METHODS: The 3D-printed cardiac insert phantom was positioned into a chest phantom and scanned with a 16-slice CT scanner. Acquisitions were performed with CCTA protocols using 120 kVp at four different tube currents, 300, 200, 100 and 50 mA (protocols A, B, C and D, respectively). The image data sets were reconstructed with a filtered back projection (FBP) and three different IR algorithm strengths. The image quality metrics of image noise, signal-noise ratio (SNR) and contrast-noise ratio (CNR) were calculated for each protocol.
RESULTS: Decrease in dose levels has significantly increased the image noise, compared to FBP of protocol A (P
AIM: To present a case of extradural temporal bone chondroblastoma and discuss the clinical presentation, radiographic findings, histology and particularly the surgical management of the case.
CASE REPORT: We report a case of a 31-year-old man who presented with a painless left temporal swelling and left sided hearing loss for four months. Computed tomography (CT) scan revealed an aggressive mass involving the left preauricular region with temporal mastoid bone erosion. Magnetic resonance imaging (MRI) showed an extra-axial left temporal mastoid mass pushing the left temporal lobe superiorly. The patient underwent complete excision of the temporal bone tumor. The final histopathological diagnosis was in keeping with chondroblastoma.
CONCLUSION: Temporal bone chondroblastoma is rare but an aggressive condition. Complete tumor resection via an appropriate approach that enables adequate exposure will lead to a favorable outcome.
MATERIALS AND METHODS: This was a retrospective study using computed tomography (CT) scans from 3 hospitals. Inclusion criteria were scans with 1-5 nodules of diameter ≥5 mm; exclusion criteria were poor-quality scans or those with nodules measuring <5mm in diameter. In the lesion detection phase, 2,147 nodules from 219 scans were used to develop and train the deep learning 3D-CNN to detect lesions. The 3D-CNN was validated with 235 scans (354 lesions) for sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) analysis. In the path planning phase, Bayesian optimization was used to propose possible needle trajectories for lesion biopsy while avoiding vital structures. Software-proposed needle trajectories were compared with actual biopsy path trajectories from intraprocedural CT scans in 150 patients, with a match defined as an angular deviation of <5° between the 2 trajectories.
RESULTS: The model achieved an overall AUC of 97.4% (95% CI, 96.3%-98.2%) for lesion detection, with mean sensitivity of 93.5% and mean specificity of 93.2%. Among the software-proposed needle trajectories, 85.3% were feasible, with 82% matching actual paths and similar performance between supine and prone/oblique patient orientations (P = .311). The mean angular deviation between matching trajectories was 2.30° (SD ± 1.22); the mean path deviation was 2.94 mm (SD ± 1.60).
CONCLUSIONS: Segmentation, lesion detection, and path planning for CT-guided lung biopsy using an AI-guided software showed promising results. Future integration with automated robotic systems may pave the way toward fully automated biopsy procedures.
AIMS: This study aimed to examine the influence of vessel volume on bolus thermodilution measurements.
METHODS: We prospectively included patients with angina with non-obstructive coronary arteries (ANOCA) undergoing bolus and continuous thermodilution assessments. All patients underwent coronary CT angiography to extract vessel volume. Coronary microvascular dysfunction was defined as coronary flow reserve (CFR)
MATERIAL AND METHODS: The study included 223 tomograms of the head and neck in sagittal projection from patients without any pathology of the studied structures. Morphometric analysis was carried out using PjaPro and Gradient programs, statistical analysis was performed by SPSS Statistics software. A fully convolutional EfficientNet-B2 neural network was used, which was trained in two stages: selection of the area of interest and solution of regression tasks.
RESULTS: Morphometric assessment and subsequent statistical analysis of the selected group of features have shown presence of the strongest correlation with age in the indicator characterizing the involution of the median atlantoaxial joint. A deep learning method using the convolutional network, which automatically selects the desired area in the image (the area of the vertebral junction), classifies the sample, and makes an assumption about the age of the unknown individual with an accuracy of 7.5 to 10.5 years has been tested.
CONCLUSION: As a result of the study, a positive experience has been obtained indicating the possibility of using convolutional neural networks to determine the age of the unknown person, which expands the evidence base and provides new opportunities for determining group-wide personality traits in forensic medicine.
METHODS: A six-year retrospective review at our institution on adult patients with TB and malignant-PPL diagnosed from rEBUS procedure from October 1, 2016, to December 31, 2022. Clinical, radiological, procedural, histological and microbiological data were extracted and analysed.
RESULTS: 387 PPLs were included in our cohort, 32 % were TB-PPL and 68 % were malignant-PPL. The median age was 63 (IQR 55-70) years, with the TB-PPL group significantly younger. The median size of the target lesion was 2.90 (IQR 2.26-4.00) cm. The overall rEBUS diagnostic yield was 85.3 %, with a 1.3 % pneumothorax risk. Multivariate analysis identified independent predictors for TB-PPL, including age <60 years (adj OR 2.635), target lesion size <2 cm (adj OR 2.385), upper lobe location (adj OR 2.020), presence of a cavity on pre-procedural CT (adj OR 4.186), and presence of rEBUS bronchogram (adj OR 2.722). These variables achieved an area under the curve of 0.729 (95 % CI 0.673-0.795) with a diagnostic accuracy of 75.49 % (95 % CI 70.68-79.88).
CONCLUSIONS: Despite non-specific radiological findings in TB-PPL, our study identifies younger age, target lesion size less than 2 cm, upper lobe location, the presence of cavitation, and rEBUS bronchogram were independent clinical predictors for TB-PPL. This prediction model potentially helps mitigate the risk of accidental TB exposure during bronchoscopic procedures. A future prospective cohort study to validate these findings is essential to allow proper triaging of patient planning for rEBUS procedure.
AIM: To compare the quality of CT brain images produced by a fixed CT scanner and a portable CT scanner (CereTom).
METHODS: This work was a single-centre retrospective study of CT brain images from 112 neurosurgical patients. Hounsfield units (HUs) of the images from CereTom were measured for air, water and bone. Three assessors independently evaluated the images from the fixed CT scanner and CereTom. Streak artefacts, visualisation of lesions and grey-white matter differentiation were evaluated at three different levels (centrum semiovale, basal ganglia and middle cerebellar peduncles). Each evaluation was scored 1 (poor), 2 (average) or 3 (good) and summed up to form an ordinal reading of 3 to 9.
RESULTS: HUs for air, water and bone from CereTom were within the recommended value by the American College of Radiology (ACR). Streak artefact evaluation scores for the fixed CT scanner was 8.54 versus 7.46 (Z = -5.67) for CereTom at the centrum semiovale, 8.38 (SD = 1.12) versus 7.32 (SD = 1.63) at the basal ganglia and 8.21 (SD = 1.30) versus 6.97 (SD = 2.77) at the middle cerebellar peduncles. Grey-white matter differentiation showed scores of 8.27 (SD = 1.04) versus 7.21 (SD = 1.41) at the centrum semiovale, 8.26 (SD = 1.07) versus 7.00 (SD = 1.47) at the basal ganglia and 8.38 (SD = 1.11) versus 6.74 (SD = 1.55) at the middle cerebellar peduncles. Visualisation of lesions showed scores of 8.86 versus 8.21 (Z = -4.24) at the centrum semiovale, 8.93 versus 8.18 (Z = -5.32) at the basal ganglia and 8.79 versus 8.06 (Z = -4.93) at the middle cerebellar peduncles. All results were significant with P-value < 0.01.
CONCLUSIONS: Results of the study showed a significant difference in image quality produced by the fixed CT scanner and CereTom, with the latter being more inferior than the former. However, HUs of the images produced by CereTom do fulfil the recommendation of the ACR.
CASE REPORT: We presented a case of left maxillary mucopyocele in a 58-year-old man that developed after radiotherapy for nasopharyngeal carcinoma. Computed tomography scan showed opacification of the left maxillary sinus expanding through the medial wall of the antrum with thinning and destruction of the adjacent structures. Endoscopic marsupialization of the lesion and left partial maxillectomy were performed. The cystic mass had yellowish thick mucopurulent fluid that was completely drained.
CONCLUSIONS: A few cases of sphenoid sinus mucocele as a late complication of radiation therapy have been reported. Maxillary mucocele and mucopyocele can be considered as one of the late complications of radiotherapy to head and neck as a result of occlusion of sinus ostia by scarred mucosa.