Automatic brain segmentation using fractional signal modeling of a multiple flip angle, spoiled gradient-recalled echo acquisition

Ahlgren, André; Wirestam, Ronnie; Ståhlberg, Freddy; Knutsson, Linda

doi:10.1007/s10334-014-0439-2

Cited by 14 publications

(18 citation statements)

References 32 publications

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“…For example, Ahlgren et al [70] utilized the signal of a spoiled gradientrecalled echo (SPGR) sequence acquired with multiple flip angles to map T1, and subsequently to fit of a multi-…”

Section: Resultsmentioning

confidence: 99%

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Partial volume effect modeling for segmentation and tissue classification of brain magnetic resonance images: A review

Tohka¹

2014

WJR

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Quantitative analysis of magnetic resonance (MR) brain images are facilitated by the development of automated segmentation algorithms. A single image voxel may contain of several types of tissues due to the finite spatial resolution of the imaging device. This phenomenon, termed partial volume effect (PVE), complicates the segmentation process, and, due to the complexity of human brain anatomy, the PVE is an important factor for accurate brain structure quantification. Partial volume estimation refers to a generalized segmentation task where the amount of each tissue type within each voxel is solved. This review aims to provide a systematic, tutorial-like overview and categorization of methods for partial volume estimation in brain MRI. The review concentrates on the statistically based approaches for partial volume estimation and also explains differences to other, similar image segmentation approaches.

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“…For example, Ahlgren et al [70] utilized the signal of a spoiled gradientrecalled echo (SPGR) sequence acquired with multiple flip angles to map T1, and subsequently to fit of a multi-…”

Section: Resultsmentioning

confidence: 99%

“…An interesting recent development in MRI segmentation and partial volume estimation is the use of quantitative tissue type maps for the purpose [70][71][72] . For example, Ahlgren et al [70] utilized the signal of a spoiled gradientrecalled echo (SPGR) sequence acquired with multiple flip angles to map T1, and subsequently to fit of a multi-…”

Section: Resultsmentioning

confidence: 99%

Partial volume effect modeling for segmentation and tissue classification of brain magnetic resonance images: A review

Tohka¹

2014

WJR

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“…Third, the method relies on the availability of PV estimates. The FSM approach has been shown to produce reliable PV estimates with high repeatability and robustness to noise . Furthermore, FSM can be implemented to yield PV estimates in the native space of ASL data (as in this work, and Refs and ).…”

Section: Discussionmentioning

confidence: 99%

Improved calculation of the equilibrium magnetization of arterial blood in arterial spin labeling

Ahlgren¹,

Wirestam²,

Knutsson

et al. 2018

Magnetic Resonance in Med

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Compared with the conventional approach, the proposed method produced more homogenous M0a maps, corresponding to a more accurate calibration. The proposed method also yielded significantly different perfusion values across the whole brain, and performed consistently in all subjects. The new M0a method improves quantitative perfusion estimation with arterial spin labeling, and can therefore be of considerable value in perfusion imaging applications.

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“…There are many previous works proposed as the methods for automatic segmentation in MR images which focus on different organs such as brain, kidneys, liver, and heart [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Fully automatic cardiac T2* relaxation time estimation using marker-controlled watershed

Wantanajittikul

Saekho

Phrommintikul

et al. 2015

2015 IEEE International Conference on Control System, Computing and Engineering (ICCSCE)

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Heart failure due to iron-overload cardiomyopathy is one of the main causes of mortality. The cardiomyopathy is reversible if intensive iron chelation treatment is done in time. However, the diagnosis is often delayed because the cardiac iron deposition is unpredictable and the symptoms are lately detected. The widely used method in many countries is by calculating a parameter called the T2* (T2-star) from magnetic resonance (MR) image sequences. In order to compute the T2* value, the region of interest (ROI) is manually selected by an expert which may require considerable time and skills. The aim of this work is hence to develop the fully automatic cardiac T2* measurement by using marker-controlled watershed for segmenting the interventricular septum in cardiac MR images. Mathematical morphologies are also used to reduce some errors. Moreover, a new approach for T2* evaluation is suggested in this work. The interventricular septa from MR images in all echo times (TE's) are segmented to calculate the signal intensities (SI's) while the classical method segments the interventricular septum only in the first TE. Thirty cardiac MR images were used in this work. The segmentation performances are evaluated only from the MR images in the first TE. The precision and recall are 0.9 and 0.8 compared with the two experts' opinions. It shows that the segmentation results of our proposed technique are quite similar to the experts' opinions. The T2* values were carried out based on the automatically segmented ROI's. The mean difference of T2* values between the proposed method and the experts' opinions is about 1 ms. This demonstrates that the T2* values from the proposed technique are also close to T2* values calculated by the experts.

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Automatic brain segmentation using fractional signal modeling of a multiple flip angle, spoiled gradient-recalled echo acquisition

Cited by 14 publications

References 32 publications

Partial volume effect modeling for segmentation and tissue classification of brain magnetic resonance images: A review

Partial volume effect modeling for segmentation and tissue classification of brain magnetic resonance images: A review

Improved calculation of the equilibrium magnetization of arterial blood in arterial spin labeling

Fully automatic cardiac T2* relaxation time estimation using marker-controlled watershed

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