Liver Motion Research Articles

Quantification of Intrafraction and Interfraction Tumor Motion Amplitude and Prediction Error for Different Liver Tumor Trajectories in Cyberknife Synchrony Tracking

To research the fiducial-based, real-time tracking intrafraction (during the fraction [intra-]) and interfraction (between fractions [inter-]) tumor respiration amplitude, motion trajectory, and prediction error and quantify their relationships for different types of motion trajectories during Cyberknife-based stereotactic ablation radiotherapy. Twelve patients with liver tumors were treated using a Cyberknife system, and 58 fractions were involved in this study. Real-time target motion tracking data were extracted and transformed from the robot coordinate system into the patient coordinate system by the rotation matrix. Only the time sessions of the beam on were studied according to the data information generated from the Cyberknife motion tracking system. The motion correlation model between the external marker signal and internal fiducial position was built to present the type of motion trajectory. Using the correlation model as a function of external marker signal and internal fiducial position, we knew 4 motion trajectories mainly existed for liver cancer patients as follows: perfect linearity (group I), simple linearity (group II), hysteresis (group III), and area respiratory (group IV) patterns. More than half of the patients had a linear breathing trajectory. Analyzing all patients together, the intra-amplitudes were slightly less than those of the inter-amplitudes. The amplitude from large to small was in the superior-inferior, left-right and anterior-posterior directions, regardless of inter- and intra-amplitudes. Then, patients with a larger peak-to-peak have a larger standard deviation of amplitude and a larger amplitude in all fractions/sessions. The prediction errors of the linear motion trajectory were generally less than 1 mm. The prediction errors of the regular hysteresis breathing model were smaller than those of the irregular hysteresis model. Scattered breathing would result in a larger tracking error, such as the area respiratory trajectory. It was logical that prediction errors were larger for patients who showed much variation in their breathing amplitude. This paper showed that the liver motion trajectory model included perfect linearity, sample linearity, hysteresis, and area. The linear motion trajectory presented the minimum tracking error and the best stability, and the hysteresis and area trajectory were the worst. Therefore, breathing management, including respiration training, control, and evaluation of motion trajectory in all directions, was significantly necessary during liver SABR treatment.

Irregular Respiratory Motion Compensation for Liver Contrast-Enhanced Ultrasound via Transport-Based Motion Estimation.

Contrast-enhanced ultrasound (CEUS) imaging has been widely applied for the detection and characterization of focal liver lesions (FLLs), for its ability to visualize the blood flow in real time. However, cyclic liver motion poses a great challenge to the recovery of perfusion curves as well as quantitative kinetic parameters estimation. Recently, a few gating methods have been proposed to eliminate unexpected intensity fluctuations by the breathing phase estimation, with the assumption that each breathing phase corresponds to a specific lesion position strictly. While practical liver motion tends to be irregular due to changes in the patient's underlying physiologic status, that is, the same phase might not correspond to the same position. To tackle the challenge of motion irregularity, we present a novel motion estimation-based respiratory compensation method, named RCME, which first estimates salient motion information through the framework of optimal transport (OT) by jointly modeling pixel intensity as well as their locations and then employs sparse subspace clustering (SSC) to identify the subset of frames acquired at the same position. Our proposed method is evaluated on 15 clinical CEUS sequences in both qualitative and quantitative manners. Experimental results demonstrate good performance on irregular liver motion compensation.

Endogenous motion of liver correlates to the severity of portal hypertension.

BACKGROUNDDegree of portal hypertension (PH) is the most important prognostic factor for the decompensation of liver cirrhosis and death, therefore adequate care for patients with liver cirrhosis requires timely detection and evaluation of the presence of clinically significant PH (CSPH) and severe PH (SPH). As the most accurate method for the assessment of PH is an invasive direct measurement of hepatic venous pressure gradient (HVPG), the search for non-invasive methods to diagnose these conditions is actively ongoing.AIMTo evaluate the feasibility of parameters of endogenously induced displacements and strain of liver to assess degree of PH.METHODSOf 36 patients with liver cirrhosis and measured HVPG were included in the case-control study. Endogenous motion of the liver was characterized by derived parameters of region average tissue displacement signal (dantero, dretro, dRMS) and results of endogenous tissue strain imaging using specific radiofrequency signal processing algorithm. Average endogenous strain µ and standard deviation σ of strain were assessed in the regions of interest (ROI) (1 cm × 1 cm and 2 cm × 2 cm in size) and different frequency subbands of endogenous motion (0-10 Hz and 10-20 Hz).RESULTSFour parameters showed statistically significant (P < 0.05) correlation with HVPG measurement. The strongest correlation was obtained for the standard deviation of strain (estimated at 0-10 Hz and 2 cm × 2 cm ROI size). Three parameters showed statistically significant differences between patient groups with CSPH, but only dretro showed significant results in SPH analysis. According to ROC analysis area under the curve (AUC) of the σROI[0…10Hz, 2 cm × 2 cm] parameter reached 0.71 (P = 0.036) for the diagnosis of CSPH; with a cut-off value of 1.28 μm/cm providing 73% sensitivity and 70% specificity. AUC for the diagnosis of CSPH for µROI[0…10Hz, 1 cm × 1 cm] was 0.78 (P = 0.0024); with a cut-off value of 3.92 μm/cm providing 73% sensitivity and 80% specificity. Dretro parameter had an AUC of 0.86 (P = 0.0001) for the diagnosis of CSPH and 0.84 (P = 0.0001) for the diagnosis of SPH. A cut-off value of -132.34 μm yielded 100% sensitivity for both conditions, whereas specificity was 80% and 72% for CSPH and SPH respectively.CONCLUSIONThe parameters of endogenously induced displacements and strain of the liver correlated with HVPG and might be used for non-invasive diagnosis of PH.

Estimating 3-dimensional liver motion using deep learning and 2-dimensional ultrasound images.

The main purpose of this study is to construct a system to track the tumor position during radiofrequency ablation (RFA) treatment. Existing tumor tracking systems are designed to track a tumor in a two-dimensional (2D) ultrasound (US) image. As a result, the three-dimensional (3D) motion of the organs cannot be accommodated and the ablation area may be lost. In this study, we propose a method for estimating the 3D movement of the liver as a preliminary system for tumor tracking. Additionally, in current 3D movement estimation systems, the motion of different structures during RFA could reduce the tumor visibility in US images. Therefore, we also aim to improve the estimation of the 3D movement of the liver by improving the liver segmentation. We propose a novel approach to estimate the relative 6-axial movement (x, y, z, roll, pitch, and yaw) between the liver and the US probe in order to estimate the overall movement of the liver. We used a convolutional neural network (CNN) to estimate the 3D displacement from two-dimensional US images. In addition, to improve the accuracy of the estimation, we introduced a segmentation map of the liver region as the input for the regression network. Specifically, we improved the extraction accuracy of the liver region by using a bi-directional convolutional LSTM U-Net with densely connected convolutions (BCDU-Net). By using BCDU-Net, the accuracy of the segmentation was dramatically improved, and as a result, the accuracy of the movement estimation was also improved. The mean absolute error for the out-of-plane direction was 0.0645mm/frame. The experimental results show the effectiveness of our novel method to identify the movement of the liver by BCDU-Net and CNN. Precise segmentation of the liver by BCDU-Net also contributes to enhancing the performance of the liver movement estimation.

Estimation of boundary conditions for patient-specific liver simulation during augmented surgery

Augmented reality can improve the outcome of hepatic surgeries, assuming an accurate liver model is available to estimate the position of internal structures. While researchers have proposed patient-specific liver simulations, very few have addressed the question of boundary conditions. Resulting mainly from ligaments attached to the liver, they are not visible in preoperative images, yet play a key role in the computation of the deformation. We propose to estimate both the location and stiffness of ligaments by using a combination of a statistical atlas, numerical simulation, and Bayesian inference. Ligaments are modeled as polynomial springs connected to a liver finite element model. They are initialized using an anatomical atlas and stiffness properties taken from the literature. These characteristics are then corrected using a reduced-order unscented Kalman filter based on observations taken from the laparoscopic image stream. Our approach is evaluated using synthetic data and phantom data. By relying on a simplified representation of the ligaments to speed up computation times, it is not estimating the true characteristics of ligaments. However, results show that our estimation of the boundary conditions still improves the accuracy of the simulation by 75% when compared to typical methods involving Dirichlet boundary conditions. By estimating patient-specific boundary conditions, using tracked liver motion from RGB-D data, our approach significantly improves the accuracy of the liver model. The method inherently handles noisy observations, a substantial feature in the context of augmented reality.

Dosimetric assessment of patient-specific breath-hold reproducibility on liver motion for SBRT planning.

PurposeTo investigate the impact of breath‐hold reproducibility on liver motion using a respiratory motion management device.MethodsForty‐four patients with hepatic tumors, treated with SBRT with breath‐hold, were randomly selected for this study. All patients underwent three consecutive computed tomography (CT) scans using active breath‐hold coordinator (ABC) with three repeated single breath‐hold during simulation. The three CT scans were labeled as ABC1‐CT, ABC2‐CT, and ABC3‐CT. Displacements of centroids of the entire livers among the three ABC‐CTs were measured as a surrogate for intrafractional motion. For each patient, two different treatment plans were prepared: (a) a clinical plan using a 5‐mm expansion of an ITV that encompassed all three GTVs from each of the three ABC‐CTs, and (b) a research plan using a 5‐mm expansion of the GTV from only ABC1‐CT to create PTV. The clinical plan acceptance criteria were that 95% of the PTV and 99% of the GTV received 100% of the prescription dose. Dosimetric endpoints were analyzed and compared for the two plans.ResultsAll shifts in the medial‐lateral direction (range: −3.9 to 2.0 mm) were within 5 mm while 7% of shifts in the anterior–posterior direction (range: −10.5 to 16.7 mm) and 11% of shifts in the superior–inferior direction (range: −17.0 to 8.7 mm) exceeded 5 mm. Six patients (14%) had an intrafraction motion greater than 5 mm in any direction. For these six patients, if a plan was created based on a PTV from a single CT (ex. ABC1‐CT), 5 of 12 GTVs captured from other ABC‐CTs would fail to meet the clinical acceptance criteria due to poor breath‐hold reproducibility.ConclusionsNon‐negligible intrafractional motion occurs in patients with poor breath‐hold reproducibility. To identify this subgroup of patients, acquiring three CTs with active breath‐hold during simulation is a feasible practical method.

Anthropomorphic phantom for deformable lung and liver CT and MR imaging for radiotherapy

In this study, a functioning and ventilated anthropomorphic phantom was further enhanced for the purpose of CT and MR imaging of the lung and liver. A deformable lung, including respiratory tract was 3D printed. Within the lung’s inner structures is a solid region shaped from a patient’s lung tumour and six nitro-glycerine capsules as reference landmarks. The full internal mesh was coated, and the tumour filled, with polyorganosiloxane based gel. A moulded liver was created with an external casing of silicon filled with polyorganosiloxane gel and flexible plastic internal structures. The liver, fitted to the inferior portion of the right lung, moves along with the lung’s ventilation. In the contralateral side, a cavity is designed to host a dosimeter, whose motion is correlated to the lung pressure. A 4DCT of the phantom was performed along with static and 4D T1 weighted MR images. The CT Hounsfield units (HU) for the flexible 3D printed material were −600–100 HU (lung and liver structures), for the polyorganosiloxane gel 30–120 HU (lung coating and liver filling) and for the silicon 650–800 HU (liver casing). The MR image intensity units were 0–40, 210–280 and 80–130, respectively. The maximum range of motion in the 4D imaging for the superior lung was 1–3.5 mm and 3.5–8 mm in the inferior portion. The liver motion was 5.5–8.0 mm at the tip and 5.7–10.0 mm at the dome. No measurable drift in motion was observed over a 2 h session and motion was reproducible over three different sessions for sin2(t), sin4(t) and a patient-like breathing curve with the interquartile range of amplitudes for all breathing cycles within 0.5 mm. The addition of features within the lung and of a deformable liver will allow the phantom to be used for imaging studies such as validation of 4DMRI and pseudo CT methods.

Failure mode and effects analysis of linac-based liver stereotactic body radiotherapy.

Although stereotactic body radiation therapy (SBRT) is an attractive noninvasive approach for liver irradiation, it presents specific challenges associated with respiration-induced liver motion, daily tumor localization due to liver deformation, and poor visualization of target with respect to adjacent normal liver in computed tomography (CT). We aim to identify potential hazards and develop a set of mitigation strategies to improve the safety of our liver SBRT program, using failure mode and effect analysis (FMEA). A multidisciplinary group consisting of two physicians, three physicists, two dosimetrists, and two therapists was formed. A process map covering ten major stages of the liver SBRT program from the initial diagnosis to posttreatment follow-up was generated. A total of 102 failure modes (FM), together with their causes and effects, were identified. The occurrence (O), severity (S), and lack of detectability (D) were independently scored using a scale from 1 (lowest risk) to 10 (largest risk). The ranking was done using the risk probability number (RPN) defined as the product of average O, S, and D numbers for each mode. Two fault tree analyses were performed. The failure modes with the highest RPN values as well as highest severity score were considered for investigation and a set of mitigation strategies was developed to address these. The median RPN (RPNmed ) values for all modes ranged from of 9 to 105 and the highest median S score (Smed ) was 8. Fourteen FMs were identified to be significant by both RPNmed and Smed (top ten RPNmed ranked and highest Smed FMs) and 12 of them were considered for risk mitigation efforts. The remaining two were omitted due to either sufficient checks already in place, or lack of practical mitigation strategies. Implemented measures consisted of five physics tasks, two physician tasks, and three workflow changes. The application of FMEA to our liver SBRT program led to the identification of potential FMs and allowed improvement measures to enhance the safety of our clinical practice.

Helical IMRT-Based Stereotactic Body Radiation Therapy Using an Abdominal Compression Technique and Modified Fractionation Regimen for Small Hepatocellular Carcinoma.

Purpose:To assess the efficacy and safety of stereotactic body radiation therapy using an abdominal compression technique and modified fractionation regimen (5-10 fractions) in patients with small-sized hepatocellular carcinoma.Methods:A total of 101 patients with small-sized hepatocellular carcinoma treated with stereotactic body radiation therapy using an abdominal compression technique and modified fractionation regimen were registered between June 2011 and June 2019 in our hospital. A total dose of 48 to 60 Gy was applied over 5 to 14 consecutive days. Liver motion was controlled by abdominal compression, and a helical intensity-modified radiation therapy-based stereotactic body radiation therapy administrated in tomotherapy platform.Results:The median follow-up period was 23.2 months (range: 4.1-99.2 months). Complete response and partial response were observed in 63 (62.4%) patients and in 24 (23.8%) patients, respectively. At the time of our analysis, the 1-, 3-, and 5-year local control rates after stereotactic body radiation therapy were 96.1%, 89.0%, and 89.0%, respectively. However, logistic regression analysis revealed no correlation between the biologically effective dose and 3-year local control rates. The 1-, 3-, and 5-year overall survival rates were 96.9%, 69.0%, and 64.3%, respectively. For patients who were treatment-naive, the 1-, 3-, and 5-year overall survival were 96.3%, 82.0%, and 82.0%, respectively. No patients experienced classic radiation-induced liver disease or nonclassic radiation-induced liver disease after stereotactic body radiation therapy completion.Conclusions:When using an abdominal compression technique and modified fractionation regimen (5-10 fractions) based on helical intensity-modified radiation therapy, stereotactic body radiation therapy led to a lower toxicity and comparative rate of local control and overall survival for patients who with small-sized hepatocellular carcinoma.

The noise navigator: a surrogate for respiratory-correlated 4D-MRI for motion characterization in radiotherapy

Respiratory-correlated 4D-MRI can characterize respiratory-induced motion of tumors and organs-at-risk for radiotherapy treatment planning and is a necessity for image guidance of moving tumors treated on an MRI-linac. Essential for 4D-MRI generation is a robust respiratory surrogate signal.We investigated the feasibility of the noise navigator as respiratory surrogate signal for 4D-MRI generation. The noise navigator is based on the respiratory-induced modulation of the thermal noise variance measured by the receive coils during MR acquisition and thus is inherently present and synchronized with MRI data acquisition. Additionally, the noise navigator can be combined with any rectilinear readout strategy (e.g. radial and cartesian) and is independent of MR image contrast and imaging orientation. For radiotherapy applications, the noise navigator provides a robust respiratory signal for patients scanned with an elevated coil setup. This is particularly attractive for widely used cartesian sequences where currently a non-interfering self-navigation means is lacking for MRI-based simulation and MRI-guided radiotherapy.The feasibility of 4D-MRI generation with the noise navigator as respiratory surrogate signal was demonstrated for both cartesian and radial readout strategies in radiotherapy setup on four healthy volunteers and two radiotherapy patients on a dedicated 1.5 T MRI scanner and two radiotherapy patients on a 1.5 T MRI-linac system. Moreover, the respiratory-correlated 4D-MR images showed liver motion comparable to a reference 2D cine MRI series for the volunteers.For 2D cartesian cine MRI acquisitions, both the noise navigator and respiratory bellows were benchmarked against an image navigator. Respiratory phase detection based on the noise navigator agreed 1.4 times better with the image navigator than the respiratory bellows did. For a 3D Stack-of-Stars acquisitions, the noise navigator was compared to radial self-navigation and a 1.7 times higher respiratory phase detection agreement was observed than for the respiratory bellows compared to radial self-navigation.

Mixed reality based respiratory liver tumor puncture navigation

This paper presents a novel mixed reality based navigation system for accurate respiratory liver tumor punctures in radiofrequency ablation (RFA). Our system contains an optical see-through head-mounted display device (OST-HMD), Microsoft HoloLens for perfectly overlaying the virtual information on the patient, and a optical tracking system NDI Polaris for calibrating the surgical utilities in the surgical scene. Compared with traditional navigation method with CT, our system aligns the virtual guidance information and real patient and real-timely updates the view of virtual guidance via a position tracking system. In addition, to alleviate the difficulty during needle placement induced by respiratory motion, we reconstruct the patient-specific respiratory liver motion through statistical motion model to assist doctors precisely puncture liver tumors. The proposed system has been experimentally validated on vivo pigs with an accurate real-time registration approximately 5-mm mean FRE and TRE, which has the potential to be applied in clinical RFA guidance.

A method to reconstruct intra-fractional liver motion in rotational radiotherapy using linear fiducial markers

Complex intra-fractional motion and deformation of the liver significantly impacts the accuracy of delivered dose in radiotherapy. It limits margin reduction, dose escalation and normal tissue sparing. A critical component of motion management is to accurately reconstruct tumor motion. In this study, we developed a six degrees of freedom projection marker matching method (6-DoF PM3) to reconstruct translational and rotational liver tumor motion in a rotational treatment delivery, such as volumetric modulated arc therapy (VMAT). Specifically, we modeled the use of two gold markers implanted in a linear form. The four endpoints of the two gold linear markers were used as tracking surrogates. During delivery, kV x-ray projection images were acquired. A method was developed to automatically identify the 2D marker-endpoints on the projection images. 3D marker positions were determined by solving an optimization problem with the objective function penalizing the distance from the reconstructed 3D position of each fiducial marker endpoint to the corresponding straight line defined by the kV x-ray projection of the endpoints. We performed a series of tests to evaluate different components of the method. For 2D marker endpoints identification, 99.9% of the marker endpoints were identified with an error (1 pixel) along both u and v directions. For 3D reconstruction of motion in simulation studies, error of rotational angle was ° without considering the 2D marker identification error. The rotational angle error was relatively sensitive to the accuracy of 2D marker identification. When the 2D error raised from 0.22 mm to 0.776 mm, the error of 3D rotational angle increased from 0.5° to 2.5°. In the experimental end-to-end tests, the mean root-mean square error of the 3D reconstructed marker positions was 0.75 mm and the mean error of rotational angle was within 1.7°. Our method can accurately determine intra-fractional liver tumor motion in rotational radiotherapy using kV projections of only two linear fiducial markers.

Designing and validating a PVA liver phantom with respiratory motion for needle-based interventions

PurposeThe purpose is to design and validate an anthropomorphic polyvinyl alcohol (PVA) liver phantom with respiratory motion to simulate needle-based interventions. Such a system can, for example, be used as a validation tool for novel needles.MethodsImage segmentations of CT scans of four patients during inspiration and expiration were used to measure liver and rib displacement. An anthropomorphic liver mold based on a CT scan was 3D printed and filled with 5% w/w PVA-to-water, undergoing two freeze–thaw cycles, in addition to a 3D-printed compliant rib cage. They were both held in place by a PVA abdominal phantom. A sinusoidal motion vector, based on the measured liver displacement, was applied to the liver phantom by means of a motion stage. Liver, rib cage and needle deflection were tracked by placing electromagnetic sensors on the phantom. Liver and rib cage phantom motion was validated by comparison with the CT images of the patients, whereas needle deflection was compared with the literature.ResultsCT analysis showed that from the state of expiration to inspiration, the livers moved predominantly toward the right (mean: 2 mm, range: − 11 to 11 mm), anterior (mean: 15 mm, range: 9–21 mm) and caudal (mean: 16 mm, range: 6–24 mm) direction. The mechatronic design of the liver phantom gives the freedom to set direction and amplitude of the motion and was able to mimic the direction of liver motion of one patient. Needle deflection inside the phantom increased from 1.6 to 3.8 mm from the initial expiration state to inspiration.ConclusionsThe developed liver phantom allows for applying different motion patterns and shapes/sizes and thus allows for patient-specific simulation of needle-based interventions. Moreover, it is able to mimic appropriate respiratory motion and needle deflection as observed in patients.

Respiratory motion compensation in interventional liver SPECT using simultaneous fluoroscopic and nuclear imaging.

PurposeQuantitative accuracy of the single photon emission computed tomography (SPECT) reconstruction of the pretreatment procedure of liver radioembolization is crucial for dosimetry; visual quality is important for detecting doses deposited outside the planned treatment volume. Quantitative accuracy is limited by respiratory motion. Conventional gating eliminates motion by count rejection but increases noise, which degrades the visual reconstruction quality. Motion compensation using all counts can be performed if the motion signal and motion vector field over time are known. The measurement of the motion signal of a patient currently requires a device (such as a respiratory belt) attached to the patient, which complicates the acquisition. The motion vector field is generally extracted from a previously acquired four‐dimensional scan and can differ from the motion in the scan performed during the intervention. The simultaneous acquisition of fluoroscopic and nuclear projections can be used to obtain both the motion vector field and the projections of the corresponding (moving) activity distribution. This eliminates the need for devices attached to the patient and provides an accurate motion vector field for SPECT reconstruction. Our approach to motion compensation would primarily be beneficial for interventional SPECT because the time‐critical setting requires fast scans and no inconvenience of an external apparatus. The purpose of this work is to evaluate the performance of the motion compensation approach for interventional liver SPECT by means of simulations.MethodsNuclear and fluoroscopic projections of a realistic digital human phantom with respiratory motion were generated using fast Monte Carlo simulators. Fluoroscopic projections were sampled at 1–5 Hz. Nuclear data were acquired continuously in list mode. The motion signal was extracted from the fluoroscopic projections by calculating the center‐of‐mass, which was then used to assign each photon to a corresponding motion bin. The fluoroscopic projections were reconstructed per bin and coregistered, resulting in a motion vector field that was used in the SPECT reconstruction. The influence of breathing patterns, fluoroscopic imaging dose, sampling rate, number of bins, and scanning time was studied. In addition, the motion compensation method was compared with conventional gating to evaluate the detectability of spheres with varying uptake ratios.ResultsThe liver motion signal was accurately extracted from the fluoroscopic projections, provided the motion was stable in amplitude and the sampling rate was greater than 2 Hz. The minimum total fluoroscopic dose for the proposed method to function in a 5‐min scan was 10 µGy. Although conventional gating improved the quantitative reconstruction accuracy, substantial background noise was observed in the short scans because of the limited counts available. The proposed method similarly improved the quantitative accuracy, but generated reconstructions with higher visual quality. The proposed method provided better visualization of low‐contrast features than when using gating.ConclusionThe proposed motion compensation method has the potential to improve SPECT reconstruction quality. The method eliminates the need for external devices to measure the motion signal and generates an accurate motion vector field for reconstruction. A minimal increase in the fluoroscopic dose is required to substantially improve the results, paving the way for clinical use.

Impact of planned dose reporting methods on Gamma pass rates for IROC lung and liver motion phantoms treated with pencil beam scanning protons

PurposeThe purpose of this study is to evaluate the impact of two methods of reporting planned dose distributions on the Gamma analysis pass rates for comparison with measured 2D film dose and simulated delivered 3D dose for proton pencil beam scanning treatment of the Imaging and Radiation Oncology Core (IROC) proton lung and liver mobile phantoms.Methods and materialsFour-dimensional (4D) computed-tomography (CT) image sets were acquired for IROC proton lung and liver mobile phantoms, which include dosimetry inserts that contains targets, thermoluminescent dosimeters and EBT2 films for plan dose verification. 4DCT measured fixed motion magnitudes were 1.3 and 1.0 cm for the lung and liver phantoms, respectively. To study the effects of motion magnitude on the Gamma analysis pass rate, three motion magnitudes for each phantom were simulated by creating virtual 4DCT image sets with motion magnitudes scaled from the scanned phantom motion by 50, 100, and 200%. The internal target volumes were contoured on the maximum intensity projection CTs of the 4DCTs for the lung phantom and on the minimum intensity projection CTs of the 4DCTs for the liver phantom. Treatment plans were optimized on the average intensity projection (AVE) CTs of the 4DCTs using the RayStation treatment planning system. Plan doses were calculated on the AVE CTs, which was defined as the planned AVE dose (method one). Plan doses were also calculated on all 10 phase CTs of the 4DCTs and were registered using target alignment to and equal-weight-summed on the 50% phase (T50) CT, which was defined as the planned 4D dose (method two). The planned AVE doses and 4D doses for phantom treatment were reported to IROC, and the 2D-2D Gamma analysis pass rates for measured film dose relative to the planned AVE and 4D doses were compared. To evaluate motion interplay effects, simulated delivered doses were calculated for each plan by sorting spots into corresponding respiratory phases using spot delivery time recorded in the log files by the beam delivery system to calculate each phase dose and accumulate dose to the T50 CTs. Ten random beam starting phases were used for each beam to obtain the range of the simulated delivered dose distributions. 3D-3D Gamma analyses were performed to compare the planned 4D/AVE doses with simulated delivered doses.ResultsThe planned 4D dose matched better with the measured 2D film dose and simulated delivered 3D dose than the planned AVE dose. Using planned 4D dose as institution reported planned dose to IROC improved IROC film dose 2D-2D Gamma analysis pass rate from 92 to 96% on average for three films for the lung phantom (7% 5 mm), and from 92 to 94% in the sagittal plane for the liver phantom (7% 4 mm), respectively, compared with using the planned AVE dose. The 3D-3D Gamma analysis (3% 3 mm) pass rate showed that the simulated delivered doses for lung and liver phantoms using 10 random beam starting phases for each delivered beam matched the planned 4D dose significantly better than the planned AVE dose for phantom motions larger than 1 cm (p ≤ 0.04).ConclusionsIt is recommended to use the planned 4D dose as the institution reported planned dose to IROC to compare with the measured film dose for proton mobile phantoms to improve film Gamma analysis pass rate in the IROC credentialing process.

Usefulness of Virtual Expiratory CT Images to Compensate for Respiratory Liver Motion in Ultrasound/CT Image Fusion: A Prospective Study in Patients with Focal Hepatic Lesions.

ObjectiveTo assess whether virtual expiratory (VE)-computed tomography (CT)/ultrasound (US) fusion imaging is more effective than conventional inspiratory (CI)-CT/US fusion imaging for hepatic interventional procedures.Materials and MethodsThis prospective study was approved by the Institutional Review Board, and informed consent was obtained from each patient. In total, 62 patients with focal hepatic lesions referred for hepatic interventional procedures were enrolled. VE-CT images were generated from CI-CT images to reduce the effects of respiration-induced liver motion. The two types of CT images were fused with real-time US images for each patient. The operators scored the visual similarity with the liver anatomy upon initial image fusion and the summative usability of complete image fusion using the respective five-point scales. The time required for complete image fusion and the number of point locks used were also compared.ResultsIn comparison with CI-CT/US fusion imaging, VE-CT/US fusion imaging showed significantly higher visual similarity with the liver anatomy on the initial image fusion (mean score, 3.9 vs. 1.7; p < 0.001) and higher summative usability for complete image fusion (mean score, 4.0 vs. 1.9; p < 0.001). The required time (mean, 11.1 seconds vs. 22.5 seconds; p < 0.001) and the number of point locks (mean, 1.6 vs. 3.0; p < 0.001) needed for complete image fusion using VE-CT/US fusion imaging were significantly lower than those needed for CI-CT/US fusion imaging.ConclusionVE-CT/US fusion imaging is more effective than CI-CT/US fusion imaging for hepatic interventional procedures.

Assessment of cardiac-driven liver movements with filtered harmonic phase image representation, optical flow quantification, and motion amplification.

To characterize cardiac-driven liver movements using a harmonic phase image representation (HARP) with an optical flow quantification and motion amplification method. The method was applied to define the cardiac trigger delay providing minimal signal losses in liver DWI images. The 16-s breath-hold balanced-SSFP time resolved 20 images/s were acquired at 3T in coronal and sagittal orientations. A peripheral pulse unit signal was recorded. Cardiac-triggered DWI images were acquired after different peripheral pulse unit delays. A steerable pyramid decomposition with multiple orientations and spatial frequencies was applied. The liver motion field-map was derived from temporal variations of the HARP representation filtered around the cardiac frequency. Liver displacements were quantified with an optical flow method; moreover the right liver motion was amplified. The largest displacements were observed in the left liver (feet-head:3.70 ± 1.06 mm; anterior-posterior: 2.35 ± 0.51 mm). Displacements were statistically significantly weaker in the middle right liver (0.47 ± 0.11 mm; P = 0.0156). The average error was 0.013 ± 0.022 mm (coronal plane) and 0.021 ± 0.041 mm (sagittal plane). The velocity field demonstrated opposing movements of the right liver extremities during the cardiac cycle. DWI signal loss was minimized in regions and instants of smallest amplitude of both velocity and velocity gradient. Cardiac-driven liver movements were quantified with combined cardiac frequency-filtered HARP and optical flow methods. A motion phase opposition between right liver extremities was demonstrated. Displacement amplitude and velocity were larger in the left liver especially along the vertical direction. Motion amplification visually emphasized cardiac-driven right liver displacements. The optimal cardiac timing minimizing signal loss in liver DWI images was derived.

Variations of MRI-assessed peristaltic motions during radiation therapy

PurposeUnderstanding complex abdominal organ motion is essential for motion management in radiation therapy (RT) of abdominal tumors. This study investigates abdominal motion induced by respiration and peristalsis, during various time durations relevant to RT, using various CT and MRI techniques acquired under free breathing (FB) and breath hold (BH).MethodsA series of CT and MRI images acquired with various techniques under free breathing and/or breath hold from 37 randomly-selected pancreatic or liver cancer patients were analyzed to assess the motion in various time frames. These data include FB 4DCT from 15 patients (for motion in time duration of 5 sec), FB 2D cine-MRI from 4 patients (time duration of 1.7 min, 1 second acquisition time per slice), FB cine-MRI acquired using MR-Linac from 6 patients in various fractions (acquisition time is less than 0.6 seconds per slice), FB 4DMRI from 2 patients (time duration of 2 min), respiration-gated T2 with gating at the end expiration (time duration of 3–5 min), and BH T1 with multiphase dynamic contrast in acquisition times of 17 seconds for each of five phases (pre-contrast, arterial, venous, portal venous and delayed post-contrast) from 10 patients. Motions of various organs including gallbladder (GB) and liver were measured based on these MRI data. The GB motion includes both respiration and peristalsis, while liver motion is primarily respiration. By subtracting liver motion (respiration) from GB motion (respiration and peristalsis), the peristaltic motion, along with small residual motion, was obtained.ResultsFrom cine-MRI, the residual motion beyond the respiratory motion was found to be up to 0.6 cm in superior-inferior (SI) and 0.55 cm in anterior-posterior (AP) directions. From 2D cine-MRI acquired by the MR-Linac, different peristaltic motions were found from different fractions for each patient. The peristaltic motion was found to vary between 0.3–1 cm. From BH T1 phase images, the average motions that were primarily due to peristalsis movements were found to be 1.2 cm in SI, 0.7 cm in AP, and 0.9 cm in left-right (LR) directions. The average motions assessed from 4DCT were 1.0 cm in SI and 0.3 cm in AP directions, which were generally smaller than the motions assessed from cine-MRI, i.e., 1.8 cm in SI and 0.6 cm in AP directions, for the same patients. However, average motions from 4DMRI, which are coming from respiratory were measured to be 1.5, 0.5, and 0.4 cm in SI, AP, and LR directions, respectively.ConclusionThe abdominal motion due to peristalsis can be similar in magnitude to respiratory motion as assessed. These motions can be irregular and persistent throughout the imaging and RT delivery procedures, and should be considered together with respiratory motion during RT for abdominal tumors.

Effect of a Device-Free Compressed Shell Fixation Method on Hepatic Respiratory Movement: Analysis for Respiratory Amplitude of the Liver and Internal Motions of a Fiducial Marker

PurposeSuppression of respiratory movement of the liver would be desirable for high-precision radiation therapy for liver tumors. We aimed to investigate the effect of our original device-free compressed shell fixation method and breathing instruction on suppression of respiratory movement. The characteristics of liver motion based on the movement of a fiducial marker were also analyzed. Methods and MaterialsFirst, respiratory amplitudes of the liver with the device-free compressed shell were analyzed from the data of 146 patients. The effect of this shell fixing method on liver movement was evaluated. Second, as another cohort study with 166 patients, interfractional internal motion of the liver for patients fixed in the shell was calculated using the fiducial marker coordinate data of images for position setting before daily irradiation. Third, in another 12 patients, intrafractional internal motion was calculated from the fiducial marker coordinate data using x-ray images before and after irradiation. ResultsThe median respiratory movement without the shell, after fixing with the shell, and after instructing on the breathing method with the shell was 14.2 (interquartile range, 10.7-19.8), 11.5 (8.6-17.5), and 10.4 mm (7.3-15.8), respectively. Systematic and random errors of interfractional internal motion were all ≤2 mm in the left-right and anteroposterior directions and 3.7 and 3.0 mm, respectively, in the craniocaudal direction. Systematic and random errors of intrafractional internal motion were all ≤1.3 mm in the left-right and anteroposterior directions and 0.8 and 2.4 mm, respectively, in the craniocaudal direction. ConclusionsThe device-free compressed shell fixation method was effective in suppressing the respiratory movement of the liver. Irradiation position matching using the fiducial marker can correct the interfractional internal motion on each day, which would contribute to the reduction of the margin to be given around the target.

Respiratory motion estimation of the liver with abdominal motion as a surrogate.

Background: Respiratory‐induced motion (RIM) causes uncertainties in localizing hepatic lesions, which could lead to inaccurate targeting during interventions. One approach to mitigate the problem is respiratory motion estimation (RME), in which the liver motion is estimated by measuring external signals called surrogates. Methods: A learning‐based approach has been developed and validated to estimate the RIM of hepatic lesions. External markers placed on the human's abdomen were chosen as surrogates. Accordingly, appropriate motion models (multivariate, Ridge and Lasso regression models) were designed to correlate the liver motion with the abdominal motion, and trained to estimate the superior–inferior (SI) motion of the liver. Three subjects volunteered for 6 sessions of such that liver images acquired by magnetic resonance imaging (MRI) were recorded alongside camera‐tracked external markers. Results and conclusions: The proposed machine learning approach was validated in MRI on human subjects and the results show that the approach could estimate the respiratory‐induced SI motion of the liver with a mean absolute error (MAE) accuracy below 2 mm.