Digital Tomosynthesis Images Research Articles

Diagnostic yield of dental radiography and digital tomosynthesis for the identification of anatomic structures in cats.

Digital tomosynthesis (DT) has emerged as a potential imaging modality for evaluating anatomic structures in veterinary medicine. This study aims to validate the diagnostic yield of DT in identifying predefined anatomic structures in feline cadaver heads, comparing it with conventional intraoral dental radiography (DR). A total of 16 feline cadaver heads were utilized to evaluate 19 predefined clinically relevant anatomic structures using both DR and DT. A semi-quantitative scoring system was employed to characterize the ability of each imaging method to identify these structures. DT demonstrated a significantly higher diagnostic yield compared to DR for all evaluated anatomic structures. Orthogonal DT imaging identified 13 additional anatomic landmarks compared to a standard 10-view feline set obtained via DR. Moreover, DT achieved statistically significant higher scores for each of these landmarks, indicating improved visualization over DR. These findings validate the utility of DT technology in reliably identifying clinically relevant anatomic structures in the cat skull. This validation serves as a foundation for further exploration of DT imaging in detecting dentoalveolar and other maxillofacial bony lesions and pathologies in cats.

Effect of network architectures in multi-agent reinforcement learning for denoising digital tomosynthesis images

Effect of network architectures in multi-agent reinforcement learning for denoising digital tomosynthesis images

Deformable 3D/3D CT-to-digital-tomosynthesis image registration in image-guided bronchoscopy interventions

Traditional navigational bronchoscopy procedures rely on preprocedural computed tomography (CT) and intraoperative chest radiography and cone-beam CT (CBCT) to biopsy peripheral lung lesions. This navigational approach is challenging due to the projective nature of radiography, and the high radiation dose, long imaging time, and large footprints of CBCT. Digital tomosynthesis (DTS) is considered an attractive alternative combining the advantages of radiography and CBCT. Only the depth resolution cannot match a full CBCT image due to the limited angle acquisition. To address this issue, preoperative CT is a good auxiliary in guiding bronchoscopy interventions. Nevertheless, CT-to-body divergence caused by anatomic changes and respiratory motion, hinders the effective use of CT imaging. To mitigate CT-to-body divergence, we propose a novel deformable 3D/3D CT-to-DTS registration algorithm employing a multistage, multiresolution approach and using affine and elastic B-spline transformation models with bone and lung mask images. A multiresolution strategy with a Gaussian image pyramid and a multigrid strategy within the B-spline model are applied. The normalized correlation coefficient is included in the cost function for the affine model and a multimetric weighted cost function is used for the B-spline model, with weights determined heuristically. Tested on simulated and real patient bronchoscopy data, the algorithm yields promising results. Assessed qualitatively by visual inspection and quantitatively by computing the Dice coefficient (DC) and the average symmetric surface distance (ASSD), the algorithm achieves mean DC of 0.82±0.05 and 0.74±0.05, and mean ASSD of 0.65±0.29mm and 0.93±0.43mm for simulated and real data, respectively. This algorithm lays the groundwork for CT-aided intraoperative DTS imaging in image-guided bronchoscopy interventions with future studies focusing on automated metric weight setting.

A denoising model based on multi-agent reinforcement learning with data transformation for digital tomosynthesis

Objective. Denoising models based on the supervised learning have been proposed for medical imaging. However, its clinical availability in digital tomosynthesis (DT) imaging is limited due to the necessity of a large amount of training data for providing acceptable image quality and the difficulty in minimizing a loss. Reinforcement learning (RL) can provide the optimal pollicy, which maximizes a reward, with a small amount of training data for implementing a task. In this study, we presented a denoising model based on the multi-agent RL for DT imaging in order to improve the performance of the machine learning-based denoising model. Approach. The proposed multi-agent RL network consisted of shared sub-network, value sub-network with a reward map convolution (RMC) technique and policy sub-network with a convolutional gated recurrent unit (convGRU). Each sub-network was designed for implementing feature extraction, reward calculation and action execution, respectively. The agents of the proposed network were assigned to each image pixel. The wavelet and Anscombe transformations were applied to DT images for delivering precise noise features during network training. The network training was implemented with the DT images obtained from the three-dimensional digital chest phantoms, which were constructed by using clinical CT images. The performance of the proposed denoising model was evaluated in terms of signal-to-noise ratio (SNR), structural similarity (SSIM) and peak signal-to-noise ratio (PSNR). Main results. Comparing the supervised learning, the proposed denoising model improved the SNRs of the output DT images by 20.64% while maintaining the similar SSIMs and PSNRs. In addition, the SNRs of the output DT images with the wavelet and Anscombe transformations were 25.88 and 42.95% higher than that for the supervised learning, respectively. Significance. The denoising model based on the multi-agent RL can provide high-quality DT images, and the proposed method enables the performance improvement of machine learning-based denoising models.

AI-based computer-aided diagnostic system of chest digital tomography synthesis: Demonstrating comparative advantage with X-ray-based AI systems

Background:Compared with chest X-ray (CXR) imaging, which is a single image projected from the front of the patient, chest digital tomosynthesis (CDTS) imaging can be more advantageous for lung lesion detection because it acquires multiple images projected from multiple angles of the patient. Various clinical comparative analysis and verification studies have been reported to demonstrate this, but there is no artificial intelligence (AI)-based comparative analysis studies. Existing AI-based computer-aided detection (CAD) systems for lung lesion diagnosis have been developed mainly based on CXR images; however, CAD-based on CDTS, which uses multi-angle images of patients in various directions, has not been proposed and verified for its usefulness compared to CXR-based counterparts.Background and objective:This study develops and tests a CDTS-based AI CAD system to detect lung lesions to demonstrate performance improvements compared to CXR-based AI CAD.Methods:We used multiple (e.g., five) projection images as input for the CDTS-based AI model and a single-projection image as input for the CXR-based AI model to compare and evaluate the performance between models. Multiple/single projection input images were obtained by virtual projection on the three-dimensional (3D) stack of computed tomography (CT) slices of each patient’s lungs from which the bed area was removed. These multiple images result from shooting from the front and left and right 30/60∘. The projected image captured from the front was used as the input for the CXR-based AI model. The CDTS-based AI model used all five projected images. The proposed CDTS-based AI model consisted of five AI models that received images in each of the five directions, and obtained the final prediction result through an ensemble of five models. Each model used WideResNet-50. To train and evaluate CXR- and CDTS-based AI models, 500 healthy data, 206 tuberculosis data, and 242 pneumonia data were used, and three three-fold cross-validation was applied.Results:The proposed CDTS-based AI CAD system yielded sensitivities of 0.782 and 0.785 and accuracies of 0.895 and 0.837 for the (binary classification) performance of detecting tuberculosis and pneumonia, respectively, against normal subjects. These results show higher performance than the sensitivity of 0.728 and 0.698 and accuracies of 0.874 and 0.826 for detecting tuberculosis and pneumonia through the CXR-based AI CAD, which only uses a single projection image in the frontal direction. We found that CDTS-based AI CAD improved the sensitivity of tuberculosis and pneumonia by 5.4% and 8.7% respectively, compared to CXR-based AI CAD without loss of accuracy.Conclusions:This study comparatively proves that CDTS-based AI CAD technology can improve performance more than CXR. These results suggest that we can enhance the clinical application of CDTS. Our code is available at https://github.com/kskim-phd/CDTS-CAD-P.

Digital Tomosynthesis as a Problem-Solving Technique to Confirm or Exclude Pulmonary Lesions in Hidden Areas of the Chest.

To evaluate the capability of digital tomosynthesis (DTS) to characterize suspected pulmonary lesions in the so-called hidden areas at chest X-ray (CXR). Among 726 patients with suspected pulmonary lesions at CXR who underwent DTS, 353 patients (201 males, 152 females; age 71.5 ± 10.4 years) revealed suspected pulmonary lesions in the apical, hilar, retrocardiac, or paradiaphragmatic lung zones and were retrospectively included. Two readers analyzed CXR and DTS images and provided a confidence score: 1 or 2 = definitely or probably benign pulmonary or extra-pulmonary lesion, or pulmonary pseudo-lesion deserving no further diagnostic work-up; 3 = indeterminate lesion; 4 or 5 = probably or definitely pulmonary lesion deserving further diagnostic work-up by CT. The nature of DTS findings was proven by CT (n = 108) or CXR during follow-up (n = 245). In 62/353 patients the suspected lung lesions were located in the lung apex, in 92/353 in the hilar region, in 59/353 in the retrocardiac region, and in 140/353 in the paradiaphragmatic region. DTS correctly characterized the CXR findings as benign pulmonary or extrapulmonary lesion (score 1 or 2) in 43/62 patients (69%) in the lung apex region, in 56/92 (61%) in the pulmonary hilar region, in 40/59 (67%) in the retrocardiac region, and in 106/140 (76%) in the paradiaphragmatic region, while correctly recommending CT in the remaining cases due to the presence of true solid pulmonary lesion, with the exception of 22 false negative findings (60 false positive findings). DTS showed a significantly (p < 0.05) increased sensitivity, specificity, and overall diagnostic accuracy and area under ROC curve compared to CXR alone. DTS allowed confirmation or exclusion of the presence of true pulmonary lesions in the hidden areas of the chest.

Advances in Bone Joint Imaging-Metal Artifact Reduction.

Numerous types of metal implants have been introduced in orthopedic surgery and are used in everyday practice. To precisely evaluate the postoperative condition of arthroplasty or trauma surgery, periprosthetic infection, and the loosening of implants, it is important to reduce artifacts induced by metal implants. In this review, we focused on technical advances in metal artifact reduction using digital tomosynthesis, computed tomography, and magnetic resonance imaging. We discussed new developments in diagnostic imaging methods and the continuous introduction of novel technologies to reduce metal artifacts; however, these innovations have not yet completely removed metal artifacts. Different algorithms need to be selected depending on the size, shape, material and implanted body parts of an implant. Future advances in metal artifact reduction algorithms and techniques and the development of new sequences may enable further reductions in metal artifacts even on original images taken previously. Moreover, the combination of different imaging modalities may contribute to further reductions in metal artifacts. Clinicians must constantly update their knowledge and work closely with radiologists to select the best diagnostic imaging method for each metal implant.

Usefulness of digital tomosynthesis in diagnosing cervical ossification of the posterior longitudinal ligament: a comparative study with other imaging modalities.

The diagnosis and classification of ossification of the posterior longitudinal ligament (OPLL) can be difficult with radiography alone; therefore, computed tomography (CT) is also usually performed. There are many reports on the usefulness of digital tomosynthesis (DTS) for image analysis in orthopedics. This study aimed to compare the accuracy of DTS with radiography and CT for the diagnosis and classification of cervical OPLL (C-OPLL). We included 31 patients with OPLL and 30 with cervical spondylotic myelopathy. The patients' cervical spine radiography, DTS, and CT images were each evaluated twice by three specialists and three residents. In the intra-observer reliability study, there was one observer with a fair level of kappa values for radiography and DTS among three residents. The kappa values for CT were the best for all observers. In the inter-observer reliability study, the interclass correlation coefficient (ICC) values were high for both diagnosis and classification by specialists at the almost perfect level for all three imaging modalities. On the other hand, the ICC values for both diagnosis and classification for radiography by the residents were lower than those for DTS and CT. This study revealed that DTS may be an alternative to CT for the diagnosis and classification of C-OPLL by specialists. Caution should be exercised in diagnosing and classifying C-OPLL using radiography and DTS by residents, and the use of CT is recommended.

"Digital Tomosynthesis" As a Technique for the Evaluation of Endobronchial Stents in Lung Transplant Recipients.

Lung transplant patients often suffer from posttransplant airway pathologies that require placement of endobronchial stents. In addition to surveillance bronchoscopy, patients often undergo radiographic stent evaluations. Chest x-rays are extremely limited in their ability to diagnose stent complications, so many patients require chest computed tomography (CT) scans for stent evaluation. Chest CT scans are costly and expose patients to higher cumulative radiation doses. Digital tomosynthesis (DTS) is an imaging modality that provides high-resolution images using limited angle tomography. The costs and radiation doses are comparable to conventional x-ray. We present a series of 4 postlung transplant patients with bronchial stents in whom we performed DTS and chest x-ray simultaneously. The DTS images were far superior to chest x-ray and comparable with CT in evaluating the placement and patency of the stents, especially in the case of silicone stents. Furthermore, the improved resolution provided clinically relevant diagnostic information that resulted in therapeutic bronchoscopy for suctioning of mucus impaction in one of the patients.

Shape-Based Breast Lesion Classification Using Digital Tomosynthesis Images: The Role of Explainable Artificial Intelligence

Computer-aided diagnosis (CAD) systems can help radiologists in numerous medical tasks including classification and staging of the various diseases. The 3D tomosynthesis imaging technique adds value to the CAD systems in diagnosis and classification of the breast lesions. Several convolutional neural network (CNN) architectures have been proposed to classify the lesion shapes to the respective classes using a similar imaging method. However, not only is the black box nature of these CNN models questionable in the healthcare domain, but so is the morphological-based cancer classification, concerning the clinicians. As a result, this study proposes both a mathematically and visually explainable deep-learning-driven multiclass shape-based classification framework for the tomosynthesis breast lesion images. In this study, authors exploit eight pretrained CNN architectures for the classification task on the previously extracted regions of interests images containing the lesions. Additionally, the study also unleashes the black box nature of the deep learning models using two well-known perceptive explainable artificial intelligence (XAI) algorithms including Grad-CAM and LIME. Moreover, two mathematical-structure-based interpretability techniques, i.e., t-SNE and UMAP, are employed to investigate the pretrained models’ behavior towards multiclass feature clustering. The experimental results of the classification task validate the applicability of the proposed framework by yielding the mean area under the curve of 98.2%. The explanability study validates the applicability of all employed methods, mainly emphasizing the pros and cons of both Grad-CAM and LIME methods that can provide useful insights towards explainable CAD systems.

Diagnostic Accuracy of Chest Digital Tomosynthesis in Patients Recovering after COVID-19 Pneumonia.

Purpose: To assess the diagnostic accuracy of traditional chest X-ray (CXR) and digital tomosynthesis (DTS) compared to computed tomography (CT) in detecting pulmonary interstitial changes in patients having recovered from severe COVID-19. Materials and Methods: This was a retrospective observational study, and received local ethics committee approval. Patients suspected of having COVID-19 pneumonia upon emergency department admission between 1 March and 31 August 2020, and who underwent CXR followed by DTS and CT, were considered. Inclusion criteria were as follows: (1) patients with previous SARS-CoV-2 infection proven by a positive RT-PCR on nasopharyngeal swabs performed upon admission to the hospital, and with complete clinical recovery; (2) a diagnosis of SARS-CoV-2-related ARDS, according to the Berlin criteria, during hospitalization; (3) no recent history of other lung disease; and (4) complete imaging follow-up by CXR, DTS, and CT for at least 6 months and up to one year. Analysis of DTS images was carried out independently by two radiologists with 16 and 10 years of experience in chest imaging, respectively. The following findings were evaluated: (1) ground-glass opacities (GGOs); (2) air-space consolidations with or without air bronchogram; (3) reticulations; and (4) linear consolidation. Indicators of diagnostic performance of RX and digital tomosynthesis were calculated using CT as a reference. All data were analyzed using R statistical software (version 4.0.2, 2020). Results: Out of 44 patients initially included, 25 patients (17 M/8 F), with a mean age of 64 years (standard deviation (SD): 12), met the criteria and were included. The overall average numbers of findings confirmed by CT were GGOs in 11 patients, lung consolidations in 8 patients, 7 lung interstitial reticulations, and linear consolidation in 20 patients. DTS showed a significantly higher diagnostic accuracy compared to CXR in recognizing interstitial lung abnormalities—especially GGOs (p = 0.0412) and linear consolidations (p = 0.0009). The average dose for chest X-ray was 0.10 mSv (0.07–0.32), for DTS was 1.03 mSv (0.74–2.00), and for CT scan was 3 mSv. Conclusions: According to our results, DTS possesses a high diagnostic accuracy, compared with CXR, in revealing lung fibrotic changes in patients who have recovered from COVID-19 pneumonia.

A Novel Quantitative Evaluation of Bone Formation After Opening Wedge High Tibial Osteotomy Using Tomosynthesis.

This study aimed to establish and validate a novel evaluation method using digital tomosynthesis to quantify bone formation in the gap after opening wedge high tibial osteotomy (OW-HTO). We retrospectively analyzed bone formation in the gap in 22 patients who underwent OW-HTO using digital tomosynthesis at 1, 2, 3, 6, 9, and 12months postoperatively. Bone formation was semi-quantitatively assessed using the modified van Hemert's score and density measurements on digital tomosynthesis images. The gap filling value (GFV) was calculated as the ratio of the intensities of the opening gap and the tibial shaft. In addition, the relationship between the modified van Hemert's score and GFV was evaluated. The reproducibility of GFV had an interclass correlation coefficient (ICC [1,2]) of 0.958 for intraobserver reliability and an ICC (2,1) of 0.975 for interobserver reliability. The GFV increased in a time-dependent manner and was moderately correlated with the modified van Hemert's score (r = 0.630, p &lt; 0.001). The GFV plateaued at 6months postoperatively. In addition, the GFV was higher in patientswith a modified van Hemert's score of 2 than in patientswith a modified van Hemert's score of 3 (p = 0.008). The GFVs obtained using digital tomosynthesis can be used to assess postoperative bone formation in the opening gap after OW-HTO with high accuracy and reproducibility.

The relationship of whole human vertebral body creep to bone density and texture via clinically available imaging modalities

Creep deformation of human vertebrae accumulates under physiological levels of load and is understood to contribute to the progression toward clinically observable vertebral fracture. However, little information is available in terms of clinically measurable predictors of creep behavior in human vertebrae. In this study, creep tests were performed on 22 human cadaveric T12 vertebrae (13 male, 9 female; age 41–90). Areal and volumetric bone density parameters were measured from the same specimens using dual x-ray absorptiometry and high resolution computed tomography. Image textural analyses (which probe the organization of image intensities within the cancellous bone in low resolution clinical imaging) were performed using digital tomosynthesis (DTS) images. Multiple regression models were constructed to examine the relationship between creep properties and bone density and DTS image textural parameters. For the standard clinical imaging configuration, models including DTS derived image textural parameters alone were generally more explanatory (adjusted R2: 0.14–0.68) than those with bone density parameters forced in the models (adjusted R2: 0.17–0.61). Metrics of textural heterogeneity and anisotropy presented as the most explanatory imaging markers for creep deformation and recovery from creep. These metrics of image texture may help provide, independent from bone mass, important clinically measurable indicators of the time dependent deformation of human vertebrae.

Diagnostic performance of digital tomosynthesis to evaluate silicone airway stents and related complications.

BackgroundDigital tomosynthesis (DTS) is an imaging technique with benefits in reconstructing sequential cross-sectional images. We evaluated the diagnostic performance of DTS for silicone airway stents and stent-related complications in patients who underwent bronchoscopic intervention.MethodsThis retrospective study included patients who underwent bronchoscopic intervention after chest radiography (CXR) and DTS examinations from September 2013 to August 2020. The interval between CXR, DTS, and bronchoscopic intervention was a maximum of 10 days. CXR and DTS images were evaluated using a bronchoscopic view as a reference. We calculated the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for assessing the diagnostic performance.ResultsThe total CXR, DTS, and bronchoscopic intervention-matching datasets comprised 213 cases from 119 patients and, silicone stents were present in 167 of them. The ability of DTS to detect silicone stents was better than that of CXR (sensitivity, 92.8% vs. 71.3%, P<0.001). Of the 167 cases with silicone stents, 53 experienced stent migration and 121 experienced stent obstructions due to granulation tissue or fibrosis. The sensitivity for detecting stent migration was also higher with DTS than with CXR (45.3% vs. 24.5%, P=0.025). The sensitivity for detecting the stent obstruction was better with DTS than with CXR (64.5% vs. 19.0%, P<0.001).ConclusionsDTS was more sensitive and accurate in revealing silicone airway stents and silicone stent-related complications than CXR. However, there were limitations in confirming stent migration and obstruction with DTS due to granulation tissue growth and fibrosis.

Improved detection of solitary pulmonary nodules on radiographs compared with deep bone suppression imaging.

The present study aimed to investigate whether deep bone suppression imaging (BSI) could increase the diagnostic performance for solitary pulmonary nodule detection compared with digital tomosynthesis (DTS), dual-energy subtraction (DES) radiography, and conventional chest radiography (CCR). A total of 256 patients (123 with a solitary pulmonary nodule, 133 with normal findings) were included in the study. The confidence score of 6 observers determined the presence or absence of pulmonary nodules in each patient. These were first analyzed using a CCR image, then with CCR plus deep BSI, then with CCR plus DES radiography, and finally with DTS images. Receiver-operating characteristic curves were used to evaluate the performance of the 6 observers in the detection of pulmonary nodules. For the 6 observers, the average area under the curve improved significantly from 0.717 with CCR to 0.848 with CCR plus deep BSI (P<0.01), 0.834 with CCR plus DES radiography (P<0.01), and 0.939 with DTS (P<0.01). Comparisons between CCR and CCR plus deep BSI found that the sensitivities of the assessments by the 3 residents increased from 53.2% to 69.5% (P=0.014) for nodules located in the upper lung field, from 30.6% to 44.6% (P=0.015) for nodules that were partially/completely obscured by the bone, and from 33.2% to 45.8% (P=0.006) for nodules <10 mm. The deep BSI technique can significantly increase the sensitivity of radiology residents for solitary pulmonary nodules compared with CCR. Increased detection was seen mainly for smaller nodules, nodules with partial/complete obscuration, and nodules located in the upper lung field.

Novel method for selecting slices of the same cross-sectional view from digital tomosynthesis for monitoring posterior spinal instrumentation

BackgroundA method to evaluate pedicle screw loosening on digital tomosynthesis images is yet to be established owing to lack of methods for selecting slices of the same cross-sectional view. We aimed to develop an objective method for selecting slices of the same cross-sectional view on digital tomosynthesis images. MethodsFirst, an objective method of pixel selection was developed by measuring the size of glass disk and titanium alloy screw on digital tomosynthesis images followed by comparison with the actual sizes. Second, a method for selecting slices of the same cross-sectional view was explored on a bone model with posterior spinal instrumentation using the screw centerline and rod curvature as indicators of the same cross section. The angle between the screw centerline and rod was calculated to verify the accuracy in obtaining the same cross-sectional view. Third, the method for selecting slices of the same cross-sectional view was applied to six patients after posterior lumbar spinal instrumentation. ResultsThe pixel selection method enabled objective determination of a pixel on the peripheral lines of objects with an error as low as 200 μm in distance measurements on titanium alloy and glass. The mean differences of rod-screw angles between two slices were less than 1° and were not statistically significant in the bone model and patient images. ConclusionA method for selecting slices of the same cross-sectional view on digital tomosynthesis images was successfully developed. This method can enable objective and quantitative evaluations of pedicle screw loosening.

Use of Digital Tomosynthesis in Assessing Accurate Medial Epicondyle Fracture Displacement as Compared With Conventional Radiography and Computed Tomography.

Introduction:Medial epicondyle fracture displacement is notoriously difficult to determine on conventional radiography, and follow-up computed tomography (CT) is often obtained to measure precise displacement. Another option for fracture characterization is digital tomosynthesis (DT), a technology providing high in-plane resolution of bony anatomy by acquiring multiple low-dose images in a linear arc. Advantages of DT include lower radiation exposure and lower cost than CT, rapid image acquisition, and a similar patient experience to conventional radiography. The digital application of tomosynthesis is relatively new and is integrated as an add-on feature with modern radiography equipment. This study compares DT, CT and conventional radiography for measurement accuracy in medial epicondyle fractures with the goal of determining relative accuracy in measuring medial epicondyle fracture displacement.Methods:Medial epicondyle fractures were created in 5 cadaveric elbow specimens. Each specimen was imaged with conventional radiography, DT, and CT. True displacement measured by digital calipers was compared with “measured” displacement for each image acquisition. CT images included axial, sagittal, and coronal reformats. DT images of the elbow included anteroposterior (AP) longitudinal and transverse, lateral longitudinal and transverse, and axial longitudinal and transverse. Conventional radiographs included AP, lateral, and axial distal humerus images. Four physicians reviewed all images 3 months later. Each reviewer independently measured maximum apparent fracture displacement to the nearest 0.1 mm. Measurement accuracy was calculated as percent difference [(measured displacement−actual displacement)/actual displacement] for each acquisition. Mean, median, and SD for measurement accuracy were calculated. Two-tailed paired t tests were performed on each acquisition to compare the measurement accuracy.Results:Compared with conventional radiographs, accuracy of DT was superior in AP longitudinal (P=0.03), AP transverse (P=0.01), axial longitudinal (P=0.0001), and axial transverse projections (P=0.001). Accuracy of CT was superior to conventional radiography in the AP projection (P=0.03), but was equivalent in the axial projection (P=0.9). Accuracy of CT was similar to DT in AP longitudinal (P=0.6), AP transverse (P=0.5), and axial longitudinal projections (P=0.07). Accuracy of DT in the axial transverse projection was superior to CT (P=0.03).Conclusion:DT is more accurate than conventional radiography (both AP and axial views) and as accurate as CT in assessing millimeters of displacement of medial epicondyle fracture fragments.Level of Evidence:Level IV—diagnostic study.

Quantitative Breast Density in Contrast-Enhanced Mammography.

Contrast-enhanced mammography (CEM) demonstrates a potential role in personalized screening models, in particular for women at increased risk and women with dense breasts. In this study, volumetric breast density (VBD) measured in CEM images was compared with VBD obtained from digital mammography (DM) or tomosynthesis (DBT) images. A total of 150 women who underwent CEM between March 2019 and December 2020, having at least a DM/DBT study performed before/after CEM, were included. Low-energy CEM (LE-CEM) and DM/DBT images were processed with automatic software to obtain the VBD. VBDs from the paired datasets were compared by Wilcoxon tests. A multivariate regression model was applied to analyze the relationship between VBD differences and multiple independent variables certainly or potentially affecting VBD. Median VBD was comparable for LE-CEM and DM/DBT (12.73% vs. 12.39%), not evidencing any statistically significant difference (p = 0.5855). VBD differences between LE-CEM and DM were associated with significant differences of glandular volume, breast thickness, compression force and pressure, contact area, and nipple-to-posterior-edge distance, i.e., variables reflecting differences in breast positioning (coefficient of determination 0.6023; multiple correlation coefficient 0.7761). Volumetric breast density was obtained from low-energy contrast-enhanced spectral mammography and was not significantly different from volumetric breast density measured from standard mammograms.

Role of digital tomosynthesis in the context of tuberculosis contact investigation: comparisons with digital radiography.

Chest radiography value as a screening tool in those exposed to pulmonary tuberculosis (TB) is reduced by its lower sensitivity to detect small intrapulmonary lesions. To evaluate the efficacy of digital tomosynthesis (DTS) screening of individuals that had contacted persons with active TB using low-dose computed tomography (CT) as the reference standard methods. This retrospective, community-based screening study of 90 adults who had been in close contact with a TB case was undertaken at our institution. All individuals underwent clinical evaluation, digital radiography (DR), DTS, and low-dose chest CT. Observers assessed and classified DR and DTS images using CT as the reference-standard method. Based on clinical and imaging findings, TB status was classified as normal, latent, minimal, subclinical, and active. Diagnostic performances of DTS and DR for the interpretation of correct diagnosis were calculated. The estimated effective doses for DR, DTS, and low-dose CT were 0.01 mSv, 0.1 mSv, and 0.33 mSv, respectively. TB statuses of the 90 individuals were as follows: 62 latent (68.9%); two subclinical (2.2%); and one minimal (1.1%). The sensitivities, specificities, and accuracies of DTS and DR in the interpretation of correct diagnosis were 75.8%, 100%, 91.1% and 48.5%, 96.5%, 78.9%, respectively. DTS appears to be superior to DR for the detection of lung lesions in individuals with TB contacts. DTS can offer a reasonable option for TB contact investigation.

Enhancing digital tomosynthesis (DTS) for lung radiotherapy guidance using patient-specific deep learning model

Digital tomosynthesis (DTS) has been proposed as a fast low-dose imaging technique for image-guided radiation therapy (IGRT). However, due to the limited scanning angle, DTS reconstructed by the conventional FDK method suffers from significant distortions and poor plane-to-plane resolutions without full volumetric information, which severely limits its capability for image guidance. Although existing deep learning-based methods showed feasibilities in restoring volumetric information in DTS, they ignored the inter-patient variabilities by training the model using group patients. Consequently, the restored images still suffered from blurred and inaccurate edges. In this study, we presented a DTS enhancement method based on a patient-specific deep learning model to recover the volumetric information in DTS images. The main idea is to use the patient-specific prior knowledge to train the model to learn the patient-specific correlation between DTS and the ground truth volumetric images. To validate the performance of the proposed method, we enrolled both simulated and real on-board projections from lung cancer patient data. Results demonstrated the benefits of the proposed method: (1) qualitatively, DTS enhanced by the proposed method shows CT-like high image quality with accurate and clear edges; (2) quantitatively, the enhanced DTS has low-intensity errors and high structural similarity with respect to the ground truth CT images; (3) in the tumor localization study, compared to the ground truth CT-CBCT registration, the enhanced DTS shows 3D localization errors of ≤0.7 mm and ≤1.6 mm for studies using simulated and real projections, respectively; and (4), the DTS enhancement is nearly real-time. Overall, the proposed method is effective and efficient in enhancing DTS to make it a valuable tool for IGRT applications.