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The use of three-dimensional (3D) models for education, pre-operative assessment, presurgical planning, and measurement have become more prevalent. With the increase in prevalence of 3D models there has also been an increase in 3D reconstructive software programs that are used to create these models. These software programs differ in reconstruction concepts, operating system requirements, user features, cost, and no one program has emerged as the standard. The purpose of this study was to conduct a systematic comparison of three widely available 3D reconstructive software programs, Amira(), OsiriX, and Mimics() , with respect to the software's ability to be used in two broad themes: morphometric research and education to translate morphological knowledge. Cost, system requirements, and inherent features of each program were compared. A novel concept selection tool, a decision matrix, was used to objectify comparisons of usability of the interface, quality of the output, and efficiency of the tools. Findings indicate that Mimics was the best-suited program for construction of 3D anatomical models and morphometric analysis, but for creating a learning tool the results were less clear. OsiriX was very user-friendly; however, it had limited capabilities. Conversely, although Amira had endless potential and could create complex dynamic videos, it had a challenging interface. These results provide a resource for morphometric researchers and educators to assist the selection of appropriate reconstruction programs when starting a new 3D modeling project.
Keywords: anatomy education; anatomy research; decision matrix; digital anatomy; image segmentation; medical education; morphometric research; reconstructive technologies; three-dimensional modeling.
The CNIC researchers partnered with Philips Ultrasound and Philips Research Paris-Medisys to develop a new probe and software for real 3D ultrasound to facilitate exploration of the carotid and femoral arteries and speed up quantification of atherosclerotic plaque volume. As Dr. Fuster explained, \"it is clear that traditional clinical evaluations based on measurements of cholesterol, blood pressure, blood glucose, and lifestyle habits cannot, on their own, accurately determine accumulated damage in the cardiovascular system, and without this crucial information we cannot take appropriate decisions to prevent acute events such as myocardial infarction or stroke.\"
The newly validated 3D vascular probe incorporates 3D matrix technology, which underpins the most advanced 3D ultrasound techniques. CNIC Clinical Research Director Dr. Borja Ibáñez explained that the new technology allows simultaneous analysis by 2D and 3D ultrasound, includes all functionalities (color doppler, power-doppler, and contrast ultrasound), and is easily incorporated into daily clinical practice by technical and medical teams already experienced in ultrasound, emphasizing that \"the integrated analysis software incorporates real 3D data processing.\"
In addition to demonstrating the accuracy of 3D matrix ultrasound, the study demonstrates that the new technique takes just half the time needed by previous methods to obtain all the information required for the definition of carotid and femoral plaque burden, essential information for correct patient management.
For patients, the outstanding feature of the new method is that the software generates a virtual 3D image of their own arteries, allowing them to see the accumulated damage. \"When patients see the state of their arteries, this impresses upon them the need to change their lifestyle, in graphic manner not achieved by reading a list of analytical data,\" said first author Dr. Beatriz López Melgar, a cardiologist at Hospital Universitario La Princesa and head of the 3D Cardioprevention Program at Hospital HM Montepríncipe in Madrid.
\"Likewise, this technological advance will soon allow us to analyze plaque composition and to use this information to assess the burden of 'adverse plaques' (plaques with a high lipid content that may be at increased risk of rupture and triggering events, such as stroke). Adverse plaque burden is a very promising marker that until now could only be assessed using highly advanced techniques that involve radiation, such as CAT and PET. Now, with 3D matrixtechnology, measuring adverse plaque burden is a realistic goal of cardiovascular ultrasound studies.\"
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Abstract:The musculoskeletal system is a vital body system that protects internal organs, supports locomotion, and maintains homeostatic function. Unfortunately, musculoskeletal disorders are the leading cause of disability worldwide. Although implant surgeries using autografts, allografts, and xenografts have been conducted, several adverse effects, including donor site morbidity and immunoreaction, exist. To overcome these limitations, various biomedical engineering approaches have been proposed based on an understanding of the complexity of human musculoskeletal tissue. In this review, the leading edge of musculoskeletal tissue engineering using 3D bioprinting technology and musculoskeletal tissue-derived decellularized extracellular matrix bioink is described. In particular, studies on in vivo regeneration and in vitro modeling of musculoskeletal tissue have been focused on. Lastly, the current breakthroughs, limitations, and future perspectives are described.Keywords: musculoskeletal tissue; tissue engineering; 3D bioprinting; decellularized extracellular matrix bioink
This topic aims to provide knowledge about spatial transformations in general and how they are implemented in BrainVoyager, which is important to understand subsequent topics about coordinate systems used in BrainVoyager and relevant neuroimaging file formats. The topic describes how affine spatial transformation matrices are used to represent the orientation and position of a coordinate system within a \"world\" coordinate system and how spatial transformation matrices can be used to map from one coordinate system to another one. It will be described how sub-transformations such as scale, rotation and translation are properly combined in a single transformation matrix as well as how such a matrix is properly decomposed into elementary transformations that are useful e.g. for display purposes. The presented information is aimed towards advanced users who want to understand how position and orientation information is stored in matrices and how to convert transformation results from and to third party (neuroimaging) software.
Such a 4 by 4 matrix M corresponds to a afﬁne transformation T() that transforms point (or vector) x to point (or vector) y. The upper-left 3 3 sub-matrix of the matrix shown above (blue rectangle on left side) represents a rotation transform, byt may also include scales and shears. The last column of the matrix represents a translation (blue rectangle on right side). When used as a coordinate system, the upper-left 3 x 3 sub-matrix represents an orientation in space while the last column vector represents a position in space. The transformation T() of point x to point y is obtained by performing the matrix-vector multiplication
The 4 by 4 transformation matrix uses homogeneous coordinates, which allow to distinguish between points and vectors. Vectors have a direction and magnitude whereas points are positions specified by 3 coordinates with respect to the origin and three base vectors i, j and k that are stored in the first three columns. Points and vectors are both represented as mathematical column vectors (column-matrix representation scheme, see note below) in homogeneous coordinates with the difference that points have a 1 in the fourth position whereas vectors have a zero at this position, which removes translation operations (4th column) for vectors. The transformation of the point x to point x' is thus written as x' = Mx or:
For vectors the value in row 4 would be 0 instead of 1 removing the translation operation by multiplying the 4th vector of matrix M by 0. Performing the matrix-vector product multiplies each column vector of matrix M with the corresponding value (x, y, z, 1) of column vector x. and the sum of these four scalar-vector products results in the output vector x'.
We next consider the nature of elementary 3D transformations and how to compose them into a single afﬁne transformation matrix. Note that for an afﬁne transformation matrix, the ﬁnal row of the matrix is always (0 0 0 1) leaving 12 parameters in the upper 3 by 4 matrix that are used to store combinations of translations, rotations, scales and shears (the values in row 4 can be used for implementing perspective viewing transformations, used e.g. in OpenGL, but this is not needed for the spatial transformations needed in neuroimaging). Homogeneous coordinates (4-element vectors and 4x4 matrices) are necessary to allow treating translation transformations (values in 4th column) in the same way as any other (scale, rotation, shear) transformation (values in upper-left 3x3 matrix), which is not possible with 3 coordinate points and 3-row matrices. Note that separate affine matrices may store individual transformations. With homogeneous coordinates any number and type of elementary transformation stored in its own matrix can be combined in any order by matrix-matrix multiplication resulting in a single transformation matrix. 153554b96e