scholarly journals Reproducibility of the Amygdalohippocampal Volumetry on Magnetic Resonance Imaging: Manual and Image Analyzer Measurements Versus Computer Assisted Measurement.

1995 ◽  
Vol 13 (2) ◽  
pp. 105-112
Author(s):  
Tohru Hoshida ◽  
Toshisuke Sakaki ◽  
Tetsuya Morimoto ◽  
Hiroshi Hashimoto ◽  
Shinichiro Kurokawa ◽  
...  
2009 ◽  
Vol 15 (3) ◽  
pp. 280-284
Author(s):  
E. V. Fedorenko ◽  
H. -J. Wittsack ◽  
A. M. Russina ◽  
N. L. Afanasieva ◽  
V. M. Gulyaev ◽  
...  

A multimodal diagnostic study of the brain was carried out in 22 patients with arterial hypertension (mean systolic blood pressure 152,8 ± 7,6 mm Hg, mean diastolic blood pressure 94,6 ± 5,2 mm Hg), without cardiovascular events in anamnesis. Magnetic resonance imaging (MRI) imaging and dynamic contrast-enhanced perfusion X-ray computer assisted tomography scan (DynCT) of the brain were performed at admission and after six months of antihypertensive treatment. Based on the MRI and DynCT visual data the extent of periventricular oedema, dimensions of liquor system and regional cerebral blood flow (as ml/min/100 g tissue) were quantified. The quantitative MRI and DynCT indices were analyzed regarding the decrease of blood pressure. Significant decrease of periventricular oedema and improvement in perfusion of basal ganglii area were observed in patients demonstrated decrease in systolic blood pressure for 12-28 mm Hg. The degree of the blood pressure decrease was not associated with the significant MRI and DynCT data improvement. Hencefore, we conclude that the brain MRI and perfusion DynCT data can be employed for evaluation of cerebroprotective effects of antihypertensive therapy.


2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Rachid Sammouda ◽  
Abdu Gumaei ◽  
Ali El-Zaart

Prostate Cancer (PCa) is one of the common cancers among men in the world. About 16.67% of men will be affected by PCa in their life. Due to the integration of magnetic resonance imaging in the current clinical procedure for detecting prostate cancer and the apparent success of imaging techniques in the estimation of PCa volume in the gland, we provide a more detailed review of methodologies that use specific parameters for prostate tissue representation. After collecting over 200 researches on image-based systems for diagnosing prostate cancer, in this paper, we provide a detailed review of existing computer-aided diagnosis (CAD) methods and approaches to identify prostate cancer from images generated using Near-Infrared (NIR), Mid-Infrared (MIR), and Magnetic Resonance Imaging (MRI) techniques. Furthermore, we introduce two research methodologies to build intelligent CAD systems. The first methodology applies a fuzzy integral method to maintain the diversity and capacity of different classifiers aggregation to detect PCa tumor from NIR and MIR images. The second methodology investigates a typical workflow for developing an automated prostate cancer diagnosis using MRI images. Essentially, CAD development remains a helpful tool of radiology for diagnosing prostate cancer disease. Nonetheless, a complete implementation of effective and intelligent methods is still required for the PCa-diagnostic system. While some CAD applications work well, some limitations need to be solved for automated clinical PCa diagnostic. It is anticipated that more advances should be made in computational image analysis and computer-assisted approaches to satisfy clinical needs shortly in the coming years.


2015 ◽  
Vol 44 (1) ◽  
pp. 8-14 ◽  
Author(s):  
Abhishek Chaturvedi ◽  
Puneet Bhargava ◽  
Orpheus Kolokythas ◽  
Lee M. Mitsumori ◽  
Jeffrey H. Maki

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