Thin Slice Thickness Research Articles

Application of deep learning reconstruction in abdominal magnetic resonance cholangiopancreatography for image quality improvement and acquisition time reduction

PurposeTo compare deep learning (DL)-based and conventional reconstruction through subjective and objective analysis and ascertain whether DL-based reconstruction improves the quality and acquisition speed of clinical abdominal magnetic resonance imaging (MRI). MethodsThe 124 patients who underwent abdominal MRI between January and July 2021 were retrospectively studied. For each patient, two-dimensional axial T2-weighted single-shot fast spin-echo MRI images with or without fat saturation were reconstructed using DL-based and conventional methods. The subjective image quality scores and objective metrics, including signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs) of the images were analysed. An explorative analysis was performed to compare 20 patients’ MRI images with site routine settings, high-resolution settings and high-speed settings. Paired t tests and Wilcoxon signed-rank tests were used for subjective and objective comparisons. ResultsA total of 144 patients were evaluated (mean age, 62.2 ± 14.1 years; 83 men). The MRI images reconstructed using DL-based methods had higher SNRs and CNRs than did those reconstructed using conventional methods (all p < 0.01). The subjective scores of the images reconstructed using DL-based methods were higher than those of the images reconstructed using conventional methods (p < 0.01), with significantly lower variation (p < 0.01). Exploratory analysis revealed that the DL-based reconstructions with thin slice thickness and higher temporal resolution had the highest image quality and were associated with the shortest scan times. ConclusionsDL-based reconstruction methods can be used to improve the quality with higher stability and accelerate the acquisition of abdominal MRI.

Improved noise reduction in photon-counting detector CT using prior knowledge-aware iterative denoising neural network.

We aim to reduce image noise in high-resolution (HR) virtual monoenergetic images (VMIs) from photon-counting detector (PCD) CT scans by developing a prior knowledge-aware iterative denoising neural network (PKAID-Net) that efficiently exploits the unique noise characteristics of VMIs at different energy (keV) levels. PKAID-Net offers two major features: first, it utilizes a lower-noise VMI (e.g., 70keV) as a prior input; second, it iteratively constructs a refined training dataset to improve the neural network's denoising performance. In each iteration, the denoised image from the previous module serves as an updated target image, which is included in the dataset for the subsequent training iteration. Our study includes 10 patient coronary CT angiography exams acquired on a clinical dual-source PCD-CT (NAEOTOM Alpha, Siemens Healthineers). The HR VMIs were reconstructed at 50, 70, and 100keV, using a sharp vascular kernel (Bv68) and thin (0.6mm) slice thickness (0.3mm increment). PKAID-Net's performance was evaluated in terms of image noise, spatial detail preservation, and quantitative accuracy. PKAID-Net achieved a noise reduction of 96% compared to filtered back projection and 65% relative to iterative reconstruction, all while preserving spatial and spectral fidelity and maintaining a natural noise texture. The iterative refinement of PCD-CT data during the training process substantially enhanced the robustness of deep learning-based denoising compared to the original method, which resulted in some spatial detail loss. The PKAID-Net provides substantial noise reduction while maintaining spatial and spectral fidelity of the HR VMIs from PCD-CT.

Improved overall image quality in low-dose dual-energy computed tomography enterography using deep-learning image reconstruction.

To demonstrate the clinical advantages of a deep-learning image reconstruction (DLIR) in low-dose dual-energy computed tomography enterography (DECTE) by comparing images with standard-dose adaptive iterative reconstruction-Veo (ASIR-V) images. In this Institutional review board approved prospective study, 86 participants who underwent DECTE were enrolled. The early-enteric phase scan was performed using standard-dose (noise index: 8) and images were reconstructed at 5mm and 1.25mm slice thickness with ASIR-V at a level of 40% (ASIR-V40%). The late-enteric phase scan used low-dose (noise index: 12) and images were reconstructed at 1.25mm slice thickness with ASIR-V40%, and DLIR at medium (DLIR-M) and high (DLIR-H). The 70keV monochromatic images were used for image comparison and analysis. For objective assessment, image noise, artifact index, SNR and CNR were measured. For subjective assessment, subjective noise, image contrast, bowel wall sharpness, mesenteric vessel clarity, and small structure visibility were scored by two radiologists blindly. Radiation dose was compared between the early- and late-enteric phases. Radiation dose was reduced by 50% in the late-enteric phase [(6.31 ± 1.67)mSv] compared with the early-enteric phase [(3.01 ± 1.09)mSv]. For the 1.25mm images, DLIR-M and DLIR-H significantly improved both objective and subjective image quality compared to those with ASIR-V40%. The low-dose 1.25mm DLIR-H images had similar image noise, SNR, CNR values as the standard-dose 5mm ASIR-V40% images, but significantly higher scores in image contrast [5(5-5), P < 0.05], bowel wall sharpness [5(5-5), P < 0.05], mesenteric vessel clarity [5(5-5), P < 0.05] and small structure visibility [5(5-5), P < 0.05]. DLIR significantly reduces image noise at the same slice thickness, but significantly improves spatial resolution and lesion conspicuity with thinner slice thickness in DECTE, compared to conventional ASIR-V40% 5mm images, all while providing 50% radiation dose reduction.

A challenge and solution for automatic thin slice thickness measurements on images of the Catphan phantom

Purpose. The use of the Hough transform for angle detection is quite accurate for relatively wide slice thickness. However, the Hough transform fails to accurately detect the angle for thin slice thickness. This study proposes a method for automatically measuring the thickness of thin slices on images of a Catphan phantom. Methods. In the proposed method, the angle of the phantom’s orientation was determined based on the relative coordinates of the four hole objects in the phantom. After the angles of the wires were determined, the profiles of pixel values across the wire objects were constructed. Finally, their full widths at half maximum (FWHMs) were determined and multiplied by tan 23° to obtain the slice thicknesses of the images. The results of the proposed method were compared to a previous method, which used the Hough transform to obtain the phantom’s orientation. We used slice thicknesses ranging from 0.8 mm to 5.0 mm, and phantom angles from 0° to 10°. Results. Our proposed method detected the angle of the phantom accurately for thin slices, whereas a previous method did not accurately detect the angle. The results of the slice thickness using this current method were slightly higher (within 7.9%) compared to the previous method. However, the results of the two methods did not differ significantly (p-value > 0.05). Using different angles, the current method detected all the angles more accurately. Again, the slice thicknesses were not significantly different from the previous method (p-value > 0.05). Conclusion. The proposed method for measuring the thickness of thin slices in an image of a Catphan phantom, based on the relative coordinates of the four hole objects in the phantom, outperformed a previous method based on the Hough transform.

Spiral inflow MRA with sliding-slice localized quadratic encoding.

This work proposes a 2D/3D hybrid inflow MRA technique for fast scanning and high SNR and contrast-to-noise (CNR) efficiencies. Localized quadratic (LQ) encoding was combined with a sliding-slice spiral acquisition. Inflow MRAs around the circle of Willis and the carotid bifurcations were collected on four healthy volunteers. Spiral images were deblurred without or with water-fat separation for sliding-slice LQ (ssLQ) out-of-phase (OP) and Dixon inflow MRAs, respectively. Results were compared to multiple overlapping thin slab acquisitions (MOTSA) and 2D OP inflow MRAs. Noise data were also acquired with RF and gradients turned off to compute maps of SNR and SNR efficiency. Quantitative assessment of relative contrast, CNR, and CNR efficiency for flow were performed in regions of interest. The sliding-slice spiral technique alone reduces scan time by 10% to 40% compared with a standard spiral acquisition scheme. The proposed spiral ssLQ OP achieves 50% higher scan speed than the spiral MOTSA with comparable SNR and CNR efficiencies, which are ∼100% higher than the Cartesian MOTSA for intracranial inflow MRAs. Spiral ssLQ Dixon inflow MRA provides better visibility for vessels around the fat compared to spiral ssLQ OP inflow MRA, with a trade-off of scan speed. Spiral ssLQ MRA with thinner slice thickness is two to five times faster than the 2D Cartesian inflow neck MRA around the carotid bifurcations, while also achieving higher SNR efficiency. The proposed spiral ssLQ is a fast and flexible MRA method with improved SNR and CNR efficiencies over traditional Cartesian inflow MRAs.

Diagnostic performance of artificial intelligence for pediatric pulmonary nodule detection in computed tomography of the chest

PurposeTo test the performance of a commercially available adult pulmonary nodule detection artificial intelligence (AI) tool in pediatric CT chests. Methods30 consecutive chest CTs with or without contrast of patients ages 12–18 were included. Images were retrospectively reconstructed at 3 mm and 1 mm slice thickness. AI for detection of lung nodules in adults (Syngo CT Lung Computer Aided Detection (CAD)) was evaluated. 3 mm axial images were retrospectively reviewed by two pediatric radiologists (reference read) who determined the location, type, and size of nodules. Lung CAD results at 3 mm and 1 mm slice thickness were compared to reference read by two other pediatric radiologists. Sensitivity (Sn) and positive predictive value (PPV) were analyzed. ResultsThe radiologists identified 109 nodules. At 1 mm, CAD detected 70 nodules; 43 true positive (Sn = 39 %), 26 false positive (PPV = 62 %), and 1 nodule which had not been identified by radiologists. At 3 mm, CAD detected 60 nodules; 28 true positive (Sn = 26 %), 30 false positive (PPV = 48 %) and 2 nodules which had not been identified by radiologists. There were 103 solid nodules (47 measuring < 3 mm) and 6 subsolid nodules (5 measuring < 5 mm). When excluding 52 nodules (solid < 3 mm and subsolid < 5 mm) based on algorithm conditions, the Sn increased to 68 % at 1 mm and 49 % at 3 mm but there was no significant change in the PPV measuring 60 % at 1 mm and 48 % at 3 mm. ConclusionThe adult Lung CAD showed low sensitivity in pediatric patients, but better performance at thinner slice thickness and when smaller nodules were excluded.

PKAID-Net: Prior Knowledge Aware Iterative Denoising Neural Network for Photon Counting Detector CT.

An important feature enabled by Photon-Counting Detector (PCD) CT is the simultaneous acquisition of multi-energy data, which can produce virtual monoenergetic images (VMIs) at a high spatial resolution. However, noise levels observed in the high-resolution (HR) VMIs are markedly increased. Recent work involving deep learning methods has shown great potential in CT image denoising. Many CNN applications involve training using spatially co-registered low- and high-dose CT images featuring high and low image noise, respectively. However, this is implausible in routine clinical practice. Further, typical denoising methods treat each VMI energy level independently, without consideration of the valuable information in the spectral domain. To overcome these obstacles, we propose a prior knowledge-aware iterative denoising neural network (PKAID-Net). PKAID-Net offers two major benefits: first, it utilizes spectral information by including a lower-noise VMI as a prior input; and second, it iteratively constructs refined datasets for neural network training to improve the denoising performance. This study includes 10 patient coronary CT angiography (CTA) exams acquired on a clinical HR PCD-CT (NAEOTOM Alpha, Siemens Healthineers). The HR VMIs were reconstructed at 50 and 70 keV, using a sharp kernel (Bv68) and thin (0.6 mm, 0.3 mm increment) slice thickness. Results showed that the PKAID-Net provided a noise reduction of 96% and 70% relative to FBP and iterative reconstruction, respectively while maintaining spatial and spectral fidelity and a natural noise texture. These results demonstrate the noise reduction capacity of PKAID-Net as applied to cutting-edge PCD-CT data to enable HR, multi-energy cardiac CT imaging.

Modeling of electroplated diamond wire and its application towards precision slicing of semiconductors

Electroplated diamond multi-wire sawing is becoming the mainstream technique for semiconductor slicing, because of its desirable traits such as thin slice thickness, low kerf loss, and high surface/subsurface quality. Precisely describing the electroplated diamond wire is of significant importance to slicing process analysis. This study presents an electroplated diamond wire model based on statistical measurements of the cutting-edge geometry and scratching direction of abrasives on wire. These measurements are performed on a commercial wire (Diamond Wire-160) after exposing cutting edges from plated nickel layers. Results show that up to 91 % of abrasives have a single cutting edge with the shape of a triangular or quadrangular pyramid. The geometric angles (α1, α2, γ1, γ2) of these pyramids are observed to follow an approximate Gaussian distribution, and the scratching direction follows a uniform distribution. Combined with nanoindentation theory, the developed model is further employed for slicing process analysis, in which slicing forces and bow angles of wire are calculated. Additionally, slicing experiments are conducted on silicon for model verification, and the maximum relative error between predicted and experimental bow angles is 6.25 %. The developed wire model provides an effective tool for slicing process analysis, thereby accelerating the process development for scale-up production of high-precision semiconductor substrates.

Factors influencing the accuracy of multimodal image fusion for oral and maxillofacial tumors: a retrospective study

BackgroundEnsuring high accuracy in multimodal image fusion for oral and maxillofacial tumors is crucial before further application. The aim of this study was to explore the factors influencing the accuracy of multimodal image fusion for oral and maxillofacial tumors.MethodsPairs of single-modality images were obtained from oral and maxillofacial tumor patients, and were fused using a proprietary navigation system by using three algorithms (automatic fusion, manual fusion, and registration point-based fusion). Fusion accuracy was evaluated including two aspects—overall fusion accuracy and tumor volume fusion accuracy—and were indicated by mean deviation and fusion index, respectively. Image modality, fusion algorithm, and other characteristics of multimodal images that may have potential influence on fusion accuracy were recorded. Univariate and multivariate analysis were used to identify relevant affecting factors.ResultsNinety-three multimodal images were generated by fusing 31 pairs of single-modality images. The interaction effect of image modality and fusion algorithm (P = 0.02, P = 0.003) and thinner slice thickness (P = 0.006) were shown to significantly influence the overall fusion accuracy. The tumor volume (P < 0.001), tumor location (P = 0.007), and image modality (P = 0.01) were significant influencing factors for tumor volume fusion accuracy.ConclusionsTo ensure high overall fusion accuracy, manual fusion was not preferred in CT/MRI image fusion, and neither was automatic fusion in image fusion containing PET modality. Using image sets with thinner slice thickness could increase overall fusion accuracy. CT/MRI fusion yielded higher tumor volume fusion accuracy than fusion containing PET modality. The tumor volume fusion accuracy should be taken into consideration during image fusion when the tumor volume is small and the tumor is located in the mandible.

PEMERIKSAAN CT STONOGRAFI PADA KASUS NEFROLITHIASIS

ABSTRACT&#x0D; Background : CT Stonography is a CT scan tract of the kidney that is usually used to detect the presence of stones in the kidney and other pathologies such as the presence of mass in the kidneys. CT Stonography examination with kidney stones case in Buleleng Regional General Hospital using 5 mm slice thickness. The purpose of this study is to know the examination techniques, the reasons for the use of 5 mm slice thickness, and how to determine the size of kidney stones using CT Stonography in cases of nephrolithiasis at Buleleng Regional General Hospital.&#x0D; Methods : This type of research is qualitative with case study approach. The subjects were 2 radiographers, 1 radiologist, and 1 sending doctor. The location of the research at Radiology Installation Buleleng Regional General Hospital. Data were analyzed using by interactive model.&#x0D; Results : From the results of this study examination of CT Stonography at Buleleng Regional General Hospital uses special preparation of 12 hours of fasting before the examination. Position of supine patient (head first). Area scanning from diaphragm to symphisis pubis. Scan parameters using abdomen protocol, 10 mm slice thickness. The reason for using 10 mm slice thickness is that the image can be reconstructed with a thin slice thickness number from the previous result. How to determine the size of kidney stones in patients CT Stonography at Radiology Installation Buleleng Regional General Hospital is using 3 dimensions because to see from the axial, sagittal, coronal multiplanar so that we can measure from the posterior antero direction (AP), cranio caudal (CC), or laterolaterally taken from the widest distance.&#x0D; Conclusion : Examination CT Stonography with nephrolithiasis cases using the position of supine patients (head first), selection of 10 mm slice thickness on CT stonography examination techniques with nephrolithiasis cases at Radiology Installation Buleleng Regional General Hospital for the results of the image can be reconstructed with the amount of thin slice thickness of the previous results. Determination of the size of kidney stones in patients with CT Stonography at Radiology Installation Buleleng Regional General Hospital is using 3 dimensions because to see from the axial, sagittal, coronal multiplanar so that we can measure from the posterior antero direction (AP), cranio caudal (CC), and laterolateral taken from the widest distance.

Implications of computed tomography reconstruction algorithms on coronary atheroma quantification: Comparison with intravascular ultrasound

BackgroundAdvances in coronary computed tomography angiography (CCTA) reconstruction algorithms are expected to enhance the accuracy of CCTA plaque quantification. We aim to evaluate different CCTA reconstruction approaches in assessing vessel characteristics in coronary atheroma using intravascular ultrasound (IVUS) as the reference standard. MethodsMatched cross-sections (n ​= ​7241) from 50 vessels in 15 participants with chronic coronary syndrome who prospectively underwent CCTA and 3-vessel near-infrared spectroscopy-IVUS were included. Twelve CCTA datasets per patient were reconstructed using two different kernels, two slice thicknesses (0.75 ​mm and 0.50 ​mm) and three different strengths of advanced model-based iterative reconstruction (IR) algorithms. Lumen and vessel wall borders were manually annotated in every IVUS and CCTA cross-section which were co-registered using dedicated software. Image quality was sub-optimal in the reconstructions with a sharper kernel, so these were excluded. Intraclass correlation coefficient (ICC) and repeatability coefficient (RC) were used to compare the estimations of the 6 CT reconstruction approaches with those derived by IVUS. ResultsSegment-level analysis showed good agreement between CCTA and IVUS for assessing atheroma volume with approach 0.50/5 (slice thickness 0.50 ​mm and highest strength 5 ADMIRE IR) being the best (total atheroma volume ICC: 0.91, RC: 0.67, p ​< ​0.001 and percentage atheroma volume ICC: 0.64, RC: 14.06, p ​< ​0.001). At lesion-level, there was no difference between the CCTA reconstructions for detecting plaques (accuracy range: 0.64–0.67; p ​= ​0.23); however, approach 0.50/5 was superior in assessing IVUS-derived lesion characteristics associated with plaque vulnerability (minimum lumen area ICC: 0.64, RC: 1.31, p ​< ​0.001 and plaque burden ICC: 0.45, RC: 32.0, p ​< ​0.001). ConclusionCCTA reconstruction with thinner slice thickness, smooth kernel and highest strength advanced IR enabled more accurate quantification of the lumen and plaque at a segment-, and lesion-level analysis in coronary atheroma when validated against intravascular ultrasound. Clinicaltrials.gov (NCT03556644)

Diagnostic value of three-dimensional cube fluid attenuated inversion recovery imaging and its axial MIP reconstruction in multiple sclerosis

BackgroundMagnetic resonance imaging is regarded as one of the most important markers for multiple sclerosis. It can detect lesions in order to establish dissemination in time and space, which would aid in the diagnosis. Two-dimensional FLAIR is a standard sequence in MS routine imaging because it suppresses cerebrospinal fluid signal, increasing contrast between lesions and CSF and improving white matter lesion detection. Newer 3D FLAIR sequences are expected to offer even more benefits, such as improved MS lesions detection and higher resolution due to thinner slice thickness. We aimed to compare the role of 3D Cube FLAIR imaging (versus standard 2D FLAIR) in the assessment of white matter lesions in MS patients, as well as to test the convenience of using maximum intensity projection (MIP) on 3D FLAIR images for faster and easier evaluation.ResultsThis study included 160 MS patients. A 1.5 T routine brain MRI scan was performed, which included a 2D FLAIR sequence, followed by a 3D-FLAIR sequence. All images were analyzed after 3D-FLAIR images were reformatted into axial MIP images. Lesions were counted in each sequence and classified into supra-tentorial (periventricular, deep white matter, and juxta-cortical), and infra-tentorial lesions, with the relative comparison of lesions numbers on 3D-FLAIR and MIP versus 2D-FLAIR expressed as a percentage increase or decrease. 3D FLAIR can significantly improve MS lesion detection in all areas of the brain when compared with 2D FLAIR results. At 2 mm reformatting, there is no difference in MS lesion detection between sagittal 3D FLAIR and axial MIP reconstruction, implying that the MIP algorithm can be used to simplify lesion detection by reducing the number of images while maintaining the same level of reliability.Conclusion3D FLAIR sequences should be added to conventional 2D FLAIR sequences in the MRI protocol when MS is suspected.

Short- and Medium-Wave Infrared Drying of Cantaloupe (Cucumis melon L.) Slices: Drying Kinetics and Process Parameter Optimization

The main objective of the present work was to study the drying kinetics and obtain the optimum process parameters of cantaloupe slices using short-and medium-wave infrared radiation (SMIR) drying technology. The effect of three independent variables of infrared radiation temperature (55–65 °C), slice thickness (5–9 mm) and radiation distance (80–160 mm) on the L value, color difference (∆E), hardness and vitamin C content were investigated by using the Response Surface Methodology (RSM). The results showed that the Page model can adequately predict the moisture content between 55 and 65 °C (R2 &gt; 0.99). The effective moisture diffusivity (Deff) varied from 5.26 × 10−10 to 2.09 × 10−9 m2/s and the activation energy (Ea) of the SMIR drying was 31.84 kJ/mol. Infrared radiation temperature and slice thickness exerted extremely significant effects on L value and color difference (ΔE) (p &lt; 0.01), with higher infrared radiation temperature and thin slice thickness leading to a decrease in the L value and an increase in ΔE. Hardness and vitamin C content were significantly affected by infrared radiation temperature, slice thickness and radiation distance, of which the slice thickness was the most distinct factor affecting the hardness value. Higher infrared radiation temperature and larger slice thickness and radiation distance resulted in higher vitamin C degradation. For the given constraints (maximized vitamin C content and L value, minimized ΔE and hardness value), the optimum drying parameters were infrared radiation temperature 58.2 °C, slice thickness 6 mm and radiation distance 90 mm. Under the optimum drying combination conditions, the experimental values were 65.58 (L value), 8.57 (∆E), 10.49 N (hardness) and 106.58 mg/100 g (vitamin C content), respectively. This study is beneficial to the development of the cantaloupe food processing industry and provides more insights for the application of SMIR drying technology to improve the drying rate and product quality of cantaloupe.

Accelerating the 3D T1ρ mapping of cartilage using a signal-compensated robust tensor principal component analysis model.

Magnetic resonance (MR) quantitative T1ρ imaging has been increasingly used to detect the early stages of osteoarthritis. The small volume and curved surface of articular cartilage necessitate imaging with high in-plane resolution and thin slices for accurate T1ρ measurement. Compared with 2D T1ρ mapping, 3D T1ρ mapping is free from artifacts caused by slice cross-talk and has a thinner slice thickness and full volume coverage. However, this technique needs to acquire multiple T1ρ-weighted images with different spin-lock times, which results in a very long scan duration. It is highly expected that the scan time can be reduced in 3D T1ρ mapping without compromising the T1ρ quantification accuracy and precision. To accelerate the acquisition of 3D T1ρ mapping without compromising the T1ρ quantification accuracy and precision, a signal-compensated robust tensor principal component analysis method was proposed in this paper. The 3D T1ρ-weighted images compensated at different spin-lock times were decomposed as a low-rank high-order tensor plus a sparse component. Poisson-disk random undersampling patterns were applied to k-space data in the phase- and partition-encoding directions in both retrospective and prospective experiments. Five volunteers were involved in this study. The fully sampled k-space data acquired from 3 volunteers were retrospectively undersampled at R=5.2, 7.7, and 9.7, respectively. Reference values were obtained from the fully sampled data. Prospectively undersampled data for R=5 and R=7 were acquired from 2 volunteers. Bland-Altman analyses were used to assess the agreement between the accelerated and reference T1ρ measurements. The reconstruction performance was evaluated using the normalized root mean square error and the median of the normalized absolute deviation (MNAD) of the reconstructed T1ρ-weighted images and the corresponding T1ρ maps. T1ρ parameter maps were successfully estimated from T1ρ-weighted images reconstructed using the proposed method for all accelerations. The accelerated T1ρ measurements and reference values were in good agreement for R=5.2 (T1ρ: 40.4±1.4 ms), R=7.7 (T1ρ: 40.4±2.1 ms), and R=9.7 (T1ρ: 40.9±2.2 ms) in the Bland-Altman analyses. The T1ρ parameter maps reconstructed from the prospectively undersampled data also showed promising image quality using the proposed method. The proposed method achieves the 3D T1ρ mapping of in vivo knee cartilage in eight minutes using a signal-compensated robust tensor principal component analysis method in image reconstruction.

High Rate of False Negative Diagnosis of Silent Patent Ductus Arteriosus on the Chest CT with 3 mm Slice-Thickness, Suggesting the Need for Analysis with Thinner Slice Thickness.

The purpose of this study was to evaluate the diagnostic accuracy of patent with ductus arteriosus (PDA) based on the availability of pretest information on routine chest CT with 3 mm slice-thickness. We retrospectively evaluated CT of 64 patients with PDA. The enrolled patients were categorized as group 1 (presence of pretest information) and 2 (absence of pretest information, silent PDA). CTs were read by eleven board-certified radiologists, and subsequently by two blind readers. We investigated whether a PDA was mentioned on the initial CT reading. Correct diagnosis of PDA was made in all patients with group 1 (n = 42). In contrast, only 13.7% were correctly diagnosed in group 2. All cases of missed PDA in group 2 were also missed by two blind readers. It is important to realize that the diagnostic accuracy of silent PDA is poor on the chest CT with 3 mm slice-thickness. Thus, use of axial CT images with the thinnest slice-thickness and multi-planar reformatted images (i.e., sagittal and coronal images) may be one way to reduce the number of missed PDA.

Comparison of Dentoalveolar Bony Defects Measurements by Dental Radiography and Computed Tomography in Companion Animals

Background: Dental disorders are commonly encountered in companion animals and diagnosed with dental radiography (DTR) and computed tomography (CT). No study has been reported previously that compares dental measurements from DTR and CT in companion animals with periodontitis-related bone loss. The purpose was to compare the DTR and CT of bony defects in cats and dogs with dental disease.Methods: This is a retrospective study and the records of Jeonbuk Animal Medical Center were reviewed between June 2019 and October 2020. All patients underwent both modalities. In DTR, bisecting angle technique was used. In CT, slice thickness was applied at either 0.5 mm or 1.0 mm depending on the size of the patients. Bony defects of dentoalveolar structures were measured.Result: Eight dogs and eight cats with dental disease were included. All measurements in both cats and dogs were smaller in DTR than in CT. Additionally, CT with thin slice thickness provided more sufficient and accurate information for dental diagnosis. Conclusively, CT examination for dental disease showed to be superior in the assessment of periodontitis-related bone lesions by obtaining precise images without distortion and overlap and can possibly substitute DTR in cats and small dogs.

Computed Tomography-Guided Transthoracic Needle Biopsy: Predictors for Diagnostic Failure and Tissue Adequacy for Molecular Testing.

Background: Adequate and representative tissue from lung tumor is important in the era of precision medicine. The aim of this study is to identify detailed procedure-related variables and factors influencing diagnostic success and tissue adequacy for molecular testing in CT-guided TTNB.Methods: Consecutive patients undergoing CT-guided TTNB were retrospectively enrolled between January 2013 and May 2020. Multivariate analysis was performed for predictors for diagnostic accuracy and tissue adequacy for molecular testing. Logistic regression was used to identify risk factors for procedure-related complications.Results: A total of 2,556 patients undergoing CT-guided TTNB were enrolled and overall success rate was 91.5% (2,338/2,556). For lung nodules ≤3 cm, predictors for diagnostic success included coaxial needle use [OR = 0.34 (0.16–0.71), p = 0.004], CT scan slice thickness of 2.5 mm [OR = 0.42 (0.15–0.82), p = 0.011] and additional prefire imaging [OR = 0.31 (0.14–0.68), p = 0.004]. For lung tumor >3 cm, ground glass opacity part more than 50% [OR = 7.53 (2.81–20.23), p < 0.001] or presence of obstructive pneumonitis [OR = 2.31 (1.53–3.48), p < 0.001] had higher risk of diagnostic failure. For tissue adequacy, tissue submitted in two cassettes (98.9 vs. 94.9%, p = 0.027) was a positive predictor; while male (5.7 vs. 2.5%, p = 0.032), younger age (56.61 ± 11.64 vs. 65.82 ± 11.98, p < 0.001), and screening for clinical trial (18.5 vs. 0.7%, p < 0.001) were negative predictors.Conclusions: Using a coaxial needle, with thin CT slice thickness (2.5 mm), and obtaining additional prefire imaging improved diagnostic success, while obtaining more than two tissue cores and submitting in two cassettes improved tissue adequacy for molecular testing.

PROCEDURE OF MULTI SLICE COMPUTED TOMOGRAPHY (MSCT) THORAX EXAMINATION USING POSITIVE CONTRAST MEDIA WITH BREAST CANCER CASE

Background: The protocol for MSCT Thorax examination is axial/coronal/sagittal. The slice thickness parameter has an important role in examining MSCT Thorax in breast cancer cases using contrast media. The thinner the slice thickness, the better the detailed image obtained. The aim of this study is to explain the MSCT Thorax examination procedure in breast cancer cases using positive contrast media, the role of slice thickness in diagnosis and to find out diagnostic information on the MSCT Thorax examination in cancer cases breast.&#x0D; Methods: This type of research is qualitative with a literature study approach. The data were obtained by identifying the problem then looking for keywords, namely MSCT Thorax, Slice thickness, breast cancer. Literature reviews are carried out through journal search engine searches, such as: Google Scholar, American Journal Rontgenology (AJR), Pubmed, Proquest. The collected journals are reduced based on inclusion criteria so that 3 relevant journals are obtained then analyzed descriptively so that they can answer the objectives to be drawn conclusions.&#x0D; Results: The results of a literature study show that the MSCT Thorax examination procedure in cases of breast cancer using contrast media is fasting 6 hours before the examination, laboratory checks (urea cratinin within normal limits), releasing all metals in the body, CT scan plane, fixation tools, blankets. , contrast media, injector set.&#x0D; Conclusion: Contrast media dosage 1-2 ml / kg body weight, flow rate 2-4 ml / s, concentration 300-350 mgl / ml, patient position supine feet first, upper limit of lung Apex and lower limit of diaphragm (depending on needs), axial cut, coronal, sagittal, the parameters used were kV, mAs, slice thickness, matrix, WW, WL. A thin slice thickness will provide more accurate diagnostic information and a clear picture of metastases and small lesions can be seen.

The Influence of Quantitative Parameters Measured on CT with Different Slice Thicknesses on Evaluating the Growth of Pulmonary Nodules

We did this study to investigate the effect of thick (5mm) and thin (1 or 0.625 mm) slice thickness of CT images on evaluating pulmonary nodules' growth to improve their diagnostic accuracy. The clinical and CT data of 251 patients with lung nodules and two follow-up CTs from October 2016 to October 2019 were analyzed retrospectively. Malignant nodules were confirmed by pathology, and benign nodules were confirmed by pathology or follow-up. Two radiologists double-blindly assessed the CT features (density, shape, lobes, border), maximum diameter, and volume of nodules on the thick (5MM) and thin (≤1MM) images of two follow-up CTs. We use One-way analysis of variance for quantitative data; the X2 test or FISHER exact probability method was used for qualitative data; and the ROC curve was used to analyze the diagnostic power of nodule size, volume, and change in differentiating benign and malignant lesions. Among 251 pulmonary nodules, 117 (46.6%) benign nodules and 134 (53.3%) malignant nodules. During the CT follow-up, the volume measured on the thick-section image, the diameter, and the volume measured on the thin-section image were statistically different in benign and malignant lung nodules (P<0.001). In contrast, the diameter measured on the thick-section image was similar between these two groups (P=0.328). For benign and malignant pulmonary nodules, the diameter, volume, and change measured on the thin-section image were significantly larger than the thick-section image's data (P<0.001). The ROC curve showed that the diagnostic efficiency of volume was higher compared to the diameter. There were significant differences in nodule type, density change, shape, lobulation, and pleural retraction between benign and malignant nodules for CT features. Accurately assessing the volume changes combined with CT characteristics will help improve lung nodules' diagnosis accuracy. Volume measured on thin-section (1mm) CT images is the best quantitative parameter for assessing the change of pulmonary nodules. Combining Volume change with CT characteristics would help to improve the diagnostic accuracy.

Noise reduction in CT image using prior knowledge aware iterative denoising

The clinical demand for low image noise often limits the slice thickness used in many CT applications. However, a thick-slice image is more susceptible to longitudinal partial volume effects, which can blur key anatomic structures and pathologies of interest. In this work, we develop a prior knowledge aware iterative denoising (PKAID) framework that utilizes spatial data redundancy in the slice increment direction to generate low-noise, thin-slice images, and demonstrate its application in non-contrast head CT exams. The proposed technique takes advantage of the low noise of thicker images and exploits the structural similarity between the thick- and thin-slice images to reduce noise in the thin-slice image. Phantom data and patient cases (n = 3) of head CT were used to assess performance of this method. Images were reconstructed at clinically utilized slice thickness (5 mm) and thinner slice thickness (2 mm). PKAID was used to reduce image noise in 2 mm images using the 5 mm images as low-noise prior. Noise amplitude, noise power spectra (NPS), modulation transfer function (MTF), and slice sensitivity profiles (SSPs) of images before/after denoising were analyzed. The NPS and MTF analysis showed that PKAID preserved noise texture and resolution of the original thin-slice image, while reducing noise to the level of thick-slice image. The SSP analysis showed that the slice thickness of the original thin-slice image was retained. Patient examples demonstrated that PKAID-processed, thin-slice images better delineated brain structures and key pathologies such as subdural hematoma compared to the clinical 5 mm images, while additionally reducing image noise. To test an alternative PKAID utilization for dose reduction, a head exam with 40% dose reduction was simulated using projection-domain noise insertion. The image of 5 mm slice thickness was then denoised using PKAID. The results showed that the PKAID-processed reduced-dose images maintained similar noise and image quality compared to the full-dose images.