scholarly journals Robustness of radiomic features in CT images with different slice thickness, comparing liver tumour and muscle

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lorena Escudero Sanchez ◽  
Leonardo Rundo ◽  
Andrew B. Gill ◽  
Matthew Hoare ◽  
Eva Mendes Serrao ◽  
...  
Keyword(s):  
Large Fraction ◽  
Therapy Response ◽  
Response Assessment ◽  
Ct Images ◽  
Optimal Number ◽  
Image Features ◽  
Liver Tumour ◽  
Slice Thickness ◽  
Invasive Method ◽  
Feature Values

AbstractRadiomic image features are becoming a promising non-invasive method to obtain quantitative measurements for tumour classification and therapy response assessment in oncological research. However, despite its increasingly established application, there is a need for standardisation criteria and further validation of feature robustness with respect to imaging acquisition parameters. In this paper, the robustness of radiomic features extracted from computed tomography (CT) images is evaluated for liver tumour and muscle, comparing the values of the features in images reconstructed with two different slice thicknesses of 2.0 mm and 5.0 mm. Novel approaches are presented to address the intrinsic dependencies of texture radiomic features, choosing the optimal number of grey levels and correcting for the dependency on volume. With the optimal values and corrections, feature values are compared across thicknesses to identify reproducible features. Normalisation using muscle regions is also described as an alternative approach. With either method, a large fraction of features (75–90%) was found to be highly robust (< 25% difference). The analyses were performed on a homogeneous CT dataset of 43 patients with hepatocellular carcinoma, and consistent results were obtained for both tumour and muscle tissue. Finally, recommended guidelines are included for radiomic studies using variable slice thickness.

scholarly journals Multi-factorial considerations for intra-thoracic lymph node evaluations of healthy cats on computed tomographic images

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Ninlawan Thammasiri ◽  
Chutimon Thanaboonnipat ◽  
Nan Choisunirachon ◽  
Damri Darawiroj
Keyword(s):  
Lymph Node ◽  
Lymph Nodes ◽  
Ct Images ◽  
Slice Thickness ◽  
Maximum Width ◽  
Post Contrast ◽  
Computed Tomographic ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct

Abstract Background It is difficult to examine mild to moderate feline intra-thoracic lymphadenopathy via and thoracic radiography. Despite previous information from computed tomographic (CT) images of intra-thoracic lymph nodes, some factors from animals and CT setting were less elucidated. Therefore, this study aimed to investigate the effect of internal factors from animals and external factors from the CT procedure on the feasibility to detect the intra-thoracic lymph nodes. Twenty-four, client-owned, clinically healthy cats were categorized into three groups according to age. They underwent pre- and post-contrast enhanced CT for whole thorax followed by inter-group evaluation and comparison of sternal, cranial mediastinal, and tracheobronchial lymph nodes. Results Post contrast-enhanced CT appearances revealed that intra-thoracic lymph nodes of kittens were invisible, whereas the sternal, cranial mediastinal, and tracheobronchial nodes of cats aged over 7 months old were detected (6/24, 9/24 and 7/24, respectively). Maximum width of these lymph nodes were 3.93 ± 0.74 mm, 4.02 ± 0.65 mm, and 3.51 ± 0.62 mm, respectively. By age, lymph node sizes of these cats were not significantly different. Transverse lymph node width of males was larger than that of females (P = 0.0425). Besides, the detection score of lymph nodes was affected by slice thickness (P < 0.01) and lymph node width (P = 0.0049). Furthermore, an irregular, soft tissue structure, possibly the thymus, was detected in all juvenile cats and three mature cats. Conclusions Despite additional information on intra-thoracic lymph nodes in CT images, which can be used to investigate lymphatic-related abnormalities, age, sex, and slice thickness of CT images must be also considered.

The Labyrinthine Portion of the Facial Canal in Patients with Bell's Palsy Investigated by Computed Tomography

1987 ◽  
Vol 28 (1) ◽  
pp. 25-30 ◽  
Author(s):  
K. Wadin ◽  
L. Thomander ◽  
H. Wilbrand
Keyword(s):  
Computed Tomography ◽  
Temporal Bone ◽  
No Value ◽  
Bell’S Palsy ◽  
Ct Images ◽  
Slice Thickness ◽  
Facial Canal ◽  
Bell's Palsy ◽  
Unreliable Method ◽  
Different Parts

The reproducibility of the labyrinthine portion of the facial canal by computed tomography was investigated in 22 patients with Bell's palsy. The CT images were compared with those obtained in 18 temporal bone specimens. Measurements of the diameters of different parts of the facial canal were made on these images and also microscopically in plastic casts of the temporal bone specimens. No marked difference was found between the dimensions of the labyrinthine portion of the facial canal of the involved and healthy temporal bone in the patient, nor did these differ from the dimensions in the specimens. CT of the slender, curved labyrinthine portion was found to be of doubtful value for metric estimation of small differences in width. The anatomic variations of the canal rendered the evaluation more difficult. CT with a slice thickness of 2 mm was of no value for assessment of this part of the canal. Measurement of the diameters of the labyrinthine portion on CT images is an inappropriate and unreliable method for clinical purposes.

scholarly journals Computerized Tomography Image Features under the Reconstruction Algorithm in the Evaluation of the Effect of Ropivacaine Combined with Dexamethasone and Dexmedetomidine on Assisted Thoracoscopic Lobectomy

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yan Cui ◽  
Yang Sun ◽  
Meng Xia ◽  
Dan Yao ◽  
Jun Lei
Keyword(s):  
Fourier Transform ◽  
Iterative Reconstruction ◽  
Analytical Method ◽  
Reconstruction Algorithm ◽  
Ct Images ◽  
Image Features ◽  
Anesthesia Induction ◽  
Iterative Reconstruction Algorithm ◽  
Thoracoscopic Lobectomy ◽  
The Fourier Transform

This research was aimed to study CT image features based on the backprojection filtering reconstruction algorithm and evaluate the effect of ropivacaine combined with dexamethasone and dexmedetomidine on assisted thoracoscopic lobectomy to provide reference for clinical diagnosis. A total of 110 patients undergoing laparoscopic resection were selected as the study subjects. Anesthesia induction and nerve block were performed with ropivacaine combined with dexamethasone and dexmedetomidine before surgery, and chest CT scan was performed. The backprojection image reconstruction algorithm was constructed and applied to patient CT images for reconstruction processing. The results showed that when the overlapping step size was 16 and the block size was 32 × 32, the running time of the algorithm was the shortest. The resolution and sharpness of reconstructed images were better than the Fourier transform analytical method and iterative reconstruction algorithm. The detection rates of lung nodules smaller than 6 mm and 6–30 mm (92.35% and 95.44%) were significantly higher than those of the Fourier transform analytical method and iterative reconstruction algorithm (90.98% and 87.53%; 88.32% and 90.87%) ( P < 0.05 ). After anesthesia induction and lobectomy with ropivacaine combined with dexamethasone and dexmedetomidine, the visual analogue scale (VAS) decreased with postoperative time. The VAS score decreased to a lower level (1.76 ± 0.54) after five days. In summary, ropivacaine combined with dexamethasone and dexmedetomidine had better sedation and analgesia effects in patients with thoracoscopic lobectomy. CT images based on backprojection reconstruction algorithm had a high recognition accuracy for lung lesions.

scholarly journals A multi-label classification model for full slice brain computerised tomography image

2020 ◽  
Vol 21 (S6) ◽  
Author(s):  
Jianqiang Li ◽  
Guanghui Fu ◽  
Yueda Chen ◽  
Pengzhi Li ◽  
Bo Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ct Images ◽  
Image Features ◽  
Classification Model ◽  
Brain Diseases ◽  
Ct Scans ◽  
Computerised Tomography ◽  
Brain Ct ◽  
Proposed Model ◽  
The Brain

Abstract Background Screening of the brain computerised tomography (CT) images is a primary method currently used for initial detection of patients with brain trauma or other conditions. In recent years, deep learning technique has shown remarkable advantages in the clinical practice. Researchers have attempted to use deep learning methods to detect brain diseases from CT images. Methods often used to detect diseases choose images with visible lesions from full-slice brain CT scans, which need to be labelled by doctors. This is an inaccurate method because doctors detect brain disease from a full sequence scan of CT images and one patient may have multiple concurrent conditions in practice. The method cannot take into account the dependencies between the slices and the causal relationships among various brain diseases. Moreover, labelling images slice by slice spends much time and expense. Detecting multiple diseases from full slice brain CT images is, therefore, an important research subject with practical implications. Results In this paper, we propose a model called the slice dependencies learning model (SDLM). It learns image features from a series of variable length brain CT images and slice dependencies between different slices in a set of images to predict abnormalities. The model is necessary to only label the disease reflected in the full-slice brain scan. We use the CQ500 dataset to evaluate our proposed model, which contains 1194 full sets of CT scans from a total of 491 subjects. Each set of data from one subject contains scans with one to eight different slice thicknesses and various diseases that are captured in a range of 30 to 396 slices in a set. The evaluation results present that the precision is 67.57%, the recall is 61.04%, the F1 score is 0.6412, and the areas under the receiver operating characteristic curves (AUCs) is 0.8934. Conclusion The proposed model is a new architecture that uses a full-slice brain CT scan for multi-label classification, unlike the traditional methods which only classify the brain images at the slice level. It has great potential for application to multi-label detection problems, especially with regard to the brain CT images.

How Thick Should CT/MR Slices be to Plan Conformal Radiotherapy? A Study on the Accuracy of Three-Dimensional Volume Reconstruction

1996 ◽  
Vol 82 (5) ◽  
pp. 470-472 ◽  
Author(s):  
Anna Somigliana ◽  
Giancarlo Zonca ◽  
Gianfranco Loi ◽  
Adele Emilia Sichirollo
Keyword(s):  
Radiation Therapy ◽  
Three Dimensional ◽  
Conformal Radiotherapy ◽  
Ct Images ◽  
Planning System ◽  
Slice Thickness ◽  
Therapy Planning ◽  
Volume Reconstruction ◽  
Radiation Therapy Planning ◽  
Ct Slices

Aim and background The aim of this experimental study was to correlate the thickness of acquired CT slices (2, 4 and 8 mm) or MR slices (4 and 7 mm) with the accuracy of three-dimensional volume reconstruction as performed by a commercially available radiation therapy planning system. Methods We used a cylindrical phantom, with a 15-cm diameter and 20-cm height, containing 5 spheres (12.7-31.8 mm diameter) of solid Plexiglas sunk in a 3% agar jelly solution. The phantom was scanned by the CT scan with 3 different slice thicknesses (2, 4 and 8 mm and a distance of 0 mm between the slices). Two different acquisition techniques (slice thickness of 4 and 7 mm with 0.8 and 1.4 mm slice distance, respectively) were compared in the MR study. The volume values calculated from measurements were compared with the known true volume values of the spheres. Results The average percentage volume difference between calculated and true values for the smaller spheres reconstructed with CT images 2 and 4 mm thick was generally less than 8%, whereas the error for volumes reconstructed with 8-mm-thick CT slices was more than 20%. For the larger spheres, the error was generally less than 5%. The data produced by MR acquisition agreed with those obtained using CT sections. Conclusions For targets less than 1.5 cm in diameter on our system it is reasonable to acquire CT images with the smallest thickness available. For targets between 1.5 and 3 cm, it seems sufficient to acquire the localization images with a slice thickness of 4 mm. For targets more than 4 cm in diameter, considering that with our radiation therapy planning system the time spent for manual contouring and for isodose calculation highly increased with the number of acquired images, we suggest that the acquisition of CT-MR slices 8-10-mm thick is totally adequate even for Conformal radiotherapy treatments.

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