Patient Respiratory Motion Research Articles

A robotic system for transthoracic puncture of pulmonary nodules based on gated respiratory compensation

Background and objectiveWith the urgent demands for rapid and precise localization of pulmonary nodules in procedures such as transthoracic puncture biopsy and thoracoscopic surgery, many surgical navigation and robotic systems are applied in the clinical practice of thoracic operation. However, current available positioning methods have certain limitations, including high radiation exposure, large errors from respiratory, complicated and time-consuming procedures, etc. MethodsTo address these issues, a preoperative computed tomography (CT) image-guided robotic system for transthoracic puncture was proposed in this study. Firstly, an algorithm for puncture path planning based on constraints from clinical knowledge was developed. This algorithm enables the calculation of Pareto optimal solutions for multiple clinical targets concerning puncture angle, puncture length, and distance from hazardous areas. Secondly, to eradicate intraoperative radiation exposure, a fast registration method based on preoperative CT and gated respiration compensation was proposed. The registration process could be completed by the direct selection of points on the skin near the sternum using a hand-held probe. Gating detection and joint optimization algorithms are then performed on the collected point cloud data to compensate for errors from respiratory motion. Thirdly, to enhance accuracy and intraoperative safety, the puncture guide was utilized as an end effector to restrict the movement of the optically tracked needle, then risky actions with patient contact would be strictly limited. ResultsThe proposed system was evaluated through phantom experiments on our custom-designed simulation test platform for patient respiratory motion to assess its accuracy and feasibility. The results demonstrated an average target point error (TPE) of 2.46 ± 0.68 mm and an angle error (AE) of 1.49 ± 0.45° for the robotic system. ConclusionsIn conclusion, our proposed system ensures accuracy, surgical efficiency, and safety while also reducing needle insertions and radiation exposure in transthoracic puncture procedures, thus offering substantial potential for clinical application.

Experimental comparison of linear regression and LSTM motion prediction models for MLC-tracking on an MRI-linac.

Magnetic resonance imaging (MRI)-guided radiotherapy with multileaf collimator (MLC)-tracking is a promising technique for intra-fractional motion management, achieving high dose conformality without prolonging treatment times. To improve beam-target alignment, the geometric error due to system latency should be reduced by using temporal prediction. To experimentally compare linear regression (LR) and long-short-term memory (LSTM) motion prediction models for MLC-tracking on an MRI-linac using multiple patient-derived traces with different complexities. Experiments were performed on a prototype 1.0T MRI-linac capable of MLC-tracking. A motion phantom was programmed to move a target in superior-inferior (SI) direction according to eight lung cancer patient respiratory motion traces. Target centroid positions were localized from sagittal 2D cine MRIs acquired at 4Hz using a template matching algorithm. The centroid positions were input to one of four motion prediction models. We used (1) a LSTM network which had been optimized in a previous study on patient data from another cohort (offline LSTM). We also used (2) the same LSTM model as a starting point for continuous re-optimization of its weights during the experiment based on recent motion (offline+online LSTM). Furthermore, we implemented (3) a continuously updated LR model, which was solely based on recent motion (online LR). Finally, we used (4) the last available target centroid without any changes as a baseline (no-predictor). The predictions of the models were used to shift the MLC aperture in real-time. An electronic portal imaging device (EPID) was used to visualize the target and MLC aperture during the experiments. Based on the EPID frames, the root-mean-square error (RMSE) between the target and the MLC aperture positions was used to assess the performance of the different motion predictors. Each combination of motion trace and prediction model was repeated twice to test stability, for a total of 64 experiments. The end-to-end latency of the system was measured to be (389 ± 15)ms and was successfully mitigated by both LR and LSTM models. The offline+online LSTM was found to outperform the other models for all investigated motion traces. It obtained a median RMSE over all traces of (2.8 ± 1.3)mm, compared to the (3.2 ± 1.9)mm of the offline LSTM, the (3.3 ± 1.4)mm of the online LR and the (4.4 ± 2.4)mm when using the no-predictor. According to statistical tests, differences were significant (p-value <0.05) among all models in a pair-wise comparison, but for the offline LSTM and online LR pair. The offline+online LSTM was found to be more reproducible than the offline LSTM and the online LR with a maximum deviation in RMSE between two measurements of 10%. This study represents the first experimental comparison of different prediction models for MRI-guided MLC-tracking using several patient-derived respiratory motion traces. We have shown that among the investigated models, continuously re-optimized LSTM networks are the most promising to account for the end-to-end system latency in MRI-guided radiotherapy withMLC-tracking.

Evaluation of Patient-Specific Quality Control (QC) for Markerless Dynamic Tumor Tracking (MDTT) Deliveries

To report on the patient-specific QC for MDTT for liver and lung tumors treated using the dome-of-liver/diaphragm as a surrogate for the target position and compare to the QC of conventional fiducial-based tracking. Vero4DRT is a linear accelerator that has a gimbal-mounted waveguide and collimation system allowing the radiation beam to perform dynamic tumor tracking based on a patient's respiratory motion. A correlation model is built between an external IR marker placed on the chest and an internal structure, which may be implanted fiducial surrogates or in the case of MDTT, an anatomic landmark. During treatment an orthogonal kV image pair is taken every second. The auto detected internal structure position versus the predicted position is recorded in treatment log files. Five IMRT tracking plans were delivered to two commercial motion phantoms with in-house additions for patient-specific tracking QC. For point dose measurements, a 0.6cc farmer chamber was placed inside the dynamic tracking phantom which contains fiducials for fiducial-based tracking delivery and an anatomic landmark for MDTT. For 2D dose distribution measurements, a radiographic film insert for the Quasar Respiratory Motion Phantom was constructed. The phantom contains both fiducials and has a liver-dome shape at one end to facilitate MDTT. Both motion platforms were interfaced with software that enabled patient-specific respiratory motion traces acquired during a 4DCT scan. Each plan was delivered three times on each phantom: 1) in static mode, 2) during fiducial-based tracking, and 3) during MDTT. Chamber measurements taken during static and tracked delivery were compared to the average chamber dose from the treatment planning software (TPS). Film distributions measured during tracked deliveries were compared to the static film distribution using gamma analysis with FILMQA PRO software. Finally, dynamic tracking treatment statistics, extracted from log files, were compared between fiducial and markerless tracking deliveries. All chamber measurements resulted in dose differences <2.5% compared with the TPS. Dose differences between fiducial-based and MDTT were 0.26% on average (range 0.03-0.62%). For film dose map analysis, gamma pass rates were >95% for all plans for all tracking methods. The average gamma pass rate for 3%/3mm was 99.4% (fiducial-based) vs 98.4% (markerless). For 2%/2mm, the average gamma pass rate was 96.4% (fiducial-based tracking) vs 95.6% (MDTT). Dynamic treatment statistics from logs files reported an average 3D absolute deviation from predicted position of 0.52mm (±0.24) for fiducial tracking and 1.19mm (±0.50) for MDTT. Both fiducial-based and MDTT plans meet passing criteria for patient-specific QC. 3D absolute deviation of detected versus predicted position are larger for MDTT compared to fiducial tracking, however no significant impact on dose delivery was measured.

Real-time continuous image guidance for endoscopic retrograde cholangiopancreatography based on 3D/2D registration and respiratory compensation.

It has been confirmed that three-dimensional (3D) imaging allows easier identification of bile duct anatomy and intraoperative guidance of endoscopic retrograde cholangiopancreatography (ERCP), which reduces the radiation dose and procedure time with improved safety. However, current 3D biliary imaging does not have good real-time fusion with intraoperative imaging, a process meant to overcome the influence of intraoperative respiratory motion and guide navigation. The present study explored the feasibility of real-time continuous image-guided ERCP. To explore the feasibility of real-time continuous image-guided ERCP. We selected 2 3D-printed abdominal biliary tract models with different structures to simulate different patients. The ERCP environment was simulated for the biliary phantom experiment to create a navigation system, which was further tested in patients. In addition, based on the estimation of the patient's respiratory motion, preoperative 3D biliary imaging from computed tomography of 18 patients with cholelithiasis was registered and fused in real-time with 2D fluoroscopic sequence generated by the C-arm unit during ERCP. Continuous image-guided ERCP was applied in the biliary phantom with a registration error of 0.46 mm ± 0.13 mm and a tracking error of 0.64 mm ± 0.24 mm. After estimating the respiratory motion, 3D/2D registration accurately transformed preoperative 3D biliary images to each image in the X-ray image sequence in real-time in 18 patients, with an average fusion rate of 88%. Continuous image-guided ERCP may be an effective approach to assist the operator and reduce the use of X-ray and contrast agents.

4DCT is long overdue for improvement.

4DCT is long overdue for improvement.

Image-based motion artifact reduction on liver dynamic contrast enhanced MRI

Liver MRI images often suffer from degraded quality due to ghosting or blurring artifacts caused by patient respiratory or bulk motion. In this study, we developed a two-stage deep learning model to reduce motion artifact on dynamic contrast enhanced (DCE) liver MRIs. The stage-I network utilized a deep residual network with a densely connected multi-resolution block (DRN-DCMB) network to remove most motion artifacts. The stage-II network applied the generative adversarial network (GAN) and perceptual loss compensation to preserve image structural features. The stage-I network served as the generator of GAN and its pretrained parameters in stage-I were further updated via backpropagation during stage-II training. The stage-I network was trained using small image patches with simulated motion artifacts including image-space rotational and translational motion, and K-space based centric and interleaved linear motion, sinusoidal, and rotational motion to mimic liver motion patterns. The stage-II network training used full-size images with the same types of simulated motion. The liver DCE-MRI image volumes without obvious motion artifacts in 10 patients were used for the training process, of which 1020 images of 8 patients were used for training and 240 images of 2 patients for validation. Finally, the whole two-stage deep learning model was tested with simulated motion images (312 clean images from 5 test patients) and patient images with real motion artifacts (28 motion images from 12 patients). The resulted images after two-stage processing demonstrated reduced motion artifacts while preserved anatomic details without image blurriness, with SSIM of 0.935 ± 0.092, MSE of 60.7 ± 9.0 × 10-3, and PSNR of 32.054 ± 2.219.

Deep learning-based 4D-synthetic CTs from sparse-view CBCTs for dose calculations in adaptive proton therapy.

Time-resolved 4D cone beam-computed tomography (4D-CBCT) allows a daily assessment of patient anatomy and respiratory motion. However, 4D-CBCTs suffer from imaging artifacts that affect the CT number accuracy and prevent accurate proton dose calculations. Deep learning can be used to correct CT numbers and generate synthetic CTs (sCTs) that can enable CBCT-based proton dose calculations. In this work, sparse view 4D-CBCTs were converted into 4D-sCT utilizing a deep convolutional neural network (DCNN). 4D-sCTs were evaluated in terms of image quality and dosimetric accuracy to determine if accurate proton dose calculations for adaptive proton therapy workflows of lung cancer patients are feasible. A dataset of 45 thoracic cancer patients was utilized to train and evaluate a DCNN to generate 4D-sCTs, based on sparse view 4D-CBCTs reconstructed from projections acquired with a 3D acquisition protocol. Mean absolute error (MAE) and mean error were used as metrics to evaluate the image quality of single phases and average 4D-sCTs against 4D-CTs acquired on the same day. The dosimetric accuracy was checked globally (gamma analysis) and locally for target volumes and organs-at-risk (OARs) (lung, heart, and esophagus). Furthermore, 4D-sCTs were also compared to 3D-sCTs. To evaluate CT number accuracy, proton radiography simulations in 4D-sCT and 4D-CTs were compared in terms of range errors. The clinical suitability of 4D-sCTs was demonstrated by performing a 4D dose reconstruction using patient specific treatment delivery log files and breathing signals. 4D-sCTs resulted in average MAEs of 48.1±6.5HU (single phase) and 37.7±6.2HU (average). The global dosimetric evaluation showed gamma pass ratios of 92.3%±3.2% (single phase) and 94.4%±2.1% (average). The clinical target volume showed high agreement in D98 between 4D-CT and 4D-sCT, with differences below 2.4% for all patients. Larger dose differences were observed in mean doses of OARs (up to 8.4%). The comparison with 3D-sCTs showed no substantial image quality and dosimetric differences for the 4D-sCT average. Individual 4D-sCT phases showed slightly lower dosimetric accuracy. The range error evaluation revealed that lung tissues cause range errors about three times higher than the other tissues. In this study, we have investigated the accuracy of deep learning-based 4D-sCTs for daily dose calculations in adaptive proton therapy. Despite image quality differences between 4D-sCTs and 3D-sCTs, comparable dosimetric accuracy was observed globally and locally. Further improvement of 3D and 4D lung sCTs could be achieved by increasing CT number accuracy in lung tissues.

特異値分解による術中X線投影動画から時間濃度曲線の推定手法の開発

It is a challenge in clinical practice to estimate Time Enhancement Curves (TEC) of a Region Of Interest ( ROI ) in real time from intraoperative X-ray projection movies. In the past, TEC was estimated by obtaining the Standard Linear Attenuation Coefficient Distribution (SLACD) from a preoperative contrast-enhanced CT and combining it with an intraoperative X-ray projection movie. However, in actual clinical practice, the SLACD is often uncertain due to the unavailability of preoperative contrast-enhanced CT images and the patient's respiratory motion. We developed a method for estimating TECs at a newly defined Node Of Interest (NOIs) by applying Singular Value Decomposition ( SVD ) and prior information to intraoperative X-ray projection movies only. To validate the effectiveness of this method, twin numerical experiments were conducted using a simulated blood vessel model, and the estimation results were compared. The estimation results show that the method is able to accurately estimate the rise time of TECs and the frame of maximum concentration.

Visually guided respiratory motion management for Ethos adaptive radiotherapy.

PurposeEthos adaptive radiotherapy (ART) is emerging with AI‐enhanced adaptive planning and high‐quality cone‐beam computed tomography (CBCT). Although a respiratory motion management solution is critical for reducing motion artifacts on abdominothoracic CBCT and improving tumor motion control during beam delivery, our institutional Ethos system has not incorporated a commercial solution. Here we developed an institutional visually guided respiratory motion management system to coach patients in regular breathing or breath hold during intrafractional CBCT scans and beam delivery with Ethos ART.MethodsThe institutional visual‐guidance respiratory motion management system has three components: (1) a respiratory motion detection system, (2) an in‐room display system, and (3) a respiratory motion trace management software. Each component has been developed and implemented in the clinical Ethos ART workflow. The applicability of the solution was demonstrated in installation, routine QA, and clinical workflow.ResultsAn air pressure sensor has been utilized to detect patient respiratory motion in real time. Either a commercial or in‐house software handled respiratory motion trace display, collection and visualization for operators, and visual guidance for patients. An extended screen and a projector on an adjustable stand were installed as the in‐room visual guidance solution for the closed‐bore ring gantry medical linear accelerator utilized by Ethos. Consistent respiratory motion traces and organ positions on intrafractional CBCTs demonstrated the clinical suitability of the proposed solution in Ethos ART.ConclusionThe study demonstrated the utilization of an institutional visually guided respiratory motion management system for Ethos ART. The proposed solution can be easily applied for Ethos ART and adapted for use with any closed bore‐type system, such as computed tomography and magnetic resonance imaging, through incorporation with appropriate respiratory motion sensors.

Interventional respiratory motion compensation by simultaneous fluoroscopic and nuclear imaging: a phantom study

Purpose. A compact and mobile hybrid c-arm scanner, capable of simultaneously acquiring nuclear and fluoroscopic projections and SPECT/CBCT, was developed to aid fluoroscopy-guided interventional procedures involving the administration of radionuclides (e.g. hepatic radioembolization). However, as in conventional SPECT/CT, the acquired nuclear images may be deteriorated by patient respiratory motion. We propose to perform compensation for respiratory motion by extracting the motion signal from fluoroscopic projections so that the nuclear counts can be gated into motion bins. The purpose of this study is to quantify the performance of this motion compensation technique with phantom experiments. Methods. Anthropomorphic phantom configurations that are representative of distributions obtained during the pre-treatment procedure of hepatic radioembolization were placed on a stage that translated with three different motion patterns. Fluoroscopic projections and nuclear counts were simultaneously acquired under planar and SPECT/CBCT imaging. The planar projections were visually assessed. The SPECT reconstructions were visually assessed and quantitatively assessed by calculating the activity recovery of the spherical inserts in the phantom. Results. The planar nuclear projections of the translating anthropomorphic phantom were blurry when no motion compensation was applied. With motion compensation, the nuclear projections became representative of the stationary phantom nuclear projection. Similar behavior was observed for the visual quality of SPECT reconstructions. The mean error of the activity recovery in the uncompensated SPECT reconstructions was 15.8% ± 0.9% for stable motion, 11.9% ± 0.9% for small variations, and 11.0% ± 0.9% for large variations. When applying motion compensation, the mean error decreased to 1.8% ± 1.6% for stable motion, 2.2% ± 1.5% for small variations, and 5.2% ± 2.5% for large variations. Conclusion. A compact and mobile hybrid c-arm scanner, capable of simultaneously acquiring nuclear and fluoroscopic projections, can perform compensation for respiratory motion. Such motion compensation results in sharper planar nuclear projections and increases the quantitative accuracy of the SPECT reconstructions.

An Adaptive Respiratory Motion Compensation Algorithm with Singular Value Decomposition for Intracardiac Catheter Tracking.

During radiofrequency catheterization for atrial fibrillation, how to accurately obtain non-X-ray intracardiac catheter position is crucial to successful endocardial mapping and ablation treatment. The major limitation of the cost-effective intracardiac catheter tracking with transthoracic electrical-fields is that the distribution of electrical conductivity within the volume torso remains dynamics and nonlinear and changes with the patient's respiratory motion. Studies have shown respiratory motion-induced catheter localization error over 20 mm. In this study, we present a novel adaptive respiratory motion compensation algorithm with singular value decomposition for reducing the interference of respiration to ensure the accuracy of intracardiac catheter localization. Animal experiments in swine were carried out for assessing the performance of the propose method through a comparison with a traditional filtering method. The obtained results demonstrate that the proposed adaptive filter based on the SVD performed well to track the original information of catheter position by accurately and timely removing the respiratory interference in case of either a fast- or slow- moving catheter operation. Future applications of this algorithm would be potentially useful for intracardiac catheter localization and real-time tracking.

Characterization of continuous bed motion effects on patient breathing and respiratory motion correction in PET/CT imaging.

Continuous bed motion (CBM) was recently introduced as an alternative to step‐and‐shoot (SS) mode for PET/CT data acquisition. In CBM, the patient is continuously advanced into the scanner at a preset speed, whereas in SS, the patient is imaged in overlapping bed positions. Previous investigations have shown that patients preferred CBM over SS for PET data acquisition. In this study, we investigated the effect of CBM versus SS on patient breathing and respiratory motion correction. One hundred patients referred for PET/CT were scanned using a Siemens mCT scanner. Patient respiratory waveforms were recorded using an Anzai system and analyzed using four methods: Methods 1 and 2 measured the coefficient of variation (COV) of the respiratory cycle duration (RCD) and amplitude (RCA). Method 3 measured the respiratory frequency signal prominence (RSP) and method 4 measured the width of the HDChest optimal gate (OG) window when using a 35% duty cycle. Waveform analysis was performed over the abdominothoracic region which exhibited the greatest respiratory motion and the results were compared between CBM and SS. Respiratory motion correction was assessed by comparing the ratios of SUVmax, SUVpeak, and CNR of focal FDG uptake, as well as Radiologists’ visual assessment of corresponding image quality of motion corrected and uncorrected images for both acquisition modes. The respiratory waveforms analysis showed that the RCD and RCA COV were 3.7% and 33.3% lower for CBM compared to SS, respectively, while the RSP and OG were 30.5% and 2.0% higher, respectively. Image analysis on the other hand showed that SUVmax, SUVpeak, and CNR were 8.5%, 4.5%, and 3.4% higher for SS compared to CBM, respectively, while the Radiologists’ visual comparison showed similar image quality between acquisition modes. However, none of the results showed statistically significant differences between SS and CBM, suggesting that motion correction is not impacted by acquisition mode.

Improving the Spatial Alignment in PET/CT Using Amplitude-Based Respiration-Gated PET and Patient-Specific Breathing-Instructed CT.

Appropriate attenuation correction is important for accurate quantification of SUVs in PET. Patient respiratory motion can introduce a spatial mismatch between respiration-gated PET and CT, reducing quantitative accuracy. In this study, the effect of a patient-specific breathing-instructed CT protocol on the spatial alignment between CT and amplitude-based optimal respiration-gated PET images was investigated. Methods: 18F-FDG PET/CT imaging was performed on 20 patients. In addition to the standard low-dose free-breathing CT, breath-hold CT was performed. The amplitude limits of the respiration-gated PET were used to instruct patients to hold their breath during CT acquisition at a similar amplitude level. Spatial mismatch was quantified using the position differences between the lung-liver transition in PET and CT images, the distance between PET and CT lesions' centroids, and the amount of overlap as indicated by the Jaccard similarity coefficient. Furthermore, the effect on attenuation correction was quantified by measuring SUVs, metabolic tumor volume, and total lesion glycolysis (TLG) of lung lesions. Results: All patients found the breathing instructions feasible; however, 4 patients had trouble complying with the instructions. In total, 18 patients were included. The average distance between the lung-liver transition between PET and CT was significantly reduced for breath-hold CT (1.7 ± 2.1 mm), compared with standard CT (5.6 ± 7.3 mm) (P = 0.049). Furthermore, the mean distance between the lesions' centroids on PET and CT was significantly smaller for breath-hold CT (3.6 ± 2.0 mm) than for standard CT (5.5 ± 6.5 mm) (P = 0.040). Quantification of lung lesion SUV was significantly affected, with a higher SUVmean when breath-hold CT (6.3 ± 3.9 g/cm3) was used for image reconstruction than for standard CT (6.1 ± 3.8 g/cm3) (P = 0.044). Though metabolic tumor volume was not significantly different, TLG reached statistical significance. Conclusion: Optimal respiration-gated PET in combination with patient-specific breathing-instructed CT results in an improved alignment between PET and CT images and shows an increased SUVmean and TLG. Even though the effects are small, a more accurate SUV and TLG determination is of importance for a more stable PET quantification, which is relevant for radiotherapy planning and therapy response monitoring.

Sin-quadratic model for chest tomosynthesis respiratory signal analysis and its application in four dimensional chest tomosynthesis reconstruction

Chest tomosynthesis (CTS) is a newly developed imaging technique which provides pseudo-3D volume anatomical information of thorax from limited-angle projections and contains much less of superimposed anatomy than the chest X-ray radiography. One of the relatively common problems in CTS is the patient respiratory motion during image acquisition, which negatively impacts the detectability. In this work, we propose a sin-quadratic model to analyze the respiratory motion during CTS scan, which is a real time method where the respiratory signal is generated by extracting the motion of diaphragm from projection radiographs. According to the estimated respiratory signal, the CTS projections were then amplitude-based sorted into four to eight phases, and an iterative reconstruction strategy with total variation regularization was adopted to reconstruct the CTS images at each phase. Simulated digital XCAT phantom data and three sets of patient data were adopted for the experiments to validate the performance of the sin-quadratic model and its application in four dimensional (4D) CTS reconstruction. Results of the XCAT phantom simulation study show that the correlation coefficient between the extracted respiratory signal and the originally designed respiratory signal is 0.9964, which suggests that the proposed model could exactly extract the respiratory signal from CTS projections. The 4D CTS reconstructions of both the phantom data and the patient data show clear reduction of motion-induced blur.

A Tool for Characterizing Randomness and Irregularity in Patient Respiratory Motion

Patient random irregular breathing undermines the fundamental assumption for 4DCT and radiotherapy based on it, either ITV-based or gated treatments. During patient’s 4DCT simulation, Respiratory Gating Signal Waveform (RGSW) is usually collected over a duration much longer than one breathing cycle. Therefore, RGSW may contain valuable information on irregularities in the patient breathing cycles and help to improve imaging and delivery in 4D radiation therapy. In this study, we developed a RGSW based tool for quantitatively accessing the randomness and irregularity level in patient respiratory motion. Totally 242 clinical RGSWs obtained with Varian Real-time Position Management (RPM) system were included in this study. For each RGSW, firstly we constructed its unwrapped phase φ(t) using Hilbert transform. Secondly, we computed the phase differences, i.e., φ(t+τ)-φ(t), between any two points along the waveform with a fixed time interval τ. Thirdly, we estimated the standard deviation σ of the phase differences. Finally, we calculated volatility (i.e., σ ⁄√τ) of the RGSW's phase in the given time interval τ. Sixteen intervals (i.e., τ=1s, 2s, 3s, …, 16s) were selected for volatility’s computation to examine if it was stable over different time intervals. The relative standard deviation of the acquired volatility values at all 16 time-intervals was computed for each RGSW. Fittings were performed on the histograms formed by observed volatility over a fixed time interval (e.g., 4s) and the relative standard deviation in its measured values with all 16 intervals from all patient RPM RGSWs. A stochastic random walk phase model was utilized to interpret the results. The unwrapped phase of an ideal periodic RGSW always has zero volatility. For our clinical RGSWs, the volatility is non-zero but reasonably stable over the selected time intervals. The observed volatility at 4s interval from the 242 RGSWs (∈ [0.07, 3.7] (1⁄√s), median=0.78 (1⁄√s)) could be approximated by a Gamma distribution Γ(3.0, 0.3). The population histogram of the relative standard deviation in volatility for the investigated time intervals (∈[2%, 35%], median=11.5%) could be fitted with a Gamma distribution Γ(3.5, 0.04). The stochastic random walk phase model, which yields a fixed volatility value over any given time interval, might serve as a useful tool in explaining our observations on phase volatility in patient's RGSW. We proposed a new application of patient’s RGSW for measuring the randomness and irregularity in patient respiratory motion. The volatility in RPM RGSW phase has a potential correlation to the eligibility and treatment response of a given patient to a given clinical procedure. Further studies are needed to explore its potential clinical role in patient 4D image acquisition and patient radiotherapy delivery.

CT imaging in assessing the severity of retroperitoneal injuries in blunt abdominal trauma

Background: Trauma is a leading cause of death in developing countries due to vehicular and railway accidents, of which abdominal trauma due to blunt injuries constitutes 13% mortality and 45% morbidity. The purpose of this study is to assess and diagnose the severity of those retroperitoneal injuries other than the common causes of abdominal trauma with multidetector computed tomography (MDCT) scanning with intravenous contrast being the gold standard diagnostic modality even in unstable patients.Methods: A total of 204 patients of these 147 patients were males (72%) and 57 were females (28%) were included of all age groups, who had sustained abdominal trauma due to vehicular, railways accidents and also due to trivial trauma and history of fall from heights.Results: Of the 204 patients included in our study, 110 patients (55%) had the maximum trauma affection to the kidneys, in which 48% constitutes grade I injury, which shows the high sensitivity of MDCT detecting early injuries. Nearly 12% patients had adrenal injuries, 24% had pancreatic injuries, although a small percentage also had delayed manifestation of pseudocyst formation in stable patients. Duodenal and colonic injuries constitute rest of 5% and 4% of the patients. All these patients had good correlation with the morphological MDCT grading of injuries and clinical correlation.Conclusions: MDCT is the imaging procedure of choice for early detection or ruling out the presence of retroperitoneal injuries in trauma patients, especially now with the newer faster MDCT, scanning can be performed more quickly, minimizing patient movement, respiratory motion and vascular pulsation.

Automated Detection of Motion Artefacts in MR Imaging Using Decision Forests.

The acquisition of a Magnetic Resonance (MR) scan usually takes longer than subjects can remain still. Movement of the subject such as bulk patient motion or respiratory motion degrades the image quality and its diagnostic value by producing image artefacts like ghosting, blurring, and smearing. This work focuses on the effect of motion on the reconstructed slices and the detection of motion artefacts in the reconstruction by using a supervised learning approach based on random decision forests. Both the effects of bulk patient motion occurring at various time points in the acquisition on head scans and the effects of respiratory motion on cardiac scans are studied. Evaluation is performed on synthetic images where motion artefacts have been introduced by altering the k-space data according to a motion trajectory, using the three common k-space sampling patterns: Cartesian, radial, and spiral. The results suggest that a machine learning approach is well capable of learning the characteristics of motion artefacts and subsequently detecting motion artefacts with a confidence that depends on the sampling pattern.

Adaptive Interference Cancellation of ECG Signals.

As an important biological signal, electrocardiogram (ECG) signals provide a valuable basis for the clinical diagnosis and treatment of several diseases. However, its reference significance is based on the effective acquisition and correct recognition of ECG signals. In fact, this mV-level weak signal can be easily affected by various interferences caused by the power of magnetic field, patient respiratory motion or contraction, and so on from the sampling terminal to the receiving and display end. The overlapping interference affects the quality of ECG waveform, leading to the false detection and recognition of wave groups, and thus causing misdiagnosis or faulty treatment. Therefore, the elimination of the interference of the ECG signal and the subsequent wave group identification technology has been a hot research topic, and their study has important significance. Based on the above, this paper introduces two improved adaptive algorithms based on the classical least mean square (LMS) algorithm by introducing symbolic functions and block-processing concepts.

Sign determination methods for the respiratory signal in data-driven PET gating

Patient respiratory motion during PET image acquisition leads to blurring in the reconstructed images and may cause significant artifacts, resulting in decreased lesion detectability, inaccurate standard uptake value calculation and incorrect treatment planning in radiation therapy. To reduce these effects data can be regrouped into (nearly) ‘motion-free’ gates prior to reconstruction by selecting the events with respect to the breathing phase. This gating procedure therefore needs a respiratory signal: on current scanners it is obtained from an external device, whereas with data driven (DD) methods it can be directly obtained from the raw PET data. DD methods thus eliminate the use of external equipment, which is often expensive, needs prior setup and can cause patient discomfort, and they could also potentially provide increased fidelity to the internal movement. DD methods have been recently applied on PET data showing promising results. However, many methods provide signals whose direction with respect to the physical motion is uncertain (i.e. their sign is arbitrary), therefore a maximum in the signal could refer either to the end-inspiration or end-expiration phase, possibly causing inaccurate motion correction. In this work we propose two novel methods, CorrWeights and CorrSino, to detect the correct direction of the motion represented by the DD signal, that is obtained by applying principal component analysis (PCA) on the acquired data. They only require the PET raw data, and they rely on the assumption that one of the major causes of change in the acquired data related to the chest is respiratory motion in the axial direction, that generates a cranio-caudal motion of the internal organs. We also implemented two versions of a published registration-based method, that require image reconstruction. The methods were first applied on XCAT simulations, and later evaluated on cancer patient datasets monitored by the Varian Real-time Position ManagementTM (RPM) device, selecting the lower chest bed positions. For each patient different time intervals were evaluated ranging from 50 to 300 s in duration. The novel methods proved to be generally more accurate than the registration-based ones in detecting the correct sign of the respiratory signal, and their failure rates are lower than 3% when the DD signal is highly correlated with the RPM. They also have the advantage of faster computation time, avoiding reconstruction. Moreover, CorrWeights is not specifically related to PCA and considering its simple implementation, it could easily be applied together with any DD method in clinical practice.

WE-E-BRB-02: Implementation of Pencil Beam Scanning (PBS) Proton Therapy Treatment for Liver Patient

Strategies for treating thoracic and liver tumors using pencil beam scanning proton therapy Thoracic and liver tumors have not been treated with pencil beam scanning (PBS) proton therapy until recently. This is because of concerns about the significant interplay effects between proton spot scanning and patient's respiratory motion. However, not all tumors have unacceptable magnitude of motion for PBS proton therapy. Therefore it is important to analyze the motion and understand the significance of the interplay effect for each patient. The factors that affect interplay effect and its washout include magnitude of motion, spot size, spot scanning sequence and speed. Selection of beam angle, scanning direction, repainting and fractionation can all reduce the interplay effect. An overview of respiratory motion management in PBS proton therapy including assessment of tumor motion and WET evaluation will be first presented. As thoracic tumors have very different motion patterns from liver tumors, examples would be provided for both anatomic sites. As thoracic tumors are typically located within highly heterogeneous environments, dose calculation accuracy is a concern for both treatment target and surrounding organs such as spinal cord or esophagus. Strategies for mitigating the interplay effect in PBS will be presented and the pros and cons of various motion mitigation strategies will be discussed. Learning Objectives: 1.Motion analysis for individual patients with respect to interplay effect 2.Interplay effect and mitigation strategies for treating thoracic/liver tumors with PBS 3.Treatment planning margins for PBS 4.The impact of proton dose calculation engines over heterogeneous treatment target and surrounding organs I have a current research funding from Varian Medical System under the master agreement between University of Pennsylvania and Varian; L. Lin, I have a current funding from Varian Medical System under the master agreement between University of Pennsylvania and Varian.; H. Li, Na