Soft Tissue Imaging Research Articles

Feasibility of delta radiomics–based pCR prediction for rectal cancer patients treated with magnetic resonance–guided adaptive radiotherapy

Magnetic resonance–guided adaptive radiotherapy (MRgART) represents the latest frontier in precision radiotherapy. It is distinguished from other modalities by the possibility of acquiring high-contrast soft tissue images, combined with the ability to recalculate and re-optimize the plan on the daily anatomy. The extensive database of available images offers ample scope for using disciplines such as radiomics to try to correlate features and outcomes. This study aimed to correlate the change of radiomics feature along the treatment to pathological complete response (pCR) for locally advanced rectal cancer (LARC) patients. Twenty-eight LARC patients undergoing neoadjuvant chemoradiotherapy (nCRT) with a short course (25 Gy, 5 Gy × 5f) MRgART at 1.5 Tesla MR-Linac were enrolled. The T2-weighted images acquired at each fraction, corresponding target delineation, pCR result of the surgical specimen, and clinical variables were collected. Seven families of features [First Order, Shape, Gray-level Co-occurrence Matrix (GLCM), Gray-level Dependence Matrix (GLDM), Gray-level Run Length Matrix (GLRLM), Gray-level Size Zone Matrix (GLSZM), and Neighborhood Gray Tone Difference Matrix (NGTDM)] were extracted, and delta features were calculated from the ratio of features of each successive fraction to those of the first fraction. Mann-Whitney U test and LASSO were utilized to reduce the dimension of features and select those features that are most significant to pCR. At last, the radiomics signatures were established by linear regression with the final set of features and their coefficients. A total of 581 radiomics features were extracted, and 2,324 delta features were calculated for each patient. Nineteen features and delta features, and one clinical variable (cN) were significant (p< 0.05) to pCR; seven predictive features were further selected and included in the linear regression to construct the radiomics signature significantly discriminating pCR and non-pCR groups (p< 0.05). Delta features based on MRI images acquired during a short course MRgART could potentially be used to predict treatment response in LARC patients undergoing nCRT.

Sudden Sensorineural Hearing Loss from a Jugular Bulb Diverticulum

A 19-year-old woman presented with an 11-month history of sudden-onset left sided hearing loss accompanied by vertigo and headache. Audiometric testing revealed profound left- sided hearing loss. A contrast-enhanced MRI of the internal auditory canal performed 5 months after symptom onset was interpreted as showing a vascular loop, probably the anterior inferior cerebellar artery, abutting and indenting on the left vestibulocochlear nerve; and a prominent and high-riding left jugular bulb. In this study, the internal auditory canals were assessed to be of normal width, with walls that were smooth and sharply defined. A cerebral CT angiogram subsequently performed did not show any abnormal findings related to the previously identified vascular loop. On the basis of these radiologic findings, the patient was advised surgery by physicians at a tertiary- care institution, presumably to address the identified vascular loop. A second opinion was sought by the patient.
 Review of the MRI initially focused on the axial high-resolution T2-weighted sequence (T2-DRIVE), as the fast spin-echo T2-weighted sequence has been recommended as a reliable and cost-effective MR screening protocol for the detection of masses in the IAC.1 In contrast to the official radiology report, stenosis of the left internal auditory canal by a protrusion (Figure 1, white asterisk) originating from the posteromedial wall of the internal auditory canal was noted. This protrusion, which had no MR signal intensity, appeared to abut and compress the cranial nerves within the IAC. Reconstruction of the images in non-orthogonal planes aligned with the orientation and direction of the left 8th cranial nerve showed the protrusion causing upward compression on and distortion of the nerve. (Figure 2, white arrows)
 Attention was directed to the axial high-resolution T1-weighted sequence (T1W-3D FFE), which revealed that the protrusion contained an isointense soft tissue structure (Figure 3, white asterisk) located within the petrous bone medial to the posterior semicircular canal. This structure appeared to be an upward extension of the jugular bulb.
 The axial high-resolution contrast-enhanced T1-weighted sequence (T1W-3D TFE Gd) showed smooth, vivid enhancement of the identified structure (Figure 4, black asterisk), which connected with the sigmoid sinus in lower cuts. This confirmed the presence of a high-riding jugular bulb that encroached on the internal auditory canal.
 Any doubt as to its true nature was dispelled by a review of the temporal bone structures on high-resolution CT which was fortunately available in the cerebral CT angiogram. This revealed a protrusion of the high-riding jugular bulb with a waist-like margin (Figure 5A and B, black arrows), allowing further characterization of the lesion as a jugular bulb diverticulum.2
 A high-riding jugular bulb that projects into the middle ear is not an uncommon anatomic variation. On the other hand, a jugular bulb diverticulum, which is an outpouching of the jugular bulb that can extend superiorly, medially, and posteriorly in the petrous bone, is a true venous anomaly that has been described rarely in the medical literature.3,4 When symptomatic, patients with this anomaly can present with sensorineural hearing loss, tinnitus, vertigo and auricular pain.3-5 Proper identification of a jugular bulb diverticulum in the evaluation of a patient with neurotologic symptoms is necessary to avoid inappropriate medical and surgical intervention.
 As demonstrated in this patient, a jugular bulb diverticulum may not be identified by a screening MRI that utilizes only a T2-weighted sequence. T1-weighted MRI sequences with and without contrast are necessary to demonstrate its soft tissue imaging characteristics. Although not the initial imaging study of choice for sudden sensorineural hearing loss, high-resolution bone-window CT may be necessary to delineate the bony anatomy of the jugular foramen and confirm the presence of this anomaly.

Integration of Prenatal Cardiovascular Magnetic Resonance Imaging in Congenital Heart Disease.

Standard of care echocardiography can have limited diagnostic accuracy in certain cases of fetal congenital heart disease. Prenatal cardiovascular magnetic resonance (CMR) imaging has potential to provide additional anatomic imaging information, including excellent soft tissue images in multiple planes, improving prenatal diagnostics and in utero hemodynamic assessment. We conducted a literature review of fetal CMR, including its development and implementation into clinical practice, and compiled and analyzed the results. Our findings included the fact that technological and innovative approaches are required to overcome some of the challenges in fetal CMR, in part due to the dynamic nature of the fetal heart. A number of reconstruction algorithms and cardiac gating strategies have been developed over time to improve fetal CMR image quality, allowing unique investigations into fetal hemodynamics, oxygenation, and growth. Studies demonstrate that incorporating CMR in the prenatal arena influences postnatal clinical management. With further refinement and experience, fetal CMR in congenital heart disease continues to evolve and demonstrate ongoing potential as a complementary imaging modality to fetal echocardiography in the care of these patients.

Deep-learning soft-tissue decomposition in chest radiography using fast fuzzy C-means clustering with CT datasets

Chest radiography is the most routinely used X-ray imaging technique for screening and diagnosing lung and chest disease, such as lung cancer and pneumonia. However, the clinical interpretation of the hidden and obscured anatomy in chest X-ray images remains challenging because of the bony structures overlapping the lung area. Thus, multi-perspective imaging with a high radiation dose is often required. In this study, to address this problem, we propose a deep-learning soft-tissue decomposition method using fast fuzzy C-means (FFCM) clustering with computed tomography (CT) datasets. In this method, FFCM clustering is used to decompose a CT image into bone and soft-tissue components, which are synthesized into digitally reconstructed radiographs (DRRs) to obtain large amounts of X-ray decomposition datasets as ground truths for training. In the training stage, chest and soft-tissue DRRs are used as input and label data, respectively, for training the network. During the testing, a chest X-ray image is fed into the trained network to output the corresponding soft-tissue image component. To verify the efficacy of the proposed method, we conducted a feasibility study on clinical CT datasets available from the AAPM Lung CT Challenge. According to our results, the proposed method effectively yielded soft-tissue decomposition from chest X-ray images; this is encouraging for reducing the visual complexity of chest X-ray images. Consequently, the finding of our feasibility study indicate that the proposed method can offer a promising outcome for this purpose.

Visualisation of drug distribution in skin using correlative optical spectroscopy and mass spectrometry imaging

A correlative methodology for label-free chemical imaging of soft tissue has been developed, combining non-linear optical spectroscopies and mass spectrometry to achieve sub-micron spatial resolution and critically improved drug detection sensitivity. The approach was applied to visualise the kinetics of drug reservoir formation within human skin following in vitro topical treatment with a commercial diclofenac gel. Non-destructive optical spectroscopic techniques, namely stimulated Raman scattering, second harmonic generation and two photon fluorescence microscopies, were used to provide chemical and structural contrast. The same tissue sections were subsequently analysed by secondary ion mass spectrometry, which offered higher sensitivity for diclofenac detection throughout the epidermis and dermis. A method was developed to combine the optical and mass spectrometric datasets using image registration techniques. The label-free, high-resolution visualisation of tissue structure coupled with sensitive chemical detection offers a powerful method for drug biodistribution studies in the skin that impact directly on topical pharmaceutical product development.

Progress of research on the diagnosis and treatment of bone cystic echinococcosis.

Bone cystic echinococcosis (CE) is one of the most complex and dangerous of all echinococcoses. The lack of typical imaging features and clinical manifestations makes diagnosis and treatment of this disease difficult. X-ray and computed tomography (CT) images of bone CE are similar to those of bone cysts, giant-cell bone tumors, and bone metastases, but magnetic resonance imaging (MRI) shows good diagnostic value due to excellent soft-tissue imaging features. Serological tests cannot be used as a definitive diagnostic method for bone CE due to cross-reactivity, which can lead to false-positive or false-negative results. The development of novel antigens can open new frontiers in the diagnosis of the disease. Currently, views conflict on how to diagnose and treat bone CE. Both surgical and pharmacological treatments can be used, but determining which is appropriate is difficult due to the different sites and clinical manifestations of bone CE. Radical resection is not indicated for large-bone injuries, and Pharmacotherapy becomes important. This article reviews the progress of research into the pathogenesis and clinical manifestations of, and diagnostic strategies and treatment options for, bone CE. We aimed to provide a reference for clinical diagnosis and -treatment options.

Analysis of Early Stroke Diagnosis Based on Brain Magnetic Resonance Imaging using Machine Learning

Stroke causes paralysis resulting from a hemorrhage in the brain or blockage of blood flow to the brain. It is third leading cause of death in Malaysia, with at least 32 deaths per day, and poses a major challenge to Malaysia's health services. A recent study showed that he could save a patient's life if he received treatment within six hours of a stroke. Unfortunately, Malaysia is facing a shortage of neuroradiologists, hampering efforts to treat its growing number of stroke patients. In this research, used Magnetic Resonance Imaging which is better compare to CT scan and CBCT because MRI will produce more detailed images of soft tissues, ligaments and organs. So that, advanced imaging using magnetic resonance imaging (MRI) has gained more attention than conventional angiography in the diagnosis of acute stroke due to its high spatial resolution and fast scan times. Traditionally, diagnosis was made manually by neuroradiologists during a highly subjective and time-consuming task. Detecting collaterals from MRI images is a challenging task due to the presence of noise and artifacts, small size, and heterogeneous structure of vessels. By the way, this paper is mainly about the early diagnosis of stroke based on brain magnetic resonance imaging using machine learning. Based on the results, can see that the Fuzzy c-means (FCM) and Watershed Transformation (WT) segmentation of brain infarcts which are original image, output of guided filter, gradient magnitude image, output of watershed transform and final detected infarct with morphological operation.

A spine segmentation method based on scene aware fusion network

BackgroundIntervertebral disc herniation, degenerative lumbar spinal stenosis, and other lumbar spine diseases can occur across most age groups. MRI examination is the most commonly used detection method for lumbar spine lesions with its good soft tissue image resolution. However, the diagnosis accuracy is highly dependent on the experience of the diagnostician, leading to subjective errors caused by diagnosticians or differences in diagnostic criteria for multi-center studies in different hospitals, and inefficient diagnosis. These factors necessitate the standardized interpretation and automated classification of lumbar spine MRI to achieve objective consistency. In this research, a deep learning network based on SAFNet is proposed to solve the above challenges.MethodsIn this research, low-level features, mid-level features, and high-level features of spine MRI are extracted. ASPP is used to process the high-level features. The multi-scale feature fusion method is used to increase the scene perception ability of the low-level features and mid-level features. The high-level features are further processed using global adaptive pooling and Sigmoid function to obtain new high-level features. The processed high-level features are then point-multiplied with the mid-level features and low-level features to obtain new high-level features. The new high-level features, low-level features, and mid-level features are all sampled to the same size and concatenated in the channel dimension to output the final result.ResultsThe DSC of SAFNet for segmenting 17 vertebral structures among 5 folds are 79.46 ± 4.63%, 78.82 ± 7.97%, 81.32 ± 3.45%, 80.56 ± 5.47%, and 80.83 ± 3.48%, with an average DSC of 80.32 ± 5.00%. The average DSC was 80.32 ± 5.00%. Compared to existing methods, our SAFNet provides better segmentation results and has important implications for the diagnosis of spinal and lumbar diseases.ConclusionsThis research proposes SAFNet, a highly accurate and robust spine segmentation deep learning network capable of providing effective anatomical segmentation for diagnostic purposes. The results demonstrate the effectiveness of the proposed method and its potential for improving radiological diagnosis accuracy.

Material decomposition using dual-energy CT with unsupervised learning.

Material decomposition (MD) is an application of dual-energy computed tomography (DECT) that decomposes DECT images into specific material images. However, the direct inversion method used in MD often amplifies noise in the decomposed material images, resulting in lower image quality. To address this issue, we propose an image-domain MD method based on the concept of deep image prior (DIP). DIP is an unsupervised learning method that can perform different tasks without using a large training dataset with known targets (i.e., basis material images). We retrospectively recruited patients who underwent non-contrast brain DECT scans and investigated the feasibility of using the proposed DIP-based method to decompose DECT images into two (i.e., bone and soft tissue) and three (i.e., bone, soft tissue, and fat) basis materials. We evaluated the decomposed material images in terms of signal-to-noise ratio (SNR) and modulation transfer function (MTF). The proposed DIP-based method showed greater improvement in SNR in the decomposed soft-tissue images compared to the direct inversion method and the iterative method. Moreover, the proposed method produced similar MTF curves in both two- and three-material decompositions. Additionally, the proposed DIP-based method demonstrated better separation ability than the other two studied methods in the case of three-material decomposition. Our results suggest that the proposed DIP-based method is capable of unsupervisedly generating high-quality basis material images from DECT images.

Three-Dimensional Point Cloud Reconstruction Method of Cardiac Soft Tissue Based on Binocular Endoscopic Images

Three-dimensional reconstruction technology based on binocular stereo vision is a key research area with potential clinical applications. Mainstream research has focused on sparse point reconstruction within the soft tissue domain, limiting the comprehensive 3D data acquisition required for effective surgical robot navigation. This study introduces a new paradigm to address existing challenges. An innovative stereoscopic endoscopic image correction algorithm is proposed, exploiting intrinsic insights into stereoscopic calibration parameters. The synergy between the stereoscopic endoscope parameters and the disparity map derived from the cardiac soft tissue images ultimately leads to the acquisition of precise 3D points. Guided by deliberate filtering and optimization methods, the triangulation process subsequently facilitates the reconstruction of the complex surface of the cardiac soft tissue. The experimental results strongly emphasize the accuracy of the calibration algorithm, confirming its utility in stereoscopic endoscopy. Furthermore, the image rectification algorithm exhibits a significant reduction in vertical parallax, which effectively enhances the stereo matching process. The resulting 3D reconstruction technique enables the targeted surface reconstruction of different regions of interest in the cardiac soft tissue landscape. This study demonstrates the potential of binocular stereo vision-based 3D reconstruction techniques for integration into clinical settings. The combination of joint calibration algorithms, image correction innovations, and precise tissue reconstruction enhances the promise of improved surgical precision and outcomes in the field of cardiac interventions.

Determining the Relationship between Mechanical Properties and Quantitative Magnetic Resonance Imaging of Joint Soft Tissues Using Patient-Specific Templates.

To determine whether the mechanical properties of joint soft tissues such as cartilage can be calculated from quantitative magnetic resonance imaging (MRI) data, we investigated whether the mechanical properties of articular cartilage and meniscus scheduled to be resected during arthroplasty are correlated with the T2 relaxation time on quantitative MRI at the same location. Six patients who had undergone knee arthroplasty and seven who had undergone hip arthroplasty were examined. For the knee joint, the articular cartilage and lateral meniscus of the distal lateral condyle of the femur and proximal lateral tibia were examined, while for the hip joint, the articular cartilage above the femoral head was studied. We investigated the relationship between T2 relaxation time by quantitative MRI and stiffness using a hand-made compression tester at 235 locations. The patient-individualized template technique was used to align the two measurement sites. The results showed a negative correlation (from -0.30 to -0.35) in the less severely damaged articular cartilage and meniscus. This indicates that tissue mechanical properties can be calculated from T2 relaxation time, suggesting that quantitative MRI is useful in determining when to start loading after interventional surgery on cartilage tissue and in managing the health of elderly patients.

Birmingham Hip Resurfacing at 20 years.

The Birmingham Hip Resurfacing (BHR) arthroplasty has been used as a surgical treatment of coxarthrosis since 1997. We present 20-year results of 234 consecutive BHRs performed in our unit. Between 1999 and 2001, there were 217 patients: 142 males (65.4%), mean age 52 years (18 to 68) who had 234 implants (17 bilateral). They had patient-reported outcome measures collected, imaging (radiograph and ultrasound), and serum metal ion assessment. Survivorship analysis was performed using Kaplan-Meier estimates. Revision for any cause was considered as an endpoint for the analysis. Mean follow-up was 20.9 years (19.3 to 22.4). Registry data revealed that 19 hips (8.1%) had been revised and 26 patients (12%) had died from causes unrelated to the BHR. Among the remaining 189 hips, 61% were available for clinical follow-up at 20 years (n = 115) and 70% of patients had biochemical follow-up (n = 132). The cumulative implant survival rate at 20 years for male patients was 96.5% (95% confidence interval (CI) 93.5 to 99.6), and for female patients 87% (95% CI 79.7 to 94.9). The difference was statistically significant (p = 0.029). The mean Oxford Hip Score, Hip disability and Osteoarthritis Outcome Score, and Forgotten Joint Score were 45 (29 to 48), 89 (43 to 100), and 84 (19 to 100), respectively. The mean scores for each of the five domains of the EuroQol five-dimension three-level questionnaire were 1.2, 1.0, 1.2, 1.3, and 1.1, and mean overall score 82.6 (50 to 100). Ultrasound showed no pseudotumour. Mean cobalt and chromium levels were 32.1 nmol/l (1 to 374) and 45.5 nmol/l (9 to 408), respectively. This study shows that BHRs provide excellent survivorship and functional outcomes in young male patients. At 20 years, soft-tissue imaging and serum metal ion studies suggest that a metal-on-metal resurfacing implant can be well tolerated in a group of young patients.

Artificial Intelligence to Preoperatively Predict Proximal Junction Kyphosis Following Adult Spinal Deformity Surgery: Soft Tissue Imaging May Be Necessary for Accurate Models.

Retrospective cohort. In a cohort of patients undergoing adult spinal deformity (ASD) surgery, we used artificial intelligence to compare three models of preoperatively predicting radiographic proximal junction kyphosis (PJK) using: (1) traditional demographics and radiographic measurements, (2) raw preoperative scoliosis radiographs, and (3) raw preoperative thoracic magnetic resonance imaging (MRI). Despite many proposed risk factors, PJK following ASD surgery remains difficult to predict. A single-institution, retrospective cohort study was undertaken for patients undergoing ASD surgery from 2009 to 2021. PJK was defined as a sagittal Cobb angle of upper-instrumented vertebra (UIV) and UIV+2>10° and a postoperative change in UIV/UIV+2>10°. For model 1, a support vector machine was used to predict PJK within 2 years postoperatively using clinical and traditional sagittal/coronal radiographic variables and intended levels of instrumentation. Next, for model 2, a convolutional neural network (CNN) was trained on raw preoperative lateral and posterior-anterior scoliosis radiographs. Finally, for model 3, a CNN was trained on raw preoperative thoracic T1 MRIs. A total of 191 patients underwent ASD surgery with at least 2-year follow-up and 89 (46.6%) developed radiographic PJK within 2 years. Model 1: Using clinical variables and traditional radiographic measurements, the model achieved a sensitivity: 57.2% and a specificity: 56.3%. Model 2: a CNN with raw scoliosis x-rays predicted PJK with a sensitivity: 68.2% and specificity: 58.3%. Model 3: a CNN with raw thoracic MRIs predicted PJK with average sensitivity: 73.1% and specificity: 79.5%. Finally, an attention map outlined the imaging features used by model 3 elucidated that soft tissue features predominated all true positive PJK predictions. The use of raw MRIs in an artificial intelligence model improved the accuracy of PJK prediction compared with raw scoliosis radiographs and traditional clinical/radiographic measurements. The improved predictive accuracy using MRI may indicate that PJK is best predicted by soft tissue degeneration and muscle atrophy.

Frequency-domain full-waveform inversion-based musculoskeletal ultrasound computed tomography.

Recently, full-waveform inversion (FWI) has become a promising tool for ultrasound computed tomography (USCT). However, as a computationally intensive technique, FWI suffers from computational burden, especially in conventional time-domain full-waveform inversion (TDFWI). On the contrary, frequency-domain full-waveform inversion (FDFWI) provides a relatively high computational efficiency as the propagation of discrete frequencies is much cheaper than full time-domain modeling. FDFWI has already been applied in soft tissue imaging, such as breast, but for the musculoskeletal model with high impedance contrast between hard and soft tissues, there is still a lack of an effective source estimation method. In this paper, a water-referenced data calibration method is proposed to address the source estimation challenge in the presence of bones, which achieves consistency between the measured and simulated data before the FDFWI procedure. To avoid the cycle-skipping local minimum effect and facilitate the algorithm convergence, a starting frequency criterion for musculoskeletal FDFWI is further proposed. The feasibility of the proposed method is demonstrated by numerical studies on retrieving the anatomies of the leg models and different musculoskeletal lesions. The study extends the advanced FDFWI method to the musculoskeletal system and provides an alternative solution for musculoskeletal USCT imaging with high computational efficiency.

X-ray phase-contrast microtomography of soft tissues using a compact laboratory system with two-directional sensitivity.

X-ray microtomography is a nondestructive, three-dimensional inspection technique applied across a vast range of fields and disciplines, ranging from research to industrial, encompassing engineering, biology, and medical research. Phase-contrast imaging extends the domain of application of x-ray microtomography to classes of samples that exhibit weak attenuation, thus appearing with poor contrast in standard x-ray imaging. Notable examples are low-atomic-number materials, like carbon-fiber composites, soft matter, and biological soft tissues. We report on a compact and cost-effective system for x-ray phase-contrast microtomography. The system features high sensitivity to phase gradients and high resolution, requires a low-power sealed x-ray tube, a single optical element, and fits in a small footprint. It is compatible with standard x-ray detector technologies: in our experiments, we have observed that single-photon counting offered higher angular sensitivity, whereas flat panels provided a larger field of view. The system is benchmarked against known-material phantoms, and its potential for soft-tissue three-dimensional imaging is demonstrated on small-animal organs: a piglet esophagus and a rat heart. We believe that the simplicity of the setup we are proposing, combined with its robustness and sensitivity, will facilitate accessing quantitative x-ray phase-contrast microtomography as a research tool across disciplines, including tissue engineering, materials science, and nondestructive testing in general.

Real-Life Management of Pancreatic Cysts: Simplified Review of Current Guidelines.

Pancreatic cysts are becoming a popular diagnostic tool due to the increased availability of high-quality cross-sectional imaging. Pancreatic cystic lesions constitute closed, liquid-containing cavities, which are either neoplastic or non-neoplastic. While serous lesions often follow a benign course, mucinous lesions can hide carcinoma and, therefore, require different management. Moreover, all cysts should be considered mucinous until proven otherwise, thus limiting the errors in managing these entities. Due to the need for high contrast soft tissue imaging, magnetic resonance imaging represents an elective, non-invasive diagnostic tool. Endoscopic ultrasound (EUS) has started gaining more prominence with regard to the proper diagnosis and management of pancreatic cysts, offering quality information with minimal risks. Enabling both the acquisition of endoscopic images of the papilla and the endosonographic high-quality evaluation of septae, mural nodules along with the vascular patterns of the lesion contribute to a definitive diagnosis. Moreover, the possibility of obtaining cytological or histological samples could become mandatory in the foreseeable future, allowing for more precise molecular testing. Future research should focus on detecting methods to quickly diagnose high-grade dysplasia or early cancer for patients with pancreatic cysts, thus allowing time for appropriate treatment and avoiding surgical overtreatment or over surveillance in selected cases.

Effectiveness of one-shot dual-energy subtraction chest radiography with flat-panel detector in distinguishing between calcified and non-calcified nodules

The purpose of this study was to evaluate the added value of the soft tissue image obtained by the one-shot dual-energy subtraction (DES) method using a flat-panel detector compared with the standard image alone in distinguishing calcified from non-calcified nodules on chest radiographs. We evaluated 155 nodules (48 calcified and 107 non-calcified) in 139 patients. Five radiologists (readers 1 − 5) with 26, 14, 8, 6 and 3 years of experience, respectively, evaluated whether the nodules were calcified using chest radiography. CT was used as the gold standard of calcification and non-calcification. Accuracy and area under the receiver operating characteristic curve (AUC) were compared between analyses with and without soft tissue images. The misdiagnosis ratio (false positive plus false negative ratios) when nodules and bones overlapped was also examined. The accuracy of all radiologists increased after adding soft tissue images (readers 1 − 5: 89.7% vs. 92.3% [P = 0.206], 83.2% vs. 87.7% [P = 0.178], 79.4% vs. 92.3% [P < 0.001], 77.4% vs. 87.1% [P = 0.007], and 63.2% vs. 83.2% [P < 0.001], respectively). AUCs for all the readers improved, except for reader 2 (readers 1 − 5: 0.927 vs. 0.937 [P = 0.495], 0.853 vs. 0.834 [P = 0.624], 0.825 vs. 0.878 [P = 0.151], 0.808 vs. 0.896 [P < 0.001], and 0.694 vs. 0.846 [P < 0.001], respectively). The misdiagnosis ratio for nodules that overlapped with the bone decreased after adding soft tissue images in all readers (11.5% vs. 7.6% [P = 0.096], 17.6% vs. 12.2% [P = 0.144], 21.4% vs. 7.6% [P < 0.001], 22.1% vs. 14.5% [P = 0.050] and 35.9% vs. 16.0% [P < 0.001], respectively), particularly that of readers 3 − 5. In conclusion, the soft tissue images obtained using one-shot DES with a flat-panel detector have added value in distinguishing calcified from non-calcified nodules on chest radiographs, especially for less experienced radiologists.

Gene expression detection in developing mouse tissue using in situ hybridization and µCT imaging

High resolution and noninvasiveness have made soft-tissue X-ray microtomography (µCT) a widely applicable three-dimensional (3D) imaging method in studies of morphology and development. However, scarcity of molecular probes to visualize gene activity with µCT has remained a challenge. Here, we apply horseradish peroxidase-assisted reduction of silver and catalytic gold enhancement of the silver deposit to insitu hybridization in order to detect gene expression in developing tissues with µCT (here called GECT, gene expression CT). We show that GECT detects expression patterns of collagen type II alpha 1and sonic hedgehogin developing mouse tissues comparably with an alkaline phosphatase-based detection method. After detection, expression patterns are visualized with laboratory µCT, demonstrating that GECT is compatible with varying levels of gene expression and varying sizes of expression regions. Additionally, we show that the method is compatible with prior phosphotungstic acid staining, a conventional contrast staining approach in µCT imaging of soft tissues. Overall, GECT is a method that can be integrated with existing laboratory routines to obtain spatially accurate 3D detection of gene expression.

Anatomical damage caused by Bacillus thuringiensis variety israelensis in yellow fever mosquito Aedes aegypti (L.) larvae revealed by micro-computed tomography

With micro-computed tomography techniques, using the single-distance phase-retrieval algorithm phase contrast, we reconstructed enhanced rendered images of soft tissues of Aedes aeqypti fourth instar larvae after Bti treatment. In contrast to previous publications based on conventional microscopy, either optical or electron microscopy, which were limited to partial studies, mostly in the form of histological sections, here we show for the first time the effects of Bti on the complete internal anatomy of an insect. Using 3D rendered images it was possible to study the effect of the bacterium in tissues and organs, not only in sections but also as a whole. We compared the anatomy of healthy larvae with the changes undergone in larvae after being exposed to Bti (for 30 min, 1 h and 6 h) and observed the progressive damage that Bti produce. Damage to the midgut epithelia was confirmed, with progressive swelling of the enterocytes, thickening epithelia, increase of the vacuolar spaces and finally cell lysis, producing openings in the midgut walls. Simultaneously, the larvae altered their motility, making it difficult for them to rise to the surface and position the respiratory siphon properly to break surface tension and breathe. Internally, osmotic shock phenomena were observed, resulting in a deformation of the cross-section shape, producing the appearance of a wide internal space between the cuticle and the internal structures and a progressive collapse of the tracheal trunks. Taken together, these results indicate the death of the larvae, not by starvation as a consequence of the destruction of the epithelia of the digestive tract as previously stated, but due to a synergic catastrophic multifactor process in addition to asphyxia due to a lack of adequate gas exchange.

Magnetic Susceptibility of Common Metals and Alloys Used in Medical Devices

Abstract Magnetic resonance imaging (MRI) is a widely used imaging modality that is ideally suited for imaging soft tissues. The use of strong magnetic and electric fields employed in MRI creates the potential for hazards when imaging patients with metallic implants. With the proper material selection, implant design, and imaging procedure, many of these hazards can be adequately managed. Magnetic susceptibility (χ) is a material property indicating a material’s tendency to interact with and distort an applied magnetic field, and it has substantial influence on both the force and torque experienced by an implant in a magnetic field, as well as the size of the image artifact. In this study, the magnetic susceptibilities of 45 different medically relevant alloys and alloy composites are determined and reported. This data will aid in material selection for medical devices intended to be labeled as MR Conditional.