Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique.

Abdominal multi-organ segmentation is one of the most important topics in the field of medical image analysis, and it plays an important role in supporting clinical workflows such as disease diagnosis and treatment planning. In this study, an efficient multi-organ segmentation method called Swin-PSAxialNet based on the nnU-Net architecture is proposed. It was designed specifically for the precise segmentation of 11 abdominal organs in CT images. The proposed network has made the following improvements compared to nnU-Net. Firstly, Space-to-depth (SPD) modules and parameter-shared axial attention (PSAA) feature extraction blocks were introduced, enhancing the capability of 3D image feature extraction. Secondly, a multi-scale image fusion approach was employed to capture detailed information and spatial features, improving the capability of extracting subtle features and edge features. Lastly, a parameter-sharing method was introduced to reduce the model's computational cost and training speed. The proposed network achieves an average Dice coefficient of 0.93342 for the segmentation task involving 11 organs. Experimental results indicate the notable superiority of Swin-PSAxialNet over previous mainstream segmentation methods. The method shows excellent accuracy and low computational costs in segmenting major abdominal organs.

Submillisievert Abdominal Photon-Counting CT versus Energy-integrating Detector CT for Urinary Calculi Detection: Impact on Diagnostic Confidence.

Background Contrast-unenhanced abdominal CT is the imaging standard for urinary calculi detection; however, studies comparing photon-counting detector (PCD) CT and energy-integrating detector (EID) CT dose-reduction potentials are lacking. Purpose To compare the radiation dose and image quality of optimized EID CT with those of an experimental PCD CT scan protocol including tin prefiltration in patients suspected of having urinary calculi. Materials and Methods This retrospective single-center study included patients who underwent unenhanced abdominal PCD CT or EID CT for suspected urinary caliculi between February 2022 and March 2023. Signal and noise measurements were performed at three anatomic levels (kidney, psoas, and obturator muscle). Nephrolithiasis and/or urolithiasis presence was independently assessed by three radiologists, and diagnostic confidence was recorded on a five-point scale (1, little to no confidence; 5, complete confidence). Reader agreement was determined by calculating Krippendorff α. Results A total of 507 patients (mean age, 51.7 years ± 17.4 [SD]; 317 male patients) were included (PCD CT group, 229 patients; EID CT group, 278 patients). Readers 1, 2, and 3 detected nephrolithiasis in 129, 127, and 129 patients and 94, 94, and 94 patients, whereas the readers detected urolithiasis in 113, 114, and 114 patients and 152, 153, and 152 patients in the PCD CT and EID CT groups, respectively. Regardless of protocol (PCD CT or EID CT) or calculus localization, near perfect interreader agreement was found (α ≥ 0.99; 95% CI: 0.99, 1). There was no evidence of a difference in reader confidence between PCD CT and EID CT (median confidence, 5; IQR, 5-5; P ≥ .57). The effective doses were 0.79 mSv (IQR, 0.63-0.99 mSv) and 1.39 mSv (IQR, 1.01-1.87 mSv) for PCD CT and EID CT, respectively. Despite the lower radiation exposure, the signal-to-noise ratios at the kidney, psoas, and obturator levels were 30%, 23%, and 17% higher, respectively, in the PCD CT group (P < .001). Conclusion Submillisievert abdominal PCD CT provided high-quality images for the diagnosis of urinary calculi; radiation exposure was reduced by 44% with a higher signal-to-noise ratio than with EID CT and with no evidence of a difference in reader confidence. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Nezami and Malayeri in this issue.

Clinical feasibility of deep learning based synthetic contrast enhanced abdominal CT in patients undergoing non enhanced CT scans.

Our objective was to develop and evaluate the clinical feasibility of deep-learning-based synthetic contrast-enhanced computed tomography (DL-SynCCT) in patients designated for nonenhanced CT (NECT). We proposed a weakly supervised learning with the utilization of virtual non-contrast CT (VNC) for the development of DL-SynCCT. Training and internal validations were performed with 2202 pairs of retrospectively collected contrast-enhanced CT (CECT) images with the corresponding VNC images acquired from dual-energy CT. Clinical validation was performed using an external validation set including 398 patients designated for true nonenhanced CT (NECT), from multiple vendors at three institutes. Detection of lesions was performed by three radiologists with only NECT in the first session and an additionally provided DL-SynCCT in the second session. The mean peak signal-to-noise ratio (PSNR) and structural similarity index map (SSIM) of the DL-SynCCT compared to CECT were 43.25 ± 0.41 and 0.92 ± 0.01, respectively. With DL-SynCCT, the pooled sensitivity for lesion detection (72.0% to 76.4%, P < 0.001) and level of diagnostic confidence (3.0 to 3.6, P < 0.001) significantly increased. In conclusion, DL-SynCCT generated by weakly supervised learning showed significant benefit in terms of sensitivity in detecting abnormal findings when added to NECT in patients designated for nonenhanced CT scans.

Evaluation of Incidental Findings on Abdominopelvic CT: Potential Benefit of Photon-counting Detector CT Over Conventional Single-energy CT.

PURPOSE: To assese of potential benefint of photon-counting detector CT (PCD-CT) over conventional single-energy CT (CSE-CT) on accurate diagnosis of incidental findings with high clinical significance (IFHCS). MATERIALS AND METHODS: This retrospective study included 365 patients who initially underwent abdominopelvic contrast-enhanced CT (AP-CECT) without non-enhancement (PCD-CT: 187 and CSE-CT: 178). We selected IFHCS and evaluated their diagnosability using CE-CT alone. IFHCSs that could not be diagnosed with only CE-CT were evaluated using additional PCD-CT postprocessing techniques, including virtual non-contrast image, low keV image, and iodine map. A PCD-CT scanner (NAEOTOM Alpha, Siemens Healthineer, Erlangen, Germany) was used. RESULTS: Thirty-nine IFHCSs (PCD-CT: 22 and CSE-CT: 17) were determined in this study. Seven IFHCSs in each group were able to diagnose with only CE-CT. Fifteen IFHCSs were able to diagnose using the additional PCD-CT postprocessing technique, which was useful for detecting and accurately diagnosing 68.2% (15/22) of lesions and 65% (13/20) of patients. All IFHCSs were accurately diagonosed with PCD-CT. CONCLUSION: PCD-CT was useful for characterizing IFHCSs that are indeterminate at CSE-CT. PCD-CT offered potential benefit of PCD-CT over conventional single-energy CT on evaluation of IFHCS on only abdominopelvic CT.

Optimizing twin-beam dual-energy CT reconstruction: Quantitative consistency and stability assessment in reference to 120 kV: An observational study.

The split filter CT can filter X-ray beam. Theoretically, the split filter CT not only provides a good low-energy beam, but also provides a more robust CT value. The aim of this study was to compare conventional single-energy computed tomography (SECT) and twin-beam dual-energy (TBDE) CT regarding the quantitative consistency and stabilities of HU measurements at different abdominal organs. Forty-four patients were prospectively enrolled to randomly receive SECT and TBDE protocols at either body part of a thorax-abdominal examination. Their overlapping scan coverage was subjected to further image analysis. For TBDE scans, composed images(c-images) and virtual monoenergetic images (VMIs) at 60, 70, 80, and 90 kiloelectron volt (keV) were reconstructed. The attenuations were measured at 5 abdominal organs and compared between SECT and TBDE to characterize quantitative consistency by intraclass correlation coefficients (ICCs), whereas their standard deviations were used to assess the Hounsfield Unit (HU) stability. The c-images, 70 keV and 80 keV VMIs from TBDE provided consistent HU values (all ICCs > 0.8) with the SECT measurements; moreover, these TBDE images had superior HU stability over SECT images in all abdominal measurements except for fat tissue. The best HU stability can be achieved in 80 keV VMIs with the lowest noise level. The c-images and VMIs derived from TBDE can produce consistent values as SECT. The 80 keV images displayed better HU stability and a lower noise level across various abdominal organs.

Deep learning image reconstruction generates thinner slice iodine maps with improved image quality to increase diagnostic acceptance and lesion conspicuity: a prospective study on abdominal dual-energy CT.

BACKGROUND: To assess the improvement of image quality and diagnostic acceptance of thinner slice iodine maps enabled by deep learning image reconstruction (DLIR) in abdominal dual-energy CT (DECT). METHODS: This study prospectively included 104 participants with 136 lesions. Four series of iodine maps were generated based on portal-venous scans of contrast-enhanced abdominal DECT: 5-mm and 1.25-mm using adaptive statistical iterative reconstruction-V (Asir-V) with 50% blending (AV-50), and 1.25-mm using DLIR with medium (DLIR-M), and high strength (DLIR-H). The iodine concentrations (IC) and their standard deviations of nine anatomical sites were measured, and the corresponding coefficient of variations (CV) were calculated. Noise-power-spectrum (NPS) and edge-rise-slope (ERS) were measured. Five radiologists rated image quality in terms of image noise, contrast, sharpness, texture, and small structure visibility, and evaluated overall diagnostic acceptability of images and lesion conspicuity. RESULTS: The four reconstructions maintained the IC values unchanged in nine anatomical sites (all p > 0.999). Compared to 1.25-mm AV-50, 1.25-mm DLIR-M and DLIR-H significantly reduced CV values (all p < 0.001) and presented lower noise and noise peak (both p < 0.001). Compared to 5-mm AV-50, 1.25-mm images had higher ERS (all p < 0.001). The difference of the peak and average spatial frequency among the four reconstructions was relatively small but statistically significant (both p < 0.001). The 1.25-mm DLIR-M images were rated higher than the 5-mm and 1.25-mm AV-50 images for diagnostic acceptability and lesion conspicuity (all P < 0.001). CONCLUSIONS: DLIR may facilitate the thinner slice thickness iodine maps in abdominal DECT for improvement of image quality, diagnostic acceptability, and lesion conspicuity.

Dual fluid silhouette in X-ray of the abdomen: a diagnostic flag for neurogenic bladder with urinary ascites.

A neonate presented with abdominal distension and decreased urinary output. X-ray revealed dual abdominal fluid condition-ascites with a distended bladder, along with vertebral anomalies. The possibility of urinary ascites and neurogenic bladder was kept, which was further confirmed on evaluation. Here, we emphasise the crucial role of abdominal X-ray as a diagnostic tool in uncovering this intricate medical puzzle. By detailing the clinical presentation, diagnostic approach and treatment strategy, the report contributes insights into the rare and complex abdominal condition.

Abdominal CT findings characteristic of Castleman disease: multi-centre review of 76 adult cases with abdominopelvic nodal involvement.

OBJECTIVE: Characterize the CT findings of abdominopelvic Castleman disease, including a new observation involving the perinodal fat. METHODS: Multi-centre search at 5 institutions yielded 76 adults (mean age, 42.1 ± 14.3 years; 38 women/38 men) meeting inclusion criteria of histopathologically proven Castleman disease with nodal involvement at abdominopelvic CT. Retrospective review of the dominant nodal mass was assessed for size, attenuation, and presence of calcification, and for prominence and soft-tissue infiltration of the perinodal fat. Hypervascular nodal enhancement was based on both subjective and objective comparison with aortic blood pool attenuation. RESULTS: Abdominal involvement was unicentric in 48.7% (37/76) and multicentric in 51.3% (39/76), including 31 cases with extra-abdominal involvement. Histopathologic subtypes included hyaline vascular variant (HVV), plasma cell variant (PCV), mixed HVV/PCV, and HHV-8 variant in 39, 25, 3 and 9 cases, respectively. The dominant nodal mass measured 4.4 ± 1.9 cm and 3.2 ± 1.7 cm in mean long- and short-axis, respectively, and appeared hypervascular in 58.6% (41/70 with IV contrast). Internal calcification was seen in 22.4% (17/76). Infiltration of the perinodal fat, with or without hypertrophy, was present in 56.6% (43/76), more frequent with hypervascular vs non-hypervascular nodal masses (80.5% vs 20.7%; P < .001). Among HVV cases, 76.9% were unicentric, 71.1% appeared hypervascular, and 69.2% demonstrated perinodal fat infiltration. CONCLUSION: Hypervascular nodal masses demonstrating prominence and infiltration of perinodal fat at CT can suggest the specific diagnosis of Castleman disease, especially the HVV. ADVANCES IN KNOWLEDGE: Abdominopelvic nodal masses that demonstrate hypervascular enhancement and prominent infiltration of the perinodal fat at CT can suggest the diagnosis of Castleman disease, but nonetheless requires tissue sampling.

Low-iodine-dose computed tomography coupled with an artificial intelligence-based contrast-boosting technique in children: a retrospective study on comparison with conventional-iodine-dose computed tomography.

BACKGROUND: Low-iodine-dose computed tomography (CT) protocols have emerged to mitigate the risks associated with contrast injection, often resulting in decreased image quality. OBJECTIVE: To evaluate the image quality of low-iodine-dose CT combined with an artificial intelligence (AI)-based contrast-boosting technique in abdominal CT, compared to a standard-iodine-dose protocol in children. MATERIALS AND METHODS: This single-center retrospective study included 35 pediatric patients (mean age 9.2 years, range 1-17 years) who underwent sequential abdominal CT scans-one with a standard-iodine-dose protocol (standard-dose group, Iobitridol 350 mgI/mL) and another with a low-iodine-dose protocol (low-dose group, Iohexol 240 mgI/mL)-within a 4-month interval from January 2022 to July 2022. The low-iodine CT protocol was reconstructed using an AI-based contrast-boosting technique (contrast-boosted group). Quantitative and qualitative parameters were measured in the three groups. For qualitative parameters, interobserver agreement was assessed using the intraclass correlation coefficient, and mean values were employed for subsequent analyses. For quantitative analysis of the three groups, repeated measures one-way analysis of variance with post hoc pairwise analysis was used. For qualitative analysis, the Friedman test followed by post hoc pairwise analysis was used. Paired t-tests were employed to compare radiation dose and iodine uptake between the standard- and low-dose groups. RESULTS: The standard-dose group exhibited higher attenuation, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) of organs and vessels compared to the low-dose group (all P-values < 0.05 except for liver SNR, P = 0.12). However, noise levels did not differ between the standard- and low-dose groups (P = 0.86). The contrast-boosted group had increased attenuation, CNR, and SNR of organs and vessels, and reduced noise compared with the low-dose group (all P < 0.05). The contrast-boosted group showed no differences in attenuation, CNR, and SNR of organs and vessels (all P > 0.05), and lower noise (P = 0.002), than the standard-dose group. In qualitative analysis, the contrast-boosted group did not differ regarding vessel enhancement and lesion conspicuity (P > 0.05) but had lower noise (P < 0.05) and higher organ enhancement and artifacts (all P < 0.05) than the standard-dose group. While iodine uptake was significantly reduced in low-iodine-dose CT (P < 0.001), there was no difference in radiation dose between standard- and low-iodine-dose CT (all P > 0.05). CONCLUSION: Low-iodine-dose abdominal CT, combined with an AI-based contrast-boosting technique exhibited comparable organ and vessel enhancement, as well as lesion conspicuity compared to standard-iodine-dose CT in children. Moreover, image noise decreased in the contrast-boosted group, albeit with an increase in artifacts.

Generation of virtual monoenergetic images at 40 keV of the upper abdomen and image quality evaluation based on generative adversarial networks.

BACKGROUND: Abdominal CT scans are vital for diagnosing abdominal diseases but have limitations in tissue analysis and soft tissue detection. Dual-energy CT (DECT) can improve these issues by offering low keV virtual monoenergetic images (VMI), enhancing lesion detection and tissue characterization. However, its cost limits widespread use. PURPOSE: To develop a model that converts conventional images (CI) into generative virtual monoenergetic images at 40 keV (Gen-VMI40keV) of the upper abdomen CT scan. METHODS: Totally 444 patients who underwent upper abdominal spectral contrast-enhanced CT were enrolled and assigned to the training and validation datasets (7:3). Then, 40-keV portal-vein virtual monoenergetic (VMI40keV) and CI, generated from spectral CT scans, served as target and source images. These images were employed to build and train a CI-VMI40keV model. Indexes such as Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity (SSIM) were utilized to determine the best generator mode. An additional 198 cases were divided into three test groups, including Group 1 (58 cases with visible abnormalities), Group 2 (40 cases with hepatocellular carcinoma [HCC]) and Group 3 (100 cases from a publicly available HCC dataset). Both subjective and objective evaluations were performed. Comparisons, correlation analyses and Bland-Altman plot analyses were performed. RESULTS: The 192nd iteration produced the best generator mode (lower MAE and highest PSNR and SSIM). In the Test groups (1 and 2), both VMI40keV and Gen-VMI40keV significantly improved CT values, as well as SNR and CNR, for all organs compared to CI. Significant positive correlations for objective indexes were found between Gen-VMI40keV and VMI40keV in various organs and lesions. Bland-Altman analysis showed that the differences between both imaging types mostly fell within the 95% confidence interval. Pearson's and Spearman's correlation coefficients for objective scores between Gen-VMI40keV and VMI40keV in Groups 1 and 2 ranged from 0.645 to 0.980. In Group 3, Gen-VMI40keV yielded significantly higher CT values for HCC (220.5HU vs. 109.1HU) and liver (220.0HU vs. 112.8HU) compared to CI (p < 0.01). The CNR for HCC/liver was also significantly higher in Gen-VMI40keV (2.0 vs. 1.2) than in CI (p < 0.01). Additionally, Gen-VMI40keV was subjectively evaluated to have a higher image quality compared to CI. CONCLUSION: CI-VMI40keV model can generate Gen-VMI40keV from conventional CT scan, closely resembling VMI40keV.

Optimization of smoothing parameter for block matching and 3D filtering algorithm in low-dose chest and abdominal computed tomography images.

Computed tomography (CT), known for its exceptionally high accuracy, is associated with a substantial dose of ionizing radiation. Low-dose protocols have been devised to address this issue; however, a reduction in the radiation dose can lead to a deficiency in the number of photons, resulting in quantum noise. Thus, the aim of this study was to optimize the smoothing parameter (σ-value) of the block matching and 3D filtering (BM3D) algorithm to effectively reduce noise in low-dose chest and abdominal CT images. Acquired images were subsequently analyze using quantitative evaluation metrics, including contrast to noise ratio (CNR), coefficient of variation (CV), and naturalness image quality evaluator (NIQE). Quantitative evaluation results demonstrated that the optimal σ-value for CNR, CV, and NIQE were 0.10, 0.11, and 0.09 in low-dose chest CT images respectively, whereas those in abdominal images were 0.12, 0.11, and 0.09, respectively. The average of the optimal σ-values, which produced the most improved results, was 0.10, considering both visual and quantitative evaluations. In conclusion, we demonstrated that the optimized BM3D algorithm with σ-value is effective for noise reduction in low-dose chest and abdominal CT images indicating its feasibility of in the clinical field.

Effect of arm position on image quality and radiation dose during thorax and abdomen computed tomography scans.

INTRODUCTION: During Computed Tomography (CT) scans of the Thorax-Abdomen-Pelvis (TAP) the patient's arms should be positioned above the head to obtain optimal image quality and expose the patient to the lowest possible radiation dose. This may be impossible with patients with shoulder problems leading to arms being positioned in other ways. This study aimed to investigate differences in objective image quality and estimated effective dose (E), when positioning the arms below shoulder level in CT-TAP. METHODS: An anthropomorphic phantom with cadaver arms was used. Four arm positions were tested: Along the torso (A), on the pelvis (B), on a pillow on the pelvis (C), and one arm on pillow on the pelvis and the other arm on the pelvis (D). A Siemens SOMATOM Definition Flash CT-scanner with CareDose 4D was used. The dose length product was read to estimate E. Image quality was assessed objectively by measuring noise within the region of interest in the liver and urinary bladder. RESULTS: Significant differences in E between all arm positions were seen (p = 0.005). The lowest E was obtained in position C, reducing E by 8.42%. Position A provided the best image quality but the highest E. CONCLUSION: This study showed no unequivocal optimal positioning of arms in CT-TAP. Position A provided the best object image quality, while position C yielded the lowest E. These results may impact the planning of diagnostic CT where positioning of arms may influence optimal image quality and radiation dose. IMPLICATION FOR PRACTICE: This study illustrates tendencies for objective image quality and E when arms are positioned below shoulder level. Further research is needed to assess the clinical relevance with the diagnostic potential.

Use of a 3D camera for automated patient positioning for chest-abdomen-pelvis CT scans: Effect on positioning accuracy and patient dose.

INTRODUCTION: 3D positioning cameras that automate the positioning of patients with respect to the CT isocentre have been developed and are in common use in CT departments. This study aimed to compare the performance of radiographers and a 3D camera system with respect to positioning accuracy and the effect on patient radiation dose for chest-abdomen-pelvis scans. METHODS: Patient positioning and dose data obtained from a dose management system was evaluated over a two-month period for patients positioned with (CAMon) and without (CAMoff) the positioning camera. Median vertical and lateral offset values were compared between the groups whilst doses were evaluated as a function of patient water equivalent diameter (WED) for the thorax and abdomen-pelvis acquisitions for both cohorts. RESULTS: Radiographers demonstrated high levels of positioning accuracy, however significant improvements in median vertical offset were identified for the CAMon cohort for both thorax (8 mm vs. 17 mm (p = 0.001)) and abdomen-pelvis (7 mm vs. 16 mm (p = 0.003)) scans. The percentage of patients positioned within 5 mm of the isocentre was 39.0% and 16.1% for the CAMon and CAMoff cohorts. For CAMoff scans, 77.4% of patients were positioned below the isocentre, but this was reduced to 45.8% for CAMon scans. No significant changes in dose as a function of WED were identified related to the camera use (thorax: p = 0.569, abdomen-pelvis: p = 0.760). CONCLUSION: Use of a 3D camera delivered significant improvements in the accuracy and reproducibility of patient positioning when compared with radiographers. IMPLICATIONS FOR PRACTICE: Improvements in positioning accuracy were observed at the research site and hence positioning camera use has the potential to become standard practice in CT to help ensure appropriate doses are delivered to patients according to their size.

The impact of X-ray scatter correction software on abdomen radiography in terms of image quality and radiation dose.

INTRODUCTION: The conventional anti-scatter grid is widely used in X-ray radiography to reduce scattered X-rays, but it increases patient dose. Scatter-correction software offers a dose-reducing alternative by correcting for scattered X-rays without a physical grid. Grids and software correction are necessary to reduce scatter radiation and improve image quality especially for the large body parts. The scatter correction can be beneficial in situations where the use of grid is challenging. The implementation of grids and advanced software correction techniques is imperative to ensure that radiographic images maintain high levels of clarity, contrast, and resolution, and ultimately facilitating more accurate diagnoses. This study compares image quality and radiation dose for abdomen exams using scatter correction software and physical grids. METHODS: An anthropomorphic phantom (abdomen) underwent imaging with varying fat and lean tissue layers and body mass index (BMI) configurations. Imaging parameters included 70 kVp tube voltage, 110 cm SID, and Automatic Exposure Control (AEC) both lateral and central chambers. AP abdomen X-ray projections were acquired with and without an anti-scatter grid, and scatter correction software was applied. Image quality was assessed using contrast to noise ratio (CNR) and signal to noise ratio (SNR) metrics. The tube current mAs was considered an exposure factor that affected radiation dose and was used to compare the VG software and physical grid. Radiation dose was measured using Dose Area Products (DAP). The effective dose was estimated using Monte Carlo simulation-PCXMC software. Paired t-tests were used to investigate the image quality difference between the Gridless and VG software, Gridless and PG, and VG software and PG approaches. For the DAP and effective dose, paired t-test was used to investigate the difference between VG software and PG. RESULTS: Images acquired with a grid had the highest mean CNR (71.3 ± 32) compared to Gridless (50 ± 33.8) and scatter correction software (59.3 ± 37.9). The mean SNR of the grid images was (82.7.3 ± 38.9), which is 18% higher than the scatter correction software images (70.4 ± 36.7) and 29% higher than in the Gridless images (62.9.3 ± 34). The mean DAP value was reduced by 81% when the scatter correction software was used compared to the grid (mean: 65.4 µGy.m2 and 338.2 µGy.m2, respectively) with a significant difference (p = 0.001). Scatter correction software resulted in a lower effective dose compared to physical grid use, (mean difference± SD = -0.3 ± 0.18 mSv) with a significant difference (P = 0.02). CONCLUSION: Scatter correction software reduced the radiation dose required but images employing a grid yielded higher CNR and SNR. However, the radiation dose reduction might affect the image quality to a level that impacts the diagnostic information available. Thus, further research needs to be conducted to optimise the use of the scatter correction software. IMPLICATION FOR PRACTICE: Objectively, X-ray scatter correction software might be promising in conditions where a grid cannot be applied.

Optimizing CT Abdomen-Pelvis Scan Radiation Dose: Examining the Role of Body Metrics (Waist Circumference, Hip Circumference, Abdominal Fat, and Body Mass Index) in Dose Efficiency.

Objective: This study investigates the correlation between patient body metrics and radiation dose in abdominopelvic CT scans, aiming to identify significant predictors of radiation exposure. Methods: Employing a cross-sectional analysis of patient data, including BMI, abdominal fat, waist, abdomen, and hip circumference, we analyzed their relationship with the following dose metrics: the CTDIvol, DLP, and SSDE. Results: Results from the analysis of various body measurements revealed that BMI, abdominal fat, and waist circumference are strongly correlated with increased radiation doses. Notably, the SSDE, as a more patient-centric dose metric, showed significant positive correlations, especially with waist circumference, suggesting its potential as a key predictor for optimizing radiation doses. Conclusions: The findings suggest that incorporating patient-specific body metrics into CT dosimetry could enhance personalized care and radiation safety. Conclusively, this study highlights the necessity for tailored imaging protocols based on individual body metrics to optimize radiation exposure, encouraging further research into predictive models and the integration of these metrics into clinical practice for improved patient management.

Active learning using adaptable task-based prioritisation.

Supervised machine learning-based medical image computing applications necessitate expert label curation, while unlabelled image data might be relatively abundant. Active learning methods aim to prioritise a subset of available image data for expert annotation, for label-efficient model training. We develop a controller neural network that measures priority of images in a sequence of batches, as in batch-mode active learning, for multi-class segmentation tasks. The controller is optimised by rewarding positive task-specific performance gain, within a Markov decision process (MDP) environment that also optimises the task predictor. In this work, the task predictor is a segmentation network. A meta-reinforcement learning algorithm is proposed with multiple MDPs, such that the pre-trained controller can be adapted to a new MDP that contains data from different institutes and/or requires segmentation of different organs or structures within the abdomen. We present experimental results using multiple CT datasets from more than one thousand patients, with segmentation tasks of nine different abdominal organs, to demonstrate the efficacy of the learnt prioritisation controller function and its cross-institute and cross-organ adaptability. We show that the proposed adaptable prioritisation metric yields converging segmentation accuracy for a new kidney segmentation task, unseen in training, using between approximately 40% to 60% of labels otherwise required with other heuristic or random prioritisation metrics. For clinical datasets of limited size, the proposed adaptable prioritisation offers a performance improvement of 22.6% and 10.2% in Dice score, for tasks of kidney and liver vessel segmentation, respectively, compared to random prioritisation and alternative active sampling strategies.

An investigation into augmentation and preprocessing for optimising X-ray classification in limited datasets: a case study on necrotising enterocolitis.

PURPOSE: Obtaining large volumes of medical images, required for deep learning development, can be challenging in rare pathologies. Image augmentation and preprocessing offer viable solutions. This work explores the case of necrotising enterocolitis (NEC), a rare but life-threatening condition affecting premature neonates, with challenging radiological diagnosis. We investigate data augmentation and preprocessing techniques and propose two optimised pipelines for developing reliable computer-aided diagnosis models on a limited NEC dataset. METHODS: We present a NEC dataset of 1090 Abdominal X-rays (AXRs) from 364 patients and investigate the effect of geometric augmentations, colour scheme augmentations and their combination for NEC classification based on the ResNet-50 backbone. We introduce two pipelines based on colour contrast and edge enhancement, to increase the visibility of subtle, difficult-to-identify, critical NEC findings on AXRs and achieve robust accuracy in a challenging three-class NEC classification task. RESULTS: Our results show that geometric augmentations improve performance, with Translation achieving +6.2%, while Flipping and Occlusion decrease performance. Colour augmentations, like Equalisation, yield modest improvements. The proposed Pr-1 and Pr-2 pipelines enhance model accuracy by +2.4% and +1.7%, respectively. Combining Pr-1/Pr-2 with geometric augmentation, we achieve a maximum performance increase of 7.1%, achieving robust NEC classification. CONCLUSION: Based on an extensive validation of preprocessing and augmentation techniques, our work showcases the previously unreported potential of image preprocessing in AXR classification tasks with limited datasets. Our findings can be extended to other medical tasks for designing reliable classifier models with limited X-ray datasets. Ultimately, we also provide a benchmark for automated NEC detection and classification from AXRs.

Robustness of radiomic features in healthy abdominal parenchyma of patients with repeated examinations on dual-layer dual-energy CT.

OBJECTIVES: Robustness of radiomic features in physiological tissue is an important prerequisite for quantitative analysis of tumor biology and response assessment. In contrast to previous studies which focused on different tumors with mostly short scan-re-scan intervals, this study aimed to evaluate the robustness of radiomic features in cancer-free patients and over a clinically encountered inter-scan interval. MATERIALS AND METHODS: Patients without visible tumor burden who underwent at least two portal-venous phase dual energy CT examinations of the abdomen between May 2016 and January 2020 were included, while macroscopic tumor burden was excluded based upon follow-up imaging for all patients (≥3 months). Further, patients were excluded if no follow-up imaging was available, or if the CT protocol showed deviations between repeated examinations. Circular regions of interest were placed and proofread by two board-certified radiologists (4 years and 5 years experience) within the liver (segments 3 and 6), the psoas muscle (left and right), the pancreatic head, and the spleen to obtain radiomic features from normal-appearing organ parenchyma using PyRadiomics. Radiomic feature robustness was tested using the concordance correlation coefficient with a threshold of 0.75 considered indicative for deeming a feature robust. RESULTS: In total, 160 patients with 480 repeated abdominal CT examinations (range: 2-4 per patient) were retrospectively included in this single-center, IRB-approved study. Considering all organs and feature categories, only 4.58 % (25/546) of all features were robust with the highest rate being found in the first order feature category (20.37 %, 22/108). Other feature categories (grey level co-occurrence matrix, grey level dependence matrix, grey level run length matrix, grey level size zone matrix, and neighborhood gray-tone difference matrix) yielded an overall low percentage of robust features (range: 0.00 %-1.19 %). A subgroup analysis revealed the reconstructed field of view and the X-ray tube current as determinants of feature robustness (significant differences in subgroups for all organs, p < 0.001) as well as the size of the region of interest (no significant difference for the pancreatic head with p = 0.135, significant difference with p < 0.001 for all other organs). CONCLUSION: Radiomic feature robustness obtained from cancer-free subjects with repeated examinations using a consistent protocol and CT scanner was limited, with first order features yielding the highest proportion of robust features.

CNN application for automated determination of the patient's size to obtain the size-specific dose estimated in CT.

Introduction. The currently available dosimetry techniques in computed tomography can be inaccurate which overestimate the absorbed dose. Therefore, we aimed to provide an automated and fast methodology to more accurately calculate the SSDE usingDwobtained by using CNN from thorax and abdominal CT study images.Methods. The SSDE was determined from the 200 records files. For that purpose, patients' size was measured in two ways: (a) by developing an algorithm following the AAPM Report No. 204 methodology; and (b) using a CNN according to AAPM Report No. 220.Results. The patient's size measured by the in-house software in the region of thorax and abdomen was 27.63 ± 3.23 cm and 28.66 ± 3.37 cm, while CNN was 18.90 ± 2.6 cm and 21.77 ± 2.45 cm. The SSDE in thorax according to 204 and 220 reports were 17.26 ± 2.81 mGy and 23.70 ± 2.96 mGy for women and 17.08 ± 2.09 mGy and 23.47 ± 2.34 mGy for men. In abdomen was 18.54 ± 2.25 mGy and 23.40 ± 1.88 mGy in women and 18.37 ± 2.31 mGy and 23.84 ± 2.36 mGy in men.Conclusions. Implementing CNN-based automated methodologies can contribute to fast and accurate dose calculations, thereby improving patient-specific radiation safety in clinical practice.