| 311. |
- Weistrand, Ola, 1975-
(författare)
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Global Shape Description of Digital Objects
- 2005
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- New methods for global shape description of three-dimensional digital objects are presented. The shape of an object is first represented by a digital surface where the faces are either triangles or quadrilaterals. Techniques for computing a high-quality parameterization of the surface are developed and this parameterization is used to approximate the shape of the object. Spherical harmonics are used as basis functions for approximations of the coordinate functions. Information about the global shape is then captured by the coefficients in the spherical harmonics expansions.For a starshaped object it is shown how a parameterization can be computed by a projection from its surface onto the unit sphere. An algorithm for computing the position at which the centre of the sphere should be placed, is presented. This algorithm is suited for digital voxel objects. Most of the work is concerned with digital objects whose surfaces are homeomorphic to the sphere. The standard method for computing parameterizations of such surfaces is shown to fail on many objects. This is due to the large distortions of the geometric properties of the surface that often occur with this method. Algorithms to handle this problem are suggested. Non-linear optimization methods are used to find a mapping between a surface and the sphere that minimizes geometric distortion and is useful as a parameterization of the surface. The methods can be applied, for example, in medical imaging for shape recognition, detection of shape deformations and shape comparisons of three-dimensional objects.
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| 312. |
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| 313. |
- West, Janne, et al.
(författare)
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Normal Appearing and Diffusely Abnormal White Matter in Patients with Multiple Sclerosis, Assessed with Quantitative MR : Optimization for clinical usage
- 2014
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Ingår i: PLOS ONE. - : Public Library of Science. - 1932-6203. ; 9:4, s. e95161-
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: Magnetic Resonance Imaging is a sensitive technique for detecting white matter (WM) MS lesions, but the relation with clinical disability is low. Because of this, changes in both ‘normal appearing white matter’ (NAWM) and ‘diffusely abnormal white matter’ (DAWM) have been of interest in recent years. MR techniques, including quantitative magnetic resonance imaging (qMRI) and quantitative magnetic resonance spectroscopy (qMRS), have been developed in order to detect and quantify such changes.In this study, a combination of qMRI and qMRS was used to investigate NAWM and DAWM in typical MS patients and in MS patients with low number of WM lesions. Patient data were compared to ‘normal white matter’ (NWM) in healthy controls.Methods: QMRI and qMRS measurements were performed on a 1.5T Philips MR-scanner. 35 patients with clinically definite MS and 20 healthy controls were included. Fifteen of the patients showed few WM lesions (‘MRIneg‘) and 20 showed radiologically typical findings (‘MRIpos’). QMRI properties were determined in ROIs of NAWM, DAWM and WM lesions in the MS groups and of NWM in controls. Descriptive statistical analysis and comparisons were performed. Correlations were calculated between qMRI measurements and (1) clinical parameters and (2) WM metabolite concentrations. Regression analyses were performed with brain parenchyma fraction and MSSS.Results: NAWM in the MRIneg group was significantly different from NAWM in the MRIpos group and NWM. In addition, R1 and R2 of NAWM in the MRIpos group correlated negatively with EDSS and MSSS. DAWM was significantly different from NWM, but similar in the two MS groups. N-acetyl aspartate correlated negatively with R1 and R2 in MRIneg. Finally, R2 of DAWM was associated with BPF.Conclusions: Changes in NAWM and DAWM are independent pathological entities in the disease. Combined qMRI and qMRS measurements of NAWM and DAWM provide important markers for disease status.
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| 314. |
- West, Janne
(författare)
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Quantitative Magnetic Resonance Imaging of the Brain : Applications for Tissue Segmentation and Multiple Sclerosis
- 2014
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Magnetic resonance imaging (MRI) is a sensitive technique for assessing white matter (WM) lesions in multiple sclerosis (MS), but there is a low correlation between MRI findings and clinical disability. Because of this, other pathological changes are of interest, including changes in normal appearing white matter (NAWM) and diffusely abnormal white matter (DAWM). Even so, the mechanisms leading to permanent disability in MS remain unclear.In contrast to conventional MRI, quantitative MRI (qMRI) is aimed at the direct measurement of the physical tissue properties, such as the relaxation times, T1 and T2, as well as the proton density (PD). QMRI is promising for characterising and quantifying changes in MS and for brain tissue segmentation.The present work describes a novel method of qMRI for the human brain (QMAP), and a segmentation method based on this. The developed methods were validated in control subjects and MR phantoms. Furthermore, an application in diseased human brain was demonstrated in MS patients. In all, 50 healthy controls and 35 MS patients were scanned with qMRI in a total of 225 acquisitions.One major finding of this work was that qMRI was able to detect and quantify changes in the MS disease that were not visible using conventional MRI. In particular, it was found that DAWM appears to constitute an intermediate between focal white matter (WM) lesions and NAWM. These changes may be caused by pathological processes that are not entirely attributable to Wallerian degeneration.This study showed that the QMAP method had high accuracy and relatively high precision, within a clinically acceptable time. This work also demonstrated that qMRI could be used for brain tissue segmentation and volume estimation of the whole brain, using pre-defined tissue characteristics. The results showed that brain tissue segmentation had high repeatability, which was somewhat lower when different geometries were acquired or different field strengths used. In particular, small differences were found between 1.5 T and 3.0 T in deep brain structures, the cerebellum and the brain stem.This work leads the way for early clinical applications of qMRI, and the challenge for the years to come is to understand the connection between qMRI properties of the brain and underlying biology.
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| 315. |
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| 316. |
- Wikström, Johan, et al.
(författare)
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Gadolinium-enhanced magnetic resonance angiography, digital subtraction angiography and duplex of the iliac arteries compared with intra-arterial pressure gradient measurements
- 2000
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Ingår i: European Journal of Vascular and Endovascular Surgery. - : Elsevier BV. - 1078-5884 .- 1532-2165. ; 19:5, s. 516-523
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Tidskriftsartikel (refereegranskat)abstract
- PURPOSE: to compare gadolinium-enhanced magnetic resonance angiography (Gd-MRA), digital subtraction angiography (DSA) and duplex of the iliac arteries with intra-arterial pressure gradient measurement as the reference method. MATERIALS AND METHODS: Gd-MRA, DSA and duplex examinations of the iliac arteries were performed in 30 patients (60 arteries) with lower-limb arterial occlusive disease. In 29 arteries, pressure measurements were made (n=25) or the artery was found to be occluded on catheterisation (n=4). An aortofemoral peak systolic pressure gradient of 20 mmHg or more was regarded as haemodynamically significant. Stenoses with a diameter reduction of 50% or more on MRA or DSA, or an increase in peak systolic velocity greater than 150% (duplex) were considered significant. MRA examinations were evaluated by means of maximum intensity projections (MRA-MIP) and using source images and curved multiplanar reconstruction (MRA-MPR). RESULTS: the sensitivity (specificity) for a significant iliac artery stenosis were 81% (75%) for MRA-MIP, 76% (75%) for MRA-MPR, 86% (88%) for DSA, and 72% (88%) for duplex. CONCLUSION: with intra-arterial pressure measurements as the reference method, similar results were achieved with Gd-MRA, DSA and duplex concerning the detection of haemodynamically significant iliac artery stenoses. The use of source images and multiplanar reconstructions resulted in higher accuracy for the detection of occlusions.
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| 317. |
- Xu, Jiangchang, et al.
(författare)
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A review on AI-based medical image computing in head and neck surgery
- 2022
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Ingår i: Physics in Medicine and Biology. - : IOP Publishing. - 0031-9155 .- 1361-6560. ; 67:17, s. 17TR01-
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Forskningsöversikt (refereegranskat)abstract
- Head and neck surgery is a fine surgical procedure with a complex anatomical space, difficult operation and high risk. Medical image computing (MIC) that enables accurate and reliable preoperative planning is often needed to reduce the operational difficulty of surgery and to improve patient survival. At present, artificial intelligence, especially deep learning, has become an intense focus of research in MIC. In this study, the application of deep learning-based MIC in head and neck surgery is reviewed. Relevant literature was retrieved on the Web of Science database from January 2015 to May 2022, and some papers were selected for review from mainstream journals and conferences, such as IEEE Transactions on Medical Imaging, Medical Image Analysis, Physics in Medicine and Biology, Medical Physics, MICCAI, etc. Among them, 65 references are on automatic segmentation, 15 references on automatic landmark detection, and eight references on automatic registration. In the elaboration of the review, first, an overview of deep learning in MIC is presented. Then, the application of deep learning methods is systematically summarized according to the clinical needs, and generalized into segmentation, landmark detection and registration of head and neck medical images. In segmentation, it is mainly focused on the automatic segmentation of high-risk organs, head and neck tumors, skull structure and teeth, including the analysis of their advantages, differences and shortcomings. In landmark detection, the focus is mainly on the introduction of landmark detection in cephalometric and craniomaxillofacial images, and the analysis of their advantages and disadvantages. In registration, deep learning networks for multimodal image registration of the head and neck are presented. Finally, their shortcomings and future development directions are systematically discussed. The study aims to serve as a reference and guidance for researchers, engineers or doctors engaged in medical image analysis of head and neck surgery.
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| 318. |
- Zachrisson, Helene, et al.
(författare)
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Soft tissue discrimination ex vivo by dual energy computed tomography
- 2010
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Ingår i: European Journal of Radiology. - : Elsevier. - 0720-048X .- 1872-7727. ; 75:2, s. E124-E128
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Tidskriftsartikel (refereegranskat)abstract
- PURPOSE: Dual Energy Computed Tomography (DECT) may provide additional information about the chemical composition of tissues compared to examination with a single X-ray energy. The aim of this in vitro study was to test whether combining two energies may significantly improve the detection of soft tissue components commonly present in arterial plaques. METHODS: Tissue samples of myocardial and psoas muscle, venous and arterial thrombus as well as fat from different locations were scanned using a SOMATOM Definition Dual Source CT system (Siemens AG, Medical Solutions, Forchheim, Germany) with simultaneous tube voltages of 140 and 80kV. The attenuation (Hounsfield units, HU) at 80 and 140kV was measured in representative regions of interest, and the association between measured HU values and tissue types was tested with logistic regression. RESULTS: The combination of two energy levels (80 and 140kV) significantly improved (p<0.001) the ability to correctly classify venous thrombus vs arterial thrombus, myocardium or psoas; arterial thrombus vs myocardium or psoas; myocardium vs psoas; as well as the differentiation between fat tissue from various locations. Single energy alone was sufficient for distinguishing fat from other tissues. CONCLUSION: DECT offers significantly improved in vitro differentiation between soft tissues occurring in plaques. If this corresponds to better tissue discrimination in vivo needs to be clarified in future studies.
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| 319. |
- Zhou, Yu, et al.
(författare)
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Synthesis of Pediatric Brain Tumor Images With Mass Effect
- 2023
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Ingår i: Medical Imaging 2023. - : SPIE-Intl Soc Optical Eng.
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Konferensbidrag (refereegranskat)abstract
- In children, brain tumors are the leading cause of cancer-related death. The amount of labeled data in children is much lower than that for adult subjects. This paper proposes a new method to synthesize high-quality pathological pediatric MRI brain images from pathological adult ones. To realistically simulate the appearance of brain tumors, the proposed method considers the mass effect, i.e., the deformation induced by the tumor to the surrounding tissue. First, a probabilistic U-Net was trained to predict a deformation field that encodes the mass effect from the healthy-pathological image pair. Second, the learned deformation field was utilized to warp the healthy mask to simulate the mass effect. The tumor mask is also added to the warped mask. Finally, a label-to-image transformer, i.e., the SPADE GAN, was trained to synthesize a pathological image from the segmentation masks of gray matter, white matter, CSF and the tumor. The synthetic images were evaluated in two quantitative ways: i) three supervised segmentation pipelines were trained on datasets with and without synthetic images. Two pipelines show over 1% improvements in the Dice scores when the datasets were augmented with synthetic data. ii) The Fréchet inception distance was measured between real and synthetic image distributions. Results show that SPADE outperforms the state-of-the-art Pix2PixHD method in both T1w and T2w modalities. The source code can be accessed on https://github.com/audreyeternal/pediatric-tumor-generation.
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| 320. |
- Zhuang, Xiahai, et al.
(författare)
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Evaluation of algorithms for Multi-Modality Whole Heart Segmentation : An open-access grand challenge.
- 2019
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Ingår i: Medical Image Analysis. - : Elsevier BV. - 1361-8415 .- 1361-8423. ; 58
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Tidskriftsartikel (refereegranskat)abstract
- Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be challenging due to the large variation of the heart shape, and different image qualities of the clinical data. To achieve this goal, an initial set of training data is generally needed for constructing priors or for training. Furthermore, it is difficult to perform comparisons between different methods, largely due to differences in the datasets and evaluation metrics used. This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The challenge provided 120 three-dimensional cardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environments with manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelve groups, have been evaluated. The results showed that the performance of CT WHS was generally better than that of MRI WHS. The segmentation of the substructures for different categories of patients could present different levels of challenge due to the difference in imaging and variations of heart shapes. The deep learning (DL)-based methods demonstrated great potential, though several of them reported poor results in the blinded evaluation. Their performance could vary greatly across different network structures and training strategies. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated good performance, though the accuracy and computational efficiency could be limited. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, continues as an ongoing benchmarking resource via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mmwhs/).
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