"Hyperdense artery sign" in early ischemic stroke: diagnostic value of model-based reconstruction approach in comparison with standard hybrid iterative reconstruction algorithm

Dual-Energy Computed Tomography Virtual Noncalcium Imaging of Intracranial Arteries in Acute Ischemic Stroke: Differentiation Between Acute Thrombus and Calcification

OBJECTIVE

Hyperdense artery sign (HAS) on noncontrast brain computed tomography (CT) indicates an acute thrombus within the cerebral artery. It is a valuable imaging biomarker for diagnosing large-vessel occlusion; however, its identification may be challenging with the presence of vascular calcification. Dual-energy CT virtual noncalcium (VNCa) imaging using a 3-material decomposition algorithm is helpful for differentiating between calcification and hemorrhage. This study aimed to clarify the potential of VNCa imaging for differentiating HAS from vascular calcification.

METHODS

Patients with acute ischemic stroke and large-vessel occlusion identified on MR angiography, who also underwent noncontrast dual-energy CT, were included. The 80 kV/Sn 140 kV mixed images, with a weighting factor of 0.4, were considered 120 kVp-equivalent images. Postprocessing using a 3-material decomposition algorithm to differentiate between calcium (Ca), cerebrospinal fluid, and hemorrhage was performed via a commercially available 3-dimensional workstation. A mixed image, VNCa image, color-coded Ca image, and color-coded Ca image with VNCa image overlay (color-coded Ca-overlay image) were obtained, and axial reconstruction with a 1-mm slice thickness was performed for each image type. Two experienced neuroradiologists conducted imaging evaluations in consensus.

RESULTS

Thirty-four patients (mean age, 76.0 years; 21 male and 13 female patients) were included. The mixed and VNCa images revealed an HAS (indicating an acute clot) corresponding to the large-vessel occlusion site in 30 patients. Among them, the VNCa and color-coded Ca-overlay images enabled clear differentiation between the acute thrombus and adjacent vessel wall calcification in 5 patients. Among the other 4 patients, the VNCa, Ca-overlay, and Ca images identified calcified cerebral emboli in the M1 segment in 1 patient. For the other 3 patients, no high attenuation corresponding to magnetic resonance angiography findings was observed in any of the mixed, VNCa, Ca-overlay, or Ca images.

CONCLUSIONS

VNCa and color-coded Ca-overlay images obtained via dual-energy brain CT enabled differentiation of acute thrombus from vessel wall calcification and calcified cerebral emboli in patients with acute ischemic stroke.

Feasibility of Low-Dose and Low-Contrast Media Volume Approach in Computed Tomography Cardiovascular Imaging Reconstructed with Model-Based Algorithm

Aim: To evaluate the dose reduction and image quality of low-dose, low-contrast media volume in computed tomography (CT) examinations reconstructed with the model-based iterative reconstruction (MBIR) algorithm in comparison with the hybrid iterative (HIR) one. Methods: We prospectively enrolled a total of 401 patients referred for cardiovascular CT, evaluated with a 256-MDCT scan with a low kVp (80 kVp) reconstructed with an MBIR (study group) or a standard HIR protocol (100 kVp-control group) after injection of a fixed dose of contrast medium volume. Vessel contrast enhancement and image noise were measured by placing the region of interest (ROI) in the left ventricle, ascending aorta; left, right and circumflex coronary arteries; main, right and left pulmonary arteries; aortic arch; and abdominal aorta. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were computed. Subjective image quality obtained by consensus was assessed by using a 4-point Likert scale. Radiation dose exposure was recorded. Results: HU values of the proximal tract of all coronary arteries; main, right and left pulmonary arteries; and of the aorta were significantly higher in the study group than in the control group (p < 0.05), while the noise was significantly lower (p < 0.05). SNR and CNR values in all anatomic districts were significantly higher in the study group (p < 0.05). MBIR subjective image quality was significantly higher than HIR in CCTA and CTPA protocols (p < 0.05). Radiation dose was significantly lower in the study group (p < 0.05). Conclusions: The MBIR algorithm combined with low-kVp can help reduce radiation dose exposure, reduce noise, and increase objective and subjective image quality.

Identifying large vessel occlusion using the hyperdense artery sign in patients treated with mechanical thrombectomy

OBJECTIVES

The hyperdense artery sign on non-contrast computed tomography-reconstructed images is useful for identifying large vessel occlusion in acute ischemic stroke. This study aimed to assess its efficacy in patients with large vessel occlusion treated with mechanical thrombectomy.

MATERIALS AND METHODS

This retrospective and prospective single-centered study from June 2019 to May 2021 evaluated the use of non-contrast computed tomography-reconstructed images for detecting hyperdense artery sign to identify large vessel occlusion from June 2020 to May 2021. We registered consecutive potential candidates for mechanical thrombectomy due to suspected stroke and assessed the accuracy of hyperdense artery sign on non-contrast computed tomography-reconstructed images for large vessel occlusion in the hyperacute setting. Non-contrast computed tomography images were reconstructed into maximum intensity projection images with iterative reconstruction algorithms to detect hyperdense artery signs. We compared the door-to-puncture time and functional outcome at 90 days before and after employing non-contrast computed tomography-reconstructed images in patients with large vessel occlusion treated with mechanical thrombectomy.

RESULTS

The cohort included 82 patients, wherein 47 were treated with mechanical thrombectomy. The sensitivity (96%) and specificity (94%) of hyperdense artery sign on non-contrast computed tomography-reconstructed images for large vessel occlusion were performed. The door-to-puncture time was significantly shortened after using non-contrast computed tomography-reconstructed images (49 versus 28 min, p = 0.001), but the functional outcome at 90 days remained unchanged.

CONCLUSIONS

Non-contrast computed tomography-reconstructed images, as a vascular imaging tool for mechanical thrombectomy, can reduce workflow time in hospitals by identifying large vessel occlusion with high sensitivity and specificity.

Usefulness of maximum intensity projection images of non-enhanced CT for detection of hyperdense middle cerebral artery sign in acute thromboembolic ischemic stroke

PURPOSE

To compare the sensitivity of the hyperdense middle cerebral artery (MCA) sign between maximum intensity projection (MIP) and conventional averaged images in patients with acute focal neurological deficits with acute thromboembolic MCA occlusion (MCA occlusion group) and patients with acute focal neurological deficits without MCA occlusion (control group).

MATERIALS AND METHODS

Initial computed tomography (CT) scans on admission were reconstructed with 5 mm thickness at every 3 mm interval for averaged and MIP images from 1 mm thickness non-contrast axial source images. Images were obtained from 30 cases each in the MCA occlusion and control groups. The CT values in the region of interests (ROIs) on the affected and unaffected sides of the MCA were compared. To compare CT values among subjects, the CT values were normalized by obtaining a ratio on the affected and unaffected sides, and the normalized CT values were analyzed using the receiver operating characteristic (ROC) curve.

RESULTS

The hyperdense MCA sign was visually detected on MIP images in 90% cases and on 5 mm averaged images in only 57% cases in the MCA occlusion group. Based on the ROC analysis of the normalized ratio on the affected and unaffected sides, area under the curve of MIP image and averaged image was 0.941 and 0.655, respectively. On MIP images, the optimal threshold of the ratio on the affected and unaffected sides was 1.152 (sensitivity: 90.0%, and specificity: 93.3%).

CONCLUSION

The hyperdense MCA sign sensitivity on 5 mm MIP images was significantly higher than that on conventional 5 mm averaged CT images. This could be useful for the early initiation of proper therapy for patients with acute focal neurological deficits.

Impact of dose reduction and iterative model reconstruction on multi-detector CT imaging of the brain in patients with suspected ischemic stroke

Non-contrast cerebral computed tomography (CT) is frequently performed as a first-line diagnostic approach in patients with suspected ischemic stroke. The purpose of this study was to evaluate the performance of hybrid and model-based iterative image reconstruction for standard-dose (SD) and low-dose (LD) non-contrast cerebral imaging by multi-detector CT (MDCT). We retrospectively analyzed 131 patients with suspected ischemic stroke (mean age: 74.2 ± 14.3 years, 67 females) who underwent initial MDCT with a SD protocol (300 mAs) as well as follow-up MDCT after a maximum of 10 days with a LD protocol (200 mAs). Ischemic demarcation was detected in 26 patients for initial and in 64 patients for follow-up imaging, with diffusion-weighted magnetic resonance imaging (MRI) confirming ischemia in all of those patients. The non-contrast cerebral MDCT images were reconstructed using hybrid (Philips “iDose4”) and model-based iterative (Philips “IMR3”) reconstruction algorithms. Two readers assessed overall image quality, anatomic detail, differentiation of gray matter (GM)/white matter (WM), and conspicuity of ischemic demarcation, if any. Quantitative assessment included signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) calculations for WM, GM, and demarcated areas. Ischemic demarcation was detected in all MDCT images of affected patients by both readers, irrespective of the reconstruction method used. For LD imaging, anatomic detail and GM/WM differentiation was significantly better when using the model-based iterative compared to the hybrid reconstruction method. Furthermore, CNR of GM/WM as well as the SNR of WM and GM of healthy brain tissue were significantly higher for LD images with model-based iterative reconstruction when compared to SD or LD images reconstructed with the hybrid algorithm. For patients with ischemic demarcation, there was a significant difference between images using hybrid versus model-based iterative reconstruction for CNR of ischemic/contralateral unaffected areas (mean ± standard deviation: SD_IMR: 4.4 ± 3.1, SD_iDose: 3.5 ± 2.3, P < 0.0001; LD_IMR: 4.6 ± 2.9, LD_iDose: 3.2 ± 2.1, P < 0.0001).  In conclusion, model-based iterative reconstruction provides higher CNR and SNR without significant loss of image quality for non-enhanced cerebral MDCT.

Model-based reconstruction algorithm in the detection of acute trauma-related lesions in brain CT examinations

BACKGROUND AND PURPOSE

To evaluate the added value of a model-based reconstruction algorithm in the assessment of acute traumatic brain lesions in emergency non-enhanced computed tomography, in comparison with a standard hybrid iterative reconstruction approach.

MATERIALS AND METHODS

We retrospectively evaluated a total of 350 patients who underwent a 256-row non-enhanced computed tomography scan at the emergency department for brain trauma. Images were reconstructed both with hybrid and model-based iterative algorithm. Two radiologists, blinded to clinical data, recorded the presence, nature, number, and location of acute findings. Subjective image quality was performed using a 4-point scale. Objective image quality was determined by computing the signal-to-noise ratio and contrast-to-noise ratio. The agreement between the two readers was evaluated using k-statistics.

RESULTS

A subjective image quality analysis using model-based iterative reconstruction gave a higher detection rate of acute trauma-related lesions in comparison to hybrid iterative reconstruction (extradural haematomas 116 vs. 68, subdural haemorrhages 162 vs. 98, subarachnoid haemorrhages 118 vs. 78, parenchymal haemorrhages 94 vs. 64, contusive lesions 36 vs. 28, diffuse axonal injuries 75 vs. 31; all P<0.001). Inter-observer agreement was moderate to excellent in evaluating all injuries (extradural haematomas k=0.79, subdural haemorrhages k=0.82, subarachnoid haemorrhages k=0.91, parenchymal haemorrhages k=0.98, contusive lesions k=0.88, diffuse axonal injuries k=0.70). Quantitatively, the mean standard deviation of the thalamus on model-based iterative reconstruction images was lower in comparison to hybrid iterative one (2.12 ± 0.92 vsa 3.52 ± 1.10; P=0.030) while the contrast-to-noise ratio and signal-to-noise ratio were significantly higher (contrast-to-noise ratio 3.06 ± 0.55 vs. 1.55 ± 0.68, signal-to-noise ratio 14.51 ± 1.78 vs. 8.62 ± 1.88; P<0.0001). Median subjective image quality values for model-based iterative reconstruction were significantly higher (P=0.003).

CONCLUSION

Model-based iterative reconstruction, offering a higher image quality at a thinner slice, allowed the identification of a higher number of acute traumatic lesions than hybrid iterative reconstruction, with a significant reduction of noise.

Application of low-dose CT combined with model-based iterative reconstruction algorithm in oncologic patients during follow-up: dose reduction and image quality

OBJECTIVES

To compare image quality and radiation dose of CT images reconstructed with model-based iterative reconstruction (MBIR) and hybrid-iterative (HIR) algorithm in oncologic patients.

METHODS

125 oncologic patients underwent both contrast-enhanced low- (100 kV), and standard (120 kV) dose CT, were enrolled. Image quality was assessed by using a 4-point Likert scale. CT attenuation values, expressed in Hounsfield unit (HU), were recorded within a regions of interest (ROI) of liver, spleen, paraspinal muscle, aortic lumen, and subcutaneous fat tissue. Image noise, expressed as standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were calculated. Radiation dose were analyzed. Paired Student's t-test was used to compare all continuous variables.

RESULTS

The overall median score assessed as image quality for CT images with the MBIR algorithm was significantly higher in comparison with HIR [4 (range 3-4) vs 3 (3-4), p = 0.017].CT attenuation values and SD were significantly higher and lower, respectively, in all anatomic districts in images reconstructed with MBIR in comparison with HIR ones (all p < 0.001). SNR and CNR values were higher in CT images reconstructed with MBIR, reaching a significant difference in all districts (all p < 0.001). Radiation dose were significantly lower in the MBIR group compared with the HIR group (p < 0.001).

CONCLUSIONS

MBIR combined with low-kV setting allows an important dose reduction in whole-body CT imaging, reaching a better image quality both qualitatively and quantitatively.

ADVANCES IN KNOWLEDGE

MBIR with low-dose approach allows a reduction of dose exposure, maintaining high image quality, especially in patients which deserve a longlasting follow-up.

Accuracy of thin-slice model-based iterative reconstruction designed for brain CT to diagnose acute ischemic stroke in the middle cerebral artery territory: a multicenter study

PURPOSE

Model-based iterative reconstruction (MBIR) yields higher spatial resolution and a lower image noise than conventional reconstruction methods. We hypothesized that thin-slice MBIR designed for brain CT could improve the detectability of acute ischemic stroke in the middle cerebral artery (MCA) territory.

METHODS

Included were 41 patients with acute ischemic stroke in the MCA territory; they were seen at 4 medical centers. The controls were 39 subjects without acute stroke. Images were reconstructed with hybrid IR and with MBIR designed for brain CT at slice thickness of 2 mm. We measured the image noise in the ventricle and compared the contrast-to-noise ratio (CNR) in the ischemic lesion. We analyzed the ability of reconstructed images to detect ischemic lesions using receiver operating characteristics (ROC) analysis; 8 observers read the routine clinical hybrid IR with 5 mm-thick images, while referring to 2 mm-thick hybrid IR images or MBIR images.

RESULTS

The image noise was significantly lower on MBIR- than hybrid IR images (1.2 vs. 3.4, p < 0.001). The CNR was significantly higher with MBIR than hybrid IR (6.3 vs. 1.6, p < 0.001). The mean area under the ROC curve was also significantly higher on hybrid IR plus MBIR than hybrid IR (0.55 vs. 0.48, p < 0.036). Sensitivity, specificity, and accuracy were 41.2%, 88.8%, and 65.7%, respectively, for hybrid IR; they were 58.8%, 86.1%, and 72.9%, respectively, for hybrid IR plus MBIR.

CONCLUSION

The additional thin-slice MBIR designed for brain CT may improve the detection of acute MCA stroke.

Optimal slice thickness of brain computed tomography using a hybrid iterative reconstruction algorithm for identifying hyperdense middle cerebral artery sign of acute ischemic stroke

PURPOSE

To determine the optimal slice thickness of brain non-contrast computed tomography using a hybrid iterative reconstruction algorithm to identify hyperdense middle cerebral artery sign in patients with acute ischemic stroke.

METHODS

We retrospectively enrolled 30 patients who had presented hyperdense middle cerebral artery sign and 30 patients who showed no acute ischemic change in acute magnetic resonance imaging. Reformatted axial images at an angle of the orbitomeatal line in slice thicknesses of 0.5, 1, 3, 5, and 7 mm were generated. Optimal slice thickness for identifying hyperdense middle cerebral artery sign was evaluated by a receiver operating characteristics curve analysis and area under the curve (AUC).

RESULTS

The mean AUC value of 0.5-mm slice (0.921; 95% confidence interval (95% CI), 0.868 to 0.975) was significantly higher than those of 3-mm (0.791; 95% CI, 0.686 to 0.895; p = 0.041), 5-mm (0.691; 95% CI, 0.583 to 0.799, p < 0.001), and 7-mm (0.695; 95% CI, 0.593 to 0.797, p < 0.001) slices, whereas it was equivalent to that of 1-mm slice (0.901; 95% CI, 0.837 to 0.965, p = 0.751).

CONCLUSION

Thin slice thickness of ≤ 1 mm has a better diagnostic performance for identifying hyperdense artery sign on brain non-contrast computed tomography with a hybrid iterative reconstruction algorithm in patients with acute ischemic stroke.

Usefulness of deep learning-assisted identification of hyperdense MCA sign in acute ischemic stroke: comparison with readers' performance

PURPOSE

To evaluate the usefulness of deep learning-assisted diagnosis for identifying hyperdense middle cerebral artery sign (HMCAS) on non-contrast computed tomography in comparison with the diagnostic performance of neuroradiologists.

MATERIALS AND METHODS

We obtained 46 HMCAS-positive and 52 HMCAS-negative test samples extracted using 50-pixel-diameter circular regions of interest. Five neuroradiologists undertook an initial diagnostic performance test by describing the HMCAS-positive prediction rate in each sample. Their diagnostic performance was compared with that of a deep convolutional neural network (DCNN) model that had been trained using another dataset in our previous study. In the second test, readers could reference the prediction rate of the DCNN model in each sample.

RESULTS

The diagnostic performance of the DCNN for HMCAS showed an accuracy of 81.6% and area under the receiver-operating characteristic curve (AUC) of 0.869, whereas the initial diagnostic performance of neuroradiologists showed an accuracy of 78.8% and AUC of 0.882. The second diagnostic test of neuroradiologists with reference to the results of the DCNN model showed an accuracy of 84.7% and AUC of 0.932. In all readers, AUC values were higher in the second test than the initial test.

CONCLUSION

The ability of DCNN to identify HMCAS is comparable with the diagnostic performance of neuroradiologists.