Hounsfield Units Research Articles

Deep learning for opportunistic, end-to-end automated assessment of epicardial adipose tissue in pre-interventional, ECG-gated spiral computed tomography.

Recently, epicardial adipose tissue (EAT) assessed by CT was identified as an independent mortality predictor in patients with various cardiac diseases. Our goal was to develop a deep learning pipeline for robust automatic EAT assessment in CT. Contrast-enhanced ECG-gated cardiac and thoraco-abdominal spiral CT imaging from 1502 patients undergoing transcatheter aortic valve replacement (TAVR) was included. Slice selection at aortic valve (AV)-level and EAT segmentation were performed manually as ground truth. For slice extraction, two approaches were compared: A regression model with a 2D convolutional neural network (CNN) and a 3DCNN utilizing reinforcement learning (RL). Performance evaluation was based on mean absolute z-deviation to the manually selected AV-level (Δz). For tissue segmentation, a 2D U-Net was trained on single-slice images at AV-level and compared to the open-source body and organ analysis (BOA) framework using Dice score. Superior methods were selected for end-to-end evaluation, where mean absolute difference (MAD) of EAT area and tissue density were compared. 95% confidence intervals (CI) were assessed for all metrics. Slice extraction using RL was slightly more precise (Δz: RL 1.8 mm (95% CI: [1.6, 2.0]), 2DCNN 2.0 mm (95% CI: [1.8, 2.3])). For EAT segmentation at AV-level, the 2D U-Net outperformed BOA significantly (Dice score: 2D U-Net 91.3% (95% CI: [90.7, 91.8]), BOA 85.6% (95% CI: [84.7, 86.5])). The end-to-end evaluation revealed high agreement between automatic and manual measurements of EAT (MAD area: 1.1 cm2 (95% CI: [1.0, 1.3]), MAD density: 2.2 Hounsfield units (95% CI: [2.0, 2.5])). We propose a method for robust automatic EAT assessment in spiral CT scans enabling opportunistic evaluation in clinical routine. Since inflammatory changes in epicardial adipose tissue (EAT)are associated with an increased risk of cardiac diseases, automated evaluation can serve as a basis for developing automated cardiac risk assessment tools, which are essential for efficient, large-scale assessment in opportunistic settings. Deep learning methods for automatic assessment of epicardial adipose tissue (EAT) have great potential. A 2-step approach with slice extraction and tissue segmentation enables robust automated evaluation of EAT. End-to-end automation enables large-scale research on the value of EAT for outcome analysis.

Comparative effectiveness of four techniques for identifying vertebral fragility fractures among elderly patients.

The incidence of vertebral fragile fractures peaked among the elderly population, and identifying individuals at high risk of vertebral fractures and promptly instituting preventions are of critical importance. This study aims to determine the efficacy and values of Hounsfield unit (HU) values, vertebral bone quality (VBQ) scores, bone mineral density (BMD) measured by dual-energy X-ray absorptiometry (DXA), and quantitative computed tomography (QCT) to discriminate between patients with and without vertebral fractures. A thorough search was conducted across PubMed, EMBASE, Cochrane Library, Web of Science, CNKI, and Wan Fang Database to identify potential studies that met the eligibility criteria. Studies that evaluated the utility of HU values, VBQ scores, QCT-measured BMD, and DXA-measured BMD in discriminating vertebral fractures were qualified. The combined results showed that there were significant differences in HU values, VBQ scores, QCT-measured BMD, and DXA-measured BMD between the fracture and non-fracture groups. Moreover, the pooled sensitivity, specificity, and AUC of HU values were 0.82, 0.67, and 0.76, respectively; the pooled sensitivity, specificity, and AUC of VBQ scores were 0.70, 0.75, and 0.78; the pooled sensitivity, specificity, and AUC of QCT-measured BMD were 0.85, 0.76 and 0.88. All four methods, namely HU values, VBQ scores, QCT-measured BMD, and DXA-measured BMD can effectively distinguish between patients with and without vertebral fragile fractures. Among these, QCT-measured BMD exhibited a relatively high efficacy in discriminating vertebral fractures. VBQ scores and HU values demonstrated comparable efficacy for discriminating vertebral fractures among elderly patients. Question Can four different imaging modalities effectively discriminate vertebral fragility fracture status among elderly patients? Findings These methods can effectively distinguish vertebral fractures status among elderly patients, and quantitative computed tomography (QCT)-measured bone mineral density (BMD) exhibited a relatively high efficacy. Clinical relevance The clinical applications of Hounsfield unit values, vertebral bone quality scores, and BMD measured by dual-energy X-ray absorptiometry and QCT show promising outcomes in identifying individuals at high risk of vertebral fractures.

Dual-energy CT for differentiation of hypodense liver lesions in pancreatic adenocarcinoma.

To assess the accuracy of CT spectral HU curve assessment of hypodense liver lesions. In this retrospective HIPAA-compliant study (January 2016 through May 2023), patients with biopsy-proven pancreatic adenocarcinoma and a biopsied indeterminate liver lesion underwent a DECT abdominal CT scan. Spectral HU curves were provided for each hypodense liver lesion, and slopes were calculated. Lesion Hounsfield units, iodine concentration and virtual enhancement were recorded. The Wilcoxon rank sum test was used to compare malignant and benign lesions. Optimal cutoff points were estimated using ROC curves and Youden's Index. Thirty-six patients (19 men, 17 women) with a mean age of 63 years ± 9 (standard deviation), a mean height of 170.9 cm ± 9.5, a mean weight of 69.8 kg ± 14.5, and a body mass index of 23.9 kg/m2 ± 3.5. Reference standard assessment identified 92 liver lesions (50 metastases, 24 cysts, 13 abscesses, 3 regions of inflammation, 2 hemangiomas) with a mean size of 1.1 cm ± 0.5. The mean interval between the CT and liver lesion biopsy was 24 days. A diagnosis of benign versus malignant was determined based on optimal cutoffs: spectral curve slope of 1.36, iodine concentration of 6.47 (100 µg/cm3), and enhancement of 10.25. The receiver operating curves (ROC) for diagnosis using spectral curve slope, iodine concentration, and virtual enhancement resulted in an area under the curve (AUC) of 0.948, 0.946, and 0.937, respectively. Spectral HU curves and iodine concentration of well-defined hypodense liver lesions are highly accurate in the diagnosis of benign versus malignant lesions. Question Limited evidence exists for spectral imaging diagnosis of liver lesions-can DECT accurately differentiate between benign and metastatic hypodense liver lesions? Findings Ninety-two hypodense liver lesions evaluated using HU keV curve slope, iodine concentration, and virtual enhancement resulted in accurate benign versus metastatic differentiation. Clinical relevance Hypodense liver lesions are a challenging issue at staging, often requiring further imaging, follow-up, and/or biopsy. The additional information from multi-energy CT can be useful to differentiate between benign and malignant lesions, thereby reducing the need for costly additional evaluation.

Opportunistic Screening for Osteoporosis by CT as Compared with DXA

Background: Osteoporosis is commonly evaluated using dual-energy X-ray absorptiometry (DXA) for bone mineral density (BMD). Non-contrast computed tomography (CT) scans provide an alternative for opportunistic osteoporosis assessment. This study aimed to evaluate screening thresholds for osteoporosis based on CT attenuation values in Hounsfield units (HU) of L1–L4 vertebrae from CT scans of the abdominal region, compared to DXA assessments of the lumbar spine and hips. Methods: Conducted retrospectively over approximately two years, the analysis included 109 patients who had both CT and DXA scans within 12 months, excluding those with metal artifacts affecting the vertebrae. CT attenuation values in the trabecular region of the vertebrae were measured and compared among three groups based on the lowest T-score from DXA. Results: In a predominantly female cohort (mean age 66.3 years), the lowest CT attenuation values for L1–L4 vertebrae showed a moderate correlation with the lowest T-score, with a Pearson correlation coefficient of 0.542 (95% CI: 0.388, 0.667). A HU threshold of ≤142 at the L1 vertebra showed 91.9% sensitivity and 48.4% specificity, while a threshold of ≤160 HU showed 97.3% sensitivity and 31.3% specificity for screening osteoporosis. Conclusions: This study supports the use of non-contrast CT with these HU thresholds as an opportunistic tool for osteoporosis assessment.

Comparison of Water-Equivalent and Effective Diameter-Based Size-Specific Dose Estimates in Computed Tomography Pulmonary Angiography: Implications for Radiation Dose Optimization

This retrospective study investigates dose estimation in computed tomography pulmonary angiography (CTPA) by analyzing 200 examinations from 96 male and 104 female patients. Two size-specific dose estimation (SSDE) methods were evaluated: one based on effective diameter (SSDEDeff) and the other on water-equivalent diameter (SSDEDw). The effective diameter (Deff) was manually measured using the lateral (LAT) and anteroposterior (AP) dimensions, while the water-equivalent diameter (Dw) was calculated from the region of interest (ROI) using mean Hounsfield Unit (HU) values and cross-sectional area. SSDE calculations were compared to the conventional CT dose metric, CTDIvol, to assess its underestimation relative to SSDEDw. The findings revealed a dose underestimation of up to 18% for CTDIvol compared to SSDEDw, particularly for patients with larger body habitus. SSDEDeff consistently overestimated the dose by 7.73% compared to SSDEDw, as Deff relied only on external dimensions while Dw considered tissue attenuation for a more individualized dose assessment. High correlations were observed between lateral diameter (dLAT) and both Deff (R2 = 0.9175) and Dw (R2 = 0.7578). However, the systematic overestimation by SSDEDeff emphasizes the importance of clearly specifying the metric used for SSDE, as differences can influence reported doses by 5-10%, affecting clinical dose monitoring and adherence to diagnostic reference levels. This study highlights the limitations of CTDIvol and SSDEDeff compared to SSDEDw, especially in regions with heterogeneous tissue. The results support the use of individualized radiation dose assessments to improve radiation safety, particularly in CTPA, which has seen increased application during the COVID-19 pandemic.

Accuracy of the Hounsfield Unit Values Measured by Implant Planning Software

Background: The measurement of Hounsfield units (HU) during implant treatment planning is important. Currently, various manufacturers’ implant planning software programs offer HU capabilities; however, their accuracy remains unverified. In this study, we aimed to validate the accuracy of HU values measured by implant planning software programs. Methods: This study used one type of multidetector computed tomography (MDCT), two types of cone–beam computed tomography (CBCT), and four implant planning software packages. Three specimens were prepared for the evaluation of HUs, and the standard values of the HUs were measured. Digital Imaging and Communications in Medicine (DICOM) data obtained from MDCT and CBCT were loaded into four implant planning software packages to measure the HU values. The HU and reference values of the four implant planning software programs obtained from MDCT and CBCT were compared. Additionally, the HU values between each implant planning software program were compared. Results: The HU values of the three specimens, as measured using the four implant planning software programs utilizing MDCT, did not exhibit a significant difference from the standard values. Conversely, those obtained from CBCT were significantly different. The measured HU values after the MDCT imaging of the specimens were not significantly different between the implant planning software programs; however, they differed after CBCT imaging. Conclusions: The results of this study indicate that it is not possible to measure HU values using CBCT with implant planning software programs. However, HU values can be measured by any implant planning software using MDCT.

Toward Closing the Loop in Image-to-Image Conversion in Radiotherapy: A Quality Control Tool to Predict Synthetic Computed Tomography Hounsfield Unit Accuracy

In recent years, synthetic Computed Tomography (CT) images generated from Magnetic Resonance (MR) or Cone Beam Computed Tomography (CBCT) acquisitions have been shown to be comparable to real CT images in terms of dose computation for radiotherapy simulation. However, until now, there has been no independent strategy to assess the quality of each synthetic image in the absence of ground truth. In this work, we propose a Deep Learning (DL)-based framework to predict the accuracy of synthetic CT in terms of Mean Absolute Error (MAE) without the need for a ground truth (GT). The proposed algorithm generates a volumetric map as an output, informing clinicians of the predicted MAE slice-by-slice. A cascading multi-model architecture was used to deal with the complexity of the MAE prediction task. The workflow was trained and tested on two cohorts of head and neck cancer patients with different imaging modalities: 27 MR scans and 33 CBCT. The algorithm evaluation revealed an accurate HU prediction (a median absolute prediction deviation equal to 4 HU for CBCT-based synthetic CTs and 6 HU for MR-based synthetic CTs), with discrepancies that do not affect the clinical decisions made on the basis of the proposed estimation. The workflow exhibited no systematic error in MAE prediction. This work represents a proof of concept about the feasibility of synthetic CT evaluation in daily clinical practice, and it paves the way for future patient-specific quality assessment strategies.

Challenges in developing response evaluation criteria for peptide receptor radionuclide therapy: A consensus report from the European Neuroendocrine Tumor Society Advisory Board Meeting 2022 and the ENETS Theranostics Task Force.

Assessing the response to systemic therapy in neuroendocrine tumors (NET) is challenging since morphological imaging response is often delayed and not necessarily reflective of clinical benefit. Peptide receptor radionuclide therapy (PRRT) has a complex mechanism of action, further complicating response assessment. In response to these challenges, the European Neuroendocrine Tumor Society (ENETS) Theranostics Task Force conducted a statement-based survey among experts to identify the current landscape and unmet needs in PRRT response assessment. The survey, presented at the 2022 ENETS Advisory Board (AB) meeting in Vienna, was completed by 70% of AB members, most of whom (81%) were from ENETS Centers of Excellence (CoE). It comprised a set of 13 questions with two substatements in three questions. Six (46%) of the statements achieved more than 75% agreement, while five (39%) additional statements reached over 60% consensus. Key points from the survey include: AB members agreed that lesions deemed equivocal on computed tomography (CT) or magnetic resonance imaging (MRI) should be characterized by somatostatin receptor (SST) positron emission tomography (PET)/CT before being designated as target lesions. It was agreed that interim response assessments should occur after the second or third PRRT cycle. Over half (54%) preferred using both conventional cross-sectional imaging (CT and/or MRI) and hybrid imaging (SST PET/CT) for this purpose. Almost all AB members supported further response assessment 3 months after the final PRRT cycle. A majority (62%) preferred using a combination of conventional cross-sectional imaging and SST PET/CT. For cases showing equivocal progression (ambiguous lesions or nontarget lesions) on CT and/or MRI, further confirmation using SST PET/CT was recommended. A significant majority (74%) preferred assessing pseudo-progression and delayed response by combining SST PET with diagnostic CT and/ or MRI. Though just below the 75% consensus threshold, there was substantial agreement on selecting target lesions based on SST PET/CT uptake intensity and homogeneity. Sixty-nine percent noted the importance of documenting and closely following heterogeneity in lesions in liver, lymph nodes, primary tumors, or other organs. As to the statement on parameters for new response criteria, AB members recommended exploring maximum standard unit value, tumor-to-background ratio, Hounsfield Unit (Choi Criteria), total tumor burden, and novel serum or molecular markers for future response evaluation criteria. Sixty-five percent supported the use of a single SST PET/CT for response assessment of NET lesions treated with PRRT. These findings highlight the importance of integrating advanced imaging techniques and recognizing the need for more nuanced criteria in assessing the efficacy of PRRT in NET patients. This approach aims to enhance the accuracy of treatment monitoring and improve patient outcomes.

Dual virtual non-contrast imaging: a Bayesian quantitative approach to determine radiotherapy quantities from contrast-enhanced DECT images

Objective.Contrast agents in computed tomography (CT) scans can compromise the accuracy of dose calculations in radiation therapy planning, especially for particle therapy. This often requires an additional non-contrast CT scan, increasing radiation exposure and introducing potential registration errors. Our goal is to resolve these issues by accurately estimating radiotherapy parameters from dual virtual non-contrast (dual-VNC) images generated by contrast-enhanced dual-energy CT (DECT) scans, while accounting for noise and variability in tissue composition.Approach.A new Bayesian model is introduced to estimate dual-VNC Hounsfield units from contrast-enhanced DECT data. The model defines a prior distribution that describes tissue variations in terms of elemental compositions and mass densities. Multiple reference tissues are used to estimate variations across human tissues. A likelihood distribution is also defined to model the noise contained in CT data. The model is thoroughly validated in a simulated environment including 12 virtual patients under low and high iodine uptake scenarios, while incorporating noise and beam hardening effects. The eigentissue decomposition technique is used to derive elemental compositions and parameters critical for radiotherapy from the dual-VNC images, such as electron density (ρe), particle stopping power (SPR), and photon energy absorption coefficient (EAC).Main results.The proposed method yields accurate voxelwise estimations forρe, SPR, and EAC, with root mean square errors of 3.09%, 3.14%, and 1.34% for highly-enhanced tissues, compared to 5.93%, 6.39%, and 17.11% when the presence of contrast agent is ignored. It also demonstrates robustness to systematic shifts in tissue composition and bandwidth variations in the prior distribution, resulting in overall uncertainties down to 1.13%, 1.33%, and 0.86% forρe, SPR, and EAC in soft tissues; 1.17%, 1.32%, and 1.34% in enhanced soft tissues; and 4.34%, 4.00%, and 2.50% in bones.Significance.The proposed method accurately derives radiotherapy parameters from contrast-enhanced DECT data and demonstrates robustness against systematic errors in reference data, highlighting its potential for clinical use.

Comparing fully automated AI body composition biomarkers at differing virtual monoenergetic levels using dual-energy CT.

To investigate the behavior of artificial intelligence (AI) CT-based body composition biomarkers at different virtual monoenergetic imaging (VMI) levels using dual-energy CT (DECT). This retrospective study included 88 contrast-enhanced abdominopelvic CTs acquired with rapid-kVp switching DECT. Images were reconstructed into five VMI levels (40, 55, 70, 85, 100keV). Fully automated algorithms for quantifying CT number (HU) in abdominal fat (subcutaneous and visceral), skeletal muscle, bone, calcium (abdominal Agatston score), and organ size (area or volume) were applied. Biomarker median difference relative to 70keV and interquartile range were reported by energy level to characterize variation. Linear regression was performed to calibrate non-70keV data and to estimate their equivalent 70keV biomarker attenuation values. Relative to 70keV, absolute median differences in attenuation-based biomarkers (excluding Agatston score) ranged 39-358, 12-102, 5-48, 9-75 HU for 40, 55, 85, 100keV, respectively. For area-based biomarkers, differences ranged 6-15, 3-4, 2-7, 0-5 cm2 for 40, 55, 85, 100keV. For volume-based biomarkers, differences ranged 12-34, 8-68, 12-52, 1-57 cm3 for 40, 55, 85, 100keV. Agatston score behavior was more spurious with median differences ranging 70-204 HU. In general, VMI < 70keV showed more variation in median biomarker measurement than VMI > 70keV. This study characterized the behavior of a fully automated AI CT biomarker toolkit across varying VMI levels obtained with DECT. The data showed relatively little biomarker value change when measured at or greater than 70keV. Lower VMI datasets should be avoided due to larger deviations in measured value as compared to 70keV, a level considered equivalent to conventional 120 kVp exams.

Automated Assessment of Sarcopenia with Hounsfield Unit Average Calculation in Computed Tomography Scans Using Deep Learning Techniques

Abstract Introduction Skeletal muscle is increasingly plastic with an ability to gain or lose tissue. Depletion of muscle mass and quality occurs due to various factors such as aging, disease, and disuse. Sarcopenia can be loosely defined as a significant loss of muscle mass and function. Sarcopenia is now recognized as an independent risk factor for various patient-related negative outcomes after various surgeries. Various computed tomography (CT) based imaging indices for assessment of sarcopenia exist in practice. The psoas muscle Hounsfield unit average calculation (HUAC) has been proven to be an effective one as it is independent of patient anthropometric data, and it can be calculated in the images provided. Aim The aim of this study is to develop automated tools for estimation of the HUAC using deep learning algorithms. Materials and Methods A total of 41 abdominal CTs were used. Ground truth was established and validated by two radiologists with more than 5 and 10 years of experience each. Models were trained to identify the psoas muscle among the slices and calculate the HUAC. Results At inference, an average intersection over union (IoU) value of 90% was obtained between the deep learning model outputs and the original annotated test images for the CT slices. The Dice coefficient was 0.90 between the ground truth labels and the output from the model. Conclusion We have demonstrated the accuracy of our deep learning–based algorithm for quantifying the psoas muscle HUAC, which is a marker for sarcopenia. There is a potential for a fully automated measure to calculate the HUAC for any patient undergoing CT scan.

Predicting prognosis prior to the combination of atezolizumab and bevacizumab on unresectable HCC: Analysis and comparison of tumor heterogeneity at CT and Gd-EOB-DTPA hepatobiliary MR imaging.

Since 2007, the combination of atezolizumab and bevacizumab, comprising an immune checkpoint inhibitor and a molecularly targeted agent, has become the first-line treatment for advanced hepatocellular carcinoma (HCC). Predicting prognosis prior to systemic chemotherapy remains a critical concern. This study included 84 advanced HCC patients who underwent enhanced computed tomography (CT) and Gd-EOB-DTPA magnetic resonance imaging (MRI) before the systemic therapy were included. In CT, the 2 radiologists measured mean CT Hounsfield unit (CTHU) value by drawing region of interest at the largest diameter of the tumor on arterial phage. The HU values were categorized into 5 groups: ≤ 0, 0 < HU ≤ 50, 50 < HU ≤ 100, 100 < HU ≤ 150, and HU > 150. The percentage of the entire tumor in each category was calculated. On MRI, hepatobiliary phase imaging features and relative enhancement ratio (RER) were also evaluated by 2 radiologists. Prognostic factors associated with progression-free survival were identified using statistical analysis. RER on HBP MRI correlated with prognosis in systemic chemotherapy. Conversely, other image features on HBP MRI and CT histogram provided consistent treatment effect.

Evaluation of predictive performance for new vertebral compression fracture between Hounsfield units value and vertebral bone quality score following percutaneous vertebroplasty or kyphoplasty

Rationale and ObjectivesNew vertebral compression fractures (NVCF) are very common in patients following percutaneous vertebroplasty (PVP) or kyphoplasty (PKP). The study aims to evaluate the comparative predictive performance of vertebral bone quality (VBQ) score and Hounsfield units (HU) value in forecasting NVCF after surgery. Materials and MethodsThis study retrospectively analyzed patients who underwent PVP/PKP at our institution between 2020 and 2021. The VBQ score and HU value were obtained from preoperative magnetic resonance imaging (MRI) and computed tomography (CT) scans, respectively. Subsequently, the forecasting capabilities of these two parameters were assessed by contrasting their receiver operating characteristic (ROC) curve. ResultsA total of 303 eligible patients (56 with NVCF and 247 without) were identified in the study. Six relevant literature factors were identified and included in the multivariate analysis revealed that lower HU value (OR = 0.967, 95% CI = 0.953-0.981, P < 0.001) and higher VBQ score (OR = 3.964, 95% CI = 2.369-6.631, P < 0.001) emerged as independent predictors of NCVF occurrence. Compared to the ROC curve of the HU value, demonstrating a diagnostic accuracy of 83.2% (95% CI = 77.5%-88.9%, P < 0.001), the VBQ score was 85.8%. And, a statistically significant negative correlation was observed between the VBQ score and the T-score (r = -0.529, P < 0.001). ConclusionIn patients undergoing PVP/PKP, VBQ score, and HU value are independently associated with the occurrence of NVCF. Assessing the HU value and the VBQ score could play an effective role in planning PVP/PKP operations.

Utilization of double contrast-enhancement boost for lower-extremity CT angiography.

We aimed to compare the efficacy of the double contrast enhancement (CE)-boost technique with that of conventional methods to improve vascular contrast attenuation in lower-extremity computed tomography (CT) angiography. This retrospective study enrolled 45 patients (mean age, 70 years; range, 26 to 90 years; 30 males). To generate the CE-boost image, the degree of CE was determined by subtracting the post-contrast CT images from the pre-contrast CT images. The double CE-boost technique involves the application of this CE process twice. Both objective assessments (CT attenuation, noise level, signal-to-noise ratio [SNR], contrast-to-noise ratio [CNR], and image sharpness) and subjective quality evaluations were conducted on three types of images (conventional, CE-boost, and double CE-boost images). Double CE-boost images demonstrated significantly reduced noise in Hounsfield units (HUs) compared with conventional and CE-boost images ( ). CT attenuation values (HUs) were substantially higher in all different locations of the lower extremity with double CE-boost images ( ), as opposed to conventional ( ) and CE-boost images ( ). The SNR and CNR were notably improved in the double CE-boost image compared with both conventional and CE-boost images. Image sharpness analysis of the popliteal artery ( ), anterior tibial artery ( ), and dorsalis pedis artery ( ) revealed consistency across conventional, CE-boost, and double CE-boost images. Subjective image analysis indicated superior ratings for the double CE-boost compared with other types. The implementation of the double CE-boost technique improves image quality by decreasing image noise, increasing CT attenuation, and improving SNR, CNR, and subjective assessment compared with CE-boost and conventional imaging.

Impact of stone density on the total laser time and other surgical outcomes in flexible ureteroscopy

Background and objectives: Several preoperative factors are assessed for the evaluation of operative time and fragmentation efficacy during retrograde intrarenal surgery. Due to limited energy capabilities, stone density is an important factor contributing to procedural time. This study aimed to evaluate the effect of stone density on the total laser time in lithotripsy. Methods: We conducted a cross-sectional prospective analysis where fifty-two patients who underwent flexible ureteroscopy using the Cyber Ho 60 holmium laser system (Quanta System) were prospectively analyzed from October 2017 to November 2020 in a cross-sectional study. These patients were divided into groups according to their stone attenuation values (Hounsfield units) and were followed up for 3 months to determine the success of stone clearance. Results: The mean stone size and density were 14.44 mm and 1043 Hounsfield units, respectively. Furthermore, the mean total laser time was 26.58 minutes, whereas the mean operative time was 41.44 minutes. The total laser time did not significantly differ between stones with attenuation &gt;1000 and those with attenuation &lt;1000 (p = 0.486). Stones measuring &gt;13 mm in size required considerably longer lasering time than their smaller counterparts (p = 0.008). Conclusion: In the era of rapid laser technology and instrumental developments, our findings suggest that stone density has no value on the outcomes of flexible ureterorenoscopy, including the total lasering time, stone-free rate, and overall complications. In contrast, stone size significantly influences the total laser time and stone-free rate.

Comparative evaluation of trabecular bone density in Hounsfield units in the lumbar native CT cross-section for osteoporosis diagnosis and fracture risk determination by different examiners

An increasing loss of bone mineral density (BMD) in the axial skeleton leads to osteoporosis and fractures, with an increase found in the thoracic and thoracolumbar regions. The extent to which an examiner-independent assessment of the extent of osteoporosis and fracture risk determination is possible by determining the trabecular density in Hounsfield units (HU) in the spine should be examined. The next question was whether quantitative BMD values can be calculated from the HU values. 225 patients (pt.) with an average age of 64.9 ± 13.1 years and abody-mass-index (BMI) of 26.8 ± 6.8 kg/m2, of which 37were men and 188 were women, were examined to determine whether they had osteoporosis. The BMD was determined in mg/cm3 using quantitative computed tomography (QCT) in the lumbar region. After anonymization by three experienced radiologists, an additional measurement of the trabecular bone density in HU, was carried out in the same vertebral bodies (atotal of 675 vertebral bodies), each using aregion of interest (ROI) positioned in the midvertebral cancellous space in the sagittal reformed CT image. In additional lateral X‑rays of the thoracic and lumbar spine, vertebral fractures were detected and graded. Sacral insufficiency fractures that occurred at the same time were also recorded. The median BMD was 73.2 (57.05-104.17) mg/cm3 and the median HU was 89.93 (67.90-126.95). With acorrelation of 0.988 (p < 0.001), quantitative values in mg/cm3 can be calculated using the following formula: Xq = 12.1 + 0.68 × HU. With HU values less than 69.84 and aBMD of the lumbar spine below 59.54 mg/cm3, there was asignificantly increased number of OVF. At least one OVF was found in 137/225 pt. In 17/137 pt., sacral fractures were also found; these patients showed the significantly lowest values with amedian BMD of 41.81 (16.2-53.7) mg/cm3. Comparable HU values were determined independently of the examiners (p > 0.05). The trabecular density measurements in HU values can be converted into quantitative BMD values in mg/cm3, which enables agood assessment of osteoporosis and fracture risk. Taking the results obtained into account, an opportunistic evaluation using HU values in native CT alone seems quite possible. Experienced examiners have arrived at comparable results.

Improved Osteoporosis Prediction in Breast Cancer Patients Using a Novel Semi-Foundational Model.

Small cohorts of certain disease states are common especially in medical imaging. Despite the growing culture of data sharing, information safety often precludes open sharing of these datasets for creating generalizable machine learning models. To overcome this barrier and maintain proper health information protection, foundational models are rapidly evolving to provide deep learning solutions that have been pretrained on the native feature spaces of the data. Although this has been optimized in Large Language Models (LLMs), there is still a sparsity of foundational models for computer vision tasks. It is in this space that we provide an investigation into pretraining Visual Geometry Group (VGG)-16, Residual Network (ResNet)-50, and Dense Network (DenseNet)-121 on an unrelated dataset of 8500 chest CTs which was subsequently fine-tuned to classify bone mineral density (BMD) in 199 breast cancer patients using the L1 vertebra on CT. These semi-foundational models showed significant improved ternary classification into mild, moderate, and severe demineralization in comparison to ground truth Hounsfield Unit (HU) measurements in trabecular bone with the semi-foundational ResNet50 architecture demonstrating the best relative performance. Specifically, the holdout testing AUC was 0.99 (p-value < 0.05, ANOVA versus no pretraining versus ImageNet transfer learning) and F1-score 0.99 (p-value < 0.05) for the holdout testing set. In this study, the use of a semi-foundational model trained on the native feature space of CT provided improved classification in a completely disparate disease state with different window levels. Future implementation with these models may provide better generalization despite smaller numbers of a disease state to be classified.

The difference in bone mineral density between femur and tibia is related to tibia deformation in endstage knee osteoarthritis

BackgroundThis study investigated bone mineral density (BMD) around the knee joint to clarify the mechanism by which tibia deformation exceeds that of the femur in patients with end-stage knee osteoarthritis (KOA). MethodsWe retrospectively analyzed 193 patients who underwent total knee arthroplasty for end-stage KOA with varus alignment. Preoperative T-score of the femur neck and femur total using dual-energy X-ray absorptiometry (DXA) and hounsfield units (HU) of the distal femur and proximal tibia using computed tomography (CT) were measured to asess the BMD. HU was measured by dividing the femur and tibia into medial, lateral, and total parts, respectively. Patients with medial proximal tibial angle (MPTA) ≥ 85° were considered ‘group 1′, and MPTA < 85° were ‘group 2′. The HU between femur and tibia were compared in group 1. T-score, HU, and HU difference between group 1 and group 2 were compared. ResultsThe HU of the proximal tibia was lower than that of the distal femur (femur lateral > femur medial > tibia medial > tibia lateral). T-score (femur neck, femur total) and HU (femur lateral, femur total, tibia lateral) were lower in group 2 than in group 1. There was no difference in femur-tibia HU difference between groups. ConclusionThe medial tibia collapse more than the medial femur in varus endstage KOA was associated with the lower BMD of the proximal tibia than that of the distal femur, and the MPTA collapse was affected by the absolute value of BMD rather than by the femur-tibia BMD difference.

Image-based finite element model stiffness and vBMD by single and dual energy CT reconstruction kernel

Single-energy quantitative computed tomography (SEQCT) provides volumetric bone mineral density (vBMD) measures for bone analysis and input to image-based finite element models (FEMs). Dual-energy CT (DECT) improves vBMD by accounting for voxel-specific material variations utilizing scans at multiple x-ray energies. vBMD is also altered by reconstruction kernel that cannot be accounted for using calibration phantoms. This study compared vBMD and FEM stiffness derived from SEQCT and DECT images reconstructed with two common kernels. SEQCT and DECT images of cadaveric shoulders (n = 10) were collected using standard (STD) and boneplus (BONE) kernels. Hounsfield Units were converted to vBMD using specimen-specific calibrations. DECT STD and BONE images were generated using an established material decomposition method with 40 and 90 keV simulated monochromatic images. A proximal humerus bone section below the anatomic neck was used for vBMD analysis and FEM generation. FEMs were loaded to 1 % apparent strain for stiffness measurements.Between STD and BONE kernel images, average vBMD differed 0.9 mgK2HPO4/cc and 4.1 mg K2HPO4/cc, in SEQCT and DECT images, respectively. Significant differences occurred in DECT images (p = 0.001). BONE reconstructed images produced higher vBMD measures across both SEQCT and DECT images. The difference between STD and BONE in both SEQCT- and DECT-based FEMs persisted, with larger estimated stiffness in BONE models. For six of the models DECT-based had higher stiffness than SEQCT-based models using the same kernel, although these models differed between STD and BONE kernels. Differences in stiffness between STD and BONE derived models were similar across image types (DECT: 17.5 kN/mm; SEQCT: 19.0 kN/mm). Stiffness values were significantly different within SECT kernels and between SEQCT BONE and DECT STD models. This study shows important differences in vBMD and FEM stiffness that occur due to CT-based imaging parameters alone. These results indicate that consistent imaging parameters should be used for vBMD analysis and FEM input to avoid systematic measurement errors.

Role of Unenhanced Computed Tomography of the Chest in the Prediction of Anaemia: A Cross-sectional Study

Introduction: Anaemia is a common clinical condition. The detection of incidental findings on unenhanced Computed Tomography (CT) that predict the presence of anaemia may play a pivotal role in early diagnosis, treatment and prognosis. Aim: To assess the role of unenhanced CT thorax in predicting anaemia by determining the sensitivity and specificity of the Interventricular Septum Sign (IVS) and Aortic Rim Sign (ARS) in the prediction of anaemia. Materials and Methods: This was a cross-sectional study conducted on 300 patients who underwent High-resolution Computed Tomography (HRCT) of the thorax using a 128-slice CT scanner in the Department of Radiology, Nizam’s Institute of Medical Sciences, Hyderabad, Telangana, India, Nizams Institute of Medical Sciences, Hyderabad, from July 2022 to September 2022. An estimation of haemoglobin levels was performed from a peripheral blood sample within 24 hours of the scan and the diagnosis of anaemia was made based on World Health Organisation (WHO) guidelines of haemoglobin values according to age and gender. On imaging, the following parameters were analysed: the variables considered for each patient included age and gender, the presence or absence of ARS, the presence or absence of an IVS and the Hounsfield Units (HU) values of seven selected Regions of Interest (ROI) on each examination. Three ROIs were placed in the Left Ventricular (LV) cavity, three on the IVS and a single ROI was placed in the aorta at the same level. Mean, percentage, frequency and standard deviation were calculated. The Chi-square test was applied for statistical analysis and a p-value of &lt;0.05 was considered significant. Results: Out of the total sample size of 300, 188 were males and 112 were females with the mean age of 47.16±19.77 years. The mean haemoglobin percentage in the study was 11.62±2.25 gm%. Approximately 195 (65%) out of 300 patients were found to be anaemic based on blood reports. Among them, the majority were diagnosed with moderate anaemia 109 (36.3%), followed by mild anaemia 76 (25.3%) and severe anaemia 10 (3.3%), respectively. The sensitivity of the dense IVS was 100% for severe anaemia, 75.6% for moderate anaemia and 55.3% for mild anaemia. In comparison, the aortic rim sign showed a sensitivity of 100% for severe anaemia, 86.5% for moderate anaemia and 67.69% for mild anaemia, with a specificity of 88.57%. Conclusion: The presence of an IVS on an unenhanced CT predicts the likelihood of underlying anaemia and suggests the need for further evaluation and timely management.