water-fat shifts and B(0)-drifts, which in turn made velocity offset correction problematic. Also, the addition of a single bipolar gradient cancelled the flow compensated nature of the SSFP sequence. In combination with the prolonged TR, this resulted in flow artifacts caused by high and pulsatile through-plane flow, affecting repeatability. Given the significantly lower repeatability of PC-SSFP, cardiac output in turn is less reliable, thus impairing the use of multiecho PC-SSFP.

Constant volume of the human lens and decrease in surface area of the capsular bag during accommodation: an MRI and Scheimpflug study.

PURPOSE: A change in surface area of the capsular bag and a change in volume of the lens can indicate whether a change in the shape of the lens during accommodation is due to the compressibility or the elasticity of the lens material. METHODS: 3D magnetic resonance imaging (MRI) was used to image the complete shape of the lens in a group of five healthy subjects between 18 and 35 years of age. A parametric representation of the cross-sectional shape was fitted to the edges of the lens, which were determined with a Canny edge filter. Based on a partition of the lens into eight parts, the parametric shape makes it possible to calculate the mean cross-sectional area, the volume, and the surface area as a function of accommodation. Corrected Scheimpflug imaging was used to validate the results obtained with MRI. RESULTS: No significant difference in central anterior and posterior radius of curvature and thickness was found between the MRI and Scheimpflug measurements. In accordance with the Helmholtz accommodation theory, a decrease in the anterior and posterior radius of curvature and equatorial diameter and an increase in lens thickness occurred with accommodation. During accommodation, the mean cross-sectional area increased and the surface area decreased. However, no significant change in lens volume was found. CONCLUSIONS: The preservation of lens volume implies that the internal human lens material can be assumed to be incompressible and is undergoing elastic deformation. Furthermore, the change in surface area indicates that the capsular bag also undergoes elastic deformation.

Equivalent refractive index of the human lens upon accommodative response.

PURPOSE: To experimentally verify the suggestion of Gullstrand (1909), i.e., that the equivalent refractive index of the human lens increases with accommodation. METHODS: The left eye of five subjects was focused on different accommodation stimuli, while the right eye was imaged with Scheimpflug photography in order to obtain the shape of the lens and cornea during accommodation. The procedure was then repeated, but instead of using the Scheimpflug camera, the accommodative response of the right eye was measured objectively with an aberrometer. The axial length was measured with a Zeiss IOL-master. Combining the results of these measurements made it possible to correct the digital Scheimpflug images for corneal and lenticular refraction, and to simultaneously calculate the equivalent refractive index of the lens for all different accommodative stimuli. Furthermore, a two-compartment model of the lens was constructed, with a nucleus and a cortex. RESULTS: In all five subjects there was no significant change in the equivalent refractive index of the lens as a function of accommodation. The mean equivalent refractive index was 1.435 +/- 0.008. Furthermore, the accommodative response appeared to be lower than the accommodative stimulus (i.e., accommodative lag). It appeared to be possible to model the optical power of the lens, based on the geometry of cortex and nucleus. Based on a refractive index of 1.406 for the nucleus, the mean refractive index of the cortex was 1.381. CONCLUSIONS: Gullstrand suggested that there would be an increase in the equivalent refractive index with accommodation; the intra-capsulary mechanism of accommodation. However, we found that the equivalent refractive index of the lens does not change with accommodation when the accommodative lag is taken into account. Furthermore, it appeared to be possible to simulate the accommodative process of a subject with a two-compartment model with constant refractive indices.

Development of a ciliary muscle-driven accommodating intraocular lens.

PURPOSE: To develop a ciliary muscle-driven accommodating intraocular lens (IOL) that has a large and predictable range of variable power as a step toward spectacle independence. SETTING: Department of Physics and Medical Technology, VU University Medical Center, Amsterdam, The Netherlands. METHODS: A concept IOL that has a rotating focus mechanism and a mechanical frame that can operate within the range of ciliary muscle contraction of a typical 60-year-old human eye was designed. Prototypes were made to test the IOL's mechanical performance in an enucleated pig's eye using a laboratory lens-stretching device that mimics the action of the human ciliary muscle. Changes in focal length during stretching were measured by laser-based ray tracing and a videocamera system. To rotate the 2 lenses in the IOL with variable optical power, a frame that allows the displacement and force of the ciliary muscle to be transferred by the capsular bag was designed. RESULTS: Ray tracing showed that the modulation transfer function (MTF) of the IOL in different accommodative states did not deviate to a great extent from the MTF of a monofocal IOL. During stretching experiments, the prototype IOL achieved 8.0 diopters of accommodation. CONCLUSIONS: Evaluation of an accommodating IOL that meets the requirements for a spectacle-independent solution to presbyopia showed that the mechanical and optical designs must be further optimized to improve optical quality and functionality.

Correction for desynchronization of EEG and fMRI clocks through data interpolation optimizes artifact reduction.

Co-registration of EEG (Electroencephalogram)- and fMRI (functional magnetic resonance imaging) remains a challenge due to the large artifacts induced on the EEG by the MR (magnetic resonance) sequence gradient and RF pulses. We present an algorithm, based on the average-subtraction method, which is able to correct EEG data for gradient and RF pulse artifacts. We optimized artifact reduction by correcting the misalignment of EEG and fMRI data samples, resulting from the asynchronous sampling of EEG and fMRI data, through interpolation of EEG data. A clustering algorithm is proposed to account for the variability of the pulse artifact. Results show that the algorithm was able to keep the spontaneous brain activity while removing gradient and pulse artifacts with only a subtraction of selectively averaged data. Pulse artifact clustering showed that most of the variability was due to the time jitter of the pulse artifact markers. We show that artifact reduction by average-subtraction is optimized by interpolating the EEG data to correct for asynchronously sampled EEG and fMRI data.

DENSE and HARP: two views on the same technique of phase-based strain imaging.

PURPOSE: To discuss differences between displacement encoding with stimulated echoes (DENSE) and the harmonic phase (HARP) in imaging and reconstruction strategies. MATERIALS AND METHODS: HARP and DENSE are presented in their historical context: while the HARP method was developed from the framework of myocardial tagging, DENSE arose from the framework of stimulated echo and displacement encoding using bipolar gradients. Both techniques have evolved since their introduction, thereby becoming more similar over time and losing their distinct features. Newly introduced improvements have successfully been applied in both methods. Differences between both methods are discussed point by point. RESULTS: From this discussion it follows that almost all apparent differences are in fact nonexistent. CONCLUSION: In the literature, both techniques are still regarded as distinctly different techniques, where a more general treatment of the technique is justified. Once it is realized that both frameworks are easily merged, the benefits are 1) less confusion about the (dis)advantages of either technique, and 2) understanding of phase-based strain imaging that is more general than HARP or DENSE alone.

The spatiotemporal MEG covariance matrix modeled as a sum of Kronecker products.

The single Kronecker product (KP) model for the spatiotemporal covariance of MEG residuals is extended to a sum of Kronecker products. This sum of KP is estimated such that it approximates the spatiotemporal sample covariance best in matrix norm. Contrary to the single KP, this extension allows for describing multiple, independent phenomena in the ongoing background activity. Whereas the single KP model can be interpreted by assuming that background activity is generated by randomly distributed dipoles with certain spatial and temporal characteristics, the sum model can be physiologically interpreted by assuming a composite of such processes. Taking enough terms into account, the spatiotemporal sample covariance matrix can be described exactly by this extended model. In the estimation of the sum of KP model, it appears that the sum of the first 2 KP describes between 67% and 93%. Moreover, these first two terms describe two physiological processes in the background activity: focal, frequency-specific alpha activity, and more widespread non-frequency-specific activity. Furthermore, temporal nonstationarities due to trial-to-trial variations are not clearly visible in the first two terms, and, hence, play only a minor role in the sample covariance matrix in terms of matrix power. Considering the dipole localization, the single KP model appears to describe around 80% of the noise and seems therefore adequate. The emphasis of further improvement of localization accuracy should be on improving the source model rather than the covariance model.

The coupled dipole model: an integrated model for multiple MEG/EEG data sets.

Often MEG/EEG is measured in a few slightly different conditions to investigate the functionality of the human brain. This kind of data sets show similarities, though are different for each condition. When solving the inverse problem (IP), performing the source localization, one encounters the problem that this IP is ill-posed: constraints are necessary to solve and stabilize the solution to the IP. Moreover, a substantial amount of data is needed to avoid a signal to noise ratio (SNR) that is too poor for source localizations. In the case of similar conditions, this common information can be exploited by analyzing the data sets simultaneously. The here proposed coupled dipole model (CDM) provides an integrated method in which these similarities between conditions are used to solve and stabilize the inverse problem. The coupled dipole model is applicable when data sets contain common sources or common source time functions. The coupled dipole model uses a set of common sources and a set of common source time functions (STFs) to model all conditions in one single model. The data of each condition are mathematically described as a linear combination of these common spatial and common temporal components. This linear combination is specified in a coupling matrix for each data set. The coupled dipole model was applied in two simulation studies and in one experimental study. The simulations show that the errors in the estimated spatial and temporal parameters decrease compared to the standard separate analyses. A decrease in position error of a factor of 10 was shown for the localization of two nearby sources. In the experimental application, the coupled dipole model was shown to be necessary to obtain a plausible solution in at least 3 of 15 conditions investigated. Moreover, using the CDM, a direct comparison between parameters in different conditions is possible, whereas in separate models, the scaling of the amplitude parameters varies in general from data set to data set.

A mathematical approach to the temporal stationarity of background noise in MEG/EEG measurements.

The general spatiotemporal covariance matrix of the background noise in MEG/EEG signals is huge. To reduce the dimensionality of this matrix it is modeled as a Kronecker product of a spatial and a temporal covariance matrix. When the number of time samples is larger than, say, J = 500, the iterative Maximum Likelihood estimation of these two matrices is still too time-consuming to be useful on a routine basis. In this study we looked for methods to circumvent this computationally expensive procedure by using a parametric model with subject-dependent parameters. Such a model would additionally help with interpreting MEG/EEG signals. For the spatial covariance, models have been derived already and it has been shown that measured MEG/EEG signals can be understood spatially as random processes, generated by random dipoles. The temporal covariance, however, has not been modeled yet, therefore we studied the temporal covariance matrix in several subjects. For all subjects the temporal covariance shows an alpha oscillation and vanishes for large time lag. This gives rise to a temporal noise model consisting of two components: alpha activity and additional random noise. The alpha activity is modeled as randomly occurring waves with random phase and the covariance of the additional noise decreases exponentially with lag. This model requires only six parameters instead of 12 J(J + 1). Theoretically, this model is stationary but in practice the stationarity of the matrix is highly influenced by the baseline correction. It appears that very good agreement between the data and the parametric model can be obtained when the baseline correction window is taken into account properly. This finding implies that the background noise is in principle a stationary process and that nonstationarities are mainly caused by the nature of the preprocessing method. When analyzing events at a fixed sample after the stimulus (e.g., the SEF N20 response) one can take advantage of this nonstationarity by optimizing the baseline window to obtain a low noise variance at this particular sample.

In vivo measurement of the brain and skull resistivities using an EIT-based method and realistic models for the head.

In vivo measurements of equivalent resistivities of skull (rho(skull)) and brain (rho(brain)) are performed for six subjects using an electric impedance tomography (EIT)-based method and realistic models for the head. The classical boundary element method (BEM) formulation for EIT is very time consuming. However, the application of the Sherman-Morrison formula reduces the computation time by a factor of 5. Using an optimal point distribution in the BEM model to optimize its accuracy, decreasing systematic errors of numerical origin, is important because cost functions are shallow. Results demonstrate that rho(skull)/rho(brain) is more likely to be within 20 and 50 rather than equal to the commonly accepted value of 80. The variation in rho(brain)(average = 301 omega x cm, SD = 13%) and rho(skull)(average = 12230 omega x cm, SD = 18%) is decreased by half, when compared with the results using the sphere model, showing that the correction for geometry errors is essential to obtain realistic estimations. However, a factor of 2.4 may still exist between values of rho(skull)/rho(brain) corresponding to different subjects. Earlier results show the necessity of calibrating rho(brain) and rho(skull) by measuring them in vivo for each subject, in order to decrease errors associated with the electroencephalogram inverse problem. We show that the proposed method is suited to this goal.