Clinical Computed Tomography Scans Research Articles

Exploring synthetic datasets for computer-aided detection: a case study using phantom scan data for enhanced lung nodule false positive reduction.

Synthetic datasets hold the potential to offer cost-effective alternatives to clinical data, ensuring privacy protections and potentially addressing biases in clinical data. We present a method leveraging such datasets to train a machine learning algorithm applied as part of a computer-aided detection (CADe) system. Our proposed approach utilizes clinically acquired computed tomography (CT) scans of a physical anthropomorphic phantom into which manufactured lesions were inserted to train a machine learning algorithm. We treated the training database obtained from the anthropomorphic phantom as a simplified representation of clinical data and increased the variability in this dataset using a set of randomized and parameterized augmentations. Furthermore, to mitigate the inherent differences between phantom and clinical datasets, we investigated adding unlabeled clinical data into the training pipeline. We apply our proposed method to the false positive reduction stage of a lung nodule CADe system in CT scans, in which regions of interest containing potential lesions are classified as nodule or non-nodule regions. Experimental results demonstrate the effectiveness of the proposed method; the system trained on labeled data from physical phantom scans and unlabeled clinical data achieves a sensitivity of 90% at eight false positives per scan. Furthermore, the experimental results demonstrate the benefit of the physical phantom in which the performance in terms of competitive performance metric increased by 6% when a training set consisting of 50 clinical CT scans was enlarged by the scans obtained from the physical phantom. The scalability of synthetic datasets can lead to improved CADe performance, particularly in scenarios in which the size of the labeled clinical data is limited or subject to inherent bias. Our proposed approach demonstrates an effective utilization of synthetic datasets for training machine learning algorithms.

Automated segmentation of clinical CT scans of the cochlea and analysis of the cochlea's vertical profile

PurposeKnowledge of the cochlear anatomy in individual patients is helpful for improving electrode selection and placement during cochlear implantation, as well as in surgical planning. The aim of this study was to develop a model-free automated segmentation algorithm to obtain 3D surfaces from clinical computed tomography (CT) scans that describe an individual's cochlear anatomy and can be used to quantitatively analyze the cochlea's vertical trajectory. MethodsClinical CT scans were re-oriented and re-sliced to obtain mid-modiolar slices. Using these slices, we segmented the cross-section of the cochlea. Results3D surfaces were obtained for the first 1.5 turns of 648 cochleae. Validation of our algorithm against the manually segmented ground truth obtained from 8 micro-CT scans showed good agreement, with 90 % area overlap and an average distance of 0.11 mm between the segmentation contours. The average cochlear duct length for the basal turn was 16.1 mm along the central path and 22.4 mm along the outer wall. The use of an intrinsic, observer-independent coordinate system and principal component analysis enabled unambiguous quantitative evaluation of the vertical trajectory of the cochlea, revealing only a weak correlation between the symmetry of the commonly used basal turn diameters (B-ratio of A and B diameters) and the profile of the vertical trajectory. ConclusionA model-free segmentation algorithm can achieve similar accuracy as previously published methods relying on statistical shapes. Quantitative analysis of the vertical trajectory can replace the categorization into rollercoaster, sloping, or intermediate vertical trajectory types.

Technical challenges and benefits of photon counting detector computed tomography

X-ray detector is the essential part of a computed tomography (CT) system that controls dose efficiency and image quality. All clinical CT scanners utilized scintillating detectors up until the first clinical photon-counting-detector (PCD) system was approved in 2021. These detectors do not record information about individual photons during the two-step detection process. PCDs, on the other hand, employ a single step process that transforms X-ray radiation straight into an electrical signal. This retains information on individual photons, allowing one to count the quantity of X-rays in various energy ranges. Better spatial resolution, reduced dosages of iodinated contrast material, enhanced iodine signal, enhanced radiation dose efficiency, and the lack of electronic noise are the main benefits of PCDs. PCDs with multiple energy thresholds are able to separate detected photons into two or more energy bins, allowing for the availability of energy-resolved data for each record. In the event of dual-source CT, this permits high pitch or high temporal resolution acquisitions in addition to high spatial resolution for tasks involving material quantification or classification. PCDCT imaging of anatomy, where excellent spatial resolution provides clinical benefit, is one of the most promising uses of the technology. Inner ear, bone, small blood artery, heart, and lung imaging are among them. Photon-counting CT will become the wave of the future for workhorse CT imaging systems. An overview of the PCDCT principle, possible clinical benefits, and limitations of conventional CT is provided in this review paper, along with potential future developments for this CT imaging technology.

Size-matching in lung transplantation by computed tomography for end-stage interstitial disease: a retrospective analysis.

Size matching between donors and recipients is a major issue in lung transplantation (LTx), especially in patients with restrictive lung disease (RLD). This study aims to evaluate computed tomography (CT) as an additional method for defining the total lung capacity (TLC) in patients with end-stage interstitial disease awaiting LTx. Clinical data and CT scans from patients who underwent a first LTx from January 2014 to July 2018 in Bichat Hospital, Paris, were prospectively included in a database. CT TLC (ctTLC) was retrospectively calculated after semi-automatic contouring of the parenchyma and compared with measured TLC (mTLC) and predicted TLC (pTLC) values. The study group included 89 patients (male:female =68:21; mean age, 59.5±10.0 years). The time between pulmonary function tests (PFTs) and CT scan was 162±270 days [median, 67 days; interquartile range (IQR), 0-233 days]. ctTLC was inferior to mTLC and pTLC (respectively 2,979±1,001 mL, 3,530±1,077 and 6,381±955 mL, P<0.001). The relative difference between CT lung volume (ctLV) and measured lung volume (mLV) was higher on the left than on the right side (25.4% vs. 16.3%, respectively, P=0.11). After exclusion of two outliers, we found a significant correlation between ctTLC and mTLC (r=0.762, P<0.001). CT volume is a feasible method to assess TLC in patients with end-stage interstitial disease awaiting LTx. This study highlights potential size-mismatch for graft selection before LTx and opens the perspective of a prospective trial evaluating impact of size-matching by donor-recipient (D-R) ctTLC ratio on postoperative outcomes.

Side specific differences of Hounsfield-Units in the osteoporotic lumbar spine.

Gold standard for determining bone density as a surrogate parameter of bone quality is measurement of bone mineral density (BMD) by dual energy X-ray absorptiometry (DXA), most commonly performed on the lumbar spine (L1-L4). Computed tomography (CT) data are often available for surgical planning prior to spine procedures, but currently this information is not standardized for bone quality assessment. Besides, measuring the Hounsfield-Units (HU) is also of great importance in the context of biomechanical studies. This in vitro study aims in comparing BMD from DXA and HU based on diagnostic CT scans. In addition, methods are presented to quantify local density variations within bones. One hundred and seventy-six vertebrae (L1-L4) from 44 body donors (age 84.0±8.7 years) were studied. DXA measurements were obtained on the complete vertebrae to determine BMD, as well as axial CT scans with a slice thickness of 1 mm. Using Mimics Innovation Suite image processing software (Materialise NV, Leuven, Belgium), two volumes (whole vertebra vs. spongious bone) were formed for each vertebra, which in turn were divided in their left and right sides. From these total of six volumes, the respective mean HU was determined. HU of the whole vertebra and just spongious HU were compared with the BMD of the corresponding vertebrae. Side specific differences were calculated as relative values. Whole bone and spongious HU correlated significantly (P>0.001; α=0.01) with BMD. A positive linear correlation was found, which was more pronounced for whole bone HU (R=0.72) than for spongious HU (R=0.62). When comparing the left and right sides within each vertebra, the HU was found to be 10% larger on average on one side compared to the opposite side. In some cases, the difference of left and right spongious bone can be up to 170%. There is a tendency for the side comparison to be larger for the spongious HU than for the whole vertebra. Determination of HU from clinical CT scans is an important tool for assessing bone quality, primarily by including the cortical portion in the calculation of HU. Unlike BMD, HU can be used to distinguish precisely between individual regions. Some of the very large side-specific gradients of the HU indicate an enormous application potential for preoperative patient-specific planning.

Deep Learning Method for Rapid Simultaneous Multistructure Temporal Bone Segmentation.

To develop and validate a deep learning algorithm for the automated segmentation of key temporal bone structures from clinical computed tomography (CT) data sets. Cross-sectional study. A total of 325 CT scans from a clinical database. A state-of-the-art deep learning (DL) algorithm (SwinUNETR) was used to train a prediction model for rapid segmentation of 9 key temporal bone structures in a data set of 325 clinical CTs. The data set was manually annotated by a specialist to serve as the ground truth. The data set was randomly split into training (n = 260) and testing (n = 65) sets. The model's performance was objectively assessed through external validation on the test set using metrics including Dice, Balanced accuracy, Hausdorff distances, and processing time. The model achieved an average Dice coefficient of 0.87 for all structures, an average balanced accuracy of 0.94, an average Hausdorff distance of 0.79 mm, and an average processing time of 9.1 seconds per CT. The present DL model for the automated simultaneous segmentation of multiple structures within the temporal bone from CTs achieved high accuracy according to currently commonly employed objective analysis. The results demonstrate the potential of the method to improve preoperative evaluation and intraoperative guidance in otologic surgery.

Osteosarcopenia and Mortality in Older Adults Undergoing Transcatheter Aortic Valve Replacement

Osteosarcopenia is an emerging geriatric syndrome characterized by age-related deterioration in muscle and bone. Despite the established relevance of frailty and sarcopenia among older adults undergoing transcatheter aortic valve replacement (TAVR), osteosarcopenia has yet to be investigated in this setting. To determine the association between osteosarcopenia and adverse outcomes following TAVR. This is a post hoc analysis of the Frailty in Aortic Valve Replacement (FRAILTY-AVR) prospective multicenter cohort study and McGill extension that enrolled patients aged 70 years or older undergoing TAVR from 2012 through 2022. FRAILTY-AVR was conducted at 14 centers in Canada, the United States, and France between 2012 and 2016, and patients at the McGill University-affiliated center in Montreal, Québec, Canada, were enrolled on an ongoing basis up to 2022. Osteosarcopenia as measured on computed tomography (CT) scans prior to TAVR. Clinically indicated CT scans acquired prior to TAVR were analyzed to quantify psoas muscle area (PMA) and vertebral bone density (VBD). Osteosarcopenia was defined as a combination of low PMA and low VBD according to published cutoffs. The primary outcome was 1-year all-cause mortality. Secondary outcomes were 30-day mortality, hospital length of stay, disposition, and worsening disability. Multivariable logistic regression was used to adjust for potential confounders. Of the 605 patients (271 [45%] female) in this study, 437 (72%) were octogenarian; the mean (SD) age was 82.6 (6.2) years. Mean (SD) PMA was 22.1 (4.5) cm2 in men and 15.4 (3.5) cm2 in women. Mean (SD) VBD was 104.8 (35.5) Hounsfield units (HU) in men and 98.8 (34.1) HU in women. Ninety-one patients (15%) met the criteria for osteosarcopenia and had higher rates of frailty, fractures, and malnutrition at baseline. One-year mortality was highest in patients with osteosarcopenia (29 patients [32%]) followed by those with low PMA alone (18 patients [14%]), low VBD alone (16 patients [11%]), and normal bone and muscle status (21 patients [9%]) (P < .001). Osteosarcopenia, but not low VBD or PMA alone, was independently associated with 1-year mortality (odds ratio [OR], 3.18; 95% CI, 1.54-6.57) and 1-year worsening disability (OR, 2.11; 95% CI, 1.19-3.74). The association persisted in sensitivity analyses adjusting for the Essential Frailty Toolset, Clinical Frailty Scale, and geriatric conditions such as malnutrition and disability. The findings suggest that osteosarcopenia detected using clinical CT scans could be used to identify frail patients with a 3-fold increase in 1-year mortality following TAVR. This opportunistic method for osteosarcopenia assessment could be used to improve risk prediction, support decision-making, and trigger rehabilitation interventions in older adults.

Evaluation of deep learning-based quantitative computed tomography for opportunistic osteoporosis screening

To evaluate diagnostic efficacy of deep learning (DL)-based automated bone mineral density (BMD) measurement for opportunistic screening of osteoporosis with routine computed tomography (CT) scans. A DL-based automated quantitative computed tomography (DL-QCT) solution was evaluated with 112 routine clinical CT scans from 84 patients who underwent either chest (N:39), lumbar spine (N:34), or abdominal CT (N:39) scan. The automated BMD measurements (DL-BMD) on L1 and L2 vertebral bodies from DL-QCT were validated with manual BMD (m-BMD) measurement from conventional asynchronous QCT using Pearson’s correlation and intraclass correlation. Receiver operating characteristic curve (ROC) analysis identified the diagnostic ability of DL-BMD for low BMD and osteoporosis, determined by dual-energy X-ray absorptiometry (DXA) and m-BMD. Excellent concordance were seen between m-BMD and DL-BMD in total CT scans (r = 0.961/0.979). The ROC-derived AUC of DL-BMD compared to that of central DXA for the low-BMD and osteoporosis patients was 0.847 and 0.770 respectively. The sensitivity, specificity, and accuracy of DL-BMD compared to central DXA for low BMD were 75.0%, 75.0%, and 75.0%, respectively, and those for osteoporosis were 68.0%, 80.5%, and 77.7%. The AUC of DL-BMD compared to the m-BMD for low BMD and osteoporosis diagnosis were 0.990 and 0.943, respectively. The sensitivity, specificity, and accuracy of DL-BMD compared to m-BMD for low BMD were 95.5%, 93.5%, and 94.6%, and those for osteoporosis were 88.2%, 94.5%, and 92.9%, respectively. DL-BMD exhibited excellent agreement with m-BMD on L1 and L2 vertebrae in the various routine clinical CT scans and had comparable diagnostic performance for detecting the low-BMD and osteoporosis on conventional QCT.

CT number calibration audit in photon radiation therapy.

Inadequate computed tomography (CT) number calibration curves affect dose calculation accuracy. Although CT number calibration curves registered in treatment planning systems (TPSs) should be consistent with human tissues, it is unclear whether adequate CT number calibration is performed because CT number calibration curves have not been assessed for various types of CT number calibration phantoms and TPSs. The purpose of this study was to investigate CT number calibration curves for mass density (ρ) and relative electron density (ρe ). A CT number calibration audit phantom was sent to 24 Japanese photon therapy institutes from the evaluating institute and scanned using their individual clinical CT scan protocols. The CT images of the audit phantom and institute-specific CT number calibration curves were submitted to the evaluating institute for analyzing the calibration curves registered in the TPSs at the participating institutes. The institute-specific CT number calibration curves were created using commercial phantom (Gammex, Gammex Inc., Middleton, WI, USA) or CIRS phantom (Computerized Imaging Reference Systems, Inc., Norfolk, VA, USA)). At the evaluating institute, theoretical CT number calibration curves were created using a stoichiometric CT number calibration method based on the CT image, and the institute-specific CT number calibration curves were compared with the theoretical calibration curve. Differences in ρ and ρe over the multiple points on the curve (Δρm and Δρe,m , respectively) were calculated for each CT number, categorized for each phantom vendor and TPS, and evaluated for three tissue types: lung, soft tissues, and bones. In particular, the CT-ρ calibration curves for Tomotherapy TPSs (ACCURAY, Sunnyvale, CA, USA) were categorized separately from the Gammex CT-ρ calibration curves because the available tissue-equivalent materials (TEMs) were limited by the manufacturer recommendations. In addition, the differences in ρ and ρe for the specific TEMs (ΔρTEM and Δρe,TEM , respectively) were calculated by subtracting the ρ or ρe of the TEMs from the theoretical CT-ρ or CT-ρe calibration curve. The mean±standard deviation (SD) of Δρm and Δρe,m for the Gammex phantom were -1.1±1.2g/cm3 and -0.2±1.1, -0.3±0.9g/cm3 and 0.8±1.3, and -0.9±1.3g/cm3 and 1.0±1.5 for lung, soft tissues, and bones, respectively. The mean±SD of Δρm and Δρe,m for the CIRS phantom were 0.3±0.8g/cm3 and 0.9±0.9, 0.6±0.6g/cm3 and 1.4±0.8, and 0.2±0.5g/cm3 and 1.6±0.5 for lung, soft tissues, and bones, respectively. The mean±SD of Δρm for Tomotherapy TPSs was 2.1±1.4g/cm3 for soft tissues, which is larger than those for other TPSs. The mean±SD of Δρe,TEM for the Gammex brain phantom (BRN-SR2) was -1.8±0.4, implying that the tissue equivalency of the BRN-SR2 plug was slightly inferior to that of other plugs. Latent deviations between human tissues and TEMs were found by comparing the CT number calibration curves of the various institutes.

Decoding calcific mitral valve disease: a novel deep learning model uncovers the role of calcium burden

Abstract Background Mitral annular calcification (MAC) is a common degenerative disease that affects older adults and causes calcium deposition in the mitral annulus, increasing the risk of mitral valve (MV) dysfunction, cardiovascular events, conduction abnormalities, and mortality. While the primary imaging modality recommended for the clinical evaluation of MAC is echocardiography, it has been reported to be found incidentally in approximately 8% of routine thoracic computed tomography (CT) scans. CT scan is a sensitive imaging modality for assessing MAC, providing the resolution necessary to understand the anatomical involvement of calcification. Research has shown that the MAC burden is associated with disease activity and progression. Still, the exact correlation between the quantitative calcium burden and the degree of severity of mitral valve disease is unclear. Purpose Our study aimed to determine the association between MAC, as assessed on CT, and significant mitral stenosis (MS) or mitral regurgitation (MR), as visualised on echocardiography. In addition, we sought to determine the quantitative MAC cutoffs that would optimally predict significant MS or MR on echocardiography. Methods A retrospective cohort study was conducted at our academic centre. Inclusion criteria were: age≥60 years, resting transthoracic echocardiogram performed between 2013-2022, non-gated chest CT performed within one year before echocardiography, and any degree of MAC documented on the echocardiography report. CT scans were requested for clinical indications unrelated to mitral valve disease. Exclusion criteria were: prior mitral valve intervention, endocarditis, and congenital mitral valve disease. Significant calcific mitral valve disease was defined as ≥moderate MR and ≥mild MS on the echocardiography report, ascertained by expert readers based on multiparametric ASE criteria. MAC volume was quantified on the multi-slice CT DICOM images using a 3D U-Net deep neural network previously trained by our group on an independent cohort. Results The cohort consisted of 1,560 unique patients with a mean age of 79 years and 61% females. The echocardiographic prevalence of significant MR and MS was 10% and 4%, respectively, for 211 affected patients. The CT-based mean MAC volume was 949 mm³ in patients with significant MR or MS, as opposed to 334 mm³ in those without (P&amp;lt;0.001). MAC volume &amp;gt;1000 mm³ was the optimal cutoff to predict significant MS (specificity 90%, sensitivity 51%, area under ROC curve 0.79) and significant MR (specificity 89%, sensitivity 21%, area under ROC curve 0.60). Adjusting for age, sex, and comorbid conditions did not affect the observed association between MAC volume and mitral valvulopathy. Conclusion A novel deep learning model for quantifying MAC volume from non-cardiac clinical CT scans was efficient in screening for calcific mitral valve disease, particularly MS, and identifying patients who may benefit from further evaluation.

Evaluation of organ and effective doses using anthropomorphic phantom: A comparison between experimental measurement and a commercial dose calculator

IntroductionThe aim of this study was to experimentally measure organ doses for computed tomography (CT) procedures using thermoluminescence dosimeters (TLDs) on a RANDO anthropomorphic phantom and verify the measured doses using CT-Expo software. MethodsThe phantom was irradiated using clinical CT scan protocols routinely used for specific procedures in the radiology department. Fifty TLD chips were used in this study. The scanning parameters (kVp, mA, s) used to scan the phantom were used as input parameters for CT-Expo dose estimations. ResultsThe TLD measured organ doses varied between 3.97 mGy for the esophagus and 56.22 mGy for the brain. High doses were recorded in the brain (37.80–56.22 mGy) and the eye lens (29.94–36.16 mGy). Comparing the organ dose measurements between TLD and CT-Expo, the maximum organ dose difference was obtained for the eye lens. A comparison between the two methods for the other organs were all less than 32 %. The effective doses from the TLD measurements for the head, chest, and abdominopelvic CT examinations were 2.78, 6.67, and 17 mSv, respectively and CT-Expo were 2.20, 10.30, and 16.70 mSv, respectively. ConclusionThe experimental and computational results are comparable, and the reliability of the TLD measurements and CT-Expo dose calculator has been proven. Implications for studyA reason for the difference in dose measurements between the two methods has been attributed to the dissimilarity in the organ position in the Rando anthropomorphic phantom and the standard mathematical phantom used by CT-Expo. The experimental and computational results have been found to be comparable.

Design and fabrication of 3D-printed patient-specific soft tissue and bone phantoms for CT imaging

The objective of this study is to create patient-specific phantoms for computed tomography (CT) that possess accurate densities and exhibit visually realistic image textures. These qualities are crucial for evaluating CT performance in clinical settings. The study builds upon a previously presented 3D printing method (PixelPrint) by incorporating soft tissue and bone structures. We converted patient DICOM images directly into 3D printer instructions using PixelPrint and utilized calcium-doped filament to increase the Hounsfield unit (HU) range. Density was modeled by controlling printing speed according to volumetric filament ratio to emulate attenuation profiles. We designed micro-CT phantoms to demonstrate the reproducibility, and to determine mapping between filament ratios and HU values on clinical CT systems. Patient phantoms based on clinical cervical spine and knee examinations were manufactured and scanned with a clinical spectral CT scanner. The CT images of the patient-based phantom closely resembled original CT images in visual texture and contrast. Micro-CT analysis revealed minimal variations between prints, with an overall deviation of ± 0.8% in filament line spacing and ± 0.022 mm in line width. Measured differences between patient and phantom were less than 12 HU for soft tissue and 15 HU for bone marrow, and 514 HU for cortical bone. The calcium-doped filament accurately represented bony tissue structures across different X-ray energies in spectral CT (RMSE ranging from ± 3 to ± 28 HU, compared to 400 mg/ml hydroxyapatite). In conclusion, this study demonstrated the possibility of extending 3D-printed patient-based phantoms to soft tissue and bone structures while maintaining accurate organ geometry, image texture, and attenuation profiles.

Computed tomography–based machine learning for donor lung screening before transplantation

Assessment and selection of donor lungs remain largely subjective and experience based. Criteria to accept or decline lungs are poorly standardized and are not compliant with the current donor pool. Using ex vivo computed tomography (CT) images, we investigated the use of a CT-based machine learning algorithm for screening donor lungs before transplantation. Clinical measures and ex situ CT scans were collected from 100 cases as part of a prospective clinical trial. Following procurement, donor lungs were inflated, placed on ice according to routine clinical practice, and imaged using a clinical CT scanner before transplantation while stored in the icebox. We trained and tested a supervised machine learning method called dictionary learning, which uses CT scans and learns specific image patterns and features pertaining to each class for a classification task. The results were evaluated with donor and recipient clinical measures. Of the 100 lung pairs donated, 70 were considered acceptable for transplantation (based on standard clinical assessment) before CT screening and were consequently implanted. The remaining 30 pairs were screened but not transplanted. Our machine learning algorithm was able to detect pulmonary abnormalities on the CT scans. Among the patients who received donor lungs, our algorithm identified recipients who had extended stays in the intensive care unit and were at 19 times higher risk of developing chronic lung allograft dysfunction within 2years posttransplant. We have created a strategy to ex vivo screen donor lungs using a CT-based machine learning algorithm. As the use of suboptimal donor lungs rises, it is important to have in place objective techniques that will assist physicians in accurately screening donor lungs to identify recipients most at risk of posttransplant complications.

Combined Method for Segmentation of Multiple Needles in 3D Liver CT

Abstract Radiofrequency ablation is a minimally invasive technique for treating liver or kidney tumors, where precisely inserted ablation probes destroy the tumor tissue with induced heat around their tips. Automatic segmentation of needles from the abdominal computed tomography (CT) scans enables large-scale procedure evaluation and creates a baseline ground truth for automated planning approaches. Our method for needle segmentation enables the detection of multiple needles from unlabeled CT scans by sequential combination of two approaches, a classical method and a neural network. The former method detects intensity-prominent needles in 3D. The latter focuses on the detection of needles with lower intensity, that remained undetected by the classical method. A well-known encoder-decoder approach on axial slices of the CT scan gives a segmentation mask for the needles prominent in slices. To train the network, we create a synthetic dataset, adding needles to clean abdominal CT slices. Our network trained on a small synthetic dataset is able to generalize to clinical CT scans. The performance of the combined method is evaluated on ten anonymous patients from the Medical University of Innsbruck each with up to 18 needles inserted in the liver without any template.

Triple contrast computed tomography reveals site-specific biomechanical differences in the human knee joint-A proof of concept study.

Cartilage and synovial fluid are challenging to observe separately in native computed tomography (CT). We report the use of triple contrast agent (bismuth nanoparticles [BiNPs], CA4+, and gadoteridol) to image and segment cartilage in cadaveric knee joints with a clinical CT scanner. We hypothesize that BiNPs will remain in synovial fluid while the CA4+ and gadoteridol will diffuse into cartilage, allowing (1) segmentation of cartilage, and (2) evaluation of cartilage biomechanical properties based on contrast agent concentrations. To investigate these hypotheses, triple contrast agent was injected into both knee joints of a cadaver (N = 1), imaged with a clinical CT at multiple timepoints during the contrast agent diffusion. Knee joints were extracted, imaged with micro-CT (µCT), and biomechanical properties of the cartilage surface were determined by stress-relaxation mapping. Cartilage was segmented and contrast agent concentrations (CA4+ and gadoteridol) were compared with the biomechanical properties at multiple locations (n = 185). Spearman's correlation between cartilage thickness from clinical CT and reference µCT images verifies successful and reliable segmentation. CA4+ concentration is significantly higher in femoral than in tibial cartilage at 60 min and further timepoints, which corresponds to the higher Young's modulus observed in femoral cartilage. In this pilot study, we show that (1) large BiNPs do not diffuse into cartilage, facilitating straightforward segmentation of human knee joint cartilage in a clinical setting, and (2) CA4+ concentration in cartilage reflects the biomechanical differences between femoral and tibial cartilage. Thus, the triple contrast agent CT shows potential in cartilage morphology and condition estimation in clinical CT.

Addressing Challenges of Opportunistic Computed Tomography Bone Mineral Density Analysis.

Computed tomography (CT) offers advanced biomedical imaging of the body and is broadly utilized for clinical diagnosis. Traditionally, clinical CT scans have not been used for volumetric bone mineral density (vBMD) assessment; however, computational advances can now leverage clinically obtained CT data for the secondary analysis of bone, known as opportunistic CT analysis. Initial applications focused on using clinically acquired CT scans for secondary osteoporosis screening, but opportunistic CT analysis can also be applied to answer research questions related to vBMD changes in response to various disease states. There are several considerations for opportunistic CT analysis, including scan acquisition, contrast enhancement, the internal calibration technique, and bone segmentation, but there remains no consensus on applying these methods. These factors may influence vBMD measures and therefore the robustness of the opportunistic CT analysis. Further research and standardization efforts are needed to establish a consensus and optimize the application of opportunistic CT analysis for accurate and reliable assessment of vBMD in clinical and research settings. This review summarizes the current state of opportunistic CT analysis, highlighting its potential and addressing the associated challenges.

DuSFE: Dual-Channel Squeeze-Fusion-Excitation co-attention for cross-modality registration of cardiac SPECT and CT.

Myocardial perfusion imaging (MPI) using single-photon emission computed tomography (SPECT) is widely applied for the diagnosis of cardiovascular diseases. Attenuation maps (μ-maps) derived from computed tomography (CT) are utilized for attenuation correction (AC) to improve the diagnostic accuracy of cardiac SPECT. However, in clinical practice, SPECT and CT scans are acquired sequentially, potentially inducing misregistration between the two images and further producing AC artifacts. Conventional intensity-based registration methods show poor performance in the cross-modality registration of SPECT and CT-derived μ-maps since the two imaging modalities might present totally different intensity patterns. Deep learning has shown great potential in medical imaging registration. However, existing deep learning strategies for medical image registration encoded the input images by simply concatenating the feature maps of different convolutional layers, which might not fully extract or fuse the input information. In addition, deep-learning-based cross-modality registration of cardiac SPECT and CT-derived μ-maps has not been investigated before. In this paper, we propose a novel Dual-Channel Squeeze-Fusion-Excitation (DuSFE) co-attention module for the cross-modality rigid registration of cardiac SPECT and CT-derived μ-maps. DuSFE is designed based on the co-attention mechanism of two cross-connected input data streams. The channel-wise or spatial features of SPECT and μ-maps are jointly encoded, fused, and recalibrated in the DuSFE module. DuSFE can be flexibly embedded at multiple convolutional layers to enable gradual feature fusion in different spatial dimensions. Our studies using clinical patient MPI studies demonstrated that the DuSFE-embedded neural network generated significantly lower registration errors and more accurate AC SPECT images than existing methods. We also showed that the DuSFE-embedded network did not over-correct or degrade the registration performance of motion-free cases. The source code of this work is available at https://github.com/XiongchaoChen/DuSFE_CrossRegistration.

Application of OSL strips in CT dosimetry according to the AAPM methodology

Computed tomography (CT) images contribute to high-quality medical diagnosis, but radiation dose can be quite high, requiring accurate assessment. CT dose index (CTDI) was developed for dosimetric purposes, but for scanners operated exclusively in axial mode. Nowadays, CTDI underestimate patient dose in helical CT exams. AAPM report TG111 (2010) suggested a new metric in which the patient's radiation dose is obtained from dose profiles constructed from several measurements made with a small ionization chamber. It is also possible to obtain dose profiles using properly calibrated OSL (optically stimulated luminescence) strips. The main objective of the present work is to contribute to optimizing CT dosimetry, comparing dose profiles obtained with OSL strips with measurements obtained by other authors. In this work, a “pencil” ionization chamber and 20 cm x 0.3 cm OSL strips were X-ray-irradiated, in air and in the holes of two cylindrical CT phantoms, using 100, 120, 140 kV peak voltages, both in lab and in a clinical CT scanner. Irradiated strips were read using an OSL reader built in the GDRFM. OSL profiles were calibrated against ionization chamber. From them, CTDIw and CTDIvol values were determined, differing approximately 3.9% from those of the CT scanner. From the profiles, also the planar equilibrium dose Deq,p (TG111) was evaluated in some CT protocols; Deq,p exceeded the CTDI values from the CT scanner in every case. E.g.: The percentage difference between Deq,p and CTDIvol for the head phantom ranged between 33-25%. Thus, in some cases, it could be advantageous to use calibrated OSL dosimeters instead of ionization chambers to obtain the profiles, saving time, because it is possible to obtain five OSL profiles from a single phantom irradiation.

Radiation Dose Management in Computed Tomography: Introduction to the Practice at a Single Facility.

Although the clinical benefits of computed tomography (CT) are undoubtedly high, radiation doses received by patients are also relatively high; therefore, radiation dose management is mandatory to optimize CT radiation doses and prevent excessive radiation events. This article describes CT dose management practice at a single facility. Many imaging protocols are used in CT depending on the clinical indications, scan region, and CT scanner; thus, managing the protocols is the first step for optimization. The appropriateness of the radiation dose for each protocol and scanner is verified, while answering whether the dose is the minimum to obtain diagnostic-quality images. Moreover, examinations with exceptionally high doses are identified, and the cause and clinical validity of the high dose are assessed. Daily imaging practice should follow standardized procedures, avoiding operator-dependent errors, and information required for radiation dose management should be recorded at each examination. The imaging protocols and procedures are reviewed for continuous improvement based on regular dose analysis and multidisciplinary team collaboration. The participation of many staff members in the dose management process is expected to contribute to promoting radiation safety through increased staff awareness.

A RETROSPECTIVE STUDY BETWEEN CLINICAL EXAMINATION, FOCUSSED ABDOMINAL SONOGRAM IN TRAUMA AND CT SCAN IN BLUNT INJURY ABDOMEN IN A TERTIARY CARE CENTRE

Aims and Objectives: Blunt abdominal trauma (BAT) accounts for the majority (80 percent) of abdominal injuries seen in the Emergency Department and is responsible for substantial morbidity and mortality in developing countries. It requires high degree of suspicion, investigation and management. The most commonly injured abdominal organs are liver and spleen. The aim of this study was to nd etiology, early diagnosis and management of patients with blunt abdominal trauma based on clinical examination, FAST and CT scan. A retrospective study of 60 cases of blunt Abdominal trauma patients Methods: presenting to emergency and outpatient department of Surgery of Silchar Medical College and Hospital from September 2020 to September 2021 was done. Amongst the studied cases most Results: common age group involved was (21–30) years (28 cases). Liver was found to be the most common injured organ (22 cases) followed by bowel and spleen. FAST was the most commonly used investigation after blunt abdominal trauma. CT was used only in hemodynamically stable patients (19 cases). The most common intra-operative nding was Intestinal perforation and the most common surgery performed was the repair or resection and anastomosis of intestinal perforation. Most common mode of injury wa Conclusions: s road trafc accidents and predominantly, men were affected. Clinical examination alone is inadequate because patients may have altered mental status. Initial resuscitation followed by physical examination and monitoring of clinical parameters and FAST and computed tomography (CT) abdomen are very important to detect patients with minimal and clinically untraceable sign of abdominal injury. Rapid diagnosis, early timed referral, adequate and trained staff, careful monitoring, early decision to go for operative or nonoperative management can help save many lives.