| 141. |
|
|
| 142. |
|
|
| 143. |
- Romu, Thobias, et al.
(författare)
-
MANA - Multi scale adaptive normalized averaging
- 2011
-
Ingår i: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. - : IEEE conference proceedings. - 9781424441280 ; , s. 361-364
-
Konferensbidrag (refereegranskat)abstract
- It is possible to correct intensity inhomogeneity in fat–water Magnetic Resonance Imaging (MRI) by estimating a bias field based on the observed intensities of voxels classified as the pure adipose tissue. The same procedure can also be used to quantify fat volume and its distribution which opens up for new medical applications. The bias field estimation method has to be robust since pure fat voxels are irregularly located and the density varies greatly within and between image volumes. This paper introduces Multi scale Adaptive Normalized Average (MANA) that solves this problem bybasing the estimate on a scale space of weighted averages. By usingthe local certainty of the data MANA preserves details where the local data certainty is high and provides realistic values in sparse areas.
|
|
| 144. |
|
|
| 145. |
- Romu, Thobias, et al.
(författare)
-
Robust Water Fat Separated Dual-Echo MRI by Phase-Sensitive Reconstruction
- 2017
-
Ingår i: Magnetic Resonance in Medicine. - : Wiley-Blackwell. - 0740-3194 .- 1522-2594. ; 78:3, s. 1208-1216
-
Tidskriftsartikel (refereegranskat)abstract
- Purpose: To develop and evaluate a robust water-fat separation method for T1-weighted symmetric two-point Dixon data.Methods: A method for water-fat separation by phase unwrapping of the opposite-phase images by phase-sensitive reconstruction (PSR) is introduced. PSR consists of three steps; 1, identification of clusters of tissue voxels; 2, unwrapping of the phase in each cluster by solving Poisson’s equation; 3, find the correct sign of each unwrapped opposite-phase cluster, so that the water-fat images are assigned the correct identities. The robustness was evaluated by counting the number of water-fat swap artifacts in a total of 733 image volumes. The method was also compared to commercial software.Results: In the water-fat separated image volumes, the PSR method failed to unwrap the phase of one cluster and misclassified 10. One swap was observed in areas affected by motion and was constricted to the affected area. Twenty swaps were observed surrounding susceptibility artifacts, none of which spread outside the artifact affected regions. The PSR method had fewer swaps when compared to commercial software.Conclusion: The PSR method can robustly produce water-fat separated whole-body images based on symmetric two-echo spoiled gradient echo images, under both ideal conditions and in the presence of common artifacts.
|
|
| 146. |
- Romu, Thobias, et al.
(författare)
-
The effect of flip-angle on body composition using calibrated water-fat MRI.
- 2016
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- This study tested how the flip angle affects body composition analysis by MRI, if adipose tissue is used as an internal intensity reference. Whole-body water-fat images with flip angle 5° and 10° were collected from 29 women in an ongoing study. The images were calibrated based on the adipose tissue signal and whole-body total adipose, lean and soft tissue volumes were measured. A mean difference of 0.29 L, or 0.90 % of the average volume, and a coefficient of variation of 0.40 % was observed for adipose tissue.
|
|
| 147. |
|
|
| 148. |
- Rydell, Joakim, 1979-, et al.
(författare)
-
Adaptive filtering of fMRI data based on correlation and BOLD response similarity
- 2006
-
Ingår i: Acoustics, Speech and Signal Processing, 2006. ICASSP 2006. Vol. 2. - : IEEE conference proceedings. - 142440469X ; , s. II-997-II-1000
-
Konferensbidrag (refereegranskat)abstract
- In analysis of fMRI data, it is common to average neighboring voxels in order to obtain robust estimates of the correlations between voxel time-series and the model of the signal expected to be present in activated regions. We have previously proposed a method where only voxels with similar correlation coefficients are averaged. In this paper we extend this idea, and present a novel method for analysis of fMRI data. In the proposed method, only voxels with similar correlation coefficients and similar time-series are averaged. The proposed method is compared to our previous method and to two well-known filtering strategies, and is shown to have superior ability to discriminate between active and inactive voxels
|
|
| 149. |
- Rydell, Joakim, 1979-, et al.
(författare)
-
Adaptive fMRI data filtering based in tissue and signal similarities
- 2007
-
Ingår i: Joint Annual Meeting ISMRM-ESMRMB,2007.
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- A novel method for analyzing fMRI data is presented. In order to detect activation with the highest possible accuracy, adaptive filtering is used to enahancethe signal to noise ratio. Using a method similar to bilateral filtering, signals from different voxels are averaged if the voxels belong to the same type oftissue and their signal variations over time are similar. The detection performance is evaluated on synthetic and real data, and it is shown that the twocriterions for averaging complement each other, providing very good detection of activation.
|
|
| 150. |
- Rydell, Joakim, 1979-
(författare)
-
Advanced MRI Data Processing
- 2007
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Magnetic resonance imaging (MRI) is a very versatile imaging modality which can be used to acquire several different types of images. Some examples include anatomical images, images showing local brain activation and images depicting different types of pathologies. Brain activation is detected by means of functional magnetic resonance imaging (fMRI). This is useful e.g. in planning of neurosurgical procedures and in neurological research. To find the activated regions, a sequence of images of the brain is collected while a patient or subject alters between resting and performing a task. The variations in image intensity over time are then compared to a model of the variations expected to be found in active parts of the brain. Locations with high correlation between the intensity variations and the model are considered to be activated by the task.Since the images are very noisy, spatial filtering is needed before the activation can be detected. If adaptive filtering is used, i.e. if the filter at each location is adapted to the local neighborhood, very good detection performance can be obtained. This thesis presents two methods for adaptive spatial filtering of fMRI data. One of these is a modification of a previously proposed method, which at each position maximizes the similarity between the filter response and the model. A novel feature of the presented method is rotational invariance, i.e. equal sensitivity to activated regions in different orientations. The other method is based on bilateral filtering. At each position, this method averages pixels which are located in the same type of brain tissue and have similar intensity variation over time.A method for robust correlation estimation is also presented. This method automatically detects local bursts of noise in a signal and disregards the corresponding signal segments when the correlation is estimated. Hence, the correlation estimate is not affected by the noise bursts. This method is useful not only in analysis of fMRI data, but also in other applications where correlation is used to determine the similarity between signals.Finally, a method for correcting artifacts in complex MR images is presented. Complex images are used e.g. in the Dixon technique for separate imaging of water and fat. The phase of these images is often affected by artifacts and therefore need correction before the actual water and fat images can be calculated. The presented method for phase correction is based on an image integration technique known as the inverse gradient. The method is shown to provide good results even when applied to images with severe artifacts.
|
|
