Soft Tissue Imaging Research Articles

Super-resolution neural networks improve the spatiotemporal resolution of adaptive MRI-guided radiation therapy

BackgroundMagnetic resonance imaging (MRI) offers superb non-invasive, soft tissue imaging of the human body. However, extensive data sampling requirements severely restrict the spatiotemporal resolution achievable with MRI. This limits the modality’s utility in real-time guidance applications, particularly for the rapidly growing MRI-guided radiation therapy approach to cancer treatment. Recent advances in artificial intelligence (AI) could reduce the trade-off between the spatial and the temporal resolution of MRI, thus increasing the clinical utility of the imaging modality.MethodsWe trained deep learning-based super-resolution neural networks to increase the spatial resolution of real-time MRI. We developed a framework to integrate neural networks directly onto a 1.0 T MRI-linac enabling real-time super-resolution imaging. We integrated this framework with the targeting system of the MRI-linac to demonstrate real-time beam adaptation with super-resolution-based imaging. We tested the integrated system using large publicly available datasets, healthy volunteer imaging, phantom imaging, and beam tracking experiments using bicubic interpolation as a baseline comparison.ResultsDeep learning-based super-resolution increases the spatial resolution of real-time MRI across a variety of experiments, offering measured performance benefits compared to bicubic interpolation. The temporal resolution is not compromised as measured by a real-time adaptation latency experiment. These two effects, an increase in the spatial resolution with a negligible decrease in the temporal resolution, leads to a net increase in the spatiotemporal resolution.ConclusionsDeployed super-resolution neural networks can increase the spatiotemporal resolution of real-time MRI. This has applications to domains such as MRI-guided radiation therapy and interventional procedures.

368 Preoperative Soft Tissue Imaging Features Elucidated by Artificial Intelligence May Best Predict Radiographic Proximal Junction Kyphosis Following Adult Spinal Deformity Surgery

INTRODUCTION: Proximal junction kyphosis (PJK) following adult spinal deformity surgery (ASD) remains difficult to predict. In a cohort of patients undergoing ASD surgery, we used artificial intelligence to compare three models of preoperatively predicting PJK using: 1) traditional radiographic measurements and demographics 2) raw preoperative scoliosis x-rays, and 3) raw preoperative thoracic T1 MRIs. METHODS: A single-institution retrospective cohort study was undertaken for patients undergoing ASD surgery from 2009-21. 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/posterior-anterior scoliosis x-rays. Finally, for Model-3, a CNN was trained on raw thoracic T1 MRIs. RESULTS: A total of 187 patients underwent ASD surgery with at least 2-year follow-up and 89 (47.6%) developed radiographic PJK within 2 years. Model-1: Using clinical variables and traditional radiographic measurements, the support vector machine predicted PJK with a sensitivity:57.2% and specificity:56.3%. Model-2: a CNN with raw scoliosis x-rays predicted PJK with sensitivity:68.2% and specificity:58.3%. Model-3: a CNN with raw thoracic MRIs predicted PJK with sensitivity:73.1% and specificity:79.5%. Finally, an attention map outlined the imaging features used by Model-3 to predict PJK. Soft tissue features predominated all true positive PJK predictions. CONCLUSIONS: The use of raw MRIs in an artificial intelligence model improved the accuracy of PJK prediction compared to raw scoliosis X-rays and traditional clinical/radiographic measurements. The improved predictive ability using MRI may indicate that PJK is best predicted by soft-tissue degeneration and muscle atrophy.

Enhanced contrast imaging with polyamide 6/Fe(OH)3 nanofibrous scaffolds: A focus on high T1 relaxivity

Nanofibers serve as widely employed tissue engineering scaffolds in diverse biomedical applications. When implanted in vivo, it is crucial for tissue engineering scaffolds to be visualizable, enabling the monitoring of their shape, position, and performance. This capability facilitates the effective assessment of implant deformations, displacements, degradations, and functionalities. However, in many biomedical imaging techniques such as magnetic resonance imaging (MRI), the contrast of tissue engineering scaffolds is often inadequate. MRI is particularly notable for its effectiveness in imaging soft tissues. Previous endeavors to enhance the contrast of tissue engineering scaffolds in MRI have involved the use of negative contrast agents (CAs). Nonetheless, negative CAs can result in artifacts, thus favoring the preference for positive CAs due to their ability to generate clearer boundaries. In this study, we successfully prepared composite polyamide 6 nanofibrous scaffolds with ultrafine dispersion Fe(OH)3 nanoparticles using electrospinning and in-situ growth techniques. The relaxation properties of the magnetic nanofibrous scaffolds confirmed the successful production of scaffolds suitable for positive imaging. In vitro cell seeding experiments demonstrated the efficient proliferation and adhesion of endothelial cells and fibroblasts. In vivo studies further revealed the biocompatibility and functionality of the scaffolds. These findings indicate that the prepared PA6/Fe(OH)3 composite nanofibrous scaffolds can enable straightforward, safe, and efficient in vivo positive contrast MRI monitoring, thereby playing a pivotal role in the integration of diagnosis and treatment within tissue engineering scaffolds.

Impact of AI-Based Post-Processing on Image Quality of Non-Contrast Computed Tomography of the Chest and Abdomen.

Non-contrast computed tomography (CT) is commonly used for the evaluation of various pathologies including pulmonary infections or urolithiasis but, especially in low-dose protocols, image quality is reduced. To improve this, deep learning-based post-processing approaches are being developed. Therefore, we aimed to compare the objective and subjective image quality of different reconstruction techniques and a deep learning-based software on non-contrast chest and low-dose abdominal CTs. In this retrospective study, non-contrast chest CTs of patients suspected of COVID-19 pneumonia and low-dose abdominal CTs suspected of urolithiasis were analysed. All images were reconstructed using filtered back-projection (FBP) and were post-processed using an artificial intelligence (AI)-based commercial software (PixelShine (PS)). Additional iterative reconstruction (IR) was performed for abdominal CTs. Objective and subjective image quality were evaluated. AI-based post-processing led to an overall significant noise reduction independent of the protocol (chest or abdomen) while maintaining image information (max. difference in SNR 2.59 ± 2.9 and CNR 15.92 ± 8.9, p < 0.001). Post-processing of FBP-reconstructed abdominal images was even superior to IR alone (max. difference in SNR 0.76 ± 0.5, p ≤ 0.001). Subjective assessments verified these results, partly suggesting benefits, especially in soft-tissue imaging (p < 0.001). All in all, the deep learning-based denoising-which was non-inferior to IR-offers an opportunity for image quality improvement especially in institutions using older scanners without IR availability. Further studies are necessary to evaluate potential effects on dose reduction benefits.

Algorithms for Polarization-singular processing of Mueller-matrix images of Soft Tissues for Biomedical Applications

Algorithms for Polarization-singular processing of Mueller-matrix images of Soft Tissues for Biomedical Applications

Analyzer-free hard x-ray interferometry

Objective. To enable practical interferometry-based phase contrast CT using standard incoherent x-ray sources, we propose an imaging system where the analyzer grating is replaced by a high-resolution detector. Since there is no need to perform multiple exposures (with the analyzer grating at different positions) at each scan angle, this scheme is compatible with continuous-rotation CT apparatus, and has the potential to reduce patient radiation dose and patient motion artifacts. Approach. Grating-based x-ray interferometry is a well-studied technique for imaging soft tissues and highly scattering objects embedded in such tissues. In addition to the traditional x-ray absorption-based image, this technique allows reconstruction of the object phase and small-angle scattering information. When using conventional incoherent, polychromatic, hard x-ray tubes as sources, three gratings are usually employed. To sufficiently resolve the pattern generated in these interferometers with contemporary x-ray detectors, an analyzer grating is used, and consequently multiple images need to be acquired for each view angle. This adds complexity to the imaging system, slows image acquisition and thus increases sensitivity to patient motion, and is not dose efficient. By simulating image formation based on wave propagation, and proposing a novel phase retrieval algorithm based on a virtual grating, we assess the potential of a analyzer-grating-free system to overcome these limitations. Main results. We demonstrate that the removal of the analyzer-grating can produce equal image contrast-to-noise ratio at reduced dose (by a factor of 5), without prolonging scan duration. Significance. By demonstrating that an analyzer-free CT system, in conjuction with an efficient phase retrieval algorithm, can overcome the prohibitive dose and workflow penalties associated grating-stepping, an alternative path towards realizing clinical inteferometric CT appears possible.

Sine Spin flat detector CT can improve cerebral soft tissue imaging: a retrospective in vivo study

BackgroundFlat detector computed tomography (FDCT) is frequently applied for periinterventional brain imaging within the angiography suite. Novel technical developments such as the Sine Spin FDCT (S-FDCT) may provide an improved cerebral soft tissue contrast. This study investigates the effect of S-FDCT on the differentiation between gray and white matter compared to conventional FDCT (C-FDCT) and multidetector computed tomography (MDCT).MethodsA retrospective analysis of a prospectively maintained patient database was performed, including patients who underwent mechanical thrombectomy in our institution and received S-FDCT or C-FDCT as well as MDCT. Differentiation between gray and white matter on the contralateral hemisphere to the ischemic stroke was analyzed quantitatively by contrast-to-noise ratio (CNR) and qualitatively (5-point ordinal scale).ResultsIn a cohort of 109 patients, MDCT demonstrated the best differentiation between gray and white matter compared to both FDCT techniques (p ≤ 0.001). Comparing both generations of FDCT, S-FDCT provided better visibility of the basal ganglia (p = 0.045) and the supratentorial cortex (p = 0.044) compared to C-FDCT both in quantitative and qualitative analyses. Median CNR were as follows: S-FDCT 2.41 (interquartile range [IQR] 1.66–3.21), C-FDCT 0.96 (0.46–1.70), MDCT 3.43 (2.83–4.17). For basal ganglia, median score and IQR were as follows: S-FDCT 2.00 (2.00–3.00), C-FDCT 1.50 (1.00–2.00), MDCT 5.00 (4.00–5.00).ConclusionsThe novel S-FDCT improves the periinterventional imaging quality of cerebral soft tissue compared to C-FDCT. Thus, it may improve the diagnosis of complications within the angiography suite. MDCT provides the best option for x-ray-based imaging of the brain tissue.Relevance statementFlat detector computed tomography is a promising technique for cerebral soft tissue imaging, while the novel Sine Spin flat detector computed tomography technique improves imaging quality compared to conventional flat detector computed tomography and thus may facilitate periinterventional diagnosis of gray and white matter.Key points• Flat detector computed tomography (FDCT) is frequently applied for periinterventional brain imaging.• The potential of novel Sine Spin FDCT (S-FDCT) is unknown so far.• S-FDCT improves the visibility of cerebral soft tissue compared to conventional FDCT.• Multidetector computed tomography is superior to both FDCT techniques.• S-FDCT may facilitate the evaluation of brain parenchyma within the angiography suite.Graphical

Development of an integrated dual-modality 3D bioluminescence tomography and ultrasound imaging system for small animal tumor imaging.

Ultrasound (US) is a valuable tool for imaging soft tissue and visualizing tumor contours. Taking the benefits of US, we presented an integrated dual-modality imaging system in this paper that achieves three-dimensional (3D) bioluminescence tomography (BLT) with multi-view bioluminescence images and 3D US imaging. The purpose of this system is to perform non-invasive, long-term monitoring of tumor growth in 3D images. US images can enhance the accuracy of the 3D BLT reconstruction and the bioluminescence dose within an object. Furthermore, an integrated co-registered scanning geometry was used to capture the fused BLT and US images. We validated the system with an in vivo experiment involving tumor-bearing mice. The results demonstrated the feasibility of reconstructing 3D BLT images in the tumor region using 3D US images. We used the dice coefficient and locational error to evaluate the similarity between the reconstructed source region and the actual source region. The dice coefficient was 88.5%, and the locational error was 0.4 mm when comparing the BLT and 3D US images. The hybrid BLT/US system could provide significant benefits for reconstructing the source of tumor location and conducting quantitative analysis of tumor size.

Evaluation of Spectral X-Ray Imaging for Panoramic Dental Images Based on a Simulation Framework.

Modern photon counting detectors allow the calculation of virtual monoenergetic or material decomposed X-ray images but are not yet used for dental panoramic radiography systems. To assess the diagnostic potential and image quality of photon counting detectors in dental panoramic radiography, ethics approval from the local ethics committee was obtained for this retrospective study. Conventional CT scans of the head and neck region were segmented into bone and soft tissue. The resulting datasets were used to calculate panoramic equivalent thickness bone and soft tissue images by forward projection, using a geometry like that of conventional panoramic radiographic systems. The panoramic equivalent thickness images were utilized to generate synthetic conventional panoramic radiographs and panoramic virtual monoenergetic radiographs at various energies. The conventional, two virtual monoenergetic images at 40keV and 60keV, and material-separated bone and soft tissue panoramic equivalent thickness X-ray images simulated from 17 head CTs were evaluated in a reader study involving three experienced radiologists regarding their diagnostic value and image quality. Compared to conventional panoramic radiographs, the material-separated bone panoramic equivalent thickness image exhibits a higher image quality and diagnostic value in assessing the bone structure and details such as teeth or root canals . Panoramic virtual monoenergetic radiographs do not show a significant advantage over conventional panoramic radiographs. The conducted reader study shows the potential of spectral X-ray imaging for dental panoramic imaging to improve the diagnostic value and image quality.

Ultrasonography, computed tomography and magnetic resonance imaging of the dromedary camel distal limbs

BackgroundLameness associated with the distal limb region in dromedary camels is quiet prevalent. The diagnosis of lameness relies on a comprehensive orthopedic examination conjugated with an appropriate imaging modality to achieve a decisive diagnosis. Using of modern imaging tools provoked a significant breakthrough in the diagnosis of lameness. Ultrasonography (US) is widely established in dromedaries, whereas computed tomography (CT) and magnetic resonance imaging (MRI) are gaining popularity. CT provides a considerably higher bone detail than any other imaging modality. US and MRI continue to be the best options for soft tissue imaging. A truthful assessment of the clinical US, CT and MRI images dictates a comprehensive familiarity with the standard US, CT and MRI tissue deviations. Accordingly, our purposes were to present a full MRI protocol for investigating the dromedary camel distal limbs as well as comparing and illustrating the merits of using MRI, CT and US for evaluation of the front and hind distal limbs in 10 healthy lameness free dromedary camel cadavers. The limbs were scanned via a high-field 1.5 Tesla MRI magnet and a multi-detector CT scanner then subjected to a systematic US examination in both longitudinal and transverse planes. The obtained MRI, CT and US images were evaluated, correlated and compared.ResultsCT and MRI eliminated the structural superimposition in the dromedary camel distal limbs and afforded assessment of minute ligamentous and tendentious structures that were inaccessible by US including the axial collateral ligaments, ligaments supporting the proximal sesamoid bones and the palmar/plantar aspects of the inter-phalangeal joints. US and MRI were appreciated for the assessment of the articular cartilage that was not visible on the plain CT images.ConclusionsCT and MRI accurately identified and characterized bones and soft tissues constituting the dromedary camel distal limbs. US was appreciated for assessment of soft tissues, articular cartilage and bone contours. CT and MRI may be considered when US results are inconclusive or to evaluate the unreachable parts of the camel distal limbs. Images presented in this study could be used as a reference standard for evaluating dromedary camel distal limb diseases.

Identifying the Anatomical Variations of the Inferior Alveolar Nerve with Magnetic Resonance Imaging.

The inferior alveolar nerve (IAN) is located in the mandibular canal (MC). It is critical to evaluate the position of the MC during treatment planning to prevent intra or postoperative complications. This retrospective study aimed to identify the anatomy and anatomical variations of the IAN using soft tissue imaging (pulse sequence magnetic resonance imaging [MRI]). This study was designed as a retrospective Consolidated Standards of Reporting Trials (CONSORT) study. In total, 220 MR images were obtained. Nutrient canals (NCs) were classified as intraosseous and dental NCs, while bifid MCs (BMCs) were classified as forward, retromolar, and buccolingual canals. IBM SPSS Statistics 22 was used. Kolmogorov-Smirnov and Shapiro-Wilk tests, descriptive statistical methods (means, standard deviations, and frequencies), and the Chi-square test were used. Statistical significance was set at P < 0.05. In total, 220 patients (172 females and 48 males) were evaluated. NCs were present in 92.3% of all MCs and were significantly higher in patients aged <25 years. BMCs were observed in 106 patients (24.1%). The most common BMC of MC/IAN was in the forward canal (14.4%), followed by the retromolar canal (7.5%). Although previously, the dental canal was considered as an anatomical variation, this study revisited the classification and suggested that dental canals are anatomical structures.

Brain MRI detection and classification: Harnessing convolutional neural networks and multi-level thresholding.

Brain tumor detection in clinical applications is a complex and challenging task due to the intricate structures of the human brain. Magnetic Resonance (MR) imaging is widely preferred for this purpose because of its ability to provide detailed images of soft brain tissues, including brain tissue, cerebrospinal fluid, and blood vessels. However, accurately detecting brain tumors from MR images remains an open problem for researchers due to the variations in tumor characteristics such as intensity, texture, size, shape, and location. To address these issues, we propose a method that combines multi-level thresholding and Convolutional Neural Networks (CNN). Initially, we enhance the contrast of brain MR images using intensity transformations, which highlight the infected regions in the images. Then, we use the suggested CNN architecture to classify the enhanced MR images into normal and abnormal categories. Finally, we employ multi-level thresholding based on Tsallis entropy (TE) and differential evolution (DE) to detect tumor region(s) from the abnormal images. To refine the results, we apply morphological operations to minimize distortions caused by thresholding. The proposed method is evaluated using the widely used Harvard Medical School (HMS) dataset, and the results demonstrate promising performance with 99.5% classification accuracy and 92.84% dice similarity coefficient. Our approach outperforms existing state-of-the-art methods in brain tumor detection and automated disease diagnosis from MR images.

Association between skeletal malocclusion, masseter muscle volume, and bite force: CBCT study

Background/Aim: This study aimed to evaluate the masseter muscle volume and maximum bite force in individuals with different types skeletal malocclusion. The researchers conducted a comparative analysis to assess the relationship between skeletal malocclusion groups and masseter muscle volume, as well as maximum bite force. By examining these variables, the study aimed to provide insights into the underlying mechanisms of craniofacial deformities. Materials and Methods: The study was conducted on a sample of 60 young adult patients (18-30) years. These patients were referred to the clinic for Cone Beam Computed Tomography (CBCT) imaging. Radiological measurements were taken using CBCT images, and bite force was measured using a force measurement sensor. Results: The results of the study indicated a statistically significant relationship in both muscle volume and bite force among the different skeletal malocclusion groups. However, no correlation was observed between muscle volume and other variables. The statistically significant relationship between vertical groups and muscle volume/bite force suggests that these measures could potentially serve as supplementary diagnostic tools. However, the uneven distribution of vertical directional groups indicates that they may not be entirely reliable as diagnostic tools. Therefore, further investigation using larger sample sizes is necessary to better understand the relationship between skeletal malocclusion, bite force, muscle volume. Conclusions: The absence of correlation between muscle volume and bite force suggests that CBCT may not be reliable method for soft tissue imaging. This implies inadequacy of CBCT in capturing soft tissue details.

Photoacoustic Imaging using Diode Laser for Soft Tissue Visualization

This study aimed to develop a photoacoustic imaging system using a diode laser as a radiation source, and a condenser microphone as a soft-tissue image detector. These tools were set in a static position and combined with the mechanic component to move the object table in the X-Y direction and were controlled by a computer with a LabView program. The result showed the built photoacoustic imaging system was used to analyze soft tissue using a diode laser in the frequency range of over 15 kHz with a 24%-47% duty cycle modulation. Furthermore, the minimum shift of the mechanic component X-Y stage was 0.02 mm, the sampling data rate was 1 second/pixel, and the image showed soft tissue intensity is generally higher than non-biological materials.

Assessment of a deep learning model for COVID-19 classification on chest radiographs: a comparison across image acquisition techniques and clinical factors.

The purpose is to assess the performance of a pre-trained deep learning model in the task of classifying between coronavirus disease (COVID)-positive and COVID-negative patients from chest radiographs (CXRs) while considering various image acquisition parameters, clinical factors, and patient demographics. Standard and soft-tissue CXRs of 9860 patients comprised the "original dataset," consisting of training and test sets and were used to train a DenseNet-121 architecture model to classify COVID-19 using three classification algorithms: standard, soft tissue, and a combination of both types of images via feature fusion. A larger more-current test set of 5893 patients (the "current test set") was used to assess the performance of the pretrained model. The current test set contained a larger span of dates, incorporated different variants of the virus and included different immunization statuses. Model performance between the original and current test sets was evaluated using area under the receiver operating characteristic curve (ROC AUC) [95% CI]. The model achieved AUC values of 0.67 [0.65, 0.70] for cropped standard images, 0.65 [0.63, 0.67] for cropped soft-tissue images, and 0.67 [0.65, 0.69] for both types of cropped images. These were all significantly lower than the performance of the model on the original test set. Investigations regarding matching the acquisition dates between the test sets (i.e., controlling for virus variants), immunization status, disease severity, and age and sex distributions did not fully explain the discrepancy in performance. Several relevant factors were considered to determine whether differences existed in the test sets, including time period of image acquisition, vaccination status, and disease severity. The lower performance on the current test set may have occurred due to model overfitting and a lack of generalizability.

An Emerging Era: Conformable Ultrasound Electronics.

Conformable electronics are regarded as the next generation of personal healthcare monitoring and remote diagnosis devices. In recent years, piezoelectric-based conformable ultrasound electronics (cUSE) have been intensively studied due to their unique capabilities, including nonradiative monitoring, soft tissue imaging, deep signal decoding, wireless power transfer, portability, and compatibility. This review provides a comprehensive understanding of cUSE for use in biomedical and healthcare monitoring systems and a summary of their recent advancements. Following an introduction to the fundamentals of piezoelectrics and ultrasound transducers, the critical parameters for transducer design are discussed. Next, five types of cUSE with their advantages and limitations are highlighted, and the fabrication of cUSE using advanced technologies is discussed. In addition, the working function, acoustic performance, and accomplishments in various applications are thoroughly summarized. It is noted that application considerations must be given to the tradeoffs between material selection, manufacturing processes, acoustic performance, mechanical integrity, and the entire integrated system. Finally, current challenges and directions for the development of cUSE are highlighted, and research flow is provided as the roadmap for future research. In conclusion, these advances in the fields of piezoelectric materials, ultrasound transducers, and conformable electronics spark an emerging era of biomedicine and personal healthcare.

Bi-Graph Reasoning for Masticatory Muscle Segmentation From Cone-Beam Computed Tomography.

Automated segmentation of masticatory muscles is a challenging task considering ambiguous soft tissue attachments and image artifacts of low-radiation cone-beam computed tomography (CBCT) images. In this paper, we propose a bi-graph reasoning model (BGR) for the simultaneous detection and segmentation of multi-category masticatory muscles from CBCTs. The BGR exploits the local and long-range interdependencies of regions of interest and category-specific prior knowledge of masticatory muscles by reasoning on the category graph and the region graph. The category graph of the learnable muscle prior knowledge handles high-level dependencies of muscle categories, enhancing the feature representation with noise-agnostic category knowledge. The region graph models both local and global dependencies of the candidate muscle regions of interest. The proposed BGR accommodates the high-level dependencies and enhances the region features in the presence of entangled soft tissue and image artifacts. We evaluated the proposed approach by segmenting masticatory muscles on clinically acquired CBCTs. Extensive experimental results show that the BGR effectively segments masticatory muscles with state-of-the-art accuracy.

Utilizing MSK Ultrasound for Comprehensive Assessment of the Femoral Trochlea: A Game Changer in Sports Medicine.

Musculoskeletal (MSK) ultrasound has emerged as a valuable tool for sports physical therapists in the assessment and treatment of various knee pathologies. Its ability to provide high-resolution images of soft tissue and superficial bone surfaces makes it especially useful for sports physical therapists and orthopedic clinicians. Specifically, MSK-ultrasound is increasingly recognized as a potent tool for the assessment of the femoral trochlea. Its non-invasive nature and dynamic imaging capabilities make it particularly suited for visualizing the femoral trochlea, a critical component in knee function and biomechanics. The use of MSK ultrasound in the evaluating the femoral trochlea provides sports medicine professionals with a dynamic, non-invasive, and cost-effective means to diagnose, and monitor knee-related injuries. This article delves into the utility of MSK ultrasound in the anatomical and functional assessment of the femoral trochlea, elucidating its benefits, limitations, and clinical implications for athletes.

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&amp;lt; 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&amp;lt; 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.&#x0D; 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)&#x0D; 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.&#x0D; 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.&#x0D; 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&#x0D; 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.&#x0D; 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.