In this work, we utilize a Transformer-based network for precise anatomical landmark detection in chest X-ray images. By combining the strengths of Transformers and UNet architecture, our proposed model achieves robust landmark localization by effectively capturing global context and spatial dependencies. Notably, our method surpasses the current state-of-the-art approaches, exhibiting a significant reduction in Mean Radial Error and a notable improvement in the rate of accurate landmark detection. Each of the landmark points in the labels is presented as a Gaussian heatmap for training the network, using a hybrid loss function, incorporating binary cross-entropy and Dice loss functions, allowing for pixel-wise classification of the heatmaps and segmentation-based training to accurately localize the landmark heatmaps. The promising results obtained highlight the underexplored potential of Transformers in anatomical landmark detection and offer a compelling solution for accurate anatomical landmark detection in chest X-rays. Our work demonstrates the viability of Transformer-based models in addressing the challenges of landmark detection in medical imaging.
The precise placement of catheter tubes and lines is crucial for providing optimal care to critically ill patients. However, the challenge of mispositioning these tubes persists. The timely detection and correction of such errors are extremely important, especially considering the increased demand for these interventions, as seen during the COVID-19 pandemic. Unfortunately, manual diagnosis is prone to error, particularly under stressful conditions, highlighting the necessity for automated solutions. This research addresses this challenge by utilizing deep learning techniques to automatically detect and classify the positions of endotracheal tubes (ETTs) in chest x-ray images. Our approach builds upon recent advancements in deep learning for medical image analysis, providing a sophisticated solution to a critical healthcare challenge. The proposed model achieves remarkable performance, with the area under the ROC scores ranging from 0.961 to 0.993 and accuracy values ranging from 0.961 to 0.999. These results emphasize the effectiveness of the model in accurately classifying ETT positions, highlighting its potential clinical utility. Through this study, we introduce an innovative application of AI in medical diagnostics, with considerations for advancing healthcare practices.
Catheter tubes and lines are one of the most common abnormal findings on a chest x-ray. Misplaced catheters can cause serious complications, such as pneumothorax, cardiac perforation, or thrombosis, and for this reason, assessment of catheter position is of utmost importance. In order to prevent these problems, radiologists usually examine chest x-rays to evaluate their positions after insertion and throughout intensive care. However, this process is both time-consuming and prone to human error. Efficient and dependable automated interpretations have the potential to lower the expenses of surgical procedures, lessen the burden on radiologists, and enhance the level of care for patients. To address this challenge, we have investigated the task of accurate segmentation of catheter tubes and lines in chest x-rays using deep learning models. In this work, we have utilized transfer learning and transformer-based networks where we utilized two different models: a U-Net++-based model with ImageNet pre-training and an efficientnet encoder, which leverages diverse visual features in ImageNet to improve segmentation accuracy, and a transformer-based U-Net architecture due to its capability to handle long-range dependencies in complex medical image segmentation tasks. Our experiments reveal the effectiveness of the U-Net++-based model in handling noisy and artifact-laden images and TransUNET’s potential for capturing complex spatial features. We compare both models using the dice coefficient as the evaluation metric and determine that U-Net++ outperforms TransUNET in terms of these segmentation metrics. Our aim is to achieve more robust and reliable catheter tube detection in chest x-rays, ultimately enhancing clinical decision-making and patient care in critical healthcare settings.
Landmark detection is critical in medical imaging for accurate diagnosis and treatment of diseases. While there are many automated methods for landmark detection, the potential of transformers in this area has not been fully explored. This work proposes a transformer-based network for accurate anatomical landmark detection in chest x-ray images. By leveraging the combined power of transformers and U-Net, our method effectively captures global context and spatial dependencies, leading to robust landmark localization. The proposed method outperforms state-of-the-art methods on Chest x-ray datasets, reducing mean radial error from 5.57 to 4.68 pixels. The experiments show that the transformer-based method can effectively learn complex spatial patterns in medical images. The results of this method show the potential to improve the precision and efficiency of tasks such as surgical planning and detecting abnormalities in medical images.
We investigate the application of stationary Digital Tomosynthesis (DTS) using distributed x-ray source arrays for retrospectively-gated dynamic chest imaging under free-breathing conditions. The new capability might provide improved assessment of lung function impairment. A high-fidelity polychromatic x-ray system model was used to simulate a dynamic DTS configuration consisting of a linear array of 42 sources covering a 27.2° angular range in the superior-inferior direction. The array was placed 130 cm from a 42x42 cm flat-panel detector (FPD) with 0.56 mm pixels. The object center was placed at 116 cm from the source array. Dynamic acquisitions of a deformable digital chest phantom undergoing a realistic respiratory cycle (4 sec period) were simulated. The x-ray sources in the array were sequentially rastered at 8 fps; multiple passes through the array were performed to cover 3-19 breathing cycles. For reconstruction (0.5 mm voxels) of a given respiratory phase, a short sequence of projections temporally centered on the phase of interest was extracted from each cycle. The length of this sequence was varied from 1 (exact gating) to 5 frames. We investigated tradeoffs in motion blur (worse with longer gating), sampling artifacts (better with more breathing cycles), and noise, assuming a fixed total scan dose of 20 mAs regardless of the number of source array passes. A DTS acquisition over 15 respiratory cycles with exact gating yields breathing phase reconstructions at inspiration, expiration, and mid-cycle that recover >70% of the contrast in a static reconstruction and achieve adequate suppression of undersampling artifacts. For expiration and expiration, wide gating with 3 views/cycle can be used to reduce noise and improve sampling without introducing appreciable motion blur (>65% contrast recovery). For intermediate respiratory phases, wide gating causes motion blur that may require algorithmic compensation. Dynamic lung imaging with stationary DTS is feasible with approx. 1 min scan time and retrospective gating.
Purpose: We develop an Active Shape Model (ASM) framework for automated bone segmentation and anatomical landmark localization in weight-bearing Cone-Beam CT (CBCT). To achieve a robust shape model fit in narrow joint spaces of the foot (0.5 – 1 mm), a new approach for incorporating proximity constraints in ASM (coupled ASM, cASM) is proposed. Methods: In cASM, shape models of multiple adjacent foot bones are jointly fit to the CBCT volume. This coupling enables checking for proximity between the evolving shapes to avoid situations where a conventional single-bone ASM might erroneously fit to articular surfaces of neighbouring bones. We used 21 extremity CBCT scans of the weight-bearing foot to compare segmentation and landmark localization accuracy of ASM and cASM in leave-one-out validation. Each scan was used as a test image once; shape models of calcaneus, talus, navicular, and cuboid were built from manual surface segmentations of the remaining 20 scans. The models were augmented with seven anatomical landmarks used for common measurements of foot alignment. The landmarks were identified in the original CBCT volumes and mapped onto mean bone shape surfaces. ASM and cASM were run for 100 iterations, and the number of principal shape components was increased every 10 iterations. Automated landmark localization was achieved by applying known point correspondences between landmark vertices on the mean shape and vertices of the final active shape segmentation of the test image. Results: Root Mean Squared (RMS) error of bone surface segmentation improved from 3.6 mm with conventional ASM to 2.7 mm with cASM. Furthermore, cASM achieved convergence (no change in RMS error with iteration) after ~40 iterations of shape fitting, compared to ~60 iterations for ASM. Distance error in landmark localization was 25% to 55% lower (depending on the landmark) with cASM than with ASM. The importance of using a coupled model is underscored by the finding that cASM detected and corrected collisions between evolving shapes in 50% to 80% (depending on the bone) of shape model fits. Conclusion: The proposed cASM framework improves accuracy of shape model fits, especially in complexes of tightly interlocking, articulated joints. The approach enables automated anatomical analysis in volumetric imaging of the foot and ankle, where narrow joint spaces challenge conventional shape models.
Current clinical image quality assessment techniques mainly analyze image quality for the imaging system in terms of
factors such as the capture system DQE and MTF, the exposure technique, and the particular image processing method
and processing parameters. However, when assessing a clinical image, radiologists seldom refer to these factors, but
rather examine several specific regions of the image to see whether the image is suitable for diagnosis. In this work, we
developed a new strategy to learn and simulate radiologists' evaluation process on actual clinical chest images. Based on
this strategy, a preliminary study was conducted on 254 digital chest radiographs (38 AP without grids, 35 AP with 6:1
ratio grids and 151 PA with 10:1 ratio grids). First, ten regional based perceptual qualities were summarized through an
observer study. Each quality was characterized in terms of a physical quantity measured from the image, and as a first
step, the three physical quantities in lung region were then implemented algorithmically. A pilot observer study was
performed to verify the correlation between image perceptual qualities and physical quantitative qualities. The results
demonstrated that our regional based metrics have promising performance for grading perceptual properties of chest
radiographs.
The quality of a digital radiograph for diagnostic imaging depends on many factors, such as the capture system DQE and
MTF, the exposure technique factors, the patient anatomy, and the particular image processing method and processing
parameters used. Therefore, the overall image quality as perceived by the radiologists depends on many factors. This
work explores objective image quality metrics directly from display-ready patient images. A preliminary study was
conducted based on a multi-frequency analysis of anatomy contrast and noise magnitude from 250 computed
radiography (CR) chest radiographs (150 PA, 50 AP captured with anti-scatter grids, and 50 AP without grids). The
contrast and noise values were evaluated in different sub-bands separately according to their frequency properties.
Contrast-Noise ratio (CNR) was calculated, the results correlated well with the human observers' overall impression on
the images captured with and without grids.
Image blur introduced by patient motion is one of the most frequently cited reasons for image rejection in radiographic diagnostic imaging. The goal of the present work is to provide an automated method for the detection of anatomical motion blur in digital radiographic images to help improve image quality and facilitate workflow in the radiology department. To achieve this goal, the method first reorients the image to a predetermined hanging protocol. Then it locates the primary anatomy in the radiograph and extracts the most indicative region for motion blur, i.e., the region of interest (ROI). The third step computes a set of motion-sensitive features from the extracted ROI. Finally, the extracted features are evaluated by using a classifier that has been trained to detect motion blur. Preliminary experiments show promising results with 86% detection sensitivity, 72% specificity, and an overall accuracy of 76%.
The risk of radiation exposure is greatest for pediatric patients and, thus, there is a great incentive to reduce the radiation dose used in diagnostic procedures for children to "as low as reasonably achievable" (ALARA). Testing of low-dose protocols presents a dilemma, as it is unethical to repeatedly expose patients to ionizing radiation in order to determine optimum protocols. To overcome this problem, we have developed a computed-radiography (CR) dose-reduction simulation tool that takes existing images and adds synthetic noise to create realistic images that correspond to images generated with lower doses. The objective of our study was to determine the extent to which simulated, low-dose images corresponded with original (non-simulated) low-dose images. To make this determination, we created pneumothoraces of known volumes in five neonate cadavers and obtained images of the neonates at 10 mR, 1 mR and 0.1 mR (as measured at the cassette plate). The 10-mR exposures were considered "relatively-noise-free" images. We used these 10 mR-images and our simulation tool to create simulated 0.1- and 1-mR images. For the simulated and original images, we identified regions of interest (ROI) of the entire chest, free-in-air region, and liver. We compared the means and standard deviations of the ROI grey-scale values of the simulated and original images with paired t tests. We also had observers rate simulated and original images for image quality and for the presence or absence of pneumothoraces. There was no statistically significant difference in grey-scale-value means nor standard deviations between simulated and original entire chest ROI regions. The observer performance suggests that an exposure ≥0.2 mR is required to detect the presence or absence of pneumothoraces. These preliminary results indicate that the use of the simulation tool is promising for achieving ALARA exposures in children.
A four-alternative forced-choice experiment was carried out to examine the effect of 8-bit versus 10-bit grayscale resolution on the detection of subtle lung nodules on a medical grayscale liquid crystal display (LCD). Sets of four independent backgrounds from each of three regions were derived from a very low-noise X-ray acquisition of a chest-phantom with an amorphous selenium radiographic detector. Simulated nodules of fixed diameter (10 mm) and varying contrast were digitally added to the centers of selected background images. Subsequently, multifrequency image processing was performed to enhance the image structures, followed by a tonescaling procedure that resulted in pixel values being specified as p-values, according to DICOM Part 14: The Grayscale Display Function. To investigate the effect that grayscale resolution may have upon softcopy detectability, each set of four images in the experiment was quantized to both 8-bit and 10-bit resolution. The resulting images were displayed on a DICOM-calibrated LCD display supporting up to 10 bits of grayscale input. Twenty observers with imaging expertise performed the nodule detection task for which the signal and location were known exactly. Results from all readers, chest regions, and backgrounds were pooled, and statistical significance between fractions of correct responses between 8-bit and 10-bit resolution was tested. Experimental results do not demonstrate a statistically significant difference in the fraction of correct answers between these two input grayscale resolutions.
Lower x-ray exposures are commonly used in radiographic exams to reduce the patient radiation dose. An unwanted side effect is that the noise level increases as the exposure level is reduced. Image enhancement techniques increasing image contrast, such as sharpening and dynamic range compression tend to increase the appearance of noise. A Gaussian filter-based noise suppression algorithm using an adaptive soft threshold has been designed to reduce the noise appearance in low-exposure images. The advantage of this technique is that the algorithm is signal-dependent, and therefore will only impact image areas with low signal-to-noise ratio. Computed radiography images captured with lower exposure levels were collected from clinical sites, and used as controls in an observer study. The noise suppression algorithm was applied to each of the control images to generate test images. Hardcopy printed film versions of control and test images were presented side-by-side on a film alternator to six radiologists. The radiologists were asked to rate the control and test images using a 9-point diagnostic quality rating scale and a 7-point delta-preference rating scale. The results showed that the algorithm reduced noise appearance, which was preferred, while preserving the diagnostic image quality. This paper describes the noise suppression algorithm and reports on the results of the observer study.
A four-alternative forced-choice experiment was conducted to investigate the relative impact of detector noise and anatomical structure on detection of subtle lung nodules. Sets of four independent backgrounds from each of three regions (heart, ribs, and lung field between the ribs) were derived from a very low-noise chest-phantom capture. Simulated nodules of varying contrast and fixed diameter (10 mm) were digitally added to the centers of selected background images. Subsequently, signal-dependent noise was introduced to simulate amorphous selenium radiographic detector performance at typical 80, 200, 400, 800, or higher speed class exposures. Series of four nodule contrasts each were empirically selected to yield comparable ranges of detectability index (d') for each background type and exposure level. Thirty-six observers with imaging expertise performed the nodule detection task, for which the signal and location were known exactly. Equally detectable nodule contrasts for each background type and exposure level were computed and their squares plotted against detector noise variance. The intercepts and slopes of the linear regressions increased in the order of lung, heart, and ribs, correlating with apparent anatomical structural complexity. The regression results imply that the effect of anatomical structure dominated that of capture device noise at clinically relevant exposures and beyond.
The point-spread function (PSF) of a circularly symmetric imaging system is commonly inferred from the line-spread function (LSF), which is the image of a line source whose length must be larger than the spatial extent of the PSF. This constraint on the minimum length of the line source makes it impossible to measure the LSF of a system whose PSF is large in extent relative to the size of the system's isoplanatic patch. This impasse motivates one to consider the problem of inferring the PSF from the finite-length line spread function (FLSF), which is the image of a finite-length line source of arbitrary, but fixed, length. Formulas for calculating the PSF from the FLSF have been developed, but the numerical implementation of these formulas are either time consuming or unstable. In this presentation, we derive a formula for performing the FLSF-PSF conversion which is better suited for numerical purposes.
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