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Littérature scientifique sur le sujet « Aneurysm detection »

Auteur : Grafiati

Publié le 8 juin 2024

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Articles de revues sur le sujet "Aneurysm detection"

Franjić, Darjan, and Josip Mašković. "Value of 3D-DSA in the detection of intracranial aneurysms." Medicina Fluminensis 57, no. 3 (2021): 260–68. http://dx.doi.org/10.21860/medflum2021_261187.

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Aim: To determine the value of three-dimensional (3D) digital subtraction angiography (DSA) in the detection of intracranial aneurysms and to compare 3D technique with DSA. Materials and Methods: A retrospective analysis of 50 patients with 60 intracranial aneurysms who underwent both conventional DSA and 3D-DSA for the evaluation of intracranial aneurysms was conducted. The presence of aneurysms, detection of aneurysmal neck, size, location, presence of additional and small aneurysms analyzed from the two protocols were compared. Results: Three-dimensional technique detected 54 aneurysms while conventional DSA detected 38 aneurysms. There was no correlation between aneurysm detection and aneurysm neck detection in the two technologies observed, but there was a difference in detection performance depending on the technology used. Three-dimensional technique detected 52 aneurysm necks while conventional DSA detected 24 aneurysm necks. There was a statistically significant and positive relationship between the detected size of the aneurysm using 3D technique and DSA technology. Three-dimensional technique detected 24 additional aneurysms while conventional DSA detected only six additional aneurysms. Conclusions: Three-dimensional technique are more successful in the detection of aneurysms, their necks and small aneurysms in comparison to digital subtraction angiography, but difference is not statistically significant. The size of the aneurysm statistically significant affects the aneurysm neck detection by conventional DSA.

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Nakagawa, Daichi, Yasunori Nagahama, Bruno A. Policeni, et al. "Accuracy of detecting enlargement of aneurysms using different MRI modalities and measurement protocols." Journal of Neurosurgery 130, no. 2 (2019): 559–65. http://dx.doi.org/10.3171/2017.9.jns171811.

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OBJECTIVEAneurysm growth is considered predictive of future rupture of intracranial aneurysms. However, how accurately neuroradiologists can reliably detect incremental aneurysm growth using clinical MRI is still unknown. The purpose of this study was to assess the agreement rate of detecting aneurysm enlargement employing generally used MRI modalities.METHODSThree silicone flow phantom models, each with 8 aneurysms of various sizes at different sites, were used in this study. The aneurysm models were identical except for an incremental increase in the sizes of the 8 aneurysms, which ranged from 0.4 mm to 2 mm. The phantoms were imaged on 1.5-T and 3-T MRI units with both time-of-flight (TOF) and contrast-enhanced MR angiography. Three independent expert neuroradiologists measured the aneurysms in a blinded manner using different measurement approaches. The individual and agreement detection rates of aneurysm enlargement among the 3 experts were calculated.RESULTSThe mean detection rate of any increase in any aneurysmal dimension was 95.7%. The detection rates of the 3 observers (observers A, B, and C) were 98.0%, 96.6%, and 92.7%, respectively (p = 0.22). The detection rates of each MRI modality were 91.3% using 1.5-T TOF, 97.2% using 1.5-T with Gd, 95.8% using 3.0-T TOF, and 97.2% using 3.0-T with Gd (p = 0.31). On the other hand, the mean detection rate for aneurysm enlargement was 54.8%. Specifically, the detection rates of observers A, B, and C were 49.0%, 46.1%, and 66.7%, respectively (p = 0.009). As the incremental enlargement value increased, the detection rate for aneurysm enlargement increased. The use of 1.5-T Gd improved the detection rate for small incremental enlargement (e.g., 0.4–1 mm) of the aneurysm (p = 0.04). The location of the aneurysm also affected the detection rate for aneurysm enlargement (p < 0.0001).CONCLUSIONSThe detection rate and interobserver agreement were very high for aneurysm enlargement of 0.4–2 mm. The detection rate for at least 1 increase in any aneurysm dimension did not depend on the choice of MRI modality or measurement protocol. Use of Gd improved the accuracy of measurement. Aneurysm location may influence the accuracy of detecting enlargement.

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Gunia, D. J., E. T. Ekvtimishvili, and G. Z. Basiladze. "Necessity of follow-up cerebral digital subtraction angiography after endovascular coiling or microsurgical cliping of ruptured intracranial aneurysms to exclude de novo or aneurysmal regrow and avoid its rupture: report of 2 cases." Endovascular Neuroradiology 27, no. 1 (2019): 12–20. http://dx.doi.org/10.26683/2304-9359-2019-1(27)-12-20.

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Objective – to improve treatment results of patients with ruptured brain aneurysms using follow-up cerebral digital subtraction angiography to avoid de novo or aneurismal regrow.Materials and methods. Analysis of follow-up cerebral digital subtraction angiography and treatment results of two patient (60 and 64-year-old females) with brain anterior communicated artery de novo aneurysm and regrowed aneurysm of an anterior communicated artery after microsurgical clipping.Results. Two patient underwent endovascular treatment of ruptured brain aneurysms after non follow-up cerebral digital subtraction angiography. In first case de novo aneurysm of anterior communicating artery and in second – regrowed aneurys of anterior communicating artery after surgical clipping. Both patients were discharged from the clinic in I and IV modified Rankin scale. Conclusions. Digital subtraction angiography follow-up of intracranial aneurysms treated by endovascular or microsurgical approach is important for the detection and prediction for the risk of bleeding (aneurysm recurrence and de novo aneurysm). There exist no guidelines on the frequency of monitoring and imaging modality to adopt and the monitoring is adapted on a case-by-case basis. Digital subtraction angiography is the gold standard for the evaluation of aneurysmal occlusion after coiling and microsurgical clipping and remains also necessary for evaluating other devices.

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Huston, J., V. E. Torres, P. P. Sulivan, K. P. Offord, and D. O. Wiebers. "Value of magnetic resonance angiography for the detection of intracranial aneurysms in autosomal dominant polycystic kidney disease." Journal of the American Society of Nephrology 3, no. 12 (1993): 1871–77. http://dx.doi.org/10.1681/asn.v3121871.

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The association of intracranial aneurysms with autosomal dominant polycystic kidney disease (ADPKD), the 30-day mortality rate exceeding 50% for aneurysmal rupture, the effectiveness of surgical repair of unruptured aneurysms with a low surgical risk, and the development of noninvasive imaging techniques for their detection have led physicians to consider the value of screening patients with ADPKD for unruptured intracranial aneurysms. The sensitivity and specificity of high-resolution computed tomography and magnetic resonance imaging for the diagnosis of small intracranial aneurysms have been disappointing. To determine the value of magnetic resonance angiography (MRA), 85 patients with ADPKD without symptoms related to an intracranial aneurysm and 2 patients with ADPKD presenting with a subarachnoid hemorrhage or a suspected aneurysmal leak were studied. MRA was performed with the Multisequence Vascular Package (GE Medical Systems) with use of three-dimensional time-of-flight and three-dimensional phase-contrast techniques, and postprocessing maximum intensity projection images were generated to eliminate the problem of overlapping vessels. Asymptomatic intracranial aneurysms were detected in 6 (22%) of 27 patients with and 3 (5%) of 56 patients without a family history of intracranial aneurysm or subarachnoid hemorrhage (P = 0.02, information missing in 2 patients) and in the 2 patients who presented with a symptomatic aneurysm. A stepwise logistic regression analysis indicated that a family history of intracranial aneurysm or subarachnoid hemorrhage was independently associated with the presence of intracranial aneurysms. All of the aneurysms were < or = 6.5 mm in diameter.(ABSTRACT TRUNCATED AT 250 WORDS)

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Paralic, Martin, Kamil Zelenak, Patrik Kamencay, and Robert Hudec. "Automatic Approach for Brain Aneurysm Detection Using Convolutional Neural Networks." Applied Sciences 13, no. 24 (2023): 13313. http://dx.doi.org/10.3390/app132413313.

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The paper introduces an approach for detecting brain aneurysms, a critical medical condition, by utilizing a combination of 3D convolutional neural networks (3DCNNs) and Convolutional Long Short-Term Memory (ConvLSTM). Brain aneurysms pose a significant health risk, and early detection is vital for effective treatment. Traditional methods for aneurysm detection often rely on complex and time-consuming procedures. A radiologist specialist annotates each aneurysm and supports our work with true-ground annotations. From the annotated data, we extract images to train proposed neural networks. The paper experiments with two different types of networks, specifically focusing on 2D convolutional neural networks (2DCNNs), 3D convolutional neural networks (3DCNNs), and Convolutional Long Short-Term Memory (ConvLSTM). Our goal is to create a virtual assistant to improve the search for aneurysm locations, with the aim of further realizing the virtual assistant. Subsequently, a radiologist specialist will confirm or reject the presence of an aneurysm, leading to a reduction in the time spent on the searching process and revealing hidden aneurysms. Our experimental results demonstrate the superior performance of the proposed approach compared to existing methods, showcasing its potential as a valuable tool in clinical settings for early and accurate brain aneurysm detection. This innovative fusion of 3DCNN and LSTM (3DCNN-ConvLSTM) techniques not only improves diagnostic precision but also holds promise for advancing the field of medical image analysis, particularly in the domain of neurovascular diseases. Overall, our research underscores the potential of neural networks for the machine detection of brain aneurysms.

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Dupont, Stefan A., Giuseppe Lanzino, Eelco F. M. Wijdicks, and Alejandro A. Rabinstein. "The use of clinical and routine imaging data to differentiate between aneurysmal and nonaneurysmal subarachnoid hemorrhage prior to angiography." Journal of Neurosurgery 113, no. 4 (2010): 790–94. http://dx.doi.org/10.3171/2010.4.jns091932.

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Object In this study, the authors' goal was to minimize false-negative results in the detection of ruptured cerebral aneurysms. Methods The authors retrospectively reviewed the clinical and radiological information in consecutive adult patients admitted with acute subarachnoid hemorrhage (SAH) to their hospital between January 1, 2002, and January 1, 2008. Patients were grouped based on the presence or absence of a ruptured aneurysm, which was detected by catheter angiography. Multivariate logistic regression analysis was used to identify factors predicting detection of aneurysmal rupture by angiography. Results The authors identified 199 patients (121 women [61%]). A ruptured aneurysm was detected in 167 patients (84%). In multivariate analysis, loss of consciousness at the onset of SAH was a strong predictive factor associated with detection of a ruptured aneurysm on subsequent angiography (OR > 100, p = 0.0002). The positive predictive value of loss of consciousness at the onset of SAH for detection of a ruptured aneurysm was 100%. Conclusions Loss of consciousness at the onset of SAH is highly predictive of aneurysm rupture. A negative CT angiography study in these patients may be a false result, and a high-quality catheter angiography should be performed.

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Ajiboye, Norman, Nohra Chalouhi, Robert M. Starke, Mario Zanaty, and Rodney Bell. "Unruptured Cerebral Aneurysms: Evaluation and Management." Scientific World Journal 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/954954.

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The evolution of imaging techniques and their increased use in clinical practice have led to a higher detection rate of unruptured intracranial aneurysms. The diagnosis of an unruptured intracranial aneurysm is a source of significant stress to the patient because of the concerns for aneurysmal rupture, which is associated with substantial rates of morbidity and mortality. Therefore, it is important that decisions regarding optimum management are made based on the comparison of the risk of aneurysmal rupture with the risk associated with intervention. This review provides a comprehensive overview of the epidemiology, pathophysiology, natural history, clinical presentation, diagnosis, and management options for unruptured intracranial aneurysms based on the current evidence in the literature. Furthermore, the authors discuss the genetic abnormalities associated with intracranial aneurysm and current guidelines for screening in patients with a family history of intracranial aneurysms. Since there is significant controversy in the optimum management of small unruptured intracranial aneurysms, we provided a systematic approach to their management based on patient and aneurysm characteristics as well as the risks and benefits of intervention.

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Al Kasab, Sami, Daichi Nakagawa, Mario Zanaty, et al. "In vitro accuracy and inter-observer reliability of CT angiography in detecting intracranial aneurysm enlargement." Journal of NeuroInterventional Surgery 11, no. 10 (2019): 1015–18. http://dx.doi.org/10.1136/neurintsurg-2019-014737.

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Background and purposeTo evaluate the accuracy and inter-observer variability when CT angiography is used to identify unruptured intracranial aneurysm growth.MethodsTwo silicone phantom models were used in this study. Each phantom had eight aneurysms of variable size. The size and location of aneurysms in phantom 1 were representative of real patient aneurysms who presented to our institution. Phantom 2 contained aneurysms in the same locations, but with enlargement in various directions. Three blinded board-certified neuroradiologists were asked to identify the size of each aneurysm in three dimensions using CT angiography. The individual enlargement detection rates and inter-observer agreement rates of aneurysm enlargement among the three experts were calculated.ResultsThe detection rate of aneurysm enlargement in one dimension was 58.3% among the three observers. Accurate detection of enlargement in all dimensions was 12.5% among the three observers. Detection accuracy was not related to the size of enlargement. Significant inter-observer measurement variability was present.ConclusionThe use of CT angiography was associated with a poor ability to identify aneurysm enlargementaccurately. Further human studies are required to confirm our findings.

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Imaizumi, Yohichi, Tohru Mizutani, Katsuyoshi Shimizu, Yosuke Sato, and Junichi Taguchi. "Detection rates and sites of unruptured intracranial aneurysms according to sex and age: an analysis of MR angiography–based brain examinations of 4070 healthy Japanese adults." Journal of Neurosurgery 130, no. 2 (2019): 573–78. http://dx.doi.org/10.3171/2017.9.jns171191.

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OBJECTIVEThe purpose of this study was to evaluate the detection rate and occurrence site according to patient sex and age of unruptured intracranial aneurysms detected through MRI and MR angiography (MRA).METHODSA total of 4070 healthy adults 22 years or older (mean age [± SD] 50.6 ± 11.0 years; 41.9% women) who underwent a brain examination known as “Brain Dock” in the central Tokyo area between April 2014 and March 2015 were checked for unruptured saccular aneurysm using 3-T MRI/MRA. The following types of cases were excluded: 1) protrusions with a maximum diameter < 2 mm at locations other than arterial bifurcations, 2) conical protrusions at arterial bifurcations with a diameter < 3 mm, and 3) cases of suspected aneurysms with unclear imaging of the involved artery. When an aneurysm was definitively diagnosed, the case was included in the aneurysm group. The authors also investigated the relationship between aneurysm occurrence and risk factors (age, sex, smoking history, hypertension, diabetes, and hyperlipidemia).RESULTSOne hundred eighty-eight aneurysms were identified in 176 individuals (detection rate 4.32%), with the detection rate for women being significantly higher (6.2% vs 3.0%, p < 0.001). The average age in the aneurysm group was significantly higher than in the patients in whom aneurysms were not detected (53.0 ± 11.1 vs 50.5 ± 11.0 years). The detection rate tended to increase with age. The detection rates were 3.6% for people in their 30s, 3.5% for those in their 40s, 4.1% for those in their 50s, 6.9% for those in their 60s, and 6.8% for those in their 70s. Excluding persons in their 20s and 80s—age groups in which no aneurysms were discovered—the detection rate in women was higher in all age ranges. Of the individuals with aneurysms, 12 (6.81%) had multiple cerebral aneurysms; no sex difference was observed with respect to the prevalence of multiple aneurysms. Regarding aneurysm size, 2.0–2.9 mm was the most common size range, with 87 occurrences (46.3%), followed by 3.0–3.9 mm (67 [35.6%]) and 4.0–4.9 mm (20 [10.6%]). The largest aneurysm was 13 mm. Regarding location, the internal carotid artery (ICA) was the most common aneurysm site, with 148 (78.7%) occurrences. Within the ICA, C1 was the site of 46 aneurysms (24.5%); C2, 57 (30.3%); and C3, 29 (15.4%). The aneurysm detection rates for C2, C3, and C4 were 2.23%, 1.23%, and 0.64%, respectively, for women and 0.68%, 0.34%, and 0.21%, respectively, for men; ICA aneurysms were significantly more common in women than in men (5.27% vs 2.20%, p < 0.001). Multivariate logistic regression analysis revealed that age (p < 0.001, OR 1.03, 95% CI 1.01–1.04), female sex (p < 0.001, OR 2.28, 95% CI 1.64–3.16), and smoking history (p = 0.011, OR 1.52, 95% CI 1.10–2.11) were significant risk factors for aneurysm occurrenceCONCLUSIONSIn this study, both female sex and older age were independently associated with an increased aneurysm detection rate. Aneurysms were most common in the ICA, and the frequency of aneurysms in ICA sites was markedly higher in women.

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Kizilkilic, Osman, Eldeniz Huseynov, Sedat G. Kandemirli, Naci Kocer, and Civan Islak. "Detection of wall and neck calcification of unruptured intracranial aneurysms with flat-detector computed tomography." Interventional Neuroradiology 22, no. 3 (2016): 293–98. http://dx.doi.org/10.1177/1591019915626591.

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Object Microsurgical clipping is a widely used surgical technique in intracranial aneurysm treatment. It can be difficult in large sized aneurysms, and those with wide necks, thick walls and calcification located in the vicinity of the neck. This study reviewed calcification of the intracranial aneurysm wall and its relation to patient age, gender, location and size of the aneurysm. A possible cut-off value after which the aneurysm calcification rate increases was also investigated to classify patients’ risk factors for microclipping. Methods A retrospective review of all unruptured intracranial aneurysms that underwent digital subtraction angiography at a single centre was performed. Flat-detector computed tomography images of the aneurysm were reviewed for aneurysm location, size and calcification. The independent samples t test and χ2 test were used to show the relation between aneurysm wall calcification and patient age, gender, aneurysm localisation and size. Results None of the reviewed factors were statistically significantly related to aneurysm calcification except aneurysm size ( P < 0.01). Receiver operating characteristic curves showed aneurysms greater than 10.5 mm could be predicted to be calcified with a sensitivity of 80% and specificity of 63%. Conclusion In this study, the presence of calcification was related to aneurysm size. Larger aneurysms were more likely to be calcified. Aneurysms greater than 10.5 mm should be further investigated with a modality such as flat-detector computed tomography to show the calcification in detail, especially if microclipping is considered.

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Thèses sur le sujet "Aneurysm detection"

Wells, Catherine E. "Abdominal Aortic Aneurysm detection by common femoral artery Doppler ultrasound waveform analysis." Thesis, Cardiff University, 2007. http://orca.cf.ac.uk/54725/.

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Abdominal Aortic Aneurysm (AAA) is a serious, life threatening condition. AAAs are often asymptomatic and many remain silent until rupture. If detected early, the condition can be monitored and electively repaired relatively safely. Arterial disease is readily and routinely assessed using Doppler ultrasound. The effect of occlusive arterial disease on Doppler waveforms is well documented and the disease state of the aorto-iliac segment can be estimated by examination of the common femoral artery (CFA) waveform. Theoretically, aneurysmal disease should alter the blood flow pattern due to the localised increase in vessel diameter. It has been noted during routine clinical examinations of CFA Doppler waveforms, that when certain features were observed, an AAA was often present. Reported studies examining lower limb Doppler waveforms and flows through aneurysm models support these observations. The main aim of this research study was to determine if and how the Doppler waveform of the CFA is changed in the presence AAA and to utilise these changes for the early diagnosis of AAA. The interpretation of Doppler waveforms for the assessment of vascular disease requires a high level of skill and training and still remains subjective. An automated, objective detection method to detect AAA by analysis of the CFA Doppler waveform was achieved by implementing a MATLAB software based computer program to perform CFA waveform feature based analysis and determine whether or not an AAA is present. The second aim of the study was to predict the AAA size and presence of ILT using the program results. For patients with no significant atherosclerotic disease, automated MATLAB based CFA waveform analysis provided 100% sensitivity and 73.3% specificity for AAA detection. Additional waveform features set up by atherosclerotic disease made it more difficult to separate the AAA and normal patients with significant atherosclerotic disease. It was not possible to adequately predict the physical characteristics of using the AAA detection program. In conclusion, feature analysis of the CFA Doppler waveform may provide a promising, alternative method for AAA detection. AAA characterisation may be possible using different analysis methods.

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Yuk, Jongtae. "Hemorrhage and aortic aneurysm detection in the abdomen using 3D ultrasound imaging /." Thesis, Connect to this title online; UW restricted, 2001. http://hdl.handle.net/1773/5882.

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Maroney, Roy Thomas. "Missed opportunities for the detection of abdominal aortic aneurysms : a retrospective study of eighteen patients presenting with a ruptured or acute symptomatic abdominal aortic aneurysm." Master's thesis, University of Cape Town, 1997. http://hdl.handle.net/11427/25566.

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A ruptured abdominal aortic aneurysm (AAA) has a mortality of 80 percent. The majority of these cases present as medical emergencies, with 50 percent dying before they reach hospital. Twenty percent are not operated on because of an incorrect diagnosis and of the surviving 30 percent, there will be a peri-operative mortality of 40 percent. Thus only 20 percent of patients survive this condition. It is important to state that the long-term survival reported for patients undergoing AAA surgery approximates that of age-matched populations. This is in contrast to patients undergoing a coronary bypass procedure, where the long-term survival is affected by factors such as hypertension, angina and peripheral vascular disease. If the condition is diagnosed electively, the mortality rate is reduced to less than 5 percent. The researcher obtained the records of 18 patients who had presented to the vascular service at the New Kingsbury Hospital with a diagnosis of a ruptured or acute symptomatic AAA. He interviewed the referring family doctor and also obtained information from the case records to determine whether there were missed opportunities for the detection of such aneurysms. The results of the research showed that 12 general practitioners (GP's) out of a group of 13, were unaware that abdominal ultrasound is a highly specific and sensitive method for detecting AAA's. Only one of the group of 13 GP's regularly screens for AAA. The diagnosis of AAA was missed in 12 of the 18 patients. In this series the mean diameter of the aorta was 7,67 cm which is considered to be easily palpable. Five of the patients were referred to specialists for incidental reasons and they all failed to detect the AAA. The majority of patients with AAA's have at least 2 associated risk factors. The patients consulted their GP at least 5 times over the 24 month period. The GP's are not aware of the different modes of presentation, associated risk factors nor the value of screening for AAA's. Ten of the group of 13 GP's profess to engage in some form of Continuing Medical Education (CME). I have suggested a few guidelines to encourage family physicians to screen for AAA in all males over the age of 60, especially if they have risk factors, such as hypertension, a current or former cigarette smoker, coronary artery disease, peripheral vascular disease and a family history of AAA. The examination should include a thorough abdominal palpation and referral for an abdominal ultrasound examination to obtain the precise diameter of the AAA as treatment depends on the size of the AAA.

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BHADRI, PRASHANT R. "DEVELOPMENT OF AN INTEGRATED SOFTWARE/HARDWARE PLATFORM FOR THE DETECTION OF CEREBRAL ANEURYSM BY QUANTIFYING BILIRUBIN IN CEREBRAL SPINAL FLUID." University of Cincinnati / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1126815429.

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López-Linares, Karen. "Image analysis and deep learning to support endovascular repair of abdominal aortic aneurysms." Doctoral thesis, Universitat Pompeu Fabra, 2019. http://hdl.handle.net/10803/667102.

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An abdominal aortic aneurysm (AAA) is a focal dilation of the aorta that may lead to its rupture. The most common treatment for AAAs is endovascular aneurysm repair (EVAR). EVAR implies lifelong postoperative surveillance using Computed Tomography Angiography (CTA), due to the potential appearance of complications. This thesis sets the basis for intelligent CTA image analysis to support post-operative follow-up of AAAs, providing clinicians with valuable information to prognose the behavior of the aneurysm. First, novel pre-operative and post-operative AAA segmentation approaches are developed, based on Convolutional Neural Networks (CNN). Initially, 2D AAA detection and segmentation CNNs are proposed. Then, segmentation is extended to 3D to increase segmentation accuracy. Precise AAA segmentation is the basis for a good AAA follow-up. It allows to measure aneurysm volume, which is thought to be a better indicator for aneurysm rupture than the current AAA diameter measurements. Furthermore, it enables more complex analyses of AAA morphology and deformations. Subsequently, a methodology for post-operative CTA time-series registration and aneurysm biomechanical strain analysis is also proposed. From these strains, quantitative image-based descriptors are extracted and correlated with the long-term patient prognosis. The extracted descriptors are the basis for possible future imaging biomarkers to be used in clinical practice to assess patient prognosis and to enable informed decision making after EVAR. Finally, the technological developments in the thesis are applied to solve complex segmentation problems in other clinical domains, such as pectoral muscle segmentation from mammograms and pulmonary artery segmentation from CT scans. Validation of the 3D AAA segmentation approach proposed in this thesis is being carried out with the aim of integrating it in a commercial product.<br>El aneurisma de aorta abdominal (AAA) es una dilatación focal de la aorta que puede provocar su ruptura. El tratamiento habitual es la reparación endovascular (EVAR), que conlleva un seguimiento postoperatorio de por vida en base a imágenes de angiografía por tomografía computarizada (CTA) para detectar posibles complicaciones. Esta tesis establece la base para el análisis inteligente de imágenes CTA para apoyar el seguimiento postoperatorio de los AAA, proporcionando a los profesionales médicos información valiosa para predecir el comportamiento del aneurisma. Primero, se han desarrollado algoritmos de segmentación de AAA a partir de CTA preoperatorias y postoperatorias, basados en redes neuronales convolucionales (CNN). Inicialmente, se han propuesto CNNs 2D para la detección y la segmentación de AAAs. Posteriormente, el algoritmo de segmentación se ha extendido a 3D para mejorar su precisión, ya que ésta es la base para un buen seguimiento. Permite medir el volumen del aneurisma, que se considera un mejor indicador de riesgo de ruptura del AAA que la aproximación actual en base a su diámetro. Además, permite realizar análisis más complejos de la morfología y las deformaciones del AAA. Una vez obtenida la segmentación, se ha propuesto una metodología para el registro de series de CTA postoperatorias y el subsiguiente análisis biomecánico de las deformaciones del aneurisma. Dichas deformaciones se han cuantificado mediante descriptores de imagen y se han correlacionado con el pronóstico del paciente a largo plazo. Los descriptores extraídos establecen la base para el desarrollo de futuros biomarcadores de imagen que puedan ser utilizados en la práctica clínica para evaluar el pronóstico del paciente y para dar soporte al médico en sus decisiones tras una intervención EVAR. Por último, la experiencia adquirida en la tesis ha permitido aplicar algunas de las tecnologías para la resolución de problemas de segmentación complejos en otros ámbitos médicos, como la segmentación del músculo pectoral en mamografías o la segmentación de la arteria pulmonar en CTA. Actualmente, se está llevando a cabo la validación del algoritmo de segmentación de AAA 3D propuesto en esta tesis, con el objetivo de integrarlo en un producto comercial.

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Wang, Yan. "Etude de la méthode de Boltzmann sur réseau pour la segmentation d'anévrismes cérébraux." Thesis, Lyon, INSA, 2014. http://www.theses.fr/2014ISAL0078/document.

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L'anévrisme cérébral est une région fragile de la paroi d'un vaisseau sanguin dans le cerveau, qui peut se rompre et provoquer des saignements importants et des accidents vasculaires cérébraux. La segmentation de l'anévrisme cérébral est une étape primordiale pour l'aide au diagnostic, le traitement et la planification chirurgicale. Malheureusement, la segmentation manuelle prend encore une part importante dans l'angiographie clinique et elle est devenue couteuse en temps de traitement étant donné la gigantesque quantité de données générées par les systèmes d'imagerie médicale. Les méthodes de segmentation automatique d'image constituent un moyen essentiel pour faciliter et accélérer l'examen clinique et pour réduire l'interaction manuelle et la variabilité inter-opérateurs. L'objectif principal de ce travail de thèse est de développer des méthodes automatiques pour la segmentation et la mesure des anévrismes. Le présent travail de thèse est constitué de trois parties principales. La première partie concerne la segmentation des anévrismes géants qui contiennent à la fois la lumière et le thrombus. La méthode consiste d'abord à extraire la lumière et le thrombus en utilisant une procédure en deux étapes, puis à affiner la forme du thrombus à l'aide de la méthode des courbes de niveaux. Dans cette partie, la méthode proposée est également comparée à la segmentation manuelle, démontrant sa bonne précision. La deuxième partie concerne une approche LBM pour la segmentation des vaisseaux dans des images 2D+t et de l'anévrisme cérébral dans les images en 3D. La dernière partie étudie un modèle de segmentation 4D en considérant les images 3D+t comme un hypervolume 4D et en utilisant un réseau LBM D4Q81, dans lequel le temps est considéré de la même manière que les trois autres dimensions pour la définition des directions de mouvement des particules dans la LBM, considérant les données 3D+t comme un hypervolume 4D et en utilisant un réseau LBM D4Q81. Des expériences sont réalisées sur des images synthétiques d'hypercube 4D et d'hypersphere 4D. La valeur de Dice sur l'image de l'hypercube avec et sans bruit montre que la méthode proposée est prometteuse pour la segmentation 4D et le débruitage<br>Cerebral aneurysm is a fragile area on the wall of a blood vessel in the brain, which can rupture and cause major bleeding and cerebrovascular accident. The segmentation of cerebral aneurysm is a primordial step for diagnosis assistance, treatment and surgery planning. Unfortunately, manual segmentation is still an important part in clinical angiography but has become a burden given the huge amount of data generated by medical imaging systems. Automatic image segmentation techniques provides an essential way to easy and speed up clinical examinations, reduce the amount of manual interaction and lower inter operator variability. The main purpose of this PhD work is to develop automatic methods for cerebral aneurysm segmentation and measurement. The present work consists of three main parts. The first part deals with giant aneurysm segmentation containing lumen and thrombus. The methodology consists of first extracting the lumen and thrombus using a two-step procedure based on the LBM, and then refining the shape of the thrombus using level set technique. In this part the proposed method is also compared with manual segmentation, demonstrating its good segmentation accuracy. The second part concerns a LBM approach to vessel segmentation in 2D+t images and to cerebral aneurysm segmentation in 3D medical images through introducing a LBM D3Q27 model, which allows achieving a good segmentation and high robustness to noise. The last part investigates a true 4D segmentation model by considering the 3D+t data as a 4D hypervolume and using a D4Q81 lattice in LBM where time is considered in the same manner as for other three dimensions for the definition of particle moving directions in the LBM model

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White, P. M. "The detection of intracranial aneurysms by non-invasive imaging methods and the epidemiology of aneurysmal subarachnoid haemorrhage within the Scottish population." Thesis, University of Edinburgh, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.663704.

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The aims of the research project, which led to the writing of this thesis were to: Examine whether non-invasive imaging methods could replace intra-arterial angiography (IADSA) in the detection of intracranial aneurysms by: a) systematically reviewing the literature; b) prospectively determining the accuracy of non-invasive imaging methods currently available to Scotland, including the effect of observer experience on diagnostic performance and the patient acceptability of the alternative imaging modalities. To establish the incidence of aneurysmal subarachnoid haemorrhage (SAH) in families by a national retrospective study of occurrences of SAH in a one year period in Scotland, in parallel with a follow-up study of the families of patients who were admitted to the Institute of Neurosciences with aneurysmal SAH a decade earlier. The thesis is divided into three parts: Part One: a) summarises the current understanding of the epidemiology and pathophysiology of intracranial aneurysms; b) an overview of cerebrovascular anatomy with reference to aneurysm formation; c) the modalities available for imaging intracranial aneurysms and the current knowledge about their diagnostic performance are considered; d) an overview of the methods available for the treatment of intracranial aneurysms; e) the concept of screening for unruptured intracranial aneurysms is discussed and placed in context by comparison to other screening programmes. Part Two: a) describes a systematic review of the non-invasive imaging of intracranial aneurysms. CT and MR angiography had similar accuracy compared to IADSA of ~90%. Data on Transcranial Doppler Sonography (TCDS) were scantly but indicated poorer performance. Detection of very small aneurysms (<3 mm diameter) was significantly poorer for the non-invasive tests; b) describes a prospective study of 200 patients examining CTA, MRA and TCDS vs IADSA in the detection of intracranial aneurysms. CTA and MRA had an accuracy (per subject) of 0.85. TCDS had similar accuracy per subject but poorer accuracy per aneurysm than CTA or MRA. Detection of aneurysms £5mm was significantly poorer than for those >5mm. Interobserver agreement was good for all modalities; c) combining TCDS with CTA or MRA improved the detection of aneurysms on a per subject basis. Non-invasive imaging tests, especially when used in combination, are reliable at detecting aneurysms >5mm; d) examines the effect of observer experience. Neuroradiologists were more consistent and had better agreement with IADSA than non-neuroradiologists. Small aneurysms and cavernous/terminal internal carotid aneurysms were poorly detected by all observers; e) assessment of patient preferences indicated that TCDS was preferred to the other non-invasive tests and CTA or MRA, with the differences being statistically significant.

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Assis, Youssef. "Détection des anévrismes intracrâniens par apprentissage profond." Electronic Thesis or Diss., Université de Lorraine, 2024. http://www.theses.fr/2024LORR0012.

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Les anévrismes intracrâniens sont des dilatations locales des vaisseaux sanguins cérébraux, présentant un risque significatif de rupture, susceptible d'entraîner des conséquences graves. La détection précoce des anévrismes non rompus revêt donc une importance cruciale pour prévenir des complications potentiellement mortelles. Cependant, l'analyse des images médicales pour localiser ces anévrismes est une tâche complexe et chronophage, demandant du temps et d'expertise, et malgré cela reste susceptible d'erreurs d'interprétation. Face à ces défis, cette thèse explore des méthodes automatisées pour la détection des anévrismes, visant à faciliter le travail des radiologues et à améliorer l'efficacité du diagnostic. Notre approche se concentre sur l'utilisation de techniques d'intelligence artificielle, en particulier les réseaux de neurones profonds, pour la détection des anévrismes à partir d'images d'IRM angiographique par temps de vol (TOF-MRA). Nos travaux de recherche se sont articulés autour de plusieurs axes principaux. Tout d'abord, en raison de la rareté des données d'entraînement dans le domaine médical, nous avons adopté une méthode d'annotation rapide, bien qu'approximative, afin de faciliter la collection de données. Par ailleurs, nous proposons une stratégie basée sur des petits patchs. En association avec de la synthèse de données, les échantillons sont démultipliés dans la base d'apprentissage. En sélectionnant les échantillons, leur distribution est ajustée pour faciliter l'optimisation. Pour la détection automatisée des anévrismes, nous avons mise en place diverses architectures de réseaux de neurones. Une première approche a exploré les réseaux de segmentation d'images. Nous proposons dans un second temps une architecture innovante inspirée des travaux en détection d'objets. Ces architectures, notamment la dernière, ont permis d'obtenir des résultats compétitifs, en particulier en termes de sensibilité par rapport aux experts. Au-delà de la détection des anévrismes, nous avons étendu notre modèle à l'estimation de la pose des anévrismes dans les images 3D. Ceci peut grandement faciliter leur analyse et leur interprétation dans des plans de coupes reformatés. Une évaluation approfondie des modèles proposés a été systématiquement réalisée, incluant des études par ablation, l'exploitation de métriques adaptées au problème de la détection et des évaluations menées par des experts cliniciens, permettant d'apprécier leur efficacité potentielle pour une utilisation clinique. Nous avons en particulier mis en évidence les problèmes liés à l'incertitude dans l'annotation des bases de données existantes<br>Intracranial aneurysms are local dilatations of cerebral blood vessels, presenting a significant risk of rupture, which can lead to serious consequences. Early detection of unruptured aneurysms is therefore crucial to prevent potentially fatal complications. However, analyzing medical images to locate these aneurysms is a complex and time-consuming task, requiring time and expertise, and yet remains prone to errors in interpretation. Faced with these challenges, this thesis explores automated methods for the detection of aneurysms, aiming to facilitate the work of radiologists and improve diagnostic efficiency. Our approach focuses on the use of artificial intelligence techniques, particularly deep neural networks, for the detection of aneurysms from time-of-flight magnetic resonance angiography (TOF-MRA) images. Our research work is centered around several main axes. Firstly, due to the scarcity of training data in the medical field, we adopt a rapid, although approximate, annotation method to facilitate data collection. Furthermore, we propose a strategy based on small patches. In association with data synthesis, the samples are multiplied in the training database. By selecting the samples, their distribution is adjusted to facilitate optimization. Secondly, for the automated detection of aneurysms, we investigate various neural network architectures. An initial approach explores image segmentation networks. Then, we propose an innovative architecture inspired by object detection methods. These architectures, especially the latter, lead to competitive results, particularly in terms of sensitivity compared to experts. Thirdly, beyond the detection of aneurysms, we extend our model to estimate the pose of aneurysms in 3D images. This can greatly facilitate their analysis and interpretation in reformatted cross-sectional plans. A thorough evaluation of the proposed models is systematically carried out, including ablation studies, the use of metrics adapted to the problem of detection, and evaluations conducted by clinical experts, allowing us to assess their potential effectiveness for clinical use. In particular, we highlight the issues related to uncertainty in the annotation of existing databases

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Yang, Guang. "Detection of micro-aneurysms in low-resolution color retinal images." Mémoire, [S.l. : s.n.], 2001. http://savoirs.usherbrooke.ca/handle/11143/4546.

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Nikravanshalmani, Alireza. "Computer aided detection and segmentation of intracranial aneurysms in CT angiography." Thesis, Kingston University, 2012. http://eprints.kingston.ac.uk/22974/.

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Accurate detection and segmentation of intracranial aneurysms plays an important role in diagnosing and reducing the incidence of subarachnoid haemorrhage (SAH) which is associated with high rates of morbidity and mortality. This research proposes a computer aided detection (CAD) and segmentation (CAS) of intracranial aneurysm in computer tomography angiography (CTA). The efficiency of the CAD/CAS system is boosted by pre-processing the input image with non-linear diffusion to smooth the CTA data while preserving the edges. A 3D region growing-based approach is used to extract the cerebral arteries followed by entropy-based search space reduction to retain the volume of the circle of Willis (CoW) and the proximal cerebral arteries where nearly all intracranial aneurysms are located, whilst eliminating the extracranial and very distal intracranial circulation. Because cerebral aneurysms vary in size we regard the problem of cerebral aneurysm detection as an intrinsically multi-scale problem and employ a multi-scale approach to all detection analysis. Shape index analysis is employed to determine potential aneurysmal regions (PARs). Hessian analysis and gradient vector field analysis which reveal 3D local shape information are used to further characterise the initial PARs. False positive reduction is then performed based on the analysis of the shape characterisations of the PARs. A ranking score is defined based on the outcomes of the shape analysis to rank the likelihood of PARs. The system allows user to navigate through the ranked PARs and select a candidate aneurysm for further analysis (CAS). The boundary of the selected aneurysm and its parent artery is delineated by using a 3D conditional morphology-based region growing approach. The output is presented to the user to be assessed for the aneurysm orientation relative to the parent vessel. A semi-automatic process is applied to detach the aneurysm from its parent artery. To have a fine segmentation of aneurysm which can be used for characterization of the aneurysm, a 3D geodesic active contour implemented in a level set framework is applied. The volume of the separated aneurysm is quantified as a typical characterization ofthe aneurysm. The system has been validated on a clinical dataset of 62 CT A scans with average 274 slices per scan (involving 17,028 CT slices) containing 70 aneurysms. Sizes of aneurysms vary between 3-16mm. 42 CTA scans have been used as a training dataset for parameter selection and 20 CTA scans have been used as a test dataset. The sensitivity of the systems for the CAD component is 97% with the average false positive of 2.24 per dataset (0.008 per slice). CAS performance was evaluated by dual visual judgment of an expert neuroradiologist and neurosurgeon. The detection and segmentation performance indicate the approach has potential in clinical applications.

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Livres sur le sujet "Aneurysm detection"

Hennemuth, Anja, Leonid Goubergrits, Matthias Ivantsits, and Jan-Martin Kuhnigk, eds. Cerebral Aneurysm Detection. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72862-5.

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Conan, Doyle A. Complete Sherlock Holmes & other detective stories. HarperCollins Publishers, 1994.

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Before wings. Faber, 2008.

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Conan, Doyle Arthur. A study in scarlet: The hound of the Baskervilles. Reader's Digest, 1991.

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Conan, Doyle Arthur. A study in scarlet: The hound of the Baskervilles. Reader's Digest Association, 1991.

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Conan, Doyle Arthur. A study in scarlet: The hound of the Baskervilles. Reader's Digest Association, 1993.

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Conan, Doyle A. A Study in Scarlet | The Hound of the Baskervilles. Reader's Digest Association, 1986.

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Conan, Doyle Arthur. Sherlock Holmes: The complete illustrated novels. Chancellor Press, 2001.

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Klinger, Leslie S., ed. Sherlock Holmes anotado: Las Novelas. Akal, 2009.

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Conan, Doyle Arthur. Sherlock Holmes: The novels. Prion, 2008.

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Chapitres de livres sur le sujet "Aneurysm detection"

Jain, Kartik. "CADA Challenge: Rupture Risk Assessment Using Computational Fluid Dynamics." In Cerebral Aneurysm Detection. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72862-5_8.

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Jia, Yizhuan, Weibin Liao, Yi Lv, et al. "Detect and Identify Aneurysms Based on Adjusted 3D Attention UNet." In Cerebral Aneurysm Detection. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72862-5_4.

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Ivantsits, Matthias, Jan-Martin Kuhnigk, Markus Huellebrand, Titus Kuehne, and Anja Hennemuth. "Deep Learning-Based 3D U-Net Cerebral Aneurysm Detection." In Cerebral Aneurysm Detection. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72862-5_3.

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Ma, Jun, and Ziwei Nie. "Exploring Large Context for Cerebral Aneurysm Segmentation." In Cerebral Aneurysm Detection. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72862-5_7.

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Ivantsits, Matthias, Leonid Goubergrits, Jan-Martin Kuhnigk, et al. "Cerebral Aneurysm Detection and Analysis Challenge 2020 (CADA)." In Cerebral Aneurysm Detection. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72862-5_1.

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Ivantsits, Matthias, Leonid Goubergrits, Jan Brüning, Andreas Spuler, and Anja Hennemuth. "Intracranial Aneurysm Rupture Prediction with Computational Fluid Dynamics Point Clouds." In Cerebral Aneurysm Detection. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72862-5_11.

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Spuler, Andreas, and Leonid Goubergrits. "CADA: Clinical Background and Motivation." In Cerebral Aneurysm Detection. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72862-5_2.

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Shit, Suprosanna, Ivan Ezhov, Johannes C. Paetzold, and Bjoern Menze. "A$$\nu $$-Net: Automatic Detection and Segmentation of Aneurysm." In Cerebral Aneurysm Detection. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72862-5_5.

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Su, Ziyu, Yizhuan Jia, Weibin Liao, et al. "3D Attention U-Net with Pretraining: A Solution to CADA-Aneurysm Segmentation Challenge." In Cerebral Aneurysm Detection. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72862-5_6.

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Ivantsits, Matthias, Markus Huellebrand, Sebastian Kelle, Titus Kuehne, and Anja Hennemuth. "Intracranial Aneurysm Rupture Risk Estimation Utilizing Vessel-Graphs and Machine Learning." In Cerebral Aneurysm Detection. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72862-5_10.

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Actes de conférences sur le sujet "Aneurysm detection"

Byrne, Greg, Fernando Mut, and Juan R. Cebral. "Using Vortex Coreline Detection to Characterize Aneurysmal Flow Activity." In ASME 2012 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/sbc2012-80209.

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Assessing the risk of rupture of cerebral aneurysms is important for planning elective interventions. Since hemodynamics is thought to play a fundamental role in the mechanisms governing aneurysm progression and rupture, numerous investigations have tried to connect aneurysm hemodynamic conditions and rupture. In our previous work aneurysm flow patterns were qualitatively classified into simple / complex and stable / unstable categories by looking at the number of vortices within the aneurysm and their persistence during the cardiac cycle [1, 2]. The purpose of this work is to propose objective hemodynamic metrics that can capture these flow characteristics with the goal of relating them to aneurysm rupture.

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DeLeo, Michael J., Matthew J. Gounis, Bo Hong, et al. "Magnetic Resonance Detection of Inflammation in Elastase-Induced Aneurysms." In ASME 2008 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2008. http://dx.doi.org/10.1115/sbc2008-192608.

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Intracranial aneurysm rupture is the leading cause of subarachnoid hemorrhage in the United States and is associated with higher fatality rates than ischemic stroke. There is currently no objective diagnostic means to identify aneurysms that are at high risk for rupture despite significant improvements in treatment. Often with no warning signs prior to aneurysm rupture such as the transient ischemic attack in ischemic stroke, the need to develop a method for detecting aneurysm instability is necessary.

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Tsai, William W., O¨mer Savas, Duncan Maitland, et al. "Experimental Study of the Vascular Dynamics of a Saccular Basilar Aneurysm." In ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-14662.

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A majority of patients who suffer hemorrhaging as a result of a ruptured cerebral aneurysm experience reduced quality of life, long term brain damage, or death. Improved understanding of the flow patterns in cerebral aneurysms will lead to better techniques for their detection and treatment. Experiments are conducted in a generic flow phantom to characterize the basic behavior of blood flow in basilar saccular aneurysms. The test setup models a saccular aneurysm that forms at the bifurcation of the basilar artery into the Circle of Willis. Three input flow conditions are tested: a physiological flow waveform obtained from Kato (2002) [1] and two steady flow conditions corresponding to the peak and mean flow rates of the physiological flow waveform. Steady cases are examined to study the effect of increasing Reynolds number on the flow and the pulsatile case to quantify unsteady effects. The results reveal highly three dimensional flow patterns within the aneurysm. Velocity and vorticity fluctuations are extracted to investigate the nature of the unsteady flows. Wall shear stress is estimated around the aneurysm dome to examine its role in wall weakening and failure.

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Hentschke, Clemens M., Klaus D. Tonnies, Oliver Beuing, and Rosa Nickl. "A new feature for automatic aneurysm detection." In 2012 IEEE 9th International Symposium on Biomedical Imaging (ISBI 2012). IEEE, 2012. http://dx.doi.org/10.1109/isbi.2012.6235669.

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Bindhya, P. S., R. Chitra, and V. S. Bibin Raj. "Sparse auto-encoder based micro-aneurysm detection." In INTERNATIONAL SCIENTIFIC AND PRACTICAL CONFERENCE “INNOVATIVE TECHNOLOGIES IN AGRICULTURE”. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0170568.

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Real, Eusebio, Jose Fernando Val-Bernal, Alejandro Ponton, et al. "OCT for anomaly detection in aortic aneurysm resection." In 2014 IEEE Sensors. IEEE, 2014. http://dx.doi.org/10.1109/icsens.2014.6985094.

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Hentschke, Clemens M., Oliver Beuing, Rosa Nickl, and Klaus D. Tonnies. "Automatic cerebral aneurysm detection in multimodal angiographic images." In 2011 IEEE Nuclear Science Symposium and Medical Imaging Conference (2011 NSS/MIC). IEEE, 2011. http://dx.doi.org/10.1109/nssmic.2011.6152566.

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Le, Nam H., Edgar A. Samaniego, Ashrita Raghuram, Sebastian Sanchez, Honghai Zhang, and Milan Sonka. "Semi-automated intracranial aneurysm segmentation and neck detection." In Image Processing, edited by Ivana Išgum and Olivier Colliot. SPIE, 2022. http://dx.doi.org/10.1117/12.2613145.

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Watton, Paul N., Marc Homer, Justin Penrose, et al. "Patient-Specific Modelling of Intracranial Aneurysm Evolution." In ASME 2011 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2011. http://dx.doi.org/10.1115/sbc2011-53223.

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Intracranial aneurysms appear as sac-like outpouchings of the cerebral vasculature wall; inflated by the pressure of the blood that fills them. They are relatively common and affect up to 5% of the adult population. Fortunately, most remain asymptomatic. However, there is a small but inherent risk of rupture: 0.1% to 1% of detected aneurysms rupture every year. If rupture does occur there is a 30% to 50% chance of fatality. Consequently, if an aneurysm is detected, clinical intervention may be deemed appropriate. Therapy is currently aimed at pre-rupture detection and preventative treatment. However, interventional procedures are not without risk to the patient. The improvement and optimization of interventional techniques is an important concern for patient welfare and is necessary for rationalisation of healthcare priorities. Hence there is a need to develop methodologies to assist in identifying those ICAs most at risk of rupture. We focus on the mathematical modelling and computational simulation of ICA evolution. Models must take into consideration: (i) the biomechanics of the arterial wall; (ii) the biology of the arterial wall and (iii) the complex interplay between (i) and (ii), i.e. the mechanobiology of the arterial wall. The ultimate ambition of such models is to aid clinical diagnosis on a patient-specific basis. However, due to the significant biological complexity coupled with limited histological information such models are still in their relative infancy. Current research focuses on simulating the evolution of an ICA with an aim to yield insight into the growth and remodelling (G&R) processes that give rise to inception, enlargement, stabilisation and rupture. We present a novel Fluid-Structure-Growth computational framework for modelling aneurysm evolution.

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Spiclin, Ziga, Ziga Bizjak, Tim Jerman, Boštjan Likar, Franjo Pernuš, and Aichi Chien. "Registration based detection and quantification of intracranial aneurysm growth." In Computer-Aided Diagnosis, edited by Horst K. Hahn and Kensaku Mori. SPIE, 2019. http://dx.doi.org/10.1117/12.2512781.

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Rapports d'organisations sur le sujet "Aneurysm detection"

Wang, Ting-Wei, Yun-Hsuan Tzeng, Jia-Sheng Hong, et al. The Role of Deep Learning in Aortic Aneurysm Segmentation and Detection from CT Scans: A Systematic Review and Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2024. http://dx.doi.org/10.37766/inplasy2024.3.0126.

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