Purpose: The purpose of our study is to compare basilar artery diameters (BAD) measured by T2WI to diameters measured by TOF MR angiography (MRA). By doing this, we hope to understand how compatible these two methodologies are with each other.
Methods: We used data from 100 patients (59 females, 41 males) who underwent a session of both T2W MRI and TOF MRA at the same time (ages between 18 and 83). We measured BAD by both T2WI and TOF MRA in three different levels. We then compared these diameters measured by two different methodologies to each other.
Results: In an area between the vertebrobasilar junction and posterior cerebral artery, all data measured by T2WI and TOF MRA in three different levels were analyzed. Average diameters measured by T2WI and TOF MRA turned out to be 79.5% correlated with each other. As a result of our mathematical model that we came up with through regression analysis, we calculated that measurements taken by T2WI on mid-pontine levels could predict TOF MRA measures with 78.3% accuracy. In T2WI and TOF MRA, average diameters measured were 2.982 ± 0.4717 and 3.205 ± 0.4281 mm, respectively. Statistical analyses showed that images measured by T2W series were significantly smaller than those measured by TOF MRA (p < 0.05).
Conclusion: Our study showed that BAD measured by T2WI were smaller than those measured by TOF MRA. We think that it will be beneficial to refer our results to avoid T2WI and TOF MRA mismatch when evaluating BAD.
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http://dx.doi.org/10.1007/s00276-017-1871-6 | DOI Listing |
Quant Imaging Med Surg
December 2024
Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China.
Background: Preliminary small-sample studies suggest that silent magnetic resonance angiography (MRA) has an advantage over time-of-flight MRA (TOF MRA) in the characterization of brain arteriovenous malformation (BAVM), but did not examine whether the imaging performance of silent MRA was affected by the intrinsic features of BAVM or common clinical factors. This study sought to compare silent MRA and TOF MRA in terms of the visualization and grading of BAVMs in various clinical settings.
Methods: In total, 85 participants (50 males, 35 females; mean age: 33.
Cureus
November 2024
Radiodiagnosis, Sawai Man Singh (SMS) Medical College and Hospital, Jaipur, IND.
Background: Stroke is a leading cause of death and disability worldwide, affecting millions annually. Accurate etiological diagnosis is critical for the effective treatment and prevention of recurrent strokes. Traditional luminal imaging techniques like computed tomography (CT) and magnetic resonance angiography (MRA) provide limited information, focusing solely on vessel lumen characteristics.
View Article and Find Full Text PDFNihon Hoshasen Gijutsu Gakkai Zasshi
December 2024
Department of Radiology, Juntendo University Nerima Hospital.
Purpose: The aim of this study was to investigate imaging conditions that allow for the rapid acquisition of mask images used in the subtraction method, one of the depiction improvement methods for brain magnetic resonance angiography, by employing compressed sensing (CS) combined with segmented time-of-flight (TOF).
Methods: The experiment was performed on healthy volunteers using 3.0T-MRI.
Microbiol Resour Announc
December 2024
Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg, Denmark.
We report the genomic characteristics of the human pathogen isolated from seawater and green algae in the Baltic Sea. Initially misidentified as through culture and MALDI-TOF, whole-genome sequencing (WGS) confirmed them as , highlighting the importance of WGS analysis in accurate classification of emerging pathogens.
View Article and Find Full Text PDFPLoS One
December 2024
CReSTIC EA 3804, Université de Reims Champagne Ardenne, Reims, France.
Cerebrovascular segmentation is a crucial preliminary task for many computer-aided diagnosis tools dealing with cerebrovascular pathologies. Over the last years, deep learning based methods have been widely applied to this task. However, classic deep learning approaches struggle to capture the complex geometry and specific topology of cerebrovascular networks, which is of the utmost importance in many applications.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!