Segmentation of the skull in MRI volumes using deformable model and taking the partial volume effect into account

Fully automated quantification of in vivo viscoelasticity of prostate zones using magnetic resonance elastography with Dense U-net segmentation

Magnetic resonance elastography (MRE) for measuring viscoelasticity heavily depends on proper tissue segmentation, especially in heterogeneous organs such as the prostate. Using trained network-based image segmentation, we investigated if MRE data suffice to extract anatomical and viscoelastic information for automatic tabulation of zonal mechanical properties of the prostate. Overall, 40 patients with benign prostatic hyperplasia (BPH) or prostate cancer (PCa) were examined with three magnetic resonance imaging (MRI) sequences: T2-weighted MRI (T2w), diffusion-weighted imaging (DWI), and MRE-based tomoelastography, yielding six independent sets of imaging data per patient (T2w, DWI, apparent diffusion coefficient, MRE magnitude, shear wave speed, and loss angle maps). Combinations of these data were used to train Dense U-nets with manually segmented masks of the entire prostate gland (PG), central zone (CZ), and peripheral zone (PZ) in 30 patients and to validate them in 10 patients. Dice score (DS), sensitivity, specificity, and Hausdorff distance were determined. We found that segmentation based on MRE magnitude maps alone (DS, PG: 0.93 ± 0.04, CZ: 0.95 ± 0.03, PZ: 0.77 ± 0.05) was more accurate than magnitude maps combined with T2w and DWI_b (DS, PG: 0.91 ± 0.04, CZ: 0.91 ± 0.06, PZ: 0.63 ± 0.16) or T2w alone (DS, PG: 0.92 ± 0.03, CZ: 0.91 ± 0.04, PZ: 0.65 ± 0.08). Automatically tabulated MRE values were not different from ground-truth values (P>0.05). In conclusion, MRE combined with Dense U-net segmentation allows tabulation of quantitative imaging markers without manual analysis and independent of other MRI sequences and can thus contribute to PCa detection and classification.

Mass Segmentation in Automated 3-D Breast Ultrasound Using Adaptive Region Growing and Supervised Edge-Based Deformable Model

Automated 3-D breast ultrasound has been proposed as a complementary modality to mammography for early detection of breast cancers. To facilitate the interpretation of these images, computer aided detection systems are being developed in which mass segmentation is an essential component for feature extraction and temporal comparisons. However, automated segmentation of masses is challenging because of the large variety in shape, size, and texture of these 3-D objects. In this paper, the authors aim to develop a computerized segmentation system, which uses a seed position as the only priori of the problem. A two-stage segmentation approach has been proposed incorporating shape information of training masses. At the first stage, a new adaptive region growing algorithm is used to give a rough estimation of the mass boundary. The similarity threshold of the proposed algorithm is determined using a Gaussian mixture model based on the volume and circularity of the training masses. In the second stage, a novel geometric edge-based deformable model is introduced using the result of the first stage as the initial contour. In a data set of 50 masses, including 38 malignant and 12 benign lesions, the proposed segmentation method achieved a mean Dice of 0.74 ± 0.19 which outperformed the adaptive region growing with a mean Dice of 0.65 ± 0.2 (p-value < 0.02). Moreover, the resulting mean Dice was significantly (p-value < 0.001) better than that of the distance regularized level set evolution method (0.52 ± 0.27). The supervised method presented in this paper achieved accurate mass segmentation results in terms of Dice measure. The suggested segmentation method can be utilized in two aspects: 1) to automatically measure the change in volume of breast lesions over time and 2) to extract features for a computer aided detection or diagnosis system.

The Relationship between Brachycephalic Head Features in Modern Persian Cats and Dysmorphologies of the Skull and Internal Hydrocephalus

Background

Cat breeders observed a frequent occurrence of internal hydrocephalus in Persian cats with extreme brachycephalic head morphology.

Objective

To investigate a possible relationship among the grade of brachycephaly, ventricular dilatation, and skull dysmorphologies in Persian cats.

Animals

92 Persian‐, 10 Domestic shorthair cats.

Methods

The grade of brachycephaly was determined on skull models based on CT datasets. Cranial measurements were examined with regard to a possible correlation with relative ventricular volume, and cranial capacity. Persians with high (peke‐face Persians) and lower grades of brachycephaly (doll‐face Persians) were investigated for the presence of skull dysmorphologies.

Results

The mean cranial index of the peke‐face Persians (0.97 ± 0.14) was significantly higher than the mean cranial index of doll‐face Persians (0.66 ± 0.04; P < 0.001). Peke‐face Persians had a lower relative nasal bone length (0.15 ± 0.04) compared to doll‐face (0.29 ± 0.08; P < 0.001). The endocranial volume was significantly lower in doll‐face than peke‐face Persians (89.6 ± 1.27% versus 91.76 ± 2.07%; P < 0.001). The cranial index was significantly correlated with this variable (Spearman's r: 0.7; P < 0.0001). Mean ventricle: Brain ratio of the peke‐face group (0.159 ± 0.14) was significantly higher compared to doll‐face Persians (0.015 ± 0.01; P < 0.001).

Conclusion and Clinical Relevance

High grades of brachycephaly are also associated with malformations of the calvarial and facial bones as well as dental malformations. As these dysmorphologies can affect animal welfare, the selection for extreme forms of brachycephaly in Persian cats should be reconsidered.

Neurological software tool for reliable atrophy measurement (NeuroSTREAM) of the lateral ventricles on clinical-quality T2-FLAIR MRI scans in multiple sclerosis

Background

There is a need for a brain volume measure applicable to the clinical routine scans. Nearly every multiple sclerosis (MS) protocol includes low-resolution 2D T2-FLAIR imaging.

Objectives

To develop and validate cross-sectional and longitudinal brain atrophy measures on clinical-quality T2-FLAIR images in MS patients.

Methods

A real-world dataset from 109 MS patients from 62 MRI scanners was used to develop a lateral ventricular volume (LVV) algorithm with a longitudinal Jacobian-based extension, called NeuroSTREAM. Gold-standard LVV was calculated on high-resolution T1 1 mm, while NeuroSTREAM LVV was obtained on low-resolution T2-FLAIR 3 mm thick images. Scan-rescan reliability was assessed in 5 subjects. The variability of LVV measurement at different field strengths was tested in 76 healthy controls and 125 MS patients who obtained both 1.5T and 3T scans in 72 hours. Clinical validation of algorithm was performed in 176 MS patients who obtained serial yearly MRI 1.5T scans for 10 years.

Results

Correlation between gold-standard high-resolution T1 LVV and low-resolution T2-FLAIR LVV was r = 0.99, p < 0.001 and the scan-rescan coefficient of variation was 0.84%. Correlation between low-resolution T2-FLAIR LVV on 1.5T and 3T was r = 0.99, p < 0.001 and the scan-rescan coefficient of variation was 2.69% cross-sectionally and 2.08% via Jacobian integration. NeuroSTREAM showed comparable effect size (d = 0.39–0.71) in separating MS patients with and without confirmed disability progression, compared to SIENA and VIENA.

Conclusions

Brain atrophy measurement on clinical quality T2-FLAIR scans is feasible, accurate, reliable, and relates to clinical outcomes.

•

A robust algorithm for measuring lateral ventricular volume on clinical FLAIR scans is proposed.

•

The algorithm combines multi-atlas joint fusion labeling with level-set smoothness-constraining refinement.

•

Results show a similar relationship to disability progression as with established metrics on high-resolution scans.

Weighted Level Set Evolution Based on Local Edge Features for Medical Image Segmentation

Level set methods have been widely used to implement active contours for image segmentation applications due to their good boundary detection accuracy. In the context of medical image segmentation, weak edges and inhomogeneities remain important issues that may hinder the accuracy of any segmentation method based on active contours implemented using level set methods. This paper proposes a method based on active contours implemented using level set methods for segmentation of such medical images. The proposed method uses a level set evolution that is based on the minimization of an objective energy functional whose energy terms are weighted according to their relative importance in detecting boundaries. This relative importance is computed based on local edge features collected from the adjacent region located inside and outside of the evolving contour. The local edge features employed are the edge intensity and the degree of alignment between the image's gradient vector flow field and the evolving contour's normal. We evaluate the proposed method for segmentation of various regions in real MRI and CT slices, X-ray images, and ultra sound images. Evaluation results confirm the advantage of weighting energy forces using local edge features to reduce leakage. These results also show that the proposed method leads to more accurate boundary detection results than state-of-the-art edge-based level set segmentation methods, particularly around weak edges.

Multi-atlas and label fusion approach for patient-specific MRI based skull estimation

PURPOSE

MRI-based skull segmentation is a useful procedure for many imaging applications. This study describes a methodology for automatic segmentation of the complete skull from a single T1-weighted volume.

METHODS

The skull is estimated using a multi-atlas segmentation approach. Using a whole head computed tomography (CT) scan database, the skull in a new MRI volume is detected by nonrigid image registration of the volume to every CT, and combination of the individual segmentations by label-fusion. We have compared Majority Voting, Simultaneous Truth and Performance Level Estimation (STAPLE), Shape Based Averaging (SBA), and the Selective and Iterative Method for Performance Level Estimation (SIMPLE) algorithms.

RESULTS

The pipeline has been evaluated quantitatively using images from the Retrospective Image Registration Evaluation database (reaching an overlap of 72.46 ± 6.99%), a clinical CT-MR dataset (maximum overlap of 78.31 ± 6.97%), and a whole head CT-MRI pair (maximum overlap 78.68%). A qualitative evaluation has also been performed on MRI acquisition of volunteers.

CONCLUSION

It is possible to automatically segment the complete skull from MRI data using a multi-atlas and label fusion approach. This will allow the creation of complete MRI-based tissue models that can be used in electromagnetic dosimetry applications and attenuation correction in PET/MR.

Comparison of the Relationship between Cerebral White Matter and Grey Matter in Normal Dogs and Dogs with Lateral Ventricular Enlargement

Large cerebral ventricles are a frequent finding in brains of dogs with brachycephalic skull conformation, in comparison with mesaticephalic dogs. It remains unclear whether oversized ventricles represent a normal variant or a pathological condition in brachycephalic dogs. There is a distinct relationship between white matter and grey matter in the cerebrum of all eutherian mammals. The aim of this study was to determine if this physiological proportion between white matter and grey matter of the forebrain still exists in brachycephalic dogs with oversized ventricles. The relative cerebral grey matter, white matter and cerebrospinal fluid volume in dogs were determined based on magnetic-resonance-imaging datasets using graphical software. In an analysis of covariance (ANCOVA) using body mass as the covariate, the adjusted means of the brain tissue volumes of two groups of dogs were compared. Group 1 included 37 mesaticephalic dogs of different sizes with no apparent changes in brain morphology, and subjectively normal ventricle size. Group 2 included 35 brachycephalic dogs in which subjectively enlarged cerebral ventricles were noted as an incidental finding in their magnetic-resonance-imaging examination. Whereas no significant different adjusted means of the grey matter could be determined, the group of brachycephalic dogs had significantly larger adjusted means of lateral cerebral ventricles and significantly less adjusted means of relative white matter volume. This indicates that brachycephalic dogs with subjective ventriculomegaly have less white matter, as expected based on their body weight and cerebral volume. Our study suggests that ventriculomegaly in brachycephalic dogs is not a normal variant of ventricular volume. Based on the changes in the relative proportion of WM and CSF volume, and the unchanged GM proportions in dogs with ventriculomegaly, we rather suggest that distension of the lateral ventricles might be the underlying cause of pressure related periventricular loss of white matter tissue, as occurs in internal hydrocephalus.

Skull Segmentation and Reconstruction From Newborn CT Images Using Coupled Level Sets

This study presents a new approach for segmentation and reconstruction of newborn's skull including bones, fontanels, and sutures from computed tomography (CT) images. The segmentation approach relies on propagation of a pair of interacting smooth surfaces based on geodesic active regions. These surfaces evolve in opposite directions; the exterior surface moves inward while the interior one moves in outward direction. The moving surfaces are forced to stop when arriving at the outer or the inner surface of the cranial bones using edge information. Since fontanels and sutures are not directly detectable in CT images, this method imposes specific propagation constraints for coupled interfaces to prevent the moving surfaces from intersecting each other and penetrating into the opposite region. Finally, an algorithm for level set initialization is introduced which enforces the evolving surfaces to conform to the shape of the head. The proposed method was evaluated using 18 neonatal CT images. The segmentation results achieved by the suggested method have been compared with manual segmentations by two different raters, performed to establish a reliable reference. The comparison of the two segmentation results using the Dice similarity coefficient and modified Hausdorff distance shows that the proposed approach provides satisfactory results.

Multiscale segmentation of the skull in MR images for MRI-based attenuation correction of combined MR/PET

BACKGROUND AND OBJECTIVE

Combined magnetic resonance/positron emission tomography (MR/PET) is a relatively new, hybrid imaging modality. MR-based attenuation correction often requires segmentation of the bone on MR images. In this study, we present an automatic segmentation method for the skull on MR images for attenuation correction in brain MR/PET applications.

MATERIALS AND METHODS

Our method transforms T1-weighted MR images to the Radon domain and then detects the features of the skull image. In the Radon domain we use a bilateral filter to construct a multiscale image series. For the repeated convolution we increase the spatial smoothing in each scale and make the width of the spatial and range Gaussian function doubled in each scale. Two filters with different kernels along the vertical direction are applied along the scales from the coarse to fine levels. The results from a coarse scale give a mask for the next fine scale and supervise the segmentation in the next fine scale. The use of the multiscale bilateral filtering scheme is to improve the robustness of the method for noise MR images. After combining the two filtered sinograms, the reciprocal binary sinogram of the skull is obtained for the reconstruction of the skull image.

RESULTS

This method has been tested with brain phantom data, simulated brain data, and real MRI data. For real MRI data the Dice overlap ratios are 92.2%±1.9% between our segmentation and manual segmentation.

CONCLUSIONS

The multiscale segmentation method is robust and accurate and can be used for MRI-based attenuation correction in combined MR/PET.

A two-stage rule-constrained seedless region growing approach for mandibular body segmentation in MRI

PURPOSE

Extraction of the mandible from 3D volumetric images is frequently required for surgical planning and evaluation. Image segmentation from MRI is more complex than CT due to lower bony signal-to-noise. An automated method to extract the human mandible body shape from magnetic resonance (MR) images of the head was developed and tested.

METHODS

Anonymous MR images data sets of the head from 12 subjects were subjected to a two-stage rule-constrained region growing approach to derive the shape of the body of the human mandible. An initial thresholding technique was applied followed by a 3D seedless region growing algorithm to detect a large portion of the trabecular bone (TB) regions of the mandible. This stage is followed with a rule-constrained 2D segmentation of each MR axial slice to merge the remaining portions of the TB regions with lower intensity levels. The two-stage approach was replicated to detect the cortical bone (CB) regions of the mandibular body. The TB and CB regions detected from the preceding steps were merged and subjected to a series of morphological processes for completion of the mandibular body region definition. Comparisons of the accuracy of segmentation between the two-stage approach, conventional region growing method, 3D level set method, and manual segmentation were made with Jaccard index, Dice index, and mean surface distance (MSD).

RESULTS

The mean accuracy of the proposed method is [Formula: see text] for Jaccard index, [Formula: see text] for Dice index, and [Formula: see text] mm for MSD. The mean accuracy of CRG is [Formula: see text] for Jaccard index, [Formula: see text] for Dice index, and [Formula: see text] mm for MSD. The mean accuracy of the 3D level set method is [Formula: see text] for Jaccard index, [Formula: see text] for Dice index, and [Formula: see text] mm for MSD. The proposed method shows improvement in accuracy over CRG and 3D level set.

CONCLUSION

Accurate segmentation of the body of the human mandible from MR images is achieved with the proposed two-stage rule-constrained seedless region growing approach. The accuracy achieved with the two-stage approach is higher than CRG and 3D level set.

A fast region-based active contour model for boundary detection of echocardiographic images

This paper presents the boundary detection of atrium and ventricle in echocardiographic images. In case of mitral regurgitation, atrium and ventricle may get dilated. To examine this, doctors draw the boundary manually. Here the aim of this paper is to evolve the automatic boundary detection for carrying out segmentation of echocardiography images. Active contour method is selected for this purpose. There is an enhancement of Chan-Vese paper on active contours without edges. Our algorithm is based on Chan-Vese paper active contours without edges, but it is much faster than Chan-Vese model. Here we have developed a method by which it is possible to detect much faster the echocardiographic boundaries. The method is based on the region information of an image. The region-based force provides a global segmentation with variational flow robust to noise. Implementation is based on level set theory so it easy to deal with topological changes. In this paper, Newton-Raphson method is used which makes possible the fast boundary detection.

Age-related changes of digital endocranial volume during human ontogeny: results from an osteological reference collection

Endocranial volume (EV) estimation is widely used in physical anthropology for assessing brain size differences between taxa and monitoring the emergence of brain growth patterns in modern humans. However, to date, no reference data are available for modern human EV ontogeny. We measured 94 skulls with known sex and age (ranging from 0 to 7.5 years) from the osteological collection of Strasbourg University (OCSU) by using an accurate digital active contour model algorithm on 3D virtual models, reconstructed by CT. The OCSU data also allow us to propose improved equations for estimating EV in immature individuals from dry skull diameters (length, width, and height). Aside from the EV, the average proportional endocranial volume (PEV), corresponding to the ratio of EV at a given age to the average EV in the corresponding adult population, was also computed. EV nearly doubles during the first year of life, and later continues to expand more slowly, at least until 7 years of age. No sex differences can be demonstrated between the EV distributions of boys and girls in this sample. However, although PEV at birth is identical in girls and boys, it later displays significantly higher values in the girls of our series. PEV obtained at birth is 22%, which is quite different from values established for the brain itself from autopsied individuals, or MRI data. This suggests that assessments of EV and PEV values in fossil specimens should be conducted by using identical measures in comparative samples of extant humans and apes.

Skull segmentation in 3D neonatal MRI using hybrid Hopfield Neural Network

A fully automated method for segmentation of neonatal skull in Magnetic Resonance (MR) images for source localization of electrical/magnetic encephalography (EEG/MEG) signals is proposed. Finding the source of these signals shows the origin of an abnormality. We propose a hybrid algorithm in which a Bayesian classifying framework is combined with a Hopfield Neural Network (HNN) for neonatal skull segmentation. Due to the non-homogeneity of skull intensities in MR images, local statistical parameters are used for adaptive training of Hopfield neural network based on Bayesian classifier error. The experimental results, which are obtained on high resolution T1-weighted MR images of nine neonates with gestational ages between 39 and 42 weeks, show 65% accuracy which consistently exhibits our scheme's superiority in comparison with previous neonatal skull segmentation methods.

Extreme leg motion analysis of professional ballet dancers via MRI segmentation of multiple leg postures

PURPOSE

Professional ballet dancers are subject to constant extreme motion which is known to be at the origin of many articular disorders. To analyze their extreme motion, we exploit a unique magnetic resonance imaging (MRI) protocol, denoted as 'dual-posture' MRI, which scans the subject in both the normal (supine) and extreme (split) postures. However, due to inhomogeneous tissue intensities and image artifacts in these scans, coupled with unique acquisition protocol (split posture), segmentation of these scans is difficult. We present a novel algorithm that exploits the correlation between scans (bone shape invariance, appearance similarity) in automatically segmenting the dancer MRI images.

METHODS

While validated segmentation algorithms are available for standard supine MRI, these algorithms cannot be applied to the split scan which exhibits a unique posture and strong inter-subject variations. In this study, the supine MRI is segmented with a deformable models method. The appearance and shape of the segmented supine models are then re-used to segment the split MRI of the same subject. Models are first registered to the split image using a novel constrained global optimization, before being refined with the deformable models technique.

RESULTS

Experiments with 10 dual-posture MRI datasets in the segmentation of left and right femur bones reported accurate and robust results (mean distance error: 1.39 ± 0.31 mm).

CONCLUSIONS

The use of segmented models from the supine posture to assist the split posture segmentation was found to be equally accurate and consistent to supine results. Our results suggest that dual-posture MRI can be efficiently and robustly segmented.

Segmentation of scalp and skull in neonatal MR images using probabilistic atlas and level set method

In this paper, we present a novel automatic algorithm for scalp and skull segmentation in T1-weighted neonatal head MR images. First, the probabilistic scalp and skull atlases are constructed. Second, the scalp outer surface is extracted based on an active mesh method. Third, maximum number of boundary points corresponding to the scalp inner surface is extracted using the constructed scalp probabilistic atlas and a set of knowledge based rules. In the next step, the skull inner surface and maximum number of boundary points of the outer surface are extracted using a priori information of the head anatomy and the constructed skull probabilistic atlas. Finally, the fast sweeping, tagging and level set methods are applied to reconstruct surfaces from the detected points in three-dimensional space. The results of the new segmentation algorithm on MRI data acquired from nine newborns (including three atlas and six test subjects) were compared with manual segmented data provided by an expert radiologist. The average similarity indices for the scalp and skull segmented regions were equal to 89% and 71% for the atlas and 84% and 63% for the test data, respectively.

Visualization of calvarial fractures from MRI volumetric datasets

We present a simple yet effective technique for visualization of skull vault pathology associated with calvarial outer table damage from magnetic resonance volumetric datasets using a personal computer as a workstation and Amira software as a visualization tool. We show that rendered pictures are informative and convey a good impression of pathology features bearing a strong resemblance to computed tomography three-dimensional images.

Segmentation of skull and scalp in 3-D human MRI using mathematical morphology

We present a new technique for segmentation of skull and scalp in T(1)-weighted magnetic resonance images (MRIs) of the human head. Our method uses mathematical morphological operations to generate realistic models of the skull, scalp, and brain that are suitable for electroencephalography (EEG) and magnetoencephalography (MEG) source modeling. We first segment the brain using our Brain Surface Extractor algorithm; using this, we can ensure that the brain does not intersect our skull segmentation. We next generate a scalp mask using a combination of thresholding and mathematical morphology. We use the scalp mask in our skull segmentation procedure, as it allows us to automatically exclude background voxels with intensities similar to those of the skull. We find the inner and outer skull boundaries using thresholding and morphological operations. Finally, we mask the results with the scalp and brain volumes to ensure closed and nonintersecting skull boundaries. Visual evaluation indicated accurate segmentations of the cranium at a gross anatomical level (other than small holes in the zygomatic bone in eight subjects) in all 44 MRI volumes processed when run using default settings. In a quantitative comparison with coregistered CT images as a gold standard, MRI skull segmentation accuracy, as measured using the Dice coefficient, was found to be similar to that which would be obtained using CT imagery with a registration error of 2-3 mm.

Traitement des images et radiothérapie

Medical images are of great importance in radiotherapy, which became a privileged application field for image processing techniques. Moreover, because of the progression of the computers' performances, these techniques are also in full expansion. Today, the recent developments of the radiotherapy (3DCR, IMRT) offer a huge place to them. Effectively, they can potentially answer to the precision requirements of the modern radiotherapy, and may then contribute to improve the delivered treatments. The purpose of this article is to present the different image processing techniques that are currently used in radiotherapy (including image matching and segmentation) as they are described in the literature.

Construction of patient-specific surface models from MR images: application to bioelectromagnetism

Patient-specific geometric models are needed in many engineering problems. This work reports a novel software tool developed to construct individualized triangulated surface models from MR images. The program consists of three main parts: segmentation, triangulation and registration. The software tool was developed under the UNIX operating system. The application area demonstrated in this work is bioelectromagnetism but the program can be used as well in other engineering problems. The tool has been successfully applied in numerous cases, both for the thorax and the head.

A new accurate and precise 3-D segmentation method for skeletal structures in volumetric CT data

We developed a highly automated three-dimensionally based method for the segmentation of bone in volumetric computed tomography (CT) datasets. The multistep approach starts with three-dimensional (3-D) region-growing using local adaptive thresholds followed by procedures to correct for remaining boundary discontinuities and a subsequent anatomically oriented boundary adjustment using local values of cortical bone density. We describe the details of our approach and show applications in the proximal femur, the knee, and the skull. The accuracy of the determination of geometrical parameters was analyzed using CT scans of the semi-anthropomorphic European spine phantom. Depending on the settings of the segmentation parameters cortical thickness could be determined with an accuracy corresponding to the side length of 1 to 2.5 voxels. The impact of noise on the segmentation was investigated by artificially adding noise to the CT data. An increase in noise by factors of two and five changed cortical thickness corresponding to the side length of one voxel. Intraoperator and interoperator precision was analyzed by repeated analysis of nine pelvic CT scans. Precision errors were smaller than 1% for trabecular and total volumes and smaller than 2% for cortical thickness. Intraoperator and interoperator precision errors were not significantly different. Our segmentation approach shows: 1) high accuracy and precision and is 2) robust to noise, 3) insensitive to user-defined thresholds, 4) highly automated and fast, and 5) easy to initialize.

Calibrated magnetic resonance hydrometry: an in vitro study

PURPOSE

To demonstrate a quantitative approach to measuring fluid volumes with standard single shot RARE sequences.

MATERIALS AND METHODS

In phantom experiments, magnetic resonance hydrometry (MRH), in combination with various calibration phantoms (5 mL up to 500 mL) as internal standards, was used to quantify fluid volumes. In total, 16 volume phantoms were investigated with six different calibration phantoms. In addition, noise correction was implemented to correct the quantification results and to avoid the influence of random noise in the image.

RESULTS

All MR measurements show significant correlations of up to r = 0.99 (P <.05) with the real applied volume in the investigated phantoms. However, measurements of large volumes were more precise with large calibration phantoms. Noise reduction did not change the correlation between measured and real applied volumes, but did reduce the error of the measured volumes. Calibrated magnetic resonance hydrometry (cMRH) is able to quantify volumes of fluid fast and noninvasively. The volumes of the used calibration phantoms have to be at least in the order of magnitude of the volumes that are to be measured.

CONCLUSION

In vitro, cMRH using a single-shot rapid acquisition with refocused echoes (ssRARE) sequence and calibration phantoms is a fast and accurate method of quantifying steady amounts of fluid.

Volume delineation by fusion of fuzzy sets obtained from multiplanar tomographic images

Techniques of three-dimensional (3-D) volume delineation from tomographic medical imaging are usually based on 2-D contour definition. For a given structure, several different contours can be obtained depending on the segmentation method used or the user's choice. The goal of this work is to develop a new method that reduces the inaccuracies generally observed. A minimum volume that is certain to be included in the volume concerned (membership degree mu = 1), and a maximum volume outside which no part of the volume is expected to be found (membership degree mu = 0), are defined semi-automatically. The intermediate fuzziness region (0 < mu < 1) is processed using the theory of possibility. The resulting fuzzy volume is obtained after data fusion from multiplanar slices. The influence of the contrast-to-noise ratio was tested on simulated images. The influence of slice thickness as well as the accuracy of the method were studied on phantoms. The absolute volume error was less than 2% for phantom volumes of 2-8 cm3, whereas the values obtained with conventional methods were much larger than the actual volumes. Clinical experiments were conducted, and the fuzzy logic method gave a volume lower than that obtained with the conventional method. Our fuzzy logic method allows volumes to be determined with better accuracy and reproducibility.