Factors influencing the reliability of a CT angiography-based deep learning method for infarct volume estimation.

Objectives: CT angiography (CTA)-based machine learning methods for infarct volume estimation have shown a tendency to overestimate infarct core and final infarct volumes (FIV). Our aim was to assess factors influencing the reliability of these methods. Methods: The effect of collateral circulation on the correlation between convolutional neural network (CNN) estimations and FIV was assessed based on the Miteff system and hypoperfusion intensity ratio (HIR) in 121 patients with anterior circulation acute ischaemic stroke using Pearson correlation coefficients and median volumes. Correlation was also assessed between successful and futile thrombectomies. The timing of individual CTAs in relation to CTP studies was analysed. Results: The strength of correlation between CNN estimated volumes and FIV did not change significantly depending on collateral status as assessed with the Miteff system or HIR, being poor to moderate (r = 0.09-0.50). The strongest correlation was found in patients with futile thrombectomies (r = 0.61). Median CNN estimates showed a trend for overestimation compared to FIVs. CTA was acquired in the mid arterial phase in virtually all patients (120/121). Conclusions: This study showed no effect of collateral status on the reliability of the CNN and best correlation was found in patients with futile thrombectomies. CTA timing in the mid arterial phase in virtually all patients can explain infarct volume overestimation. Advances in knowledge: CTA timing seems to be the most important factor influencing the reliability of current CTA-based machine learning methods, emphasizing the need for CTA protocol optimization for infarct core estimation.

Automatic chest computed tomography image noise quantification using deep learning.

PURPOSE: This study aimed to develop a deep learning (DL) method for noise quantification for clinical chest computed tomography (CT) images without the need for repeated scanning or homogeneous tissue regions. METHODS: A comprehensive phantom CT dataset (three dose levels, six reconstruction methods, amounting to 9240 slices) was acquired and used to train a convolutional neural network (CNN) to output an estimate of local image noise standard deviations (SD) from a single CT scan input. The CNN model consisting of seven convolutional layers was trained on the phantom image dataset representing a range of scan parameters and was tested with phantom images acquired in a variety of different scan conditions, as well as publicly available chest CT images to produce clinical noise SD maps. RESULTS: Noise SD maps predicted by the CNN agreed well with the ground truth both visually and numerically in the phantom dataset (errors of < 5 HU for most scan parameter combinations). In addition, the noise SD estimates obtained from clinical chest CT images were similar to running-average based reference estimates in areas without prominent tissue interfaces. CONCLUSIONS: Predicting local noise magnitudes without the need for repeated scans is feasible using DL. Our implementation trained with phantom data was successfully applied to open-source clinical data with heterogeneous tissue borders and textures. We suggest that automatic DL noise mapping from clinical patient images could be used as a tool for objective CT image quality estimation and protocol optimization.

Deep learning-aided extraction of outer aortic surface from CT angiography scans of patients with Stanford type B aortic dissection.

BACKGROUND: Guidelines recommend that aortic dimension measurements in aortic dissection should include the aortic wall. This study aimed to evaluate two-dimensional (2D)- and three-dimensional (3D)-based deep learning approaches for extraction of outer aortic surface in computed tomography angiography (CTA) scans of Stanford type B aortic dissection (TBAD) patients and assess the speed of different whole aorta (WA) segmentation approaches. METHODS: A total of 240 patients diagnosed with TBAD between January 2007 and December 2019 were retrospectively reviewed for this study; 206 CTA scans from 206 patients with acute, subacute, or chronic TBAD acquired with various scanners in multiple different hospital units were included. Ground truth (GT) WAs for 80 scans were segmented by a radiologist using an open-source software. The remaining 126 GT WAs were generated via semi-automatic segmentation process in which an ensemble of 3D convolutional neural networks (CNNs) aided the radiologist. Using 136 scans for training, 30 for validation, and 40 for testing, 2D and 3D CNNs were trained to automatically segment WA. Main evaluation metrics for outer surface extraction and segmentation accuracy were normalized surface Dice (NSD) and Dice coefficient score (DCS), respectively. RESULTS: 2D CNN outperformed 3D CNN in NSD score (0.92 versus 0.90, p = 0.009), and both CNNs had equal DCS (0.96 versus 0.96, p = 0.110). Manual and semi-automatic segmentation times of one CTA scan were approximately 1 and 0.5 h, respectively. CONCLUSIONS: Both CNNs segmented WA with high DCS, but based on NSD, better accuracy may be required before clinical application. CNN-based semi-automatic segmentation methods can expedite the generation of GTs. RELEVANCE STATEMENT: Deep learning can speeds up the creation of ground truth segmentations. CNNs can extract the outer aortic surface in patients with type B aortic dissection. KEY POINTS: • 2D and 3D convolutional neural networks (CNNs) can extract the outer aortic surface accurately. • Equal Dice coefficient score (0.96) was reached with 2D and 3D CNNs. • Deep learning can expedite the creation of ground truth segmentations.

Leveraging open dataset and transfer learning for accurate recognition of chronic pulmonary embolism from CT angiogram maximum intensity projection images.

BACKGROUND: Early diagnosis of the potentially fatal but curable chronic pulmonary embolism (CPE) is challenging. We have developed and investigated a novel convolutional neural network (CNN) model to recognise CPE from CT pulmonary angiograms (CTPA) based on the general vascular morphology in two-dimensional (2D) maximum intensity projection images. METHODS: A CNN model was trained on a curated subset of a public pulmonary embolism CT dataset (RSPECT) with 755 CTPA studies, including patient-level labels of CPE, acute pulmonary embolism (APE), or no pulmonary embolism. CPE patients with right-to-left-ventricular ratio (RV/LV) < 1 and APE patients with RV/LV ≥ 1 were excluded from the training. Additional CNN model selection and testing were done on local data with 78 patients without the RV/LV-based exclusion. We calculated area under the receiver operating characteristic curves (AUC) and balanced accuracies to evaluate the CNN performance. RESULTS: We achieved a very high CPE versus no-CPE classification AUC 0.94 and balanced accuracy 0.89 on the local dataset using an ensemble model and considering CPE to be present in either one or both lungs. CONCLUSIONS: We propose a novel CNN model with excellent predictive accuracy to differentiate chronic pulmonary embolism with RV/LV ≥ 1 from acute pulmonary embolism and non-embolic cases from 2D maximum intensity projection reconstructions of CTPA. RELEVANCE STATEMENT: A DL CNN model identifies chronic pulmonary embolism from CTA with an excellent predictive accuracy. KEY POINTS: • Automatic recognition of CPE from computed tomography pulmonary angiography was developed. • Deep learning was applied on two-dimensional maximum intensity projection images. • A large public dataset was used for training the deep learning model. • The proposed model showed an excellent predictive accuracy.

Automatic head computed tomography image noise quantification with deep learning.

PURPOSE: Computed tomography (CT) image noise is usually determined by standard deviation (SD) of pixel values from uniform image regions. This study investigates how deep learning (DL) could be applied in head CT image noise estimation. METHODS: Two approaches were investigated for noise image estimation of a single acquisition image: direct noise image estimation using supervised DnCNN convolutional neural network (CNN) architecture, and subtraction of a denoised image estimated with denoising UNet-CNN experimented with supervised and unsupervised noise2noise training approaches. Noise was assessed with local SD maps using 3D- and 2D-CNN architectures. Anthropomorphic phantom CT image dataset (N = 9 scans, 3 repetitions) was used for DL-model comparisons. Mean square error (MSE) and mean absolute percentage errors (MAPE) of SD values were determined using the SD values of subtraction images as ground truth. Open-source clinical head CT low-dose dataset (Ntrain = 37, Ntest = 10 subjects) were used to demonstrate DL applicability in noise estimation from manually labeled uniform regions and in automated noise and contrast assessment. RESULTS: The direct SD estimation using 3D-CNN was the most accurate assessment method when comparing in phantom dataset (MAPE = 15.5%, MSE = 6.3HU). Unsupervised noise2noise approach provided only slightly inferior results (MAPE = 20.2%, MSE = 13.7HU). 2DCNN and unsupervised UNet models provided the smallest MSE on clinical labeled uniform regions. CONCLUSIONS: DL-based clinical image assessment is feasible and provides acceptable accuracy as compared to true image noise. Noise2noise approach may be feasible in clinical use where no ground truth data is available. Noise estimation combined with tissue segmentation may enable more comprehensive image quality characterization.

A Randomized, Sham-Controlled Trial of Repetitive Transcranial Magnetic Stimulation Targeting M1 and S2 in Central Poststroke Pain: A Pilot Trial.

OBJECTIVES: Central poststroke pain (CPSP), a neuropathic pain condition, is difficult to treat. Repetitive transcranial magnetic stimulation (rTMS) targeted to the primary motor cortex (M1) can alleviate the condition, but not all patients respond. We aimed to assess a promising alternative rTMS target, the secondary somatosensory cortex (S2), for CPSP treatment. MATERIALS AND METHODS: This prospective, randomized, double-blind, sham-controlled three-arm crossover trial assessed navigated rTMS (nrTMS) targeted to M1 and S2 (10 sessions, 5050 pulses per session at 10 Hz). Participants were evaluated for pain, depression, anxiety, health-related quality of life, upper limb function, and three plasticity-related gene polymorphisms including Dopamine D2 Receptor (DRD2). We monitored pain intensity and interference before and during stimulations and at one month. A conditioned pain modulation test was performed using the cold pressor test. This assessed the efficacy of the descending inhibitory system, which may transmit TMS effects in pain control. RESULTS: We prescreened 73 patients, screened 29, and included 21, of whom 17 completed the trial. NrTMS targeted to S2 resulted in long-term (from baseline to one-month follow-up) pain intensity reduction of ≥30% in 18% (3/17) of participants. All stimulations showed a short-term effect on pain (17-20% pain relief), with no difference between M1, S2, or sham stimulations, indicating a strong placebo effect. Only nrTMS targeted to S2 resulted in a significant long-term pain intensity reduction (15% pain relief). The cold pressor test reduced CPSP pain intensity significantly (p = 0.001), indicating functioning descending inhibitory controls. The homozygous DRD2 T/T genotype is associated with the M1 stimulation response. CONCLUSIONS: S2 is a promising nrTMS target in the treatment of CPSP. The DRD2 T/T genotype might be a biomarker for M1 nrTMS response, but this needs confirmation from a larger study.

Automatic CT Angiography Lesion Segmentation Compared to CT Perfusion in Ischemic Stroke Detection: a Feasibility Study.

In stroke imaging, CT angiography (CTA) is used for detecting arterial occlusions. These images could also provide information on the extent of ischemia. The study aim was to develop and evaluate a convolutional neural network (CNN)-based algorithm for detecting and segmenting acute ischemic lesions from CTA images of patients with suspected middle cerebral artery stroke. These results were compared to volumes reported by widely used CT perfusion-based RAPID software (IschemaView). A 42-layer-deep CNN was trained on 50 CTA volumes with manually delineated targets. The lower bound for predicted lesion size to reliably discern stroke from false positives was estimated. The severity of false positives and false negatives was reviewed visually to assess the clinical applicability and to further guide the method development. The CNN model corresponded to the manual segmentations with voxel-wise sensitivity 0.54 (95% confidence interval: 0.44-0.63), precision 0.69 (0.60-0.76), and Sørensen-Dice coefficient 0.61 (0.52-0.67). Stroke/nonstroke differentiation accuracy 0.88 (0.81-0.94) was achieved when only considering the predicted lesion size (i.e., regardless of location). By visual estimation, 46% of cases showed some false findings, such as CNN highlighting chronic periventricular white matter changes or beam hardening artifacts, but only in 9% the errors were severe, translating to 0.91 accuracy. The CNN model had a moderately strong correlation to RAPID-reported Tmax > 10 s volumes (Pearson's r = 0.76 (0.58-0.86)). The results suggest that detecting anterior circulation ischemic strokes from CTA using a CNN-based algorithm can be feasible when accompanied with physiological knowledge to rule out false positives.

Computed tomography angiography-based deep learning method for treatment selection and infarct volume prediction in anterior cerebral circulation large vessel occlusion.

BACKGROUND: Computed tomography perfusion (CTP) is the mainstay to determine possible eligibility for endovascular thrombectomy (EVT), but there is still a need for alternative methods in patient triage. PURPOSE: To study the ability of a computed tomography angiography (CTA)-based convolutional neural network (CNN) method in predicting final infarct volume in patients with large vessel occlusion successfully treated with endovascular therapy. MATERIALS AND METHODS: The accuracy of the CTA source image-based CNN in final infarct volume prediction was evaluated against follow-up CT or MR imaging in 89 patients with anterior circulation ischemic stroke successfully treated with EVT as defined by Thrombolysis in Cerebral Infarction category 2b or 3 using Pearson correlation coefficients and intraclass correlation coefficients. Convolutional neural network performance was also compared to a commercially available CTP-based software (RAPID, iSchemaView). RESULTS: A correlation with final infarct volumes was found for both CNN and CTP-RAPID in patients presenting 6-24 h from symptom onset or last known well, with r = 0.67 (p < 0.001) and r = 0.82 (p < 0.001), respectively. Correlations with final infarct volumes in the early time window (0-6 h) were r = 0.43 (p = 0.002) for the CNN and r = 0.58 (p < 0.001) for CTP-RAPID. Compared to CTP-RAPID predictions, CNN estimated eligibility for thrombectomy according to ischemic core size in the late time window with a sensitivity of 0.38 and specificity of 0.89. CONCLUSION: A CTA-based CNN method had moderate correlation with final infarct volumes in the late time window in patients successfully treated with EVT.

Performance of a 3D convolutional neural network in the detection of hypoperfusion at CT pulmonary angiography in patients with chronic pulmonary embolism: a feasibility study.

BACKGROUND: Chronic pulmonary embolism (CPE) is a life-threatening disease easily misdiagnosed on computed tomography. We investigated a three-dimensional convolutional neural network (CNN) algorithm for detecting hypoperfusion in CPE from computed tomography pulmonary angiography (CTPA). METHODS: Preoperative CTPA of 25 patients with CPE and 25 without pulmonary embolism were selected. We applied a 48%-12%-40% training-validation-testing split (12 positive and 12 negative CTPA volumes for training, 3 positives and 3 negatives for validation, 10 positives and 10 negatives for testing). The median number of axial images per CTPA was 335 (min-max, 111-570). Expert manual segmentations were used as training and testing targets. The CNN output was compared to a method in which a Hounsfield unit (HU) threshold was used to detect hypoperfusion. Receiver operating characteristic area under the curve (AUC) and Matthew correlation coefficient (MCC) were calculated with their 95% confidence interval (CI). RESULTS: The predicted segmentations of CNN showed AUC 0.87 (95% CI 0.82-0.91), those of HU-threshold method 0.79 (95% CI 0.74-0.84). The optimal global threshold values were CNN output probability ≥ 0.37 and ≤ -850 HU. Using these values, MCC was 0.46 (95% CI 0.29-0.59) for CNN and 0.35 (95% CI 0.18-0.48) for HU-threshold method (average difference in MCC in the bootstrap samples 0.11 (95% CI 0.05-0.16). A high CNN prediction probability was a strong predictor of CPE. CONCLUSIONS: We proposed a deep learning method for detecting hypoperfusion in CPE from CTPA. This model may help evaluating disease extent and supporting treatment planning.

Evaluation of a CTA-based convolutional neural network for infarct volume prediction in anterior cerebral circulation ischaemic stroke.

BACKGROUND: Computed tomography angiography (CTA) imaging is needed in current guideline-based stroke diagnosis, and infarct core size is one factor in guiding treatment decisions. We studied the efficacy of a convolutional neural network (CNN) in final infarct volume prediction from CTA and compared the results to a CT perfusion (CTP)-based commercially available software (RAPID, iSchemaView). METHODS: We retrospectively selected 83 consecutive stroke cases treated with thrombolytic therapy or receiving supportive care that presented to Helsinki University Hospital between January 2018 and July 2019. We compared CNN-derived ischaemic lesion volumes to final infarct volumes that were manually segmented from follow-up CT and to CTP-RAPID ischaemic core volumes. RESULTS: An overall correlation of r = 0.83 was found between CNN outputs and final infarct volumes. The strongest correlation was found in a subgroup of patients that presented more than 9 h of symptom onset (r = 0.90). A good correlation was found between the CNN outputs and CTP-RAPID ischaemic core volumes (r = 0.89) and the CNN was able to classify patients for thrombolytic therapy or supportive care with a 1.00 sensitivity and 0.94 specificity. CONCLUSIONS: A CTA-based CNN software can provide good infarct core volume estimates as observed in follow-up imaging studies. CNN-derived infarct volumes had a good correlation to CTP-RAPID ischaemic core volumes.

3D convolutional neural networks applied to CT angiography in the detection of acute ischemic stroke.

BACKGROUND: The aim of this study was to investigate the feasibility of ischemic stroke detection from computed tomography angiography source images (CTA-SI) using three-dimensional convolutional neural networks. METHODS: CTA-SI of 60 patients with a suspected acute ischemic stroke of the middle cerebral artery were randomly selected for this study; 30 patients were used in the neural network training, and the subsequent testing was performed using the remaining 30 patients. The training and testing were based on manually segmented lesions. Cerebral hemispheric comparison CTA and non-contrast computed tomography (NCCT) were studied as additional input features. RESULTS: All ischemic lesions in the testing data were correctly lateralized, and a high correspondence to manual segmentations was achieved. Patients with a diagnosed stroke had clinically relevant regions labeled infarcted with a 0.93 sensitivity and 0.82 specificity. The highest achieved voxel-wise area under receiver operating characteristic curve was 0.93, and the highest Dice similarity coefficient was 0.61. When cerebral hemispheric comparison was used as an input feature, the algorithm performance improved. Only a slight effect was seen when NCCT was included. CONCLUSION: The results support the hypothesis that an acute ischemic stroke lesion can be detected with 3D convolutional neural network-based software from CTA-SI. Utilizing information from the contralateral hemisphere appears to be beneficial for reducing false positive findings.

Headache as symptom of intracranial hemorrhage.

The most important signs of danger of a headache patient include exceptionally intense or acute headache, transient loss or progressive impairment of consciousness, and neurological deficit symptoms. These patients are referred to an urgent assessment by a physician. Computed tomography scanning of the head is carried out in the case of suspected hemorrhage of a headache patient. Routine diagnosis employing cerebrospinal fluid analysis can be abandoned when excluding subarachnoid hemorrhage in a patient with headache symptoms, if blood is with certainty not observed in the CT scan of the head and no more than six hours have passed after the onset of the symptom. If subarachnoid hemorrhage is detected, cerebral CT angiography will be performed at the same time and a neurosurgeon consulted about the need of operative treatment.

Characteristics and long-term outcome of 251 patients with dural arteriovenous fistulas in a defined population.

OBJECT: Management of dural arteriovenous fistulas (DAVFs) has changed during the last decades due to increased knowledge of their pathophysiology and natural history as well as advances in treatment modalities. The authors describe the characteristics and long-term outcome of a large consecutive series of patients with DAVFs. METHODS: Altogether 251 patients with 261 DAVFs were treated in 2 of the 5 neurosurgery departments at Helsinki and Kuopio University Hospitals between 1944 and 2006. Clinical data and radiological examinations were reviewed to assess patients' overall long-term clinical outcome. RESULTS: The detection rate of DAVFs increased markedly in the 1970s and again in the 1990s when digital subtraction angiography was introduced. The incidence of DAVFs in a defined southern Finnish population was 0.51 per 100,000 individuals per year, which represents 32% of all the brain arteriovenous malformations. In the early part of the series, DAVFs were treated by proximal ligation of the feeding arteries. Later, most of the patients underwent preoperative embolization and subsequent craniotomy, and since 2000 stereotactic radiosurgery has been increasingly used in the treatment of DAVFs. Fifty-nine percent of the 261 fistulas were totally occluded. Treatment-related major complications were seen in 21 patients. CONCLUSIONS: The advances in diagnostic methods (digital subtraction angiography, CT, and MRI) increased the detection rate of DAVFs, and as treatment modalities developed, the results of treatment and outcome of patients markedly improved with the introduction of endovascular techniques and stereotactic radiosurgery. Microsurgery is of limited use in DAVFs resistant to other treatment modalities.

Oxidative stress is associated with cell death, wall degradation, and increased risk of rupture of the intracranial aneurysm wall.

BACKGROUND: The cause of rupture of intracranial aneurysms (IA) is not well understood. We previously demonstrated that loss of cells from the IA wall is associated with wall degeneration and rupture. OBJECTIVE: To investigate the mechanisms mediating cell death in the IA wall. METHODS: Snap-frozen tissue samples from aneurysm fundi were studied with terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL) staining and immunostaining (14 unruptured and 20 ruptured), as well as with Western blot (12 unruptured and 12 ruptured). RESULTS: : Ruptured IA walls had more TUNEL-positive cells than unruptured walls (P < .001). Few cells positive for cleaved caspase-3 were detected. Cleaved caspase-9 (intrinsic activation of apoptosis) was significantly increased in ruptured IA walls, whereas cleaved caspase-8 (extrinsic activation of apoptosis) was not detected. Increased expression of hemeoxygenase-1, a marker for oxidative stress, was associated with IA wall degeneration and rupture. CONCLUSION: Our results show that programmed cell death is activated in the IA wall via the intrinsic pathway. High oxidative stress in the IA wall is probably a significant cause of the intrinsic activation of cell death.

Lateral supraorbital approach applied to tuberculum sellae meningiomas: experience with 52 consecutive patients.

BACKGROUND: Various surgical approaches for the removal of tuberculum sellae meningiomas (TSMs) have previously been described. OBJECTIVE: To assess the reliability and safety of the lateral supraorbital (LSO) approach to remove TSMs. METHODS: We identified all TSM patients operated on at the Department of Neurosurgery at Helsinki University Central Hospital, Finland, by the senior author (J.H.) using the LSO approach between September 1997 and August 2010. We retrospectively analyzed the clinical data, radiological findings, surgical treatment, histology, and outcome of patients and discuss the operative technique. RESULTS: Apparent complete tumor removal was achieved in 45 patients (87%). Of 42 patients, preexisting visual deficit improved in 22, remained the same in 13, and worsened in 7, and de novo visual deficit occurred in 1 patient. At 3 months post-discharge, 47 patients (90%) had a good recovery, 4 (8%) were moderately disabled, and 1 (2%) died 40 days after surgery of unexplained cardiac arrest. Seven patients (13%) had minimal residual tumors, 2 of which required reoperation. During the median follow-up of 59 months (range, 1-133 months), tumor recurred in 1 of the patients who had undergone a second operation. CONCLUSION: TSMs of all sizes can be removed via the LSO approach with minimal morbidity and mortality. Low-power or no coagulation is recommended near the optic nerves and the optic chiasm to preserve their vascular support from the internal carotid artery perforators. Our results are comparable to those obtained using more extensive and time-consuming approaches. We recommend the LSO approach to remove TSMs.

Complications of anterior clinoidectomy through lateral supraorbital approach.

OBJECTIVE: We reviewed the surgical complications from our recent experience in vascular and tumor patients who underwent anterior clinoidectomy through the lateral supraorbital (LSO) approach. METHODS: Between June 2007 and January 2011, a total of 82 patients with neoplastic and vascular lesions underwent anterior clinoidectomy by the senior author (J.H.) through the LSO approach. We analyzed the operative videos paying particular attention to the surgical technique used for removal of the anterior clinoid process (ACP) and compared the microsurgical nuances to postoperative complications related to anterior clinoidectomy. RESULTS: Forty-five patients were treated for aneurysms; 35 patients for intraorbital, parasellar, and suprasellar tumors; and 2 patients for carotid-cavernous fistulas. Intradural anterior clinoidectomy was performed in 67 (82%) cases; in 15 (18%) cases an extradural approach was used. In 51 (62%) cases, ACP was removed completely, whereas in the remaining 31 (38%) a tailored anterior clinoidectomy was performed. Four (5%) patients had new postoperative visual deficits and 3 (4%) experienced a worsening of preoperative visual deficits. Twelve (15%) patients improved their preoperative visual deficits after intradural anterior clinoidectomy. Ultrasonic bone device is a useful tool but may damage the optic nerve when performing anterior clinoidectomy. There was no mortality in our series. CONCLUSION: Anterior clinoidectomy can be performed through an LSO approach with a safety profile that is comparable to other approaches. Ultrasonic bone dissector is a useful tool but may lead to injury of the optic nerve and should be used very carefully in its vicinity.

Tailored anterior clinoidectomy through the lateral supraorbital approach: experience with 82 consecutive patients.

OBJECTIVE: To demonstrate that anterior clinoidectomy is possible through the lateral supraorbital (LSO) approach, and that extent of the clinoidectomy is tailored according to the lesion. We reviewed our recent experience on patients with vascular and tumor who underwent anterior clinoidectomy through the LSO approach. METHODS: Between June 2007 and January 2011, 82 patients with neoplastic and vascular lesions underwent anterior clinoidectomy by the senior author (J.H.) with the LSO approach. We retrospectively analyzed the surgical videos and the microsurgical techniques of anterior clinoidectomy. RESULTS: Forty-five patients were treated for aneurysms, 35 patients for intraorbital, parasellar and suprasellar tumors, and 2 patients presented with carotid-cavernous fistula. Intradural anterior clinoidectomy was performed in 67 patients (82%); in 15 patients (18%) extradural anterior clinoidectomy was used. A minimal removal of the anterior clinoid process (ACP) was performed in 5 patients, in 8 patients a partial clinoidectomy was performed, in 18 patients a subtotal removal of the ACP was needed, and in 51 patients, the entire ACP was removed. There was no operation-related mortality in the series. CONCLUSIONS: A tailored anterior clinoidectomy is useful and can be performed through the LSO approach. Intradural visualization of the internal carotid artery and optic nerve is mandatory for the exact anatomic orientation and safe anterior clinoidectomy. We recommend intradural anterior clinoidectomy for all vascular and most neoplastic lesions.

Detection of unruptured cerebral artery aneurysms by MRA at 3.0 tesla: comparison with multislice helical computed tomographic angiography.

BACKGROUND: Computed tomographic angiography (CTA) has become the primary non-invasive method for detection of cerebral artery aneurysms in many neurovascular centers. PURPOSE: To compare MR-angiography at a 3.0 tesla (3T) scanner to CTA in the detection of unruptured intracranial aneurysms. MATERIAL AND METHODS: CTA and 3T MRA data from 60 patients were evaluated. CTA was obtained with a 4-16-row helical CT-scanner after administration of 120 cc intravenous contrast agent, MRA was performed by a 3T MR-scanner using time-of-flight pulse sequence. RESULTS: Fifty-five cerebral artery aneurysms were detected by MRA and 47 aneurysms by CTA. Most of the aneurysms detected by MRA but not by CTA were small internal carotid artery (ICA) aneurysms. Bone structures and venous enhancement deteriorated CTA accuracy, especially in skull base. In one patient a fairly large anterior communicating artery aneurysm was not visible in MRA due to spin saturation, although it was clearly visualized in CTA. After contrast injection the aneurysm was also seen in MRA. Although the overall image quality of MRA and CTA were comparable, MRA was more susceptible to artifacts and thus re-formatted surface-shaded volume rendered 3-dimensional images of aneurysms from MRA were inferior compared to those from CTA. CONCLUSION: MRA at 3T appears to be at least as sensitive as CTA in the detection of unruptured cerebral artery aneurysms, however image quality control is crucial and contrast agent enhances visualization of complex and large aneurysms.

Posterior fossa dural arteriovenous fistulas: diagnosis and follow-up with time-resolved imaging of contrast kinetics (TRICKS) at 1.5T.

BACKGROUND: Time-of-flight MR angiography (TOF MRA) is currently the most widely used non-invasive imaging tool to diagnose dural arteriovenous fistula (DAVF). It is, however, not as sensitive as invasive digital subtraction angiography (DSA) for detecting the arteriovenous shunting inherent in DAVF. Dynamic contrast-enhanced MR angiography allows separation of arterial and venous phases of contrast passage though the brain and can thus demonstrate early venous filling through the arteriovenous shunt. PURPOSE: To compare the diagnostic value of TOF MRA and a commercially available dynamic contrast-enhanced MR angiography sequence (TRICKS) at 1.5T in detecting posterior fossa DAVF. MATERIAL AND METHODS: We retrospectively collected image data for 19 patients who underwent TOF MRA, TRICKS, and DSA either for primary diagnosis or for follow-up of posterior fossa DAVF and assessed the performance of TOF MRA and TRICKS in demonstrating the arteriovenous shunt, with DSA as the reference standard. RESULTS: TRICKS detected early arterial filling at 94.4% sensitivity and 83.3% specificity. TOF MRA detected high flow-related signal within venous structures at 64.7% sensitivity and 80% specificity. CONCLUSION: The commercially available dynamic MR angiography sequence TRICKS with fully automatic vendor postprocessing at 1.5T is more sensitive than TOF MRA in detecting the arteriovenous shunt in posterior fossa DAVF.

High-resolution TOF MR angiography at 4.7 Tesla for volumetric and morphologic evaluation of coiled aneurysm neck remnants in a rat model.

BACKGROUND: Detection of morphologic and volumetric changes in aneurysm necks is important when evaluating the effects of endovascular devices for aneurysm occlusion. PURPOSE: To optimize high-resolution 3D-TOF MRA at 4.7 T in order to achieve the best aneurysm-to-background contrast in experimental rat aneurysms, and to quantify the volume of the aneurysm neck by imaging. MATERIAL AND METHODS: Saccular aneurysms in the abdominal aorta of rats were coiled with platinum coils. Tissue relaxation times were measured, and used in a mathematical simulation. To optimize 3D-TOF angiography, imaging parameters were varied within the range obtained from the simulations. Tissue contrast and contrast-to-noise ratio were measured from MR images. TOF images were compared to conventional spin echo and gradient echo images and to endoscopic and histological analyses. RESULTS: Parameters yielding the best aneurysm-to-background contrast and contrast-to-noise ratio were determined. The agreement between the results from in vivo imaging and simulation was good. The optimized 3D-TOF MRA sequence (TR/TE/FA = 60 ms/6.6 ms/20°) had an isotropic voxel size of 117 µm, which enabled measurement of the aneurysm neck volume. CONCLUSION: High-resolution 3D-TOF angiography enables non-invasive quantification of changes in neck remnants of endovascularly coiled experimental aneurysms. In this model innovations like bioactive coils can be accurately tested for their efficacy for aneurysm occlusion.