Monitoring tumor motion by real time 2D/3D registration during radiotherapy.
Ontology highlight
ABSTRACT: Background and purpose
In this paper, we investigate the possibility to use X-ray based real time 2D/3D registration for non-invasive tumor motion monitoring during radiotherapy.Materials and methods
The 2D/3D registration scheme is implemented using general purpose computation on graphics hardware (GPGPU) programming techniques and several algorithmic refinements in the registration process. Validation is conducted off-line using a phantom and five clinical patient data sets. The registration is performed on a region of interest (ROI) centered around the planned target volume (PTV).Results
The phantom motion is measured with an rms error of 2.56 mm. For the patient data sets, a sinusoidal movement that clearly correlates to the breathing cycle is shown. Videos show a good match between X-ray and digitally reconstructed radiographs (DRR) displacement. Mean registration time is 0.5 s.Conclusions
We have demonstrated that real-time organ motion monitoring using image based markerless registration is feasible.
SUBMITTER: Gendrin C
PROVIDER: S-EPMC3276833 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:To provide real-time lung tumor motion estimation during radiotherapy treatment delivery without the need for implanted fiducial markers or additional imaging dose to the patient.2D radiographs from the therapy beam's-eye-view (BEV) perspective are captured at a frame rate of 12.8 Hz with a frame grabber allowing direct RAM access to the image buffer. An in-house developed real-time soft tissue localization algorithm is utilized to calculate soft tissue displacement from these images in real-time. The system is tested with a Varian TX linear accelerator and an AS-1000 amorphous silicon electronic portal imaging device operating at a resolution of 512 × 384 pixels. The accuracy of the motion estimation is verified with a dynamic motion phantom. Clinical accuracy was tested on lung SBRT images acquired at 2 fps.Real-time lung tumor motion estimation from BEV images without fiducial markers is successfully demonstrated. For the phantom study, a mean tracking error <1.0 mm [root mean square (rms) error of 0.3 mm] was observed. The tracking rms accuracy on BEV images from a lung SBRT patient (≈20 mm tumor motion range) is 1.0 mm.The authors demonstrate for the first time real-time markerless lung tumor motion estimation from BEV images alone. The described system can operate at a frame rate of 12.8 Hz and does not require prior knowledge to establish traceable landmarks for tracking on the fly. The authors show that the geometric accuracy is similar to (or better than) previously published markerless algorithms not operating in real-time.
| S-EPMC3772940 | biostudies-other
Project description:Recently, increasing numbers of cell culture experiments with 3D spheroids presented better correlating results in vivo than traditional 2D cell culture systems. 3D spheroids could offer a simple and highly reproducible model that would exhibit many characteristics of natural tissue, such as the production of extracellular matrix. In this paper numerous cell lines were screened and selected depending on their ability to form and maintain a spherical shape. The effects of increasing concentrations of doxorubicin (DXR) on the integrity and viability of the selected spheroids were then measured at regular intervals and in real-time. In total 12 cell lines, adenocarcinomic alveolar basal epithelial (A549), muscle (C2C12), prostate (DU145), testis (F9), pituitary epithelial-like (GH3), cervical cancer (HeLa), HeLa contaminant (HEp2), embryo (NIH3T3), embryo (PA317), neuroblastoma (SH-SY5Y), osteosarcoma U2OS, and embryonic kidney cells (293T), were screened. Out of the 12, 8 cell lines, NIH3T3, C2C12, 293T, SH-SY5Y, A549, HeLa, PA317, and U2OS formed regular spheroids and the effects of DXR on these structures were measured at regular intervals. Finally, 5 cell lines, A549, HeLa, SH-SY5Y, U2OS, and 293T, were selected for real-time monitoring and the effects of DXR treatment on their behavior were continuously recorded for 5 days. A potential correlation regarding the effects of DXR on spheroid viability and ATP production was measured on days 1, 3, and 5. Cytotoxicity of DXR seemed to occur after endocytosis, since the cellular activities and ATP productions were still viable after 1 day of the treatment in all spheroids, except SH-SY5Y. Both cellular activity and ATP production were halted 3 and 5 days from the start of the treatment in all spheroids. All cell lines maintained their spheroid shape, except SHSY-5, which behaved in an unpredictable manner when exposed to toxic concentrations of DXR. Cytotoxic effects of DXR towards SH-SY5Y seemed to cause degradation of the extracellular matrix, since all cells were dismantled from the spheroid upon cell death. On the other hand, 293T spheroids revealed retarded cellular activity and ATP productions upon DXR treatment throughout the experiment. Since 293T was the embryonic kidney cells, the fast clearance or neutralizations could have made them resistant towards DXR. In conclusion, the same degree of sensitivity from the 2D system did not translate to a 3D culture system, resulting in higher IC50 values than the 2D system. The varying sensitivities and tolerances to drugs could be better understood with a 3D cell culture system.
| S-EPMC5125797 | biostudies-literature
Project description:Biplane 2D-3D registration approaches have been used for measuring 3D, in vivo glenohumeral (GH) joint kinematics. Computed tomography (CT) has become the gold standard for reconstructing 3D bone models, as it provides high geometric accuracy and similar tissue contrast to video-radiography. Alternatively, magnetic resonance imaging (MRI) would not expose subjects to radiation and provides the ability to add cartilage and other soft tissues to the models. However, the accuracy of MRI-based 2D-3D registration for quantifying glenohumeral kinematics is unknown. We developed an automatic 2D-3D registration program that works with both CT- and MRI-based image volumes for quantifying joint motions. The purpose of this study was to use the proposed 2D-3D auto-registration algorithm to describe the humerus and scapula tracking accuracy of CT- and MRI-based registration relative to radiostereometric analysis (RSA) during dynamic biplanar video-radiography. The GH kinematic accuracy (RMS error) was 0.6-1.0?mm and 0.6-2.2° for the CT-based registration and 1.4-2.2?mm and 1.2-2.6° for MRI-based registration. Higher kinematic accuracy of CT-based registration was expected as MRI provides lower spatial resolution and bone contrast as compared to CT and suffers from spatial distortions. However, the MRI-based registration is within an acceptable accuracy for many clinical research questions.
| S-EPMC6310644 | biostudies-literature
Project description:Our purpose was to develop a neural network-based registration quality evaluator (RQE) that can improve the 2D/3D image registration robustness for pediatric patient setup in external beam radiotherapy. Orthogonal daily setup X-ray images of six pediatric patients with brain tumors receiving proton therapy treatments were retrospectively registered with their treatment planning computed tomography (CT) images. A neural network-based pattern classifier was used to determine whether a registration solution was successful based on geometric features of the similarity measure values near the point-of-solution. Supervised training and test datasets were generated by rigidly registering a pair of orthogonal daily setup X-ray images to the treatment planning CT. The best solution for each registration task was selected from 50 optimizing attempts that differed only by the randomly generated initial transformation parameters. The distance from each individual solution to the best solution in the normalized parametrical space was compared to a user-defined error tolerance to determine whether that solution was acceptable. A supervised training was then used to train the RQE. Performance of the RQE was evaluated using test dataset consisting of registration results that were not used in training. The RQE was integrated with our in-house 2D/3D registration system and its performance was evaluated using the same patient dataset. With an optimized sampling step size (i.e., 5 mm) in the feature space, the RQE has the sensitivity and the specificity in the ranges of 0.865-0.964 and 0.797-0.990, respectively, when used to detect registration error with mean voxel displacement (MVD) greater than 1 mm. The trial-to-acceptance ratio of the integrated 2D/3D registration system, for all patients, is equal to 1.48. The final acceptance ratio is 92.4%. The proposed RQE can potentially be used in a 2D/3D rigid image registration system to improve the overall robustness by rejecting unsuccessful registration solutions. The RQE is not patient-specific, so a single RQE can be constructed and used for a particular application (e.g., the registration for images acquired on the same anatomical site). Implementation of the RQE in a 2D/3D registration system is clinically feasible.
| S-EPMC5690212 | biostudies-literature
Project description:Noninvasive biological readouts of tumor metastatic risk and therapeutic efficacy are needed as healthcare costs rise. CTCs are the source of metastasis in distant organs that are responsible for the majority of cancer-related deaths. Here we demonstrate the acute and long-term effect of vascular disrupting therapies (high-dose radiotherapy and tumor necrosis factor-alpha (TNF)) on CTCs released from the primary tumor with a non-invasive real-time in vivo flow cytometry system. Using our innovative flow cytometry platform, we show here that radiation and nanodrug treatment can lead to short term release of CTC from the primary tumor. There was no increase in metastasis frequency or extent between control and TNF-treated mice; however, a significant reduction in lung metastasis was noted in the radiotherapy alone group. Mice treated with both TNF and radiotherapy had a slightly elevated metastatic profile between that of radiation alone and control (untreated) tumors. Possible mechanisms based on therapy specific vessel disruption and cell death are discussed. Overall, CTCs correlated with tumor progression and suggest CTC enumeration described herein may be useful in clinical management of solid tumor malignancies.
| S-EPMC5743220 | biostudies-literature
Project description:To determine how best to time respiratory surrogate-based tumor motion model updates by comparing a novel technique based on external measurements alone to three direct measurement methods.Concurrently measured tumor and respiratory surrogate positions from 166 treatment fractions for lung or pancreas lesions were analyzed. Partial-least-squares regression models of tumor position from marker motion were created from the first six measurements in each dataset. Successive tumor localizations were obtained at a rate of once per minute on average. Model updates were timed according to four methods: never, respiratory surrogate-based (when metrics based on respiratory surrogate measurements exceeded confidence limits), error-based (when localization error ≥ 3 mm), and always (approximately once per minute).Radial tumor displacement prediction errors (mean ± standard deviation) for the four schema described above were 2.4 ± 1.2, 1.9 ± 0.9, 1.9 ± 0.8, and 1.7 ± 0.8 mm, respectively. The never-update error was significantly larger than errors of the other methods. Mean update counts over 20 min were 0, 4, 9, and 24, respectively.The same improvement in tumor localization accuracy could be achieved through any of the three update methods, but significantly fewer updates were required when the respiratory surrogate method was utilized. This study establishes the feasibility of timing image acquisitions for updating respiratory surrogate models without direct tumor localization.
| S-EPMC3695968 | biostudies-other
Project description:Imaging the infant brain with MRI has improved our understanding of early neurodevelopment. However, head motion during MRI acquisition is detrimental to both functional and structural MRI scan quality. Though infants are typically scanned while asleep, they commonly exhibit motion during scanning causing data loss. Our group has shown that providing MRI technicians with real-time motion estimates via Framewise Integrated Real-Time MRI Monitoring (FIRMM) software helps obtain high-quality, low motion fMRI data. By estimating head motion in real time and displaying motion metrics to the MR technician during an fMRI scan, FIRMM can improve scanning efficiency. Here, we compared average framewise displacement (FD), a proxy for head motion, and the amount of usable fMRI data (FD ≤ 0.2 mm) in infants scanned with (n = 407) and without FIRMM (n = 295). Using a mixed-effects model, we found that the addition of FIRMM to current state-of-the-art infant scanning protocols significantly increased the amount of usable fMRI data acquired per infant, demonstrating its value for research and clinical infant neuroimaging.
| S-EPMC9157440 | biostudies-literature
Project description:PURPOSE:To automate the estimation of swallowing motion from 2D MR cine images using deformable registration for future applications of personalized margin reduction in head and neck radiotherapy and outcome assessment of radiation-associated dysphagia. METHODS:Twenty-one patients with serial 2D FSPGR-MR cine scans of the head and neck conducted through the course of definitive radiotherapy for oropharyngeal cancer. Included patients had at least one cine scan before, during, or after radiotherapy, with a total of 52 cine scans. Contours of 7 swallowing related regions-of-interest (ROIs), including pharyngeal constrictor, epiglottis, base of tongue, geniohyoid, hyoid, soft palate, and larynx, were manually delineated from consecutive frames of the cine scan covering at least one swallowing cycle. We applied a modified thin-plate-spline robust-point-matching algorithm to register the point sets of each ROI automatically over frames. The deformation vector fields from the registration were then used to estimate the motion during swallowing for each ROI. Registration errors were estimated by comparing the deformed contours with the manual contours. RESULTS:On average 22 frames of each cine scan were contoured. The registration for one cine scan (7 ROIs over 22 frames) on average took roughly 22 minutes. A number of 8018 registrations were successfully batch processed without human interaction after the contours were drawn. The average registration error for all ROIs and all patients was 0.36 mm (range: 0.06 mm- 2.06 mm). Larynx had the average largest motion in superior direction of all structures under consideration (range: 0.0 mm- 58.7 mm). Geniohyoid had the smallest overall motion of all ROIs under consideration and the superior-inferior motion was larger than the anterior-posterior motion for all ROIs. CONCLUSION:We developed and validated a deformable registration framework to automate the estimation of swallowing motion from 2D MR cine scans.
| S-EPMC7012439 | biostudies-literature
Project description:Real-time three-dimensional (RT-3D) echocardiography has entered the clinical practice but true incremental value over standard two-dimensional echocardiography (2D) remains uncertain when applied to stress echo. The aim of the present study is to establish the additional value of RT-3D stress echo over standard 2D stress echocardiography. We evaluated 23 consecutive patients (age = 65 +/- 10 years, 16 men) referred for dipyridamole stress echocardiography with Sonos 7500 (Philips Medical Systems, Palo, Alto, CA) equipped with a phased - array 1.6-2.5 MHz probe with second harmonic capability for 2D imaging and a 2-4 MHz matrix-phased array transducer producing 60 x 70 volumetric pyramidal data containing the entire left ventricle for RT-3D imaging. In all patients, images were digitally stored in 2D and 3D for baseline and peak stress with a delay between acquisitions of less than 60 seconds. Wall motion analysis was interpreted on-line for 2D and off-line for RT-3D by joint reading of two expert stress ecocardiographist. Segmental image quality was scored from 1 = excellent to 5 = uninterpretable. Interpretable images were obtained in all patients. Acquisition time for 2D images was 67 +/- 21 sec vs 40 +/- 22 sec for RT-3D (p = 0.5). Wall motion analysis time was 2.8 +/- 0.5 min for 2D and 13 +/- 7 min for 3D (p = 0.0001). Segmental image quality score was 1.4 +/- 0.5 for 2D and 2.6 +/- 0.7 for 3D (p = 0.0001). Positive test results was found in 5/23 patients. 2D and RT-3D were in agreement in 3 out of these 5 positive exams. Overall stress result (positive vs negative) concordance was 91% (Kappa = 0.80) between 2D and RT-3D. During dipyridamole stress echocardiography RT-3D imaging is highly feasible and shows a high concordance rate with standard 2D stress echo. 2D images take longer time to acquire and RT-3D is more time-consuming to analyze. At present, there is no clear clinical advantage justifying routine RT-3D stress echocardiography use.
| S-EPMC2474587 | biostudies-literature
Project description:PurposeTo enable real-time adaptive magnetic resonance imaging-guided radiotherapy (MRIgRT) by obtaining time-resolved three-dimensional (3D) deformation vector fields (DVFs) with high spatiotemporal resolution and low latency ( <500 ms). Theory and Methods: Respiratory-resolved T1 -weighted 4D-MRI of 27 patients with lung cancer were acquired using a golden-angle radial stack-of-stars readout. A multiresolution convolutional neural network (CNN) called TEMPEST was trained on up to 32 × retrospectively undersampled MRI of 17 patients, reconstructed with a nonuniform fast Fourier transform, to learn optical flow DVFs. TEMPEST was validated using 4D respiratory-resolved MRI, a digital phantom, and a physical motion phantom. The time-resolved motion estimation was evaluated in-vivo using two volunteer scans, acquired on a hybrid MR-scanner with integrated linear accelerator. Finally, we evaluated the model robustness on a publicly-available four-dimensional computed tomography (4D-CT) dataset.ResultsTEMPEST produced accurate DVFs on respiratory-resolved MRI at 20-fold acceleration, with the average end-point-error <2 mm, both on respiratory-sorted MRI and on a digital phantom. TEMPEST estimated accurate time-resolved DVFs on MRI of a motion phantom, with an error <2 mm at 28 × undersampling. On two volunteer scans, TEMPEST accurately estimated motion compared to the self-navigation signal using 50 spokes per dynamic (366 × undersampling). At this undersampling factor, DVFs were estimated within 200 ms, including MRI acquisition. On fully sampled CT data, we achieved a target registration error of 1.87±1.65 mm without retraining the model.ConclusionA CNN trained on undersampled MRI produced accurate 3D DVFs with high spatiotemporal resolution for MRIgRT.
| S-EPMC9298075 | biostudies-literature