Sequential dual-energy subtraction technique with a dynamic flat-panel detector (FPD): primary study for image-guided radiation therapy (IGRT)

Flat-panel cone-beam computed tomography for image-guided radiation therapy

Image Guided, Motion-free Patient Body Setup using 3D Volumetric Image Registration of Classified Stable Bony Landmarks

For the Automatic Target Recognition (ATR) algorithm, the quality of the input image sequence can be a major determining factor as to the ATR algorithm's ability to recognize an object. Based on quality, an image can be easy to recognize, barely recognizable or even mangled beyond recognition. If a determination of the image quality can be made prior to entering the ATR algorithm, then a confidence factor can be applied to the probability of recognition. This confidence factor can be used to rate sensors; to improve quality through selectively preprocessing image sequences prior to applying ATR; or to limit the problem space by determining which image sequences need not be processed by the ATR algorithm. It could even determine when human intervention is needed. To get a flavor for the scope of the image quality problem, this paper reviews analog and digital forms of image degradation. It looks at traditional quality metric approaches such as peak signal-to-noise ratio. It examines a newer metric based on human vision data, a metric introduced by the Institute for Telecommunication Sciences. These objective quality metrics can be used as confidence factors primarily in ATR systems that use image sequences degraded due to transmission systems. However, to determine the quality metric, a transmission system needs the original input image sequence and the degraded output image sequence. This paper suggests a more general approach to determining quality using analysis of spatial and temporal vectors where the original input sequence is not explicitly given. This novel approach would be useful where there is no transmission system but where the ATR system is part of the sensor, on-board a mobile platform. The results of this work are demonstrated on a few standard image sequences.

Detectability of lung nodules using flat panel detector with dual energy subtraction by two shot method: Evaluation by ROC method

Dual-energy chest radiography has the potential to provide better diagnosis of lung disease by removing the bone signal from the image. Dynamic dual-energy radiography is now possible with the introduction of digital flat-panel detectors. The purpose of this study is to evaluate the feasibility of using dynamic dual-energy chest radiography for functional lung imaging and tumor motion assessment. The dual-energy system used in this study can acquire up to 15 frames of dual-energy images per second. A swine animal model was mechanically ventilated and imaged using the dual-energy system. Sequences of soft-tissue images were obtained using dual-energy subtraction. Time subtracted soft-tissue images were shown to be able to provide information on regional ventilation. Motion tracking of a lung anatomic feature (a branch of pulmonary artery) was performed based on an image cross-correlation algorithm. The tracking precision was found to be better than 1 mm. An adaptive correlation model was established between the above tracked motion and an external surrogate signal (temperature within the tracheal tube). This model is used to predict lung feature motion using the continuous surrogate signal and low frame rate dual-energy images (0.1–3.0 frames per second). The average RMS error of the prediction was (1.1 ± 0.3) mm. The dynamic dual energy was shown to be potentially useful for lung functional imaging such as regional ventilation and kinetic studies. It can also be used for lung tumor motion assessment and prediction during radiation therapy.

Rationale and objectives: We aimed to report and compare accuracy, reproducibility, and reporting confidence between thoracic dual-energy subtraction (DES) and routine posterior–anterior chest radiography (PA-CR) techniques. Materials (patients) and methods: We obtained DES (D1–D4) images from 96 patients using DES and a high-resolution dynamic flat-panel detector in combination. We compared the DES images of these patients with their PA-CR images. The maximum time interval between performing DES and PA-CR was nine weeks. Two radiologists evaluated abnormal findings on DES and PA-CR images using a three-point scale, and reporting confidence was scored using a four-point scale. The intra- and interobserver agreement values of the scores were analyzed. Further, the radiation exposure doses during PA-CR and DES acquisitions were calculated. Results: The intra- and interobserver agreement values of PA-CR and DES images were good. The reporting confidence scores for DES were generally higher than those for PA-CR. Between bone-subtracted (D3) and soft-tissue-subtracted (D4) images, the former was more successful and useful in the evaluation of bone structures, whereas the latter was better in the evaluation of consolidation and/or solitary nodules. Conclusions: DES has the potential to improve the accuracy, reproducibility, and reporting confidence of thoracic radiography. It also has the potential to provide a better diagnosis of chest pathologies using relatively low dose radiation.

In this paper, we propose a computationally efficient method for medical image registration. The centerpiece of the approach is to reduce the dimensions of each image via a projection operation. The two sequences of projection images corresponding to each image are used for estimating the registration parameters. Depending upon how the projection geometry is set up, the lower dimension registration problem can be parameterized and solved for a subset of parameters from the original problem. Computation of similarity metrics on the lower dimension projection images is significantly less complex than on the original volumetric images. Furthermore, depending on the type of projection operator used, one can achieve a better signal to noise ratio for the projection images than the original images. In order to further accelerate the process, we use Graphic Processing Units (GPUs) for generating projections of the volumetric data. We also perform the similarity computation on the graphics board, using a GPU with a programmable rendering pipeline. By doing that, we avoid transferring a large amount of data from graphics memory to system memory for computation. Furthermore, the performance of the more complex algorithms exploiting the graphics processor’s capabilities is greatly improved. We evaluate the performance and the speed of the proposed projection based registration approach using various similarity measures and benchmark them against an SSE-accelerated CPU based implementation.KeywordsSimilarity MeasureGraphic Processing UnitImage RegistrationProjection ImageTarget Registration ErrorThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Many important imaging applications in medical imaging, remote sensing and other areas result in a set of images of the same scene with no relative scene-sensor motion and in which the pixel intensities are the linear sum of the contributions of the distinct features in the scene. Such image sets are called linearly additive, spatially invariant (LA SI) image sequences. Previous research has shown, both mathematically and with examples, that a K-image sequence with M distinct features (M<K) can be linearly filtered to extract the individual features from the original image sequence. In those studies, the noise was modeled as Gaussian (uniform over the image scene) or Poisson (dependent on the feature distribution). This paper explores the effectiveness of the same technique for LA SI image sequences with salt and pepper noise, which is neither uniform nor feature dependent, and compares the results with those for the same image scene with equal Gaussian and Poisson noise. It is shown here that the method extracts the features in this case with similar effectiveness.

Real-time tumor tracking in external radiotherapy can be achieved by diagnostic (kV) X-ray imaging with a dynamic flat-panel detector (FPD). It is important to keep the patient dose as low as possible while maintaining tracking accuracy. A simulation approach would be helpful to optimize the imaging conditions. This study was performed to develop a computer simulation platform based on a noise property of the imaging system for the evaluation of tracking accuracy at any noise level. Flat-field images were obtained using a direct-type dynamic FPD, and noise power spectrum (NPS) analysis was performed. The relationship between incident quantum number and pixel value was addressed, and a conversion function was created. The pixel values were converted into a map of quantum number using the conversion function, and the map was then input into the random number generator to simulate image noise. Simulation images were provided at different noise levels by changing the incident quantum numbers. Subsequently, an implanted marker was tracked automatically and the maximum tracking errors were calculated at different noise levels. The results indicated that the maximum tracking error increased with decreasing incident quantum number in flat-field images with an implanted marker. In addition, the range of errors increased with decreasing incident quantum number. The present method could be used to determine the relationship between image noise and tracking accuracy. The results indicated that the simulation approach would aid in determining exposure dose conditions according to the necessary tracking accuracy.

Lossless compression achieves only modest data reduction in noisy medical image sequences such as digital coronary angiograms. Lossy compression achieves greater reductions, but diagnostic performance criteria for use of lossy compression in dynamically displayed image sequences have yet to be established. We performed a quantitative perception study to measure low contrast feature detectability within moving simulated arterial segments inserted frame by frame into actual human cardiac cine radiographs to produce dynamic displays with realistic quantum and structure noise. Thirty-frame angiograms were displayed in loop format at 15 fps. Three subjects made 200 observations of the original images and at each of three levels of JPEG compression in a four-alternative forced-choice experiment. Compressions of 6:1, 12:1, and 18:1 resulted in average relative detectability of 102%, 95%, and 64%, respectively, compared to the original images. Only the detectability loss at 18:1 reached statistical significance at the p&lt;0.05 confidence level in this study. We conclude that observer performance techniques are able to quantify specific changes in actual diagnostic information. Our preliminary data suggest that detection of low contrast objects in coronary angiograms is not degraded for JPEG compression ratios of 12:1 or less.

This study developed a robust algorithm for realtime ultrasonic strain imaging that employs dynamic programming techniques. Since tissue motion under external mechanical stimuli should be highly continuous, the speckle tracking problem fits well within the general framework of the hidden Markov model and can be solved as an optimization problem. A cost function combining correlation and motion continuity was used to regularize motion tracking. We found that the new algorithm provides more accurate displacement estimates than our previous algorithm for in vivo clinical data. In particular, the new algorithm is capable of tracking larger frame-average tissue deformation (1-2%) and increasing strain image consistency in a sequence of images. The new algorithm can also tolerate larger local strain (approx. 10%). Preliminary results also suggest that a significantly longer sequence of high contrast strain images (e.g. 45 vs. 15 in one cancer dataset) could be obtained with the new algorithm compared to the previous algorithm. We also achieved more than 10 frames/second with a 3 cm by 3 cm region of interest, which is sufficient to provide real-time feedback during in vivo elasticity imaging

Respiratory gating based on external surrogates is performed in many clinics. We have developed a new technique for treatment verification using an electronic portal imaging device (EPID) in cine mode for gated 3D conformal therapy. Implanted radiopaque fiducial markers inside or near the target are required for this technique. The markers are contoured on the planning CT set, enabling us to create digitally reconstructed radiographs (DRRs) for each treatment beam. During the treatment, a sequence of EPID images can be acquired without disrupting the treatment. Implanted markers are visualized in the images and their positions in the beam's eye view are calculated off-line and compared to the reference position by matching the field apertures in corresponding EPID and DRR images. The precision of the patient set-up, the placement of the beam-gating window, as well as the residual tumour motion can be assessed for each treatment fraction. This technique has been demonstrated with a case study patient, who had three markers implanted in his liver. For this patient, the intra-fractional variation of all marker positions in the gating window had a 95% range of 4.8 mm in the SI direction (the primary axis of motion). This was about the same (5 mm) as the residual motion considered in the planning process. The inter-fractional variation of the daily mean positions of the markers, which indicates the uncertainty in the set-up procedure, was within +8.3 mm/−4.5 mm (95% range) in the SI direction for this case.

Prediction of pleural invasion in lung cancer is crucial in planning appropriate operating procedures and can be assessed using dynamic chest radiography (DCR). However, this assessment is negatively affected by rib shadows in conventional images. The purpose of this study was to develop a deep learning-based bone suppression technique for DCR in various projection directions. Twenty breathing XCAT phantoms with lung tumors and a pair of phantoms consisting only of bone structures were generated. The XCAT phantoms were virtually projected from six directions, resulting in 54000 multidirectional chest X-ray images and were used for training a pix2pix model to estimate bone images from original images. Bone suppression (BS) images were created by subtracting the bone images from the original images and then evaluated based on the peak signal to noise ratio (PSNR), structural similarity index measure (SSIM), and Fréchet Video Distance (FVD). Clinical cases were processed using the trained model as well. Furthermore, lung tumors on the original and BS images were tracked using OpenCV template matching, and the tracking accuracy was compared. The PSNR, SSIM, and FVD were 0.9966, 52.20 and 136.9, respectively. In the visual evaluation, we confirmed the effect of BS without temporal fluctuation of pixel values in both the resulting images of the XCAT phantom and real patients. Furthermore, precise tracking of the targeted tumor was achieved on the resulting BS images even in oblique directions, without any interruption from the rib shadows. These results indicate that our proposed method can effectively reduce bone shadows as well as temporal variations in the effect of bone suppression.

Prospective Evaluation of Dual-Energy Imaging in Patients Undergoing Image Guided Radiation Therapy for Lung Cancer: Initial Clinical Results

To use physical attributes of redness to determine the accuracy of four bulbar redness grading scales, and to cross-calibrate the scales based on these physical measures. Two image-processing metrics, fractal dimension (D) and percentage of pixel coverage (% PC), as well as photometric chromaticity were selected as physical measures, to describe and compare grades of bulbar redness among the McMonnies/Chapman-Davies scale, the Efron Scale, the Institute for Eye Research scale, and a validated scale developed at the Centre for Contact Lens Research. Two sets of images were prepared by using image processing: The first included multiple segments covering the largest possible region of interest (ROI) within the bulbar conjunctiva in the original images; the second contained modified scale images that were matched in size and resolution across scales, and a single, equally-sized ROI. To measure photometric chromaticity, the original scale images were displayed on a computer monitor, and multiple conjunctival segments were analyzed. Pearson correlation coefficients between each set of image metrics and the reference image grades were calculated to determine the accuracy of the scales. Correlations were high between reference image grades and all sets of objective metrics (all Pearson's r >or= 0.88, P <or= 0.05); each physical attribute pointed to a different scale as being most accurate. Independent of the physical attribute used, there were wide discrepancies between scale grades, with almost no overlap when cross-calibrating and comparing the scales. Despite the generally strong linear associations between the physical characteristics of reference images in each scale, the scales themselves are not inherently accurate and are too different to allow for cross-calibration.