Registration of temporal sequences of coronal and sagittal MR images through respiratory patterns

This work discusses the determination of the breathing patterns in time sequence of images obtained from magnetic resonance (MR) and their use in the temporal registration of coronal and sagittal images. The registration is made without the use of any triggering information and any special gas to enhance the contrast. The temporal sequences of images are acquired in free breathing. The real movement of the lung has never been seen directly, as it is totally dependent on its surrounding muscles and collapses without them. The visualization of the lung in motion is an actual topic of research in medicine. The lung movement is not periodic and it is susceptible to variations in the degree of respiration. Compared to computerized tomography (CT), MR imaging involves longer acquisition times and it is preferable because it does not involve radiation. As coronal and sagittal sequences of images are orthogonal to each other, their intersection corresponds to a segment in the three-dimensional space. The registration is based on the analysis of this intersection segment. A time sequence of this intersection segment can be stacked, defining a two-dimension spatio-temporal (2DST) image. The algorithm proposed in this work can detect asynchronous movements of the internal lung structures and lung surrounding organs. It is assumed that the diaphragmatic movement is the principal movement and all the lung structures move almost synchronously. The synchronization is performed through a pattern named respiratory function. This pattern is obtained by processing a 2DST image. An interval Hough transform algorithm searches for synchronized movements with the respiratory function. A greedy active contour algorithm adjusts small discrepancies originated by asynchronous movements in the respiratory patterns. The output is a set of respiratory patterns. Finally, the composition of coronal and sagittal image pairs that are in the same breathing phase is realized by comparing of respiratory patterns originated from diaphragmatic and upper boundary surfaces. When available, the respiratory patterns associated to lung internal structures are also used. The results of the proposed method are compared with the pixel-by-pixel comparison method. The proposed method increases the number of registered pairs representing composed images and allows an easy check of the breathing phase. (C) 2010 Elsevier Ltd. All rights reserved.

AUTOMATIC CORONARY WALL SEGMENTATION IN INTRAVASCULAR ULTRASOUND IMAGES USING BINARY MORPHOLOGICAL RECONSTRUCTION

Intravascular ultrasound (IVUS) image segmentation can provide more detailed vessel and plaque information, resulting in better diagnostics, evaluation and therapy planning. A novel automatic segmentation proposal is described herein; the method relies on a binary morphological object reconstruction to segment the coronary wall in IVUS images. First, a preprocessing followed by a feature extraction block are performed, allowing for the desired information to be extracted. Afterward, binary versions of the desired objects are reconstructed, and their contours are extracted to segment the image. The effectiveness is demonstrated by segmenting 1300 images, in which the outcomes had a strong correlation to their corresponding gold standard. Moreover, the results were also corroborated statistically by having as high as 92.72% and 91.9% of true positive area fraction for the lumen and media adventitia border, respectively. In addition, this approach can be adapted easily and applied to other related modalities, such as intravascular optical coherence tomography and intravascular magnetic resonance imaging. (E-mail: matheuscardosomg@hotmail.com) (C) 2011 World Federation for Ultrasound in Medicine & Biology.

Absence and hypoplasia of the mental foramen detected in CBCT images: a case report

The mental foramen (MF) is an important anatomic landmark of the mandible, through which the mental nerve and blood vessels emerge. The importance of MF in dental practice is especially related to dental implants placement and other surgical procedures in the region. It is fundamental to be careful in order to avoid nerve and vessels injury during procedures. Anatomic variations of the MF can be found, such as occurrence of multiple foramina and unusual location. In very rare occasions, the absence of MF can be detected. The observation of this variation is not always possible using only conventional radiographs. The modern imaging resource cone beam computed tomography (CBCT) allows an accurate three-dimensional assessment of MF, as well as the identification of its variations. The aim of this article is to report MF absence and hypoplasia detected in CBCT images of a 27-year-old daughter and her 63-year-old mother, both from Brazil. Despite the MF anatomic variations, they presented no sensorial disturbance in the regions supplied by the mental nerve.

Evaluation of proximal caries in images resulting from different modes of radiographic digitalization

Objective: The aim of this study was to evaluate the performances of observers in diagnosing proximal caries in digital images obtained from digital bitewing radiographs using two scanners and four digital cameras in Joint Photographic Experts Group (JPEG) and tagged image file format (TIFF) files, and comparing them with the original conventional radiographs. Method: In total, 56 extracted teeth were radiographed with Kodak Insight film (Eastman Kodak, Rochester, NY) in a Kaycor Yoshida X-ray device (Kaycor X-707;Yoshida Dental Manufacturing Co., Tokyo, Japan) operating at 70 kV and 7 mA with an exposure time of 0.40 s. The radiographs were obtained and scanned by CanonScan D646U (Canon USA Inc., Newport News, VA) and Genius ColorPage HR7X (KYE Systems Corp. America, Doral, FL) scanners, and by Canon Powershot G2 (Canon USA Inc.), Canon RebelXT (Canon USA Inc.), Nikon Coolpix 8700 (Nikon Inc., Melville, NY), and Nikon D70s (Nikon Inc.) digital cameras in JPEG and TIFF formats. Three observers evaluated the images. The teeth were then observed under the microscope in polarized light for the verification of the presence and depth of the carious lesions. Results: The probability of no diagnosis ranged from 1.34% (Insight film) to 52.83% (CanonScan/JPEG). The sensitivity ranged from 0.24 (Canon RebelXT/JPEG) to 0.53 (Insight film), the specificity ranged from 0.93 (Nikon Coolpix/JPEG, Canon Powershot/TIFF, Canon RebelXT/JPEG and TIFF) to 0.97 (CanonScan/TIFF and JPEG) and the accuracy ranged from 0.82 (Canon RebelXT/JPEG) to 0.91 (CanonScan/JPEG). Conclusion: The carious lesion diagnosis did not change in either of the file formats (JPEG and TIFF) in which the images were saved for any of the equipment used. Only the CanonScan scanner did not have adequate performance in radiography digitalization for caries diagnosis and it is not recommended for this purpose. Dentomaxillofacial Radiology (2011) 40, 338-343. doi: 10.1259/dmfr/67185962

Image quality assessment and medical physics evaluation of different portable dental X-ray units

Introduction: Recently developed portable dental X-ray units increase the mobility of the forensic odontologists and allow more efficient X-ray work in a disaster field, especially when used in combination with digital sensors. This type of machines might also have potential for application in remote areas, military and humanitarian missions, dental care of patients with mobility limitation, as well as imaging in operating rooms. Objective: To evaluate radiographic image quality acquired by three portable X-ray devices in combination with four image receptors and to evaluate their medical physics parameters. Materials and methods: Images of five samples consisting of four teeth and one formalin-fixed mandible were acquired by one conventional wall-mounted X-ray unit, MinRay (R) 60/70 kVp, used as a clinical standard, and three portable dental X-ray devices: AnyRay (R) 60 kVp, Nomad (R) 60 kVp and Rextar (R) 70 kVp, in combination with a phosphor image plate (PSP), a CCD, or a CMOS sensor. Three observers evaluated images for standard image quality besides forensic diagnostic quality on a 4-point rating scale. Furthermore, all machines underwent tests for occupational as well as patient dosimetry. Results: Statistical analysis showed good quality imaging for all system, with the combination of Nomad (R) and PSP yielding the best score. A significant difference in image quality between the combination of the four X-ray devices and four sensors was established (p < 0.05). For patient safety, the exposure rate was determined and exit dose rates for MinRay (R) at 60 kVp, MinRay (R) at 70 kVp, AnyRay (R), Nomad (R) and Rextar (R) were 3.4 mGy/s, 4.5 mGy/s, 13.5 mGy/s, 3.8 mGy/s and 2.6 mGy/s respectively. The kVp of the AnyRay (R) system was the most stable, with a ripple of 3.7%. Short-term variations in the tube output of all the devices were less than 10%. AnyRay (R) presented higher estimated effective dose than other machines. Occupational dosimetry showed doses at the operator`s hand being lowest with protective shielding (Nomad (R): 0.1 mu Gy). It was also low while using remote control (distance > 1 m: Rextar (R) < 0.2 mu Gy, MinRay (R) < 0.1 mu Gy). Conclusions: The present study demonstrated the feasibility of three portable X-ray systems to be used for specific indications, based on acceptable image quality and sufficient accuracy of the machines and following the standard guidelines for radiation hygiene. (C) 2010 Elsevier Ireland Ltd. All rights reserved.

A segmentation-based and partial-volume-compensated method for an accurate measurement of lateral ventricular volumes on T-1-weighted magnetic resonance images

Lateral ventricular volumes based on segmented brain MR images can be significantly underestimated if partial volume effects are not considered. This is because a group of voxels in the neighborhood of lateral ventricles is often mis-classified as gray matter voxels due to partial volume effects. This group of voxels is actually a mixture of ventricular cerebro-spinal fluid and the white matter and therefore, a portion of it should be included as part of the lateral ventricular structure. In this note, we describe an automated method for the measurement of lateral ventricular volumes on segmented brain MR images. Image segmentation was carried in combination of intensity correction and thresholding. The method is featured with a procedure for addressing mis-classified voxels in the surrounding of lateral ventricles. A detailed analysis showed that lateral ventricular volumes could be underestimated by 10 to 30% depending upon the size of the lateral ventricular structure, if mis-classified voxels were not included. Validation of the method was done through comparison with the averaged manually traced volumes. Finally, the merit of the method is demonstrated in the evaluation of the rate of lateral ventricular enlargement. (C) 2001 Elsevier Science Inc. All rights reserved.

Randomized comparison of the quality of realtime fetal ultrasound images transmitted by ISDN and by IP videoconferencing

We compared the quality of realtime fetal ultrasound images transmitted using ISDN and IP networks. Four experienced obstetric ultrasound specialists viewed standard recordings in a randomized trial and rated the appearance of 30 fetal anatomical landmarks, each on a seven-point scale. A total of 12 evaluations were performed for various combinations of bandwidths (128, 384 or 768 kbit/s) and networks (ISDN or IF). The intraobserver coefficient of variation was 2.9%, 5.0%, 12.7% and 14.7% for the four observers. The mean overall ratings by each of the four observers were 4.6, 4.8, 5.0 and 5.3, respectively (a rating of 4 indicated satisfactory visualization and 7 indicated as good as the original recording). Analysis of variance showed that there were no significant interobserver variations nor significant differences in the mean scores for the different types of videoconferencing machines used. The most significant variable affecting the mean score was the bandwidth used. For ISDN, the mean score was 3.7 at 128 kbit/s, which was significantly worse than the mean score of 4.9 at 384 kbit/s, which was in turn significantly worse than the mean score of 5.9 at 768 kbit/s. The mean score for transmission using IP was about 0.5 points lower than that using ISDN across all the different bandwidths, but the differences were not significant. It appears that IP transmission in a private (non-shared) network is an acceptable alternative to ISDN for fetal tele-ultrasound and one deserving further study.

Modeling and visualization of medical anesthesiology acts

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Central and Cortical Gray Mater Segmentation of Magnetic Resonance Images of the Fetal Brain

Motivation. The study of human brain development in itsearly stage is today possible thanks to in vivo fetalmagnetic resonance imaging (MRI) techniques. Aquantitative analysis of fetal cortical surfacerepresents a new approach which can be used as a markerof the cerebral maturation (as gyration) and also forstudying central nervous system pathologies [1]. However,this quantitative approach is a major challenge forseveral reasons. First, movement of the fetus inside theamniotic cavity requires very fast MRI sequences tominimize motion artifacts, resulting in a poor spatialresolution and/or lower SNR. Second, due to the ongoingmyelination and cortical maturation, the appearance ofthe developing brain differs very much from thehomogenous tissue types found in adults. Third, due tolow resolution, fetal MR images considerably suffer ofpartial volume (PV) effect, sometimes in large areas.Today extensive efforts are made to deal with thereconstruction of high resolution 3D fetal volumes[2,3,4] to cope with intra-volume motion and low SNR.However, few studies exist related to the automatedsegmentation of MR fetal imaging. [5] and [6] work on thesegmentation of specific areas of the fetal brain such asposterior fossa, brainstem or germinal matrix. Firstattempt for automated brain tissue segmentation has beenpresented in [7] and in our previous work [8]. Bothmethods apply the Expectation-Maximization Markov RandomField (EM-MRF) framework but contrary to [7] we do notneed from any anatomical atlas prior. Data set &Methods. Prenatal MR imaging was performed with a 1-Tsystem (GE Medical Systems, Milwaukee) using single shotfast spin echo (ssFSE) sequences (TR 7000 ms, TE 180 ms,FOV 40 x 40 cm, slice thickness 5.4mm, in plane spatialresolution 1.09mm). Each fetus has 6 axial volumes(around 15 slices per volume), each of them acquired inabout 1 min. Each volume is shifted by 1 mm with respectto the previous one. Gestational age (GA) ranges from 29to 32 weeks. Mother is under sedation. Each volume ismanually segmented to extract fetal brain fromsurrounding maternal tissues. Then, in-homogeneityintensity correction is performed using [9] and linearintensity normalization is performed to have intensityvalues that range from 0 to 255. Note that due tointra-tissue variability of developing brain someintensity variability still remains. For each fetus, ahigh spatial resolution image of isotropic voxel size of1.09 mm is created applying [2] and using B-splines forthe scattered data interpolation [10] (see Fig. 1). Then,basal ganglia (BS) segmentation is performed on thissuper reconstructed volume. Active contour framework witha Level Set (LS) implementation is used. Our LS follows aslightly different formulation from well-known Chan-Vese[11] formulation. In our case, the LS evolves forcing themean of the inside of the curve to be the mean intensityof basal ganglia. Moreover, we add local spatial priorthrough a probabilistic map created by fitting anellipsoid onto the basal ganglia region. Some userinteraction is needed to set the mean intensity of BG(green dots in Fig. 2) and the initial fitting points forthe probabilistic prior map (blue points in Fig. 2). Oncebasal ganglia are removed from the image, brain tissuesegmentation is performed as described in [8]. Results.The case study presented here has 29 weeks of GA. Thehigh resolution reconstructed volume is presented in Fig.1. The steps of BG segmentation are shown in Fig. 2.Overlap in comparison with manual segmentation isquantified by the Dice similarity index (DSI) equal to0.829 (values above 0.7 are considered a very goodagreement). Such BG segmentation has been applied on 3other subjects ranging for 29 to 32 GA and the DSI hasbeen of 0.856, 0.794 and 0.785. Our segmentation of theinner (red and blue contours) and outer cortical surface(green contour) is presented in Fig. 3. Finally, torefine the results we include our WM segmentation in theFreesurfer software [12] and some manual corrections toobtain Fig.4. Discussion. Precise cortical surfaceextraction of fetal brain is needed for quantitativestudies of early human brain development. Our workcombines the well known statistical classificationframework with the active contour segmentation forcentral gray mater extraction. A main advantage of thepresented procedure for fetal brain surface extraction isthat we do not include any spatial prior coming fromanatomical atlases. The results presented here arepreliminary but promising. Our efforts are now in testingsuch approach on a wider range of gestational ages thatwe will include in the final version of this work andstudying as well its generalization to different scannersand different type of MRI sequences. References. [1]Guibaud, Prenatal Diagnosis 29(4) (2009). [2] Rousseau,Acad. Rad. 13(9), 2006, [3] Jiang, IEEE TMI 2007. [4]Warfield IADB, MICCAI 2009. [5] Claude, IEEE Trans. Bio.Eng. 51(4) (2004). [6] Habas, MICCAI (Pt. 1) 2008. [7]Bertelsen, ISMRM 2009 [8] Bach Cuadra, IADB, MICCAI 2009.[9] Styner, IEEE TMI 19(39 (2000). [10] Lee, IEEE Trans.Visual. And Comp. Graph. 3(3), 1997, [11] Chan, IEEETrans. Img. Proc, 10(2), 2001 [12] Freesurfer,http://surfer.nmr.mgh.harvard.edu.

Semi-supervised segmentation of ultrasound images based on patch representation and continuous min cut.

Ultrasound segmentation is a challenging problem due to the inherent speckle and some artifacts like shadows, attenuation and signal dropout. Existing methods need to include strong priors like shape priors or analytical intensity models to succeed in the segmentation. However, such priors tend to limit these methods to a specific target or imaging settings, and they are not always applicable to pathological cases. This work introduces a semi-supervised segmentation framework for ultrasound imaging that alleviates the limitation of fully automatic segmentation, that is, it is applicable to any kind of target and imaging settings. Our methodology uses a graph of image patches to represent the ultrasound image and user-assisted initialization with labels, which acts as soft priors. The segmentation problem is formulated as a continuous minimum cut problem and solved with an efficient optimization algorithm. We validate our segmentation framework on clinical ultrasound imaging (prostate, fetus, and tumors of the liver and eye). We obtain high similarity agreement with the ground truth provided by medical expert delineations in all applications (94% DICE values in average) and the proposed algorithm performs favorably with the literature.

Comparison of fundus autofluorescence images acquired by the confocal scanning laser ophthalmoscope (488 nm excitation) and the modified Topcon fundus camera (580 nm excitation).

To compare autofluorescence (AF) images obtained with the confocal scanning laser ophthalmoscope (using the Heidelberg retina angiograph; HRA) and the modified Topcon fundus camera, in a routine clinical setting. A prospective comparative study conducted at the Jules-Gonin Eye Hospital. Fifty-six patients from the medical retina clinic. All patients had complete ophthalmic slit-lamp and fundus examinations, colour and red-free fundus photography, AF imaging with both instruments, and fluorescein angiography. Cataract and fixation were graded clinically. AF patterns were analyzed for healthy and pathological features. Differences of image noise were analyzed by cataract grading and fixation. A total of 105 eyes were included. AF patterns discovered by the retina angiograph and the fundus camera images, respectively, were a dark optic disc in 72 % versus 15 %, a dark fovea in 92 % versus 4 %, sub- and intraretinal fluid visible as hyperautofluorescence on HRA images only, lipid exudates visible as hypoautofluorescence on HRA images only. The same autofluorescent pattern was found on both images for geographic atrophy, retinal pigment changes, drusen and haemorrhage. Image noise was significantly associated with the degree of cataract and/or poor fixation, favouring the fundus camera. Images acquired by the fundus camera before and after fluorescein angiography were identical. Fundus AF images differ according to the technical differences of the instruments used. Knowledge of these differences is important not only for correctly interpreting images, but also for selecting the most appropriate instrument for the clinical situation.

Medical image registration based on random line sampling

One of the key aspects in 3D-image registration is the computation of the joint intensity histogram. We propose a new approach to compute this histogram using uniformly distributed random lines to sample stochastically the overlapping volume between two 3D-images. The intensity values are captured from the lines at evenly spaced positions, taking an initial random offset different for each line. This method provides us with an accurate, robust and fast mutual information-based registration. The interpolation effects are drastically reduced, due to the stochastic nature of the line generation, and the alignment process is also accelerated. The results obtained show a better performance of the introduced method than the classic computation of the joint histogram

Multimodal evaluation for medical image segmentation

This paper is a joint effort between five institutionsthat introduces several novel similarity measures andcombines them to carry out a multimodal segmentationevaluation. The new similarity measures proposed arebased on the location and the intensity values of themisclassified voxels as well as on the connectivity andthe boundaries of the segmented data. We showexperimentally that the combination of these measuresimprove the quality of the evaluation. The study that weshow here has been carried out using four differentsegmentation methods from four different labs applied toa MRI simulated dataset of the brain. We claim that ournew measures improve the robustness of the evaluation andprovides better understanding about the differencebetween segmentation methods.