Dual-Energy CT-based Opportunistic Volumetric Bone Mineral Density Assessment of the Distal Radius.

Background In patients with distal radius fractures (DRFs), low bone mineral density (BMD) is associated with bone substitute use during surgery and bone nonunion, but BMD information is not regularly available. Purpose To evaluate the feasibility of dual-energy CT (DECT)-based BMD assessment from routine examinations in the distal radius and the relationship between the obtained BMD values, the occurrence of DRFs, bone nonunion, and use of surgical bone substitute. Materials and Methods Scans in patients who underwent routine dual-source DECT in the distal radius between January 2016 and December 2021 were retrospectively acquired. Phantomless BMD assessment was performed using the delineated trabecular bone of a nonfractured segment of the distal radius and both DECT image series. CT images and health records were examined to determine fracture severity, surgical management, and the occurrence of bone nonunion. Associations of BMD with the occurrence of DRFs, bone nonunion, and bone substitute use at surgical treatment were examined with generalized additive models and receiver operating characteristic analysis. Results This study included 263 patients (median age, 52 years; IQR, 36-64 years; 132 female patients), of whom 192 were diagnosed with fractures. Mean volumetric BMD was lower in patients who sustained a DRF (93.9 mg/cm3 vs 135.4 mg/cm3; P < .001), required bone substitutes (79.6 mg/cm3 vs 95.5 mg/cm3; P < .001), and developed bone nonunion (71.1 mg/cm3 vs 96.5 mg/cm3; P < .001). Receiver operating characteristic curve analysis identified these patients with an area under the curve of 0.71-0.91 (P < .001). Lower BMD increased the risk to sustain DRFs, develop bone nonunion, and receive bone substitutes at surgery (P < .001). Conclusion DECT-based BMD assessment at routine examinations is feasible and could help predict surgical bone substitute use and the occurrence of bone nonunion in patients with DRFs. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Carrino in this issue.

Combining seeded region growing and k-nearest neighbours for the segmentation of routinely acquired spatio-temporal image data.

PURPOSE: The acquisition conditions of medical imaging are often precisely defined, leading to a high homogeneity among different data sets. Nonetheless, outliers or artefacts still appear and need to be reliably detected to ensure a reliable diagnosis. Thus, the algorithms need to handle small sample sizes especially, when working with domain specific imaging modalities. METHODS: In this work, we suggest a pipeline for the detection and segmentation of light pollution in near-infrared fluorescence optical imaging (NIR-FOI), based on a small sample size. NIR-FOI produces spatio-temporal data with two spatial and one temporal dimension. To calculate a two-dimensional light pollution map for the entire image stack, we combine region growing and k-nearest neighbours (kNN), which classifies pixels into fore- and background by its entire temporal component. Thus, decision-making on reduced data is omitted. RESULTS: We achieved a [Formula: see text] score of 0.99 for classifying a data set as light polluted or pollution-free. Additionally, we reached a total [Formula: see text] score of 0.90 for detecting regions of interest within the polluted data sets. Finally, an average Dice's coefficient measuring the segmentation performance over all polluted data sets of 0.80 was accomplished. CONCLUSIONS: A Dice's coefficient of 0.80 for the area segmentation does not seem perfect. However, there are two main factors, besides true prediction errors, lowering the score: Segmentation mistakes on small areas lead to a rapid decrease in the score and labelling errors due to complex data. However, in combination with the light-polluted data set and pollution area detection, these results can be considered successful and play a key role in our general goal: Exploiting NIR-FOI for the early detection of arthritis within hand joints.

Association of Phantomless Dual-Energy CT-based Volumetric Bone Mineral Density with the Prevalence of Acute Insufficiency Fractures of the Spine.

RATIONALE AND OBJECTIVES: To evaluate the bone mineral density (BMD) of the lumbar spine derived from dual-energy CT (DECT)-based volumetric material decomposition and its association with acute insufficiency fractures of the thoracolumbar spine. MATERIALS AND METHODS: L1 of 160 patients (77 men, 83 women; mean age 64.3 years, range, 22-94 years) who underwent third-generation dual-source DECT between January 2016 and December 2021 due to suspected insufficiency fractures was retrospectively analyzed. All depicted vertebrae were examined for signs of recent fractures. A dedicated DECT postprocessing software using material decomposition was applied for phantomless BMD assessment. Receiver-operating characteristic (ROC) analysis identified optimal BMD thresholds. Associations of BMD, sex, and age with the occurrence of insufficiency fractures were examined with logistic regression models. RESULTS: A DECT-derived BMD threshold of 120.40 mg/cm³ yielded 90.1% specificity and 59.32% sensitivity to differentiate patients with at least one insufficiency fracture from patients without fracture. No patient without fracture had a DECT-derived BMD below 85 mg/cm3. Lower DECT-derived bone mineral density was associated with an increased risk of insufficiency fractures (Odds ratio of 0.93, 95% CI, 0.91-0.96, p < 0.001). Overall ROC-derived AUC was 0.82 (p < 0.0001) for the differentiation of patients that sustained an insufficiency fracture from the control group. CONCLUSION: Dual-Energy CT-based BMD assessment can accurately differentiate patients with acute insufficiency fractures of the thoracolumbar spine from patients without fracture. This algorithm can be used for phantomless risk stratification of patients undergoing routine CT to sustain insufficiency fractures of the thoracolumbar spine The identified cut-off value of 120.4 mg/cm³ is in line with current American College of Radiology (ACR) recommendations to differentiate healthy individuals from those with reduced bone mineral density.

A new promising oncogenic target (p.C382R) for treatment with pemigatinib in patients with cholangiocarcinoma.

Point mutations of the fibroblast growth factor receptor (FGFR)2 receptor in intrahepatic cholangiocarcinoma (iCC) are mainly of unknown functional significance compared to FGFR2 fusions. Pemigatinib, a tyrosine kinase inhibitor, is approved for the treatment of cholangiocarcinoma with FGFR2 fusion/rearrangement. Although it is hypothesized that FGFR2 mutations may cause uncontrolled activation of the signaling pathway, the data for targeted therapies for FGFR2 mutations remain unclear. In vitro analyses demonstrated the importance of the p.C382R mutation for ligand-independent constitutive activation of FGFR2 with transforming potential. The following report describes the clinical case of a patient diagnosed with an iCC carrying a FGFR2 p.C382R point mutation which was detected in liquid, as well as in tissue-based biopsies. The patient was treated with pemigatinib, resulting in a sustained complete functional remission in fluorodeoxyglucose-positron emission tomography/computed tomography over 10 months to date. The reported case is the first description of a complete functional remission under the treatment with pemigatinib in a patient with p.C383R mutation.

Diagnostic accuracy of quantitative dual-energy CT-based volumetric bone mineral density assessment for the prediction of osteoporosis-associated fractures.

OBJECTIVES: To evaluate the predictive value of volumetric bone mineral density (BMD) assessment of the lumbar spine derived from phantomless dual-energy CT (DECT)-based volumetric material decomposition as an indicator for the 2-year occurrence risk of osteoporosis-associated fractures. METHODS: L1 of 92 patients (46 men, 46 women; mean age, 64 years, range, 19-103 years) who had undergone third-generation dual-source DECT between 01/2016 and 12/2018 was retrospectively analyzed. For phantomless BMD assessment, dedicated DECT postprocessing software using material decomposition was applied. Digital files of all patients were sighted for 2 years following DECT to obtain the incidence of osteoporotic fractures. Receiver operating characteristic (ROC) analysis was used to calculate cut-off values and logistic regression models were used to determine associations of BMD, sex, and age with the occurrence of osteoporotic fractures. RESULTS: A DECT-derived BMD cut-off of 93.70 mg/cm3 yielded 85.45% sensitivity and 89.19% specificity for the prediction to sustain one or more osteoporosis-associated fractures within 2 years after BMD measurement. DECT-derived BMD was significantly associated with the occurrence of new fractures (odds ratio of 0.8710, 95% CI, 0.091-0.9375, p < .001), indicating a protective effect of increased DECT-derived BMD values. Overall AUC was 0.9373 (CI, 0.867-0.977, p < .001) for the differentiation of patients who sustained osteoporosis-associated fractures within 2 years of BMD assessment. CONCLUSIONS: Retrospective DECT-based volumetric BMD assessment can accurately predict the 2-year risk to sustain an osteoporosis-associated fracture in at-risk patients without requiring a calibration phantom. Lower DECT-based BMD values are strongly associated with an increased risk to sustain fragility fractures. KEY POINTS: •Dual-energy CT-derived assessment of bone mineral density can identify patients at risk to sustain osteoporosis-associated fractures with a sensitivity of 85.45% and a specificity of 89.19%. •The DECT-derived BMD threshold for identification of at-risk patients lies above the American College of Radiology (ACR) QCT guidelines for the identification of osteoporosis (93.70 mg/cm3 vs 80 mg/cm3).

Graph matching survey for medical imaging: On the way to deep learning.

The interest on graph matching has not stopped growing since the late seventies. The basic idea of graph matching consists of generating graph representations of different data or structures and compare those representations by searching correspondences between them. There are manifold techniques that have been developed to find those correspondences and the choice of one or another depends on the characteristics of the application of interest. These applications range from pattern recognition (e.g. biometric identification) to signal processing or artificial intelligence. One of the aspects that make graph matching so attractive is its ability to facilitate data analysis, and medical imaging is one of the fields that can benefit from this in a greater extent. The potential of graph matching to find similarities and differences between data acquired at different points in time shows its potential to improve diagnosis, follow-up of human diseases or any other of the clinical scenarios that require comparison between different datasets. In spite of the large amount of papers that were published in this field to the date there is no survey paper of graph matching for clinical applications. This survey aims to fill this gap.

Impact of Intravenously Injected Contrast Agent on Bone Mineral Density Measurement in Dual-Source Dual-Energy CT.

PURPOSE: To assess the influence of intravenously injected contrast agent on bone mineral density (BMD) assessment in dual-source dual-energy CT. METHODS: This retrospective study included 1,031 patients (mean age, 53 ± 7 years; 519 women) who had undergone third-generation dual-source dual-energy CT in context of tumor staging between January 2019 and December 2019. Dedicated postprocessing software based on material decomposition was used for phantomless volumetric BMD assessment of trabecular bone of the lumbar spine. Volumetric trabecular BMD values derived from unenhanced and contrast-enhanced portal venous phase were compared by calculating correlation and agreement analyses using Pearson product-moment correlation, linear regression, and Bland-Altman plots. RESULTS: Mean BMD values were 115.53 ± 37.23 and 116.10 ± 37.78 mg/cm3 in unenhanced and contrast-enhanced dual-energy CT series, respectively. Values from contrast-enhanced portal venous phase differed not significantly from those of the unenhanced phase (p = 0.44) and showed high correlation (r = 0.971 [95% CI, 0.969-0.973]) with excellent agreement in Bland-Altman plots. Mean difference of the two phases was 0.61 mg/cm3 (95% limits of agreement, -17.14 and 18.36 mg/cm3). CONCLUSION: Portal venous phase dual-source dual-energy CT allows for accurate opportunistic BMD assessment of trabecular bone of the lumbar spine compared to unenhanced imaging. Therefore, dual-source CT may provide greater flexibility regarding BMD assessment in clinical routine and reduce radiation exposure by avoiding additional osteodensitometry examinations, as contrast-enhanced CT scans in context of tumor staging are increasingly performed in dual-energy mode.

Accuracy and precision of volumetric bone mineral density assessment using dual-source dual-energy versus quantitative CT: a phantom study.

BACKGROUND: Dual-source dual-energy computed tomography (DECT) offers the potential for opportunistic osteoporosis screening by enabling phantomless bone mineral density (BMD) quantification. This study sought to assess the accuracy and precision of volumetric BMD measurement using dual-source DECT in comparison to quantitative CT (QCT). METHODS: A validated spine phantom consisting of three lumbar vertebra equivalents with 50 (L1), 100 (L2), and 200 mg/cm3 (L3) calcium hydroxyapatite (HA) concentrations was scanned employing third-generation dual-source DECT and QCT. While BMD assessment based on QCT required an additional standardised bone density calibration phantom, the DECT technique operated by using a dedicated postprocessing software based on material decomposition without requiring calibration phantoms. Accuracy and precision of both modalities were compared by calculating measurement errors. In addition, correlation and agreement analyses were performed using Pearson correlation, linear regression, and Bland-Altman plots. RESULTS: DECT-derived BMD values differed significantly from those obtained by QCT (p < 0.001) and were found to be closer to true HA concentrations. Relative measurement errors were significantly smaller for DECT in comparison to QCT (L1, 0.94% versus 9.68%; L2, 0.28% versus 5.74%; L3, 0.24% versus 3.67%, respectively). DECT demonstrated better BMD measurement repeatability compared to QCT (coefficient of variance < 4.29% for DECT, < 6.74% for QCT). Both methods correlated well to each other (r = 0.9993; 95% confidence interval 0.9984-0.9997; p < 0.001) and revealed substantial agreement in Bland-Altman plots. CONCLUSIONS: Phantomless dual-source DECT-based BMD assessment of lumbar vertebra equivalents using material decomposition showed higher diagnostic accuracy compared to QCT.

A novel robust kernel principal component analysis for nonlinear statistical shape modeling from erroneous data.

Statistical Shape Models (SSMs) have achieved considerable success in medical image segmentation. A high quality SSM is able to approximate the main plausible variances of a given anatomical structure to guide segmentation. However, it is technically challenging to derive such a quality model because: (1) the distribution of shape variance is often nonlinear or multi-modal which cannot be modeled by standard approaches assuming Gaussian distribution; (2) as the quality of annotations in training data usually varies, heavy corruption will degrade the quality of the model as a whole. In this work, these challenges are addressed by introducing a generic SSM that is able to model nonlinear distribution and is robust to outliers in training data. Without losing generality and assuming a sparsity in nonlinear distribution, a novel Robust Kernel Principal Component Analysis (RKPCA) for statistical shape modeling is proposed with the aim of constructing a low-rank nonlinear subspace where outliers are discarded. The proposed approach is validated on two different datasets: a set of 30 public CT kidney pairs and a set of 49 MRI ankle bones volumes. Experimental results demonstrate a significantly better performance on outlier recovery and a higher quality of the proposed model as well as lower segmentation errors compared to the state-of-the-art techniques.

Linking Joint Impairment and Gait Biomechanics in Patients with Juvenile Idiopathic Arthritis.

Juvenile Idiopathic Arthritis (JIA) is a paediatric musculoskeletal disease of unknown aetiology, leading to walking alterations when the lower-limb joints are involved. Diagnosis of JIA is mostly clinical. Imaging can quantify impairments associated to inflammation and joint damage. However, treatment planning could be better supported using dynamic information, such as joint contact forces (JCFs). To this purpose, we used a musculoskeletal model to predict JCFs and investigate how JCFs varied as a result of joint impairment in eighteen children with JIA. Gait analysis data and magnetic resonance images (MRI) were used to develop patient-specific lower-limb musculoskeletal models, which were evaluated for operator-dependent variability (< 3.6°, 0.05 N kg-1 and 0.5 BW for joint angles, moments, and JCFs, respectively). Gait alterations and JCF patterns showed high between-subjects variability reflecting the pathology heterogeneity in the cohort. Higher joint impairment, assessed with MRI-based evaluation, was weakly associated to overall joint overloading. A stronger correlation was observed between impairment of one limb and overload of the contralateral limb, suggesting risky compensatory strategies being adopted, especially at the knee level. This suggests that knee overloading during gait might be a good predictor of disease progression and gait biomechanics should be used to inform treatment planning.

An image-based kinematic model of the tibiotalar and subtalar joints and its application to gait analysis in children with Juvenile Idiopathic Arthritis.

In vivo estimates of tibiotalar and the subtalar joint kinematics can unveil unique information about gait biomechanics, especially in the presence of musculoskeletal disorders affecting the foot and ankle complex. Previous literature investigated the ankle kinematics on ex vivo data sets, but little has been reported for natural walking, and even less for pathological and juvenile populations. This paper proposes an MRI-based morphological fitting methodology for the personalised definition of the tibiotalar and the subtalar joint axes during gait, and investigated its application to characterise the ankle kinematics in twenty patients affected by Juvenile Idiopathic Arthritis (JIA). The estimated joint axes were in line with in vivo and ex vivo literature data and joint kinematics variation subsequent to inter-operator variability was in the order of 1°. The model allowed to investigate, for the first time in patients with JIA, the functional response to joint impairment. The joint kinematics highlighted changes over time that were consistent with changes in the patient's clinical pattern and notably varied from patient to patient. The heterogeneous and patient-specific nature of the effects of JIA was confirmed by the absence of a correlation between a semi-quantitative MRI-based impairment score and a variety of investigated joint kinematics indexes. In conclusion, this study showed the feasibility of using MRI and morphological fitting to identify the tibiotalar and subtalar joint axes in a non-invasive patient-specific manner. The proposed methodology represents an innovative and reliable approach to the analysis of the ankle joint kinematics in pathological juvenile populations.

Investigation of the dependence of joint contact forces on musculotendon parameters using a codified workflow for image-based modelling.

The generation of subject-specific musculoskeletal models of the lower limb has become a feasible task thanks to improvements in medical imaging technology and musculoskeletal modelling software. Nevertheless, clinical use of these models in paediatric applications is still limited for what concerns the estimation of muscle and joint contact forces. Aiming to improve the current state of the art, a methodology to generate highly personalized subject-specific musculoskeletal models of the lower limb based on magnetic resonance imaging (MRI) scans was codified as a step-by-step procedure and applied to data from eight juvenile individuals. The generated musculoskeletal models were used to simulate 107 gait trials using stereophotogrammetric and force platform data as input. To ensure completeness of the modelling procedure, muscles' architecture needs to be estimated. Four methods to estimate muscles' maximum isometric force and two methods to estimate musculotendon parameters (optimal fiber length and tendon slack length) were assessed and compared, in order to quantify their influence on the models' output. Reported results represent the first comprehensive subject-specific model-based characterization of juvenile gait biomechanics, including profiles of joint kinematics and kinetics, muscle forces and joint contact forces. Our findings suggest that, when musculotendon parameters were linearly scaled from a reference model and the muscle force-length-velocity relationship was accounted for in the simulations, realistic knee contact forces could be estimated and these forces were not sensitive the method used to compute muscle maximum isometric force.

Evaluation of segmentation methods on head and neck CT: Auto-segmentation challenge 2015.

PURPOSE: Automated delineation of structures and organs is a key step in medical imaging. However, due to the large number and diversity of structures and the large variety of segmentation algorithms, a consensus is lacking as to which automated segmentation method works best for certain applications. Segmentation challenges are a good approach for unbiased evaluation and comparison of segmentation algorithms. METHODS: In this work, we describe and present the results of the Head and Neck Auto-Segmentation Challenge 2015, a satellite event at the Medical Image Computing and Computer Assisted Interventions (MICCAI) 2015 conference. Six teams participated in a challenge to segment nine structures in the head and neck region of CT images: brainstem, mandible, chiasm, bilateral optic nerves, bilateral parotid glands, and bilateral submandibular glands. RESULTS: This paper presents the quantitative results of this challenge using multiple established error metrics and a well-defined ranking system. The strengths and weaknesses of the different auto-segmentation approaches are analyzed and discussed. CONCLUSIONS: The Head and Neck Auto-Segmentation Challenge 2015 was a good opportunity to assess the current state-of-the-art in segmentation of organs at risk for radiotherapy treatment. Participating teams had the possibility to compare their approaches to other methods under unbiased and standardized circumstances. The results demonstrate a clear tendency toward more general purpose and fewer structure-specific segmentation algorithms.

Accurate Physics-Based Registration for the Outcome Validation of Minimal Invasive Interventions and Open Liver Surgeries.

OBJECTIVE: The purpose of this paper is to present an outcome validation tool for tumor radiofrequency (RF) ablation and resection. METHODS: Intervention assessment tools require an accurate registration of both pre- and postoperative computed tomographies able to handle big deformations. Therefore, a physics-based method is proposed with that purpose. To increase the accuracy both automatically detected internal and surface physical landmarks are incorporated in the registration process. RESULTS: The algorithm has been evaluated in 25 clinical datasets containing RF ablations, resections, and patients with recurrent tumors. The achieved accuracy is 1.2 mm measured as mean internal distance between vessel landmarks and a positive predictive value of 0.95. The quantitative and qualitative results of the outcome validation tool show that in 50% of the cases tumors were only partially covered by the treatment. CONCLUSION: The use of internal and surface landmarks combined with a physics-based registration method increases the accuracy of the results compared to the accuracy of state of the art methods. An accurate outcome validation tool is important in order to certify that the tumor and its safety margin were fully covered by the treatment. SIGNIFICANCE: An accurate outcome validation tool can result in a decrease of the tumor recurrence rate.

A Patient-Specific Foot Model for the Estimate of Ankle Joint Forces in Patients with Juvenile Idiopathic Arthritis.

Juvenile idiopathic arthritis (JIA) is the leading cause of childhood disability from a musculoskeletal disorder. It generally affects large joints such as the knee and the ankle, often causing structural damage. Different factors contribute to the damage onset, including altered joint loading and other mechanical factors, associated with pain and inflammation. The prediction of patients' joint loading can hence be a valuable tool in understanding the disease mechanisms involved in structural damage progression. A number of lower-limb musculoskeletal models have been proposed to analyse the hip and knee joints, but juvenile models of the foot are still lacking. This paper presents a modelling pipeline that allows the creation of juvenile patient-specific models starting from lower limb kinematics and foot and ankle MRI data. This pipeline has been applied to data from three children with JIA and the importance of patient-specific parameters and modelling assumptions has been tested in a sensitivity analysis focused on the variation of the joint reaction forces. This analysis highlighted the criticality of patient-specific definition of the ankle joint axes and location of the Achilles tendon insertions. Patient-specific detection of the Tibialis Anterior, Tibialis Posterior, and Peroneus Longus origins and insertions were also shown to be important.

Quantitative dual-energy CT for phantomless evaluation of cancellous bone mineral density of the vertebral pedicle: correlation with pedicle screw pull-out strength.

OBJECTIVES: To evaluate quantitative dual-energy computed tomography (DECT) for phantomless analysis of cancellous bone mineral density (BMD) of vertebral pedicles and to assess the correlation with pedicle screw pull-out strength. METHODS: Twenty-nine thoracic and lumbar vertebrae from cadaver specimens were examined with DECT. Using dedicated post-processing software, a pedicle screw vector was mapped (R1, intrapedicular segment of the pedicle vector; R2, intermediate segment; R3, intracorporal segment; global, all segments) and BMD was calculated. To invasively evaluate pedicle stability, pedicle screws were drilled through both pedicles and left pedicle screw pull-out strength was measured. Resulting values were correlated using the paired t test and Pearson's linear correlation. RESULTS: Average pedicle screw vector BMD (R1, 0.232 g/cm(3); R2, 0.166 g/cm(3); R3, 0.173 g/cm(3); global, 0.236 g/cm(3)) showed significant differences between R1-R2 (P < 0.002) and R1-R3 (P < 0.034) segments while comparison of R2-R3 did not reach significance (P > 0.668). Average screw pull-out strength (639.2 N) showed a far stronger correlation with R1 (r = 0.80; P < 0.0001) than global BMD (r = 0.42; P = 0.025), R2 (r = 0.37; P = 0.048) and R3 (r = -0.33; P = 0.078) segments. CONCLUSIONS: Quantitative DECT allows for phantomless BMD assessment of the vertebral pedicle. BMD of the intrapedicular segment shows a significantly stronger correlation with pedicle screw pull-out strength than other segments. KEY POINTS: • Quantitative dual-energy CT enables evaluation of pedicle bone mineral density. • Intrapedicular segments show significant differences regarding bone mineral density. • Pedicle screw pull-out strength correlated strongest with R1 values. • Dual-energy CT may improve preoperative assessment before transpedicular screw fixation.

Single-portal-phase low-tube-voltage dual-energy CT for short-term follow-up of acute pancreatitis: evaluation of CT severity index, interobserver agreement and radiation dose.

OBJECTIVES: To intra-individually compare single-portal-phase low-tube-voltage (100-kVp) computed tomography (CT) with 120-kVp images for short-term follow-up assessment of CT severity index (CTSI) of acute pancreatitis, interobserver agreement and radiation dose. METHODS: We retrospectively analysed 66 patients with acute pancreatitis who underwent initial dual-contrast-phase CT (unenhanced, arterial, portal phase) at admission and short-term (mean interval 11.4 days) follow-up dual-contrast-phase dual-energy CT. The 100-kVp and linearly blended images representing 120-kVp acquisition follow-up CT images were independently evaluated by three radiologists using a modified CTSI assessing pancreatic inflammation, necrosis and extrapancreatic complications. Scores were compared with paired t test and interobserver agreement was evaluated using intraclass correlation coefficients (ICC). RESULTS: Mean CTSI scores on unenhanced, portal- and dual-contrast-phase images were 4.9, 6.1 and 6.2 (120 kVp) and 5.0, 6.0 and 6.1 (100 kVp), respectively. Contrast-enhanced series showed a higher CTSI compared to unenhanced images (P < 0.05) but no significant differences between single- and dual-contrast-phase series (P > 0.7). CTSI scores were comparable for 100-kVp and 120-kVp images (P > 0.05). Interobserver agreement was substantial for all evaluated series and subcategories (ICC 0.67-0.93). DLP of single-portal-phase 100-kVp images was reduced by 41 % compared to 120-kVp images (363.8 versus 615.9 mGy cm). CONCLUSIONS: Low-tube-voltage single-phase 100-kVp CT provides sufficient information for follow-up evaluation of acute pancreatitis and significantly reduces radiation exposure. KEY POINTS: • Single-portal-phase CT provides sufficient evaluation for follow-up of acute pancreatitis. • Follow-up CT does not benefit from unenhanced or arterial-phase acquisition. • CT severity index scores are equal for dual-contrast-phase 100-/120-kVp acquisition (P > 0.05). • 100-kVp single-portal-phase follow-up CT of acute pancreatitis significantly reduces radiation exposure.

Dual-energy CT-based phantomless in vivo three-dimensional bone mineral density assessment of the lumbar spine.

PURPOSE: To evaluate the feasibility of phantomless in vivo dual-energy computed tomography (CT)-based three-dimensional (3D) bone mineral density (BMD) assessment in comparison with dual x-ray absorptiometry (DXA). MATERIALS AND METHODS: This retrospective study was approved by the institutional review board, and the requirement to obtain informed consent was waived. Data from clinically indicated dual-energy CT and DXA examinations within 2 months, comprising the lumbar spine of 40 patients, were included. By using automated dedicated postprocessing dual-energy CT software, the trabecular bone of lumbar vertebrae L1-L4 were analyzed and segmented. A mixed-effects model was used to assess the correlations between BMD values derived from dual-energy CT and DXA. RESULTS: One hundred sixty lumbar vertebrae were analyzed in 40 patients (mean age, 57.1 years; range, 24-85 years), 21 male (mean age, 54.3 years; range, 24-85 years) and 19 female (mean age, 58.5 years; range, 31-80 years). Mean BMD of L1-L4 determined with DXA was 0.995 g/cm(2), and 18 patients (45%) showed an osteoporotic BMD (T score less than -2.5) of at least two vertebrae. Mean dual-energy CT-based BMD of L1-L4 was 0.254 g/cm(3). Bland-Altman analysis with mixed effects demonstrated a lack of correlation between dual-energy CT-based and DXA-based BMD values, with a mean difference of 0.7441 and 95% limits of agreement of 0.7441 ± 0.4080. CONCLUSION: Dedicated postprocessing of dual-energy CT data allows for phantomless in vivo BMD assessment of the trabecular bone of lumbar vertebrae and enables freely rotatable color-coded 3D visualization of intravertebral BMD distribution.

Model-based pancreas segmentation in portal venous phase contrast-enhanced CT images.

This study aims to automatically detect and segment the pancreas in portal venous phase contrast-enhanced computed tomography (CT) images. The institutional review board of the University of Erlangen-Nuremberg approved this study and waived the need for informed consent. Discriminative learning is used to build a pancreas tissue classifier incorporating spatial relationships between the pancreas and surrounding organs and vessels. Furthermore, discrete cosine and wavelet transforms are used to build texture features to describe local tissue appearance. Classification is used to guide a constrained statistical shape model to fit the data. The algorithm to detect and segment the pancreas was evaluated on 40 consecutive CT data that were acquired in the portal venous contrast agent phase. Manual segmentation of the pancreas was carried out by experienced radiologists and served as reference standard. Threefold cross validation was performed. The algorithm-based detection and segmentation yielded an average surface distance of 1.7 mm and an average overlap of 61.2 % compared with the reference standard. The overall runtime of the system was 20.4 min. The presented novel approach enables automatic pancreas segmentation in portal venous phase contrast-enhanced CT images which are included in almost every clinical routine abdominal CT examination. Reliable pancreatic segmentation is crucial for computer-aided detection systems and an organ-specific decision support.