Mice with a deletion in the first intron of the Col1a1 gene develop dissection and rupture of aorta in the absence of aneurysms: high-resolution magnetic resonance imaging, at 4.7 T, of the aorta and cerebral arteries.

Deletion of the majority of the first intron of the Col1a1 gene in mice leads to decreased type I collagen synthesis and content in the aortic wall. In 54% of cases, mice homozygous for the Col1a1 mutation die of thoracic hemorrhage by the age of 18 months. It is unknown whether the fatal bleeding results from an acute dissection of the aortic wall or a gradually developing dilatation of the medial layer prior to rupture. We optimized high-resolution MRI methods using a 4.7 T MR scanner to obtain in vivo images of the entire mouse aorta. The MR images were acquired in three imaging planes using gradient echo, spin echo, and spin echo with inversion recovery pulse sequences with a maximum in-plane resolution of 68 x 68 microm and acquisition times less than 10 min. In five Col1a1 mutated mice aged 16 months, the MR images showed no signs of aneurysmal dilatation, wall defects, or former dissection, suggesting that the mechanism for aortic rupture is an acute dissection of the aortic medial layer. Cerebral arteries were imaged using a three-dimensional time of fight pulse sequence. The resolution of 73 x 73 x 94 microm showed normal cerebral arteries. Histology showed a 22% thinner cerebral artery wall in Col1a1 mutated mice.

Linguistic description of relative positions in images.

Fuzzy set methods have been used to model and manage uncertainty in various aspects of image processing, pattern recognition, and computer vision. High-level computer vision applications hold a great potential for fuzzy set theory because of its links to natural language. Linguistic scene description, a language-based interpretation of regions and their relationships, is one such application that is starting to bear the fruits of fuzzy set theoretic involvement. In this paper, we are expanding on two earlier endeavors. We introduce new families of fuzzy directional relations that rely on the computation of histograms of forces. These families preserve important relative position properties. They provide inputs to a fuzzy rule base that produces logical linguistic descriptions along with assessments as to the validity of the descriptions. Each linguistic output uses hedges from a dictionary of about 30 adverbs and other terms that can be tailored to individual users. Excellent results from several synthetic and real image examples show the applicability of this approach.