CT Volume Research Articles

Improved radiological imaging of congenital aural atresia using flat-panel volume CT.

Precise preoperative radiological evaluation of aural atresia is of utmost importance for surgical planning. Until now, multislice computed tomography (MSCT) has been used but it cannot adequately visualize small structures such as the stapes. Flat-panel volume CT (fpVCT) with its secondary reconstructions (fpVCTSECO) offers ahigh-resolution visualization of the middle ear. New otosurgical planning software also enables detailed 3D reconstruction of the middle ear anatomy. Evaluation of the use of fpVCTSECO in combination with an otosurgical planning software for amore accurate diagnosis and treatment of congenital aural atresia. Sevenpatients with congenital aural atresia underwent preoperative MSCT (600 µm slice thickness) and corresponding fpVCT (466 µm slice thickness). In addition, fpVCTSECO (99 µm slice thickness) were reconstructed. The Jahrsdoerfer and Siegert grading scores were determined and their applicability in the abovementioned imaging modalities was evaluated. In addition, the malleus incus complex was analyzed in 3D rendering. Imaging with fpVCTSECO enabled reliable visualization of the abnormalities, in particular the ossicular chain. Asignificant difference in the Siegert grading score was found. In addition, the malleus-incus complex could be visualized better in 3D. The introduction of new imaging techniques and surgical planning techniques into the diagnostic concept of aural atresia facilitates the identification of malformed anatomy and enables systematic analysis. This combination can also help to more accurately classify the pathology and thus increase the safety and success of the surgical procedure.

APPLICATION OF SIZE SPECIFIC DOSE ESTIMATE (SSDE) AS A LOCAL DIAGNOSTIC REFERENCE LEVEL IN HEAD CT SCAN EXAMINATION AT INDRIATI HOSPITAL SOLOBARU, DR. MOEWARDI HOSPITAL SURAKARTA, SLEMAN HOSPITAL, AND NATIONAL BRAIN CENTER HOSPITAL JAKARTA

CT scan is a modality that produces a higher radiation dose compared to other modalities in radiodiagnostic installations. The radiation dose received by patients from examinations with this CT scan device is expressed in the volume CT Dose Index (CTDIvol) and Dose Length Product (DLP), which are dose indicators from the Indonesian Diagnostic Reference Level (DRL) value regulated in the Decree of the Head of the Nuclear Energy Regulatory Agency (BAPETEN) nationally. However, the CTDIvol value is only an indicator of the output dose from the CT scan, and the DLP value is an indicator of the total dose during the examination, which is influenced by the length of the scan. Both do not represent the specific dose received by the patient because the patient's dose depends not only on the output dose but also on the patient's characteristics. The patient's dose, which includes the output dose and patient characteristics, is called the Size-Specific Dose Estimate (SSDE), which is used to estimate the dose received by the patient by including a correction factor depending on the patient's dimensions. This study aims to calculate the SSDE value in a CT Head examination with a standard protocol using the same type of device, namely a 128-slice CT scan in several hospitals. The values of CTDIvol and DLP patients are obtained from the dose report displayed on the CT scan monitor screen in the IndoseCT software. The patient samples studied were patients undergoing CT Head examinations from March 2023 to February 2024, with 100 patients in each hospital. The results of this study obtained local DRL values based on SSDE calculated at Indriati Hospital, Moewardi Hospital, Sleman Hospital, and the National Brain Center Hospital and then compared them with the national TPD values. Based on the SSDE value from Indriati Hospital, the TPD value was 30.26 mGy, Sleman Hospital was 39.5 mGy, Dr. Moewardi Hospital was 49.3 mGy, and RSPON was 51.7 mGy. The TPD value of CT Head examinations without contrast from each hospital was still below the national TPD value of 60 mGy. Keywords: CT scan, diagnostic reference level (DRL), local DRL, national DRL, Size-Specific Dose Estimate (SSDE), IndoseCT.

DSIFNet: Implicit Feature Network for Nasal Cavity and Vestibule Segmentation from 3D Head CT

This study is dedicated to accurately segment the nasal cavity and its intricate internal anatomy from head CT images, which is critical for understanding nasal physiology, diagnosing diseases, and planning surgeries. Nasal cavity and it’s anatomical structures such as the sinuses, and vestibule exhibit significant scale differences, with complex shapes and variable microstructures. These features require the segmentation method to have strong cross-scale feature extraction capabilities. To effectively address this challenge, we propose an image segmentation network named the Deeply Supervised Implicit Feature Network (DSIFNet). This network uniquely incorporates an Implicit Feature Function Module Guided by Local and Global Positional Information (LGPI-IFF), enabling effective fusion of features across scales and enhancing the network's ability to recognize details and overall structures. Additionally, we introduce a deep supervision mechanism based on implicit feature functions in the network's decoding phase, optimizing the utilization of multi-scale feature information, thus improving segmentation precision and detail representation. Furthermore, we constructed a dataset comprising 7116 CT volumes (including 1,292,508 slices) and implemented PixPro-based self-supervised pretraining to utilize unlabeled data for enhanced feature extraction. Our tests on nasal cavity and vestibule segmentation, conducted on a dataset comprising 128 head CT volumes (including 34006 slices), demonstrate the robustness and superior performance of proposed method, achieving leading results across multiple segmentation metrics.

Adversarial robustness improvement for X-ray bone segmentation using synthetic data created from computed tomography scans

Deep learning-based image analysis offers great potential in clinical practice. However, it faces mainly two challenges: scarcity of large-scale annotated clinical data for training and susceptibility to adversarial data in inference. As an example, an artificial intelligence (AI) system could check patient positioning, by segmenting and evaluating relative positions of anatomical structures in medical images. Nevertheless, data to train such AI system might be highly imbalanced with mostly well-positioned images being available. Thus, we propose the use of synthetic X-ray images and annotation masks forward projected from 3D photon-counting CT volumes to create realistic non-optimally positioned X-ray images for training. An open-source model (TotalSegmentator) was used to annotate the clavicles in 3D CT volumes. We evaluated model robustness with respect to the internal (simulated) patient rotation α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha$$\\end{document} on real-data-trained models and real&synthetic-data-trained models. Our results showed that real&synthetic- data-trained models have Dice score percentage improvements of 3% to 15% across different α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha$$\\end{document} groups compared to the real-data-trained model. Therefore, we demonstrated that synthetic data could be supplementary used to train and enrich heavily underrepresented conditions to increase model robustness.

Size-specific dose estimates calculated using patient size measurements from scanned projection radiograph in high-resolution chest computed tomography.

Size-specific dose estimates (SSDE) are used to assess patient-specific radiation exposure in Computed Tomography (CT), complementing the volume CT dose index (CTDIvol). This study compared SSDE calculated using patient's lateral size from scan projection radiograph (SPR) with SSDE calculated using water equivalent diameter (Dw) from tomographic images in adult chest high-resolution CT (HRCT). In a single-centre study, the CTDIvol and dose-length product (DLP) were recorded from HRCT dose reports of adult patients. Lateral width (SLat), at the centre of the scan range, from the SPR was measured and the SSDE (SSDER) was calculated using conversion factors related to SLat. Average CT number, area of the slice, and lateral size of the patient (AxLat) were measured on the middle slice. The Dw and SSDE from Dw (SSDEW) were calculated. SSDER and SSDEW were compared using Wilcoxon signed rank test. Correlation between patient size and dosimetry parameters were investigated using Spearman Correlation test with statistical significance at P < 0.05. Bland-Altman plot was also used to test agreement between the two SSDE values. Median CTDIvol, DLP, SSDER and SSDEW were 11.0 mGy, 372 mGy.cm, 11.6 mGy and 12.9 mGy, respectively. Small but statistically significant differences (P < 0.03) were found between SLat and AxLat as well as between SSDER and SSDEW. Bland-Altman analysis resulted in borderline agreement between SSDE values. Moderate correlations were observed between dosimetry quantities and patient size measurements (ρ > 0.640; P < 0.001). SSDEw showed statistically significant correlation (ρ = 0.587 and P < 0.001) with SSDER. SSDER may be used to assess patients' absorbed radiation dose, before the scan, in adult chest HRCT. The median value of SSDER was about 10% lower than the median value SSDEW. However, the SSDEW should be used after the scan to establish effective dose and radiation risk to the patient.

Quality assurance for online adaptive radiotherapy: a secondary dose verification model with geometry-encoded U-Net

Abstract Background &amp; Purpose: In online adaptive radiotherapy (ART), quick computation-based secondary dose verification is crucial for ensuring the quality of ART plans while the patient is positioned on the treatment couch. However, traditional dose verification algorithms are generally time-consuming, reducing the efficiency of ART workflow. This study aims to develop an ultra-fast deep-learning (DL) based secondary dose verification algorithm to accurately estimate dose distributions using CT and fluence maps (FMs). &amp;#xD;Methods: We integrated FMs into the CT image domain by explicitly resolving the geometry of treatment delivery. For each gantry angle, an FM was constructed based on the optimized multi-leaf collimator apertures and corresponding monitoring units (MUs). To effectively encode treatment beam configuration, the constructed FMs were back-projected to 30 cm away from the isocenter with respect to the exact geometry of the treatment machines. Then, a 3D U-Net was utilized to take the integrated CT and FM volume as input to estimate dose. Training and validation were performed on 381 prostate cancer cases, with an additional 40 testing cases for independent evaluation of model performance.&amp;#xD;Results: The proposed model can estimate dose in ~15 ms for each patient. The average γ passing rate (3%/2mm, 10% threshold) for the estimated dose was 99.9% ± 0.15% on testing patients. The mean dose differences for the planning target volume (PTV) and organs at risk (OARs) were 0.07%±0.34% and 0.48%±0.72%, respectively.&amp;#xD;Conclusion: We have developed a geometry-resolved DL framework for accurate dose estimation and demonstrated its potential in real-time online ART doses verification.&amp;#xD;

Radiographically Apparent Acetabular Sourcil Landmarks Are Created by Comparable Regions of the Pelvis With Extraarticular Bone Variably Confounding Estimates of Joint Coverage.

The severity of hip dysplasia is characterized by radiographic measurements that require user definition of the acetabular sourcil edge, a bony landmark for which the corresponding three-dimensional (3D) anatomy is not well defined in any imaging plane. To use digitally reconstructed radiographs to determine: (1) What 3D anatomy is contributing to the "acetabular sourcil" location used to make lateral center-edge angle (LCEA) and anterior center-edge angle (ACEA) measurements in standing AP and false-profile radiographic views, respectively? (2) How do intraobserver and interobserver agreement in LCEA and ACEA translate into agreement of the 3D anatomy being evaluated? (3) How distinct are regions around the acetabular rim circumference that are evaluated by LCEA and ACEA measurements on radiographs? Between January 2018 and May 2019, 72 patients were indicated for periacetabular osteotomy to treat hip dysplasia or acetabular retroversion at our institution. From these patients, a series of 10 patients were identified of the first 12 patients in 2018 who were treated with periacetabular osteotomy, excluding two with missing or low-quality clinical imaging. A second series of 10 patients was identified of the first 11 patients in 2019 who were treated with periacetabular osteotomy and concurrent hip arthroscopy, excluding one who was missing clinical imaging. Pelvis and femoral bone surface models were generated from CT scans of these two series of 10 patients. There were 15 female and five male patients, with a median patient age of 18 years (IQR 17 to 23 years), a preoperative LCEA of 22° (IQR 18° to 24°), and a preoperative ACEA of 23° (IQR 18° to 27°). Exclusion criteria included missing preoperative CT or standard clinical radiographic imaging or severe joint incongruity. To address our first study question, digitally reconstructed radiographs matching each patient's standing AP and false-profile clinical radiographs were created from the segmented CT volumes. A board-certified orthopaedic surgeon and three trained researchers measured LCEA and ACEA on the digitally reconstructed radiographs, and the selected sourcil points were projected back into coordinates in the 3D anatomic space. To address our second study question, intraobserver and interobserver agreement in radiographic coverage angles were related to variations in 3D coordinates of the selected bony anatomy. Lastly, to address our third study question, 3D locations around the acetabular rim identified as contributing to the lateral and anterior sourcil points were summarized across patients in a clockface coordinate system, and statistical analysis of the "time" separating the 3D acetabular contributions of the sourcil edges was performed. The 3D anatomy contributing to the lateral sourcil was a variable length (27 mm [IQR 15 to 34 mm]) span of the laterosuperior acetabular edges, with contributions by the anterior inferior iliac spine in 35% (7 of 20) of hips. The anterior sourcil reflected a 28-mm (IQR 25 to 31 mm) span of bone from the medial ilium (posterior-medial to the anterior-inferior iliac spine and anterior-lateral to the arcuate line) to the anterior and lateral edges of the acetabulum. Interobserver variability was good for LCEA (intraclass correlation coefficient [ICC] 0.82 to 0.83) and moderate to good for ACEA (ICC 0.73 to 0.79), whereas the agreement in identified 3D sourcil locations varied widely (ICC 0.32 to 0.95). The acetabular edge of the 3D anatomy contributing to the anterior sourcil overlapped the circumferential range of the acetabular rim contributing to the lateral sourcil. Projection of two-dimensional radiographic landmarks contributing to the diagnosis of structural hip deformity into 3D allowed for the identification of the overlapping bony anatomy contributing to radiographically visible anterior and lateral sourcil edges. This work leveraging digitally reconstructed radiographs and 3D pelvis anatomy has found that bone outside the joint contributes to the radiographic appearance of the sourcil and may variably confound estimates of joint coverage. Furthermore, the substantial overlap between the acetabular bone contributing to measurement of the LCEA and ACEA would indicate that these angles measure similar acetabular deformity, and that additional measures are needed to assess anterior coverage independent of lateral coverage.

Prediction of preoperative lymph-vascular space invasion and survival outcomes of cervical squamous cell carcinoma by utilizing 18 F-FDG PET/CT imaging at early stage.

To establish nomograms for predicting preoperative lymph-vascular space invasion (LVSI) and survival outcomes of cervical squamous cell carcinoma (CSCC) based on PET/CT radiomics. One hundred and twenty-three patients with CSCC and LVSI status were enrolled retrospectively. Independent predictors of LVSI were identified through clinicopathological factors and PET/CT metabolic parameters. We extracted 1316 features from PET and CT volume of interest, respectively. Additionally, four models (PET-RS: radiomic signature of PET only; CT-RS: radiomic signature of CT only; PET/CT-RS + clinical data; PET/CT-RS: radiomic signature of PET and CT) were established to predict LVSI status. Calculation of radiomics scores of PET/CT was executed for assessment of the survival outcomes, followed by development of nomograms with radiomics (NR) or without radiomics (NWR). One hundred and twenty-three patients with pathologically confirmed CSCC had been categorized into two sets (training and testing sets). It was found that only maximum standardized uptake value (SUV max ) and squamous cell carcinoma antigen were independent predictors of LVSI. Meanwhile, the PET/CT-RS + clinical data outperformed the other three models in the training set [area under the curve (AUC): 0.91 vs. 0.861 vs. 0.81 vs. 0.814] and the testing set (AUC: 0.885 vs. 0.857 vs. 0.783 vs. 0.798). Additionally, SUV max and LVSI had been demonstrated to be independent prognostic indicators for progression-free survival and overall survival. Decision curve analysis and calibration curve indicated that NRs were superior to NWRs. The survival outcomes were assessed. PET/CT-based radiomic signature nomogram enables a new method for preoperative prediction of LVSI and survival prognosis for patients with CSCC.

Prostate volume on computed tomography correlates well with magnetic resonance imaging measurements and is reproducible across rater training levels.

Data are lacking for the accuracy of computed tomography (CT) in measuring prostate size, which can streamline care and prevent invasive procedures. We evaluate agreement and intra/inter-observer variability in prostate sizing between CT and magnetic resonance imaging (MRI) planimetry for a wide range of gland sizes. We retrospectively reviewed 700 patients who underwent MRI fusion biopsy at a single institution and identified 89 patients that had a CT within 2years of the MRI. Six reviewers from different training levels were categorized as student, resident, or attending and each measured prostate size on CT by the prolate ellipse method. Bland-Altman analysis determined the degree of agreement between CT and MRI. Inter- and intra-observer reliability was calculated for CT. Mean CT volume was higher than MRI volume in the < 60g group (51.5g vs. 44.5g, p = 0.004), but not in the ≥ 60g group (101g vs. 100g, p = 0.458). The bias for overestimation of prostate volume by CT was 4.1g across prostate volumes, but the proportional agreement between modalities improved with size. The Pearson correlation coefficient between CT/MRI and inter/intra-rater reliability for CT increased in the ≥ 60g vs. the < 60g group for all training levels. Our data show that there is greater clinical utility for prostate size estimation by CT than previously established, particularly for larger glands where accurate size estimation may influence therapeutic decisions. In larger glands, prostate size estimation by CT is also reproducible across various training levels.

The Evaluation of Gastric Cancer Lymphovascular Invasion Using CT Volume Perfusion.

The best treatment option for patients with resectable gastric cancer is radical gastric cancer surgery. However, the postoperative overall survival rate is low. Lymphovascular invasion (LVI) is a risk factor for cancer recurrence and a stand-alone predictor of a poor post-operative prognosis for gastric cancer (GC) patients. Current evaluation of tumor LVI performed on histological specimens, which can only be assessed after surgery, is also limited by intra-tumoural heterogeneity via biopsy. This study explored the value of CT volume perfusion in assessing tumors' lymphovascular invasion of gastric cancer. 59 gastric cancer patients confirmed by pathology who underwent both computed tomography (CT) volume perfusion examinations and gastrectomy surgery were prospectively included. Tumour lymphovascular invasion (LVI, positive or negative) was evaluated. The relationship between clinicopathological variables associated with LVI and CT perfusion parameters was analyzed by univariate analysis, followed by multivariate logistic regression analysis and receiver operating characteristic (ROC) analysis. The LVI-positive and LVI-negative groups differed significantly in terms of time to start (TTS), mean transit time (MTT), Tmax, and flow extraction product (FEP). Both FEP (odds ratio (OR), 1.048; 95% confidence interval (CI): 1.005-1.092) and MTT (OR, 0.549; 95% CI: 0.351-0.858) have the potential to be employed as independent predictors of LVI (both p < 0.05). There were different correlations between LVI, lower MTT and greater FEP. The specificity of MTT (87.88%) was higher than that of FEP (72.73%), while the sensitivity of MTT (53.85%) was lower than that of FEP (57.69%). Compared to MTT and FEP alone, the combination demonstrated a comparatively higher area under the curve (AUC) (0.797) and sensitivity (84.62%). CT volume perfusion helps evaluate LVI in gastric cancer before surgery. MTT and FEP are independent predictors for LVI, and the combination variation has better diagnostic performance. Clinical Trial Register: Jiangmen Central Hospital, https://www.chictr.org.cn/showproj.html?proj=24375, ChiCTR1800014455.

Differential diagnosis between low-risk and high-risk thymoma: Comparison of diagnostic performance of radiologists with and without deep learning model.

There are few CT-based deep learning (DL) studies on thymoma according to the World Health Organization classification. To develop a CT-based DL model to distinguish between low-risk and high-risk thymoma and to compare the diagnostic performance of radiologists with and without the DL model. 159 patients with 160 thymomas were included. A fine-tuning VGG16 network model with Adam optimizer was used, followed by k-fold cross validation. The dataset consisted of three axial slices, including the maximum tumor size from the CT volume data. The data were augmented 50 times by rotation, zoom, shear, and horizontal/vertical flip. Three independent networks for the CT dataset were considered, and the result was determined by voting. Three radiologists independently diagnosed thymomas with and without the model. The area under the curve (AUC) of the diagnostic performance was compared using receiver operating characteristic analysis. Accuracy of the DL model was 71.3%. Diagnostic performance of the radiologists was as follows: AUC and accuracy without the DL model, 0.61-0.68 and 61.9%-69.3%; and with the DL model, 0.66-0.69 and 68.1%-70.0%, respectively. AUC of the diagnostic performance showed no significant differences between radiologists with and without the DL model. The DL model tended to increase the diagnostic accuracy, but AUC was not significantly improved. Diagnostic performance of the DL was comparable to that of radiologists. The DL model assistance tended to increase diagnostic accuracy.

Pore-scale deformation characteristics of hydrate-bearing sediments with gas replacement

Gas replacement method enables the simultaneous exploitation of natural gas and the realization of carbon capture, utilization, and storage (CCUS). Safe exploitation of hydrate-bearing sediments (HBS) has garnered significant attention, particularly concerning the engineering geological risks involved. Understanding deformation characteristics during shear after the replacement of HBS is crucial for safe and efficient exploitation. This study employs microfocus computer tomography and digital volume correlation (DVC) to investigate the deformation characteristics of HBS samples with varying replacement percentages. Key findings include: 1. An increase in failure strength of HBS is observed with higher replacement percentages due to improved hydrate cementation and consolidation under confining pressure. 2. DVC analysis shows that narrower radial displacement ranges are associated with increased pore compression, while wider ranges indicate greater particle repositioning. Frequent large axial displacements suggest significant pore compaction, whereas smaller axial displacements indicate particle movement and pore-filling phenomena. 3. The gas replacement process enhances the cementation structure of HBS without altering hydrate saturation, resulting in thinner shear bands and accelerated strain softening with higher replacement percentages. 4. The DVC approach effectively captures volumetric strain and deformation behaviors, offering valuable insights into sediment responses under shear. This study provides a theoretical reference for geological safety evaluation during gas replacement exploitation.

Intra‐arterial contrast enhanced computed tomography of the deep digital flexor tendon and palmar veins in the distal forelimb in Jeju horses: Evaluating contrast‐enhancing factors

AbstractBackgroundFew studies have investigated the adequate contrast enhancement (CE) evaluation depending on concentration, volume, and rate of administration of contrast media (CM) and the scan parameters in equine contrast enhanced computed tomography (CECT).ObjectivesTo investigate CE of the deep digital flexor tendon (DDFT) and veins depending on the computed tomography (CT) voltage and concentration, volume, and rate of CM administration during intra‐arterial CECT of equine distal forelimbs.Study DesignIn vivo experiments.MethodsSix horses underwent 54 CT scans. First, the CE of DDFT and veins was evaluated depending on the voltage (80 kV and 250 mA or 120 kV and 150 mA) and CM concentration (150, 120, or 90 mg I/mL in 50 mL of CM). Second, CE of DDFT and veins was evaluated depending on the CM volume (50, 100, or 150 mL) and administration rate (2, 4, or 6 mL/s) with a fixed iodine delivery rate (IDR; 300 or 180 mg I/s).ResultsContrast enhancement of DDFT was significantly higher at 80 kV of voltage and 150 mg I/mL of CM concentration (Median: 29.65; IQR: 1.74; p &lt; 0.05). CE of the DDFT positively correlated with CM concentration (p &lt; 0.001; r = 0.75). At 180 mg I/s IDR, contrast attenuation in the medial and lateral palmar veins significantly decreased at a CM of 30 mg I/mL, rate of 6 mL/s, and volume of 150 mL (median and IQR: 985.93 and 71.8 Hounsfield units [HU] and 988.73 and 41.16 HU, respectively); the CE was sufficient to distinguish between the vessels and the adjacent structures.Main LimitationsThe number of animals was small.ConclusionsOur results suggest that a low CM concentration could yield sufficient CE of the DDFT and arteries with adjusted CT scanning parameters or volume and injection rate of CM.

Does stereotactic thrombolysis with alteplase for intracerebral haemorrhage alter intraventricular haematoma volume? A secondary analysis of the MISTIE-III trial

BackgroundStereotactic thrombolysis reduces intracerebral haemorrhage (ICH) volume in patients with spontaneous ICH. Whether intrahaematomal alteplase administration is associated with a change in intraventricular haemorrhage volume (deltaIVH) and functional outcomes is...

Improved radiological imaging of congenital aural atresia using flat-panel volume CT. German version

Precise preoperative radiological evaluation of aural atresia is of utmost importance for surgical planning. Until now, multislice computed tomography (MSCT) has been used but it cannot adequately visualize small structures such as the stapes. Flat-panel volume CT (fpVCT) with its secondary reconstructions (fpVCTSECO) offers ahigh-resolution visualization of the middle ear. New otosurgical planning software also enables detailed 3D reconstruction of the middle ear anatomy. Evaluation of the use of fpVCTSECO in combination with an otosurgical planning software for amore accurate diagnosis and treatment of congenital aural atresia. Sevenpatients with congenital aural atresia underwent preoperative MSCT (600 µm slice thickness) and corresponding fpVCT (466 µm slice thickness). In addition, fpVCTSECO (99 µm slice thickness) were reconstructed. The Jahrsdoerfer and Siegert grading scores were determined and their applicability in the abovementioned imaging modalities was evaluated. In addition, the malleus incus complex was analyzed in 3D rendering. Imaging with fpVCTSECO enabled reliable visualization of the abnormalities, in particular the ossicular chain. Asignificant difference in the Siegert grading score was found. In addition, the malleus-incus complex could be visualized better in 3D. The introduction of new imaging techniques and surgical planning techniques into the diagnostic concept of aural atresia facilitates the identification of malformed anatomy and enables systematic analysis. This combination can also help to more accurately classify the pathology and thus increase the safety and success of the surgical procedure.

Helical to wide volume acquisition in lumbar spine CT: Structures scoring based analysis

This study aims to compare the effectiveness of standard helical CT (HMCT) and volume CT (VMCT) protocols for lumbar spine imaging in terms of radiation dose and image quality. Two groups of 56 patients each (32 males, 24 females) who underwent lumbar spine CT were evaluated. The first group was imaged using HMCT and the second with VMCT. Dosimetric parameters including CTDIvol, DLP, and SSDE for both protocols were assesed and compared to evaluate radiation exposure. Image quality was assessed through quantitative analysis of Hounsfield Units (HU) and image noise, and qualitative analysis based on anatomical structure scoring by four reviewers. The VMCT protocol significantly reduced the mean CTDIvol (13 ± 3 mGy vs. 26 ± 6 mGy), DLP (270 ± 29 mGy cm vs. 609 ± 71 mGy cm), and effective dose (5 ± 0 mSv vs. 11 ± 1 mSv) compared to HMCT. SSDE was also lower for VMCT (15.6 ± 4.8 mGy) versus HMCT (31.8 ± 9.1 mGy). Despite this reduction in radiation dose, VMCT showed increased image noise across all anatomical regions, though differences in HU values were region-specific. Qualitative scoring revealed that VMCT images were similar in clarity and sharpness to HMCT images, with only marginally lower scores for most structures. VMCT significantly reduces radiation exposure compared to HMCT with observed difference in noise and CT number. The VMCT protocol can represent a promising tool in terms of radiation protection of patients while image quality needs further evaluation to take into consideration other parameters.

Constructing machine learning models based on non-contrast CT radiomics to predict hemorrhagic transformation after stoke: a two-center study.

Machine learning (ML) models were constructed according to non-contrast computed tomography (NCCT) images as well as clinical and laboratory information to assess risk stratification for the occurrence of hemorrhagic transformation (HT) in acute ischemic stroke (AIS) patients. A retrospective cohort was constructed with 180 AIS patients who were diagnosed at two centers between January 2019 and October 2023 and were followed for HT outcomes. Patients were analyzed for clinical risk factors for developing HT, infarct texture features were extracted from NCCT images, and the radiomics score (Rad-score) was calculated. Then, five ML models were established and evaluated, and the optimal ML algorithm was used to construct the clinical, radiomics, and clinical-radiomics models. Receiver operating characteristic (ROC) curves were used to compare the performance of the three models in predicting HT. Based on the outcomes of the AIS patients, 104 developed HT, and the remaining 76 had no HT. The HT group consisted of 27 hemorrhagic infarction (HI) and 77 parenchymal-hemorrhage (PH). Patients with HT had a greater neutrophil-to-lymphocyte ratio (NLR), baseline National Institutes of Health Stroke Scale (NIHSS) score, infarct volume, and Rad-score and lower Alberta stroke program early CT score (ASPECTS) (all p < 0.01) than patients without HT. The best ML algorithm for building the model was logistic regression. In the training and validation cohorts, the AUC values for the clinical, radiomics, and clinical-radiomics models for predicting HT were 0.829 and 0.876, 0.813 and 0.898, and 0.876 and 0.957, respectively. In subgroup analyses with different treatment modalities, different infarct sizes, and different stroke time windows, the assessment accuracy of the clinical-radiomics model was not statistically meaningful (all p > 0.05), with an overall accuracy of 79.5%. Moreover, this model performed reliably in predicting the PH and HI subcategories, with accuracies of 82.9 and 92.9%, respectively. ML models based on clinical and NCCT radiomics characteristics can be used for early risk evaluation of HT development in AIS patients and show great potential for clinical precision in treatment and prognostic assessment.

Quantification of bone microarchitecture using photon-counting CT at different radiation doses: A comparison with µCT

PurposeAccurate measurements of trabecular bone microarchitecture are required for a proper assessment of bone fragility. Photon-counting detector CT (PCD-CT) has different technical properties than conventional CT, resulting in higher resolution and thereby potentially enabling in-vivo measurement of trabecular microarchitecture. The purpose of this study was to quantify trabecular bone microarchitectural parameters with PCD-CT at varying radiation doses and compare this to µCT as gold standard. MethodBoth distal radii, distal tibiae, femoral heads, and two vertebrae were dissected from one human. All specimens were scanned ex-vivo on a PCD-CT system (slice increment 0.1 mm; pixel size 0.1042–0.127 mm) and a µCT system (isotropic voxel size 49–68.4 µm). The radiation doses of the PCD-CT scans were varied from 2.5 to 120 mGy based on the volume CT dose index (CTDIvol32). For the PCD-CT scans, contrast-to-noise ratio and trabecular sharpness were calculated and compared between radiation doses. µCT and PCD-CT scans were registered. The trabecular bone was then segmented from all PCD-CT and µCT scans and split into cubes with 6-mm edge length. For each cube, bone volume over total volume, trabecular thickness, trabecular number, and trabecular heterogeneity were calculated and compared between corresponding PCD-CT and µCT cubes. ResultsWith increasing dose, contrast-to-noise ratio and trabecular sharpness values increased for the PCD-CT images. Already at the lowest dose, high correlations between the trabecular microarchitectural parameters between µCT and PCD-CT were found (R2 = 0.55–0.95), which improved with increasing radiation dose (R2 = 0.76–0.96 at 20 mGy). ConclusionsPCD-CT can be used to quantify trabecular bone microarchitecture, with accuracy comparable to µCT and at clinically relevant radiation doses.

The impact of deep learning image reconstruction of spectral CTU virtual non contrast images for patients with renal stones

PurposeTo compare image quality and detection accuracy of renal stones between deep learning image reconstruction (DLIR) and Adaptive Statistical Iterative Reconstruction-Veo (ASIR-V) reconstructed virtual non-contrast (VNC) images and true non-contrast (TNC) images in spectral CT Urography (CTU). MethodsA retrospective analysis was conducted on images of 70 patients who underwent abdominal-pelvic CTU in TNC phase using non-contrast scan and contrast-enhanced corticomedullary phase (CP) and excretory phase (EP) using spectral scan. The TNC scan was reconstructed using ASIR-V70 % (TNC-AR70), contrast-enhanced scans were reconstructed using AR70, DLIR medium-level (DM), and high-level (DH) to obtain CP-VNC-AR70/DM/DH and EP-VNC-AR70/DM/DH image groups, respectively. CT value, image quality and kidney stones quantification accuracy were measured and compared among groups. The subjective evaluation was independently assessed by two senior radiologists using the 5-point Likert scale for image quality and lesion visibility. ResultsDH images were superior to AR70 and DM images in objective image quality evaluation. There was no statistical difference in the liver and spleen (both P > 0.05), or within 6HU in renal and fat in CT value between VNC and TNC images. EP-VNC-DH had the lowest image noise, highest SNR, and CNR, and VNC-AR70 images had better noise and SNR performance than TNC-AR70 images (all p < 0.05). EP-VNC-DH had the highest subjective image quality, and CP-VNC-DH performed the best in lesion visibility. In stone CT value and volume measurements, there was no statistical difference between VNC and TNC (P > 0.05). ConclusionThe DLIR-reconstructed VNC images in CTU provide better image quality than the ASIR-V reconstructed TNC images and similar quantification accuracy for kidney stones for potential dose savings.The study highlights that deep learning image reconstruction (DLIR)-reconstructed virtual non-contrast (VNC) images in spectral CT Urography (CTU) offer improved image quality compared to traditional true non-contrast (TNC) images, while maintaining similar accuracy in kidney stone detection, suggesting potential dose savings in clinical practice.

TopoRoot+: computing whorl and soil line traits of field-excavated maize roots from CT imaging

BackgroundThe use of 3D imaging techniques, such as X-ray CT, in root phenotyping has become more widespread in recent years. However, due to the complexity of the root structure, analyzing the resulting 3D volumes to obtain detailed architectural root traits remains a challenging computational problem. When it comes to image-based phenotyping of excavated maize root crowns, two types of root features that are notably missing from existing methods are the whorls and soil line. Whorls refer to the distinct areas located at the base of each stem node from which roots sprout in a circular pattern (Liu S, Barrow CS, Hanlon M, Lynch JP, Bucksch A. Dirt/3D: 3D root phenotyping for field-grown maize (zea mays). Plant Physiol. 2021;187(2):739–57. https://doi.org/10.1093/plphys/kiab311.). The soil line is where the root stem meets the ground. Knowledge of these features would give biologists deeper insights into the root system architecture (RSA) and the below- and above-ground root properties.ResultsWe developed TopoRoot+, a computational pipeline that produces architectural traits from 3D X-ray CT volumes of excavated maize root crowns. Building upon the TopoRoot software (Zeng D, Li M, Jiang N, Ju Y, Schreiber H, Chambers E, et al. Toporoot: A method for computing hierarchy and fine-grained traits of maize roots from 3D imaging. Plant Methods. 2021;17(1). https://doi.org/10.1186/s13007-021-00829-z.) for computing fine-grained root traits, TopoRoot + adds the capability to detect whorls, identify nodal roots at each whorl, and compute the soil line location. The new algorithms in TopoRoot + offer an additional set of fine-grained traits beyond those provided by TopoRoot. The addition includes internode distances, root traits at every hierarchy level associated with a whorl, and root traits specific to above or below the ground. TopoRoot + is validated on a diverse collection of field-grown maize root crowns consisting of nine genotypes and spanning across three years. TopoRoot + runs in minutes for a typical volume size of \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:40{0}^{3}$$\\end{document} on a desktop workstation. Our software and test dataset are freely distributed on Github.ConclusionsTopoRoot + advances the state-of-the-art in image-based phenotyping of excavated maize root crowns by offering more detailed architectural traits related to whorls and soil lines. The efficiency of TopoRoot + makes it well-suited for high-throughput image-based root phenotyping.