A 4D global respiratory motion model of the thorax based on CT images: A proof of concept

A Patient-Specific correspondence model to track tumor location in thorax during radiation therapy

PURPOSE

We present a patient-specific model to estimate tumor location in the thorax during radiation therapy using chest surface displacement as the surrogate signal.

METHODS

Two types of data are used for model construction: Four-dimensional computed tomography (4D-CT) images of the patient and the displacement of two points on the patient's skin on the thoracic area. Principal component analysis is used to fit the correspondence model. This model incorporates the recorded surrogate signals during radiation delivery as input and delivers the 3D trajectory of the tumor as output. We evaluated the accuracy of the proposed model on a respiratory phantom and five lung cancer patients.

RESULTS

For the respiratory phantom, the location of the center of the sphere during treatment was calculated in three directions: Left-Right (LR), Anterior-Posterior (AP) and, Superior-Inferior (SI). The error of localization was less than 1 mm in the LR and AP directions and less than 2 mm in the SI direction. The location of the tumor center for two of the patients, and the location of the apex of the diaphragm for the other three, was calculated in three directions. For all patients, the localization error in the LR and AP directions was less than 1.1 mm for two fractions and the maximum localization error in the SI direction was 6.4 mm.

CONCLUSIONS

This work presents a feasibility study of utilizing surface displacement data to locate the tumor in the thorax during radiation treatment. Future work will validate the model on a larger patient population.

Superiority of integrated cervicothoracic immobilization in the setup of lung cancer patients treated with supraclavicular station irradiation

Objective

To investigate the superiority of the integrated cervicothoracic immobilization devices (ICTID) on the mobility of the supraclavicular station in lung cancer patients requiring both primary lung lesion and positive supraclavicular lymph nodes irradiation.

Methods

One hundred patients with lung cancer were prospectively enrolled in the study. The following four different fixation methods are used for CT simulation positioning: thoracoabdominal flat immobilization device fixation with arms lifting (TAFID group), head-neck-shoulder immobilization device fixation with arms on the body sides (HNSID group), ICTID fixation with arms on the body sides (ICTID arms-down group), and n ICTID fixation with arms lifting (ICTID arms-up group). Cone-beam computed tomography (CBCT) images are taken daily or weekly before treatment, to assess anatomical changes during the radiotherapy course.

Results

The translation errors in X (left-right direction), Y (head-foot direction), and Z (abdomen-back direction) directions of the ICTID arms-up, TAFID, ICTID arms-down and HNSID groups were (0.15 ± 0.18) cm, (0.15 ± 0.16) cm, (0.16 ± 0.16) cm, and (0.15 ± 0.20) cm; (0.15 ± 0.15) cm, (0.21 ± 0.25) cm, (0.28 ± 0.23) cm, and (0.27 ± 0.21) cm; (0.13 ± 0.14) cm, (0.15 ± 0.14) cm, (0.17 ± 0.13) cm, and (0.16 ± 0.14) cm, respectively. Among them, the ICTID arms-up group had the minimal setup errors in X direction than those in ICTID arms-down (p=0.001) and HNSID groups (p=0.001), and in Y direction than those in TAFID (p<0.001), and in Z direction than those in ICTID arms-down (p<0.001) and TAFID groups (p=0.034). For the rotational errors of the four groups in the directions of sagittal plane, transverse plane, and coronal plane, the ICTID arms-up group had the smallest setup errors in the sagittal plane than that of TAFID groups and similar rotation setup errors with those of the other three groups.

Conclusion

For patients requiring radiation of primary lung lesion and positive supraclavicular lymph nodes, an integrated frame fixation device is preferred the ICTID arms-up methods provide the smallest set up error and satisfied repeatability of body position, compared with TAFID and HNSID.

Real-time 3D MRI reconstruction from cine-MRI using unsupervised network in MRI-guided radiotherapy for liver cancer

PURPOSE

Respiration has a major impact on the accuracy of radiation treatment for thorax and abdominal tumours. Instantaneous volumetric imaging could provide precise knowledge of tumour and normal organs' three-dimensional (3D) movement, which is the key to reducing the negative effect of breathing motion. Therefore, this study proposed a real-time 3D MRI reconstruction method from cine-MRI using an unsupervised network.

METHODS AND MATERIALS

Cine-MRI and setup 3D-MRI from eight patients with liver cancer were utilized to establish and validate the deep learning network for 3D-MRI reconstruction. Unlike previous methods that required 4D-MRI for network training, the proposed method utilized a reference 3D-MRI and cine-MRI to generate the training data. Then, a network was trained in an unsupervised manner to estimate the relationship between the cine-MRI acquired on coronal plane and deformation vector field (DVF) that describes the patient's breathing motion. After the training process, the coronal cine-MRI were inputted into the network, and the corresponding DVF was obtained. By wrapping the reference 3D-MRI with the generated DVF, the 3D-MRI could be reconstructed.

RESULTS

The reconstructed 3D-MRI slices were compared with the corresponding phase-sorted cine-MRI using dice similarity coefficients (DSCs) of liver contours and blood vessel localization error. In all patients, the liver DSC had mean value >96.1% and standard deviation < 1.3%; the blood vessel localization error had mean value <2.6 mm, and standard deviation was <2.0 mm. Moreover, the time for 3D-MRI reconstruction was approximately 100 ms. These results indicated that the proposed method could accurately reconstruct the 3D-MRI in real time.

CONCLUSIONS

The proposed method could accurately reconstruct the 3D-MRI from cine-MRI in real time. This method has great potential in improving the accuracy of radiotherapy for moving tumours.

PET respiratory motion correction: quo vadis?

Positron emission tomography (PET) respiratory motion correction has been a subject of great interest for the last twenty years, prompted mainly by the development of multimodality imaging devices such as PET/computed tomography (CT) and PET/magnetic resonance imaging (MRI). PET respiratory motion correction involves a number of steps including acquisition synchronization, motion estimation and finally motion correction. The synchronization steps include the use of different external device systems or data driven approaches which have been gaining ground over the last few years. Patient specific or generic motion models using the respiratory synchronized datasets can be subsequently derived and used for correction either in the image space or within the image reconstruction process. Similar overall approaches can be considered and have been proposed for both PET/CT and PET/MRI devices. Certain variations in the case of PET/MRI include the use of MRI specific sequences for the registration of respiratory motion information. The proposed review includes a comprehensive coverage of all these areas of development in field of PET respiratory motion for different multimodality imaging devices and approaches in terms of synchronization, estimation and subsequent motion correction. Finally, a section on perspectives including the potential clinical usage of these approaches is included.

Modeling intra-fractional abdominal configuration changes using breathing motion-corrected radial MRI

Abdominal organ motions introduce geometric uncertainties to gastrointestinal radiotherapy. This study investigated slow drifting motion induced by changes of internal anatomic organ arrangements using a 3D radial MRI sequence with a scan length of 20 min. Breathing motion and cyclic GI motion were first removed through multi-temporal resolution image reconstruction. Slow drifting motion analysis was performed using an image time series consisting of 72 image volumes with a temporal sampling rate of 17 s. B-spline deformable registration was performed to align image volumes of the time series to a reference volume. The resulting deformation fields were used for motion velocity evaluation and patient-specific motion model construction through principal component analysis (PCA). Geometric uncertainties introduced by slow drifting motion were assessed by Hausdorff distances between unions of organs at risk (OARs) at different motion states and reference OAR contours as well as probabilistic distributions of OARs predicted using the PCA model. Thirteen examinations from 11 patients were included in this study. The averaged motion velocities ranged from 0.8 to 1.9 mm min^-1, 0.7 to 1.6 mm min^-1, 0.6 to 2.0 mm min^-1and 0.7 to 1.4 mm min^-1for the small bowel, colon, duodenum and stomach respectively; the averaged Hausdorff distances were 5.6 mm, 5.3 mm, 5.1 mm and 4.6 mm. On average, a margin larger than 4.5 mm was needed to cover a space with OAR occupancy probability higher than 55%. Temporal variations of geometric uncertainties were evaluated by comparing across four 5 min sub-scans extracted from the full scan. Standard deviations of Hausdorff distances across sub-scans were less than 1 mm for most examinations, indicating stability of relative margin estimates from separate time windows. These results suggested slow drifting motion of GI organs is significant and geometric uncertainties introduced by such motion should be accounted for during radiotherapy planning and delivery.

Roadmap toward the 10 ps time-of-flight PET challenge

Since the seventies, positron emission tomography (PET) has become an invaluable medical molecular imaging modality with an unprecedented sensitivity at the picomolar level, especially for cancer diagnosis and the monitoring of its response to therapy. More recently, its combination with x-ray computed tomography (CT) or magnetic resonance (MR) has added high precision anatomic information in fused PET/CT and PET/MR images, thus compensating for the modest intrinsic spatial resolution of PET. Nevertheless, a number of medical challenges call for further improvements in PET sensitivity. These concern in particular new treatment opportunities in the context personalized (also called precision) medicine, such as the need to dynamically track a small number of cells in cancer immunotherapy or stem cells for tissue repair procedures. A better signal-to-noise ratio (SNR) in the image would allow detecting smaller size tumours together with a better staging of the patients, thus increasing the chances of putting cancer in complete remission. Moreover, there is an increasing demand for reducing the radioactive doses injected to the patients without impairing image quality. There are three ways to improve PET scanner sensitivity: improving detector efficiency, increasing geometrical acceptance of the imaging device and pushing the timing performance of the detectors. Currently, some pre-localization of the electron-positron annihilation along a line-of-response (LOR) given by the detection of a pair of annihilation photons is provided by the detection of the time difference between the two photons, also known as the time-of-flight (TOF) difference of the photons, whose accuracy is given by the coincidence time resolution (CTR). A CTR of about 10 picoseconds FWHM will ultimately allow to obtain a direct 3D volume representation of the activity distribution of a positron emitting radiopharmaceutical, at the millimetre level, thus introducing a quantum leap in PET imaging and quantification and fostering more frequent use of 11C radiopharmaceuticals. The present roadmap article toward the advent of 10 ps TOF-PET addresses the status and current/future challenges along the development of TOF-PET with the objective to reach this mythic 10 ps frontier that will open the door to real-time volume imaging virtually without tomographic inversion. The medical impact and prospects to achieve this technological revolution from the detection and image reconstruction point-of-views, together with a few perspectives beyond the TOF-PET application are discussed.

Development and prospective in-patient proof-of-concept validation of a surface photogrammetry + CT-based volumetric motion model for lung radiotherapy

PURPOSE

We develop and validate a motion model that uses real-time surface photogrammetry acquired concurrently with four-dimensional computed tomography (4DCT) to estimate respiration-induced changes within the entire irradiated volume, over arbitrarily many respiratory cycles.

METHODS

A research, couch-mounted, VisionRT (VRT) system was used to acquire optical surface data (15 Hz, ROI = 15 × 20 cm² ) from the thoraco-abdominal surface of a consented lung SBRT patient, concurrently with their standard-of-care 4DCT. The end-exhalation phase from the 4DCT was regarded as reference and for each remaining phase, deformation vector fields (DVFs) with respect to the reference phase were computed. To reduce dimensionality, the first two principal components (PCs) of the matrix of nine DVFs were calculated. In parallel, ten phase-averaged VRT surfaces were created. Surface DVFs and corresponding PCs were computed. A principal least squares regression was used to relate the PCs of surface DVF to those of volume DVFs, establishing a relationship between time-varying surface and the underlying time-varying volume. Proof-of-concept validation was performed during each treatment fraction by concurrently acquiring 30 s time series of real-time surface data and "ground truth" kV fluoroscopic data (FL). A ray-tracing algorithm was used to create a digitally reconstructed fluorograph (DRF), and motion trajectories of high-contrast, soft-tissue, anatomical features in the DRF were compared with those from kV FL.

RESULTS

For five of the six fluoroscopic acquisition sessions, the model out-performed 4DCT in predicting contour Dice coefficient with respect to fluoroscopy-derived contours. Similarly, the model exhibited a marked improvement over 4DCT for patch positions on the diaphragm. Model patch position errors varied from 5 to -15 mm while 4DCT errors ranged between 5 and -22.4 mm. For one fluoroscopic acquisition, a marked change in the a priori internal-external correlation resulted in model errors comparable to those of 4DCT.

CONCLUSIONS

We described the development and a proof-of-concept validation for a volumetric motion model that uses surface photogrammetry to correlate the time-varying thoraco-abdominal surface to the time-varying internal thoraco-abdominal volume. These early results indicate that the proposed approach can result in a marked improvement over 4DCT. While limited by the duration of the fluoroscopic acquisitions as well as the resolution of the acquired images, the DRF-based proof-of-concept technique developed here is model-agnostic, and therefore, has the potential to be used as an in-patient validation tool for other volumetric motion models.

Continuous generation of volumetric images during stereotactic body radiation therapy using periodic kV imaging and an external respiratory surrogate

We present a technique for continuous generation of volumetric images during SBRT using periodic kV imaging and an external respiratory surrogate signal to drive a patient-specific PCA motion model. Using the on-board imager, kV radiographs are acquired every 3 s and used to fit the parameters of a motion model so that it matches observed changes in internal patient anatomy. A multi-dimensional correlation model is established between the motion model parameters and the external surrogate position and velocity, enabling volumetric image reconstruction between kV imaging time points. Performance of the algorithm was evaluated using 10 realistic eXtended CArdiac-Torso (XCAT) digital phantoms including 3D anatomical respiratory deformation programmed with 3D tumor positions measured with orthogonal kV imaging of implanted fiducial gold markers. The clinically measured ground truth 3D tumor positions provided a dataset with realistic breathing irregularities, and the combination of periodic on-board kV imaging with recorded external respiratory surrogate signal was used for correlation modeling to account for any changes in internal-external correlation. The three-dimensional tumor positions are reconstructed with an average root mean square error (RMSE) of 1.47 mm, and an average 95th percentile 3D positional error of 2.80 mm compared with the clinically measured ground truth 3D tumor positions. This technique enables continuous 3D anatomical image generation based on periodic kV imaging of internal anatomy without the additional dose of continuous kV imaging. The 3D anatomical images produced using this method can be used for treatment verification and delivered dose computation in the presence of irregular respiratory motion.

A hybrid proton and hyperpolarized gas tagging MRI technique for lung respiratory motion imaging: a feasibility study

The aim of this work was to develop a novel hybrid 3D hyperpolarized (HP) gas tagging MRI (t-MRI) technique and to evaluate it for lung respiratory motion measurement with comparison to deformable image registrations (DIR) methods. Three healthy subjects underwent a hybrid MRI which combines 3D HP gas t-MRI with a low resolution (Low-R, 4.5 mm isotropic voxels) 3D proton MRI (p-MRI), plus a high resolution (High-R, 2.5 mm isotropic voxels) 3D p-MRI, during breath-holds at the end-of-inhalation (EOI) and the end-of-exhalation (EOE). Displacement vector field (DVF) of the lung motion was determined from the t-MRI images by tracking tagging grids and from the High-R p-MRI using three DIR methods (B-spline based method implemented by Velocity, Free Form Deformation by MIM, and B-spline by an open source software Elastix: denoted as A, B, and C, respectively), labeled as tDVF and dDVF, respectively. The tDVF from the HP gas t-MRI was used as ground-truth reference to evaluate performance of the three DIR methods. Differences in both magnitude and angle between the tDVF and dDVFs were analyzed. The mean lung motion of the three subjects was 37.3 mm, 8.9 mm and 12.9 mm, respectively. Relatively large discrepancies were observed between the tDVF and the dDVFs as compared to previously reported DIR errors. The mean  ±  standard deviation (SD) DVF magnitude difference was 8.3  ±  5.6 mm, 9.2  ±  4.5 mm, and 9.3  ±  6.1 mm, and the mean  ±  SD DVF angular difference was 29.1  ±  12.1°, 50.1  ±  28.6°, and 39.0  ±  6.3°, for the DIR Methods A, B, and C, respectively. These preliminary results showed that the hybrid HP gas t-MRI technique revealed different lung motion patterns as compared to the DIR methods. It may provide unique perspectives in developing and evaluating DIR of the lungs. Novelty and Significance We designed a MRI protocol that includes a novel hybrid MRI technique (3D HP gas t-MRI with a low resolution 3D p-MRI) plus a high resolution 3D p-MRI. We tested the novel hybrid MRI technique on three healthy subjects for measuring regional lung respiratory motion with comparison to deformable image registrations (DIR) methods, and observed relatively large discrepancies in lung motion between HP gas t-MRI and DIR methods.