A Novel Deep Learning Framework for Internal Gross Target Volume Definition from 4D Computed Tomography of Lung Cancer Patients

In this paper, we study the reliability of a novel deep learning framework for internal gross target volume (IGTV) delineation from four-dimensional computed tomography (4DCT), which is applied to patients with lung cancer treated by Stereotactic Body Radiation Therapy (SBRT). 77 patients who underwent SBRT followed by 4DCT scans were incorporated in a retrospective study. The IGTV_DL was delineated using a novel deep machine learning algorithm with a linear exhaustive optimal combination framework, for the purpose of comparison, three other IGTVs base on common methods was also delineated, we compared the relative volume difference (RVI), matching index (MI) and encompassment index (EI) for the above IGTVs. Then, multiple parameter regression analysis assesses the tumor volume and motion range as clinical influencing factors in the MI variation. Experimental results demonstrated that the deep learning algorithm with linear exhaustive optimal combination framework has a higher probability of achieving optimal MI compared with other currently widely used methods. For patients after simple breathing training by keeping the respiratory frequency in 10 BMP, the four phase combinations of 0%, 30%, 50% and 90% can be considered as a potential candidate for an optimal combination to synthesis IGTV in all respiration amplitudes

Comparison of 3D and 4D CBCT for the Localization of Moving Targets

Purpose: Compare the localization accuracy of 3D cone beam computed tomography (CBCT) and 4D CBCT for a dynamic respiratory phantom, considering cases with and without respiratory variations between planning and treatment. Methods and Materials: A respiratory phantom was imaged using 3D CBCT and 4D CBCT. Measurements of the amplitude of motion in 3D CBCT and 4D CBCT and size of the sphere at its exhale position in 4D CBCT were acquired. The phantom was then misaligned by known distances relative to a reference position. 3D CBCT and 4D CBCT were used to re-align the phantom by comparing localization images to reference images. Because the ideal 4D CBCT registration approach was to be determined, multiple localization-reference pairs of images were examined. Comparison was performed for multiple respiratory waveforms at two amplitudes as well as for waveforms where amplitudes were increased or decreased by 30%, simulating changes in respiratory behavior between simulation and localization. Error of localization trials was defined as the difference between the recommended table shifts and the known displacement. Results: Measurements of the respiratory amplitude were underestimated for all cases. Measurement of the sphere diameter exhibited \u3c 2 mm error. Localization errors were less than 2.6 mm for all cases. When the motion amplitude was the same during localization as planning, automatic registration of the exhale frame from the 4D CBCT to the reference exhale frame had the smallest error in localization of 0.54 ± 0.03 mm. However, automatic registration of the 4D CBCT AIP to the reference AIP had the smallest errors when the motion amplitude changed between planning and localization, 1.59 ± 0.12 mm for the +30% amplitude change and 0.69 ± 0.02 mm for the -30% amplitude change. Conclusion: Because lung tumor motion amplitude throughout treatment may vary between planning and treatment, the use of 4D CBCT AIP registered to reference AIP was recommended for localization. 4D CBCT provided additional benefits compared to 3D CBCT of substantial reduction in target blurring and verification of respiratory motion characteristics prior to treatment

Surrogate-driven respiratory motion models for MRI-guided lung radiotherapy treatments

An MR-Linac integrates an MR scanner with a radiotherapy delivery system, providing non-ionizing real-time imaging of the internal anatomy before, during and after radiotherapy treatments. Due to spatio-temporal limitations of MR imaging, only high-resolution 2D cine-MR images can be acquired in real-time during MRI-guided radiotherapy (MRIgRT) to monitor the respiratory-induced motion of lung tumours and organs-at-risk. However, temporally-resolved 3D anatomical information is essential for accurate MR guidance of beam delivery and dose estimation of the actually delivered dose. Surrogate-driven respiratory motion models can estimate the 3D motion of the internal anatomy from surrogate signals, producing the required information. The overall aim of this thesis was to tailor a generalized respiratory motion modelling framework for lung MRIgRT. This framework can fit the model directly to unsorted 2D MR images sampling the 3D motion, and to surrogate signals extracted from the 2D cine-MR images acquired on an MR-Linac. It can model breath-to-breath variability and produce a motion compensated super-resolution reconstruction (MCSR) 3D image that can be deformed using the estimated motion. In this work novel MRI-derived surrogate signals were generated from 2D cine-MR images to model respiratory motion for lung cancer patients, by applying principal component analysis to the control point displacements obtained from the registration of the cine-MR images. An MR multi-slice interleaved acquisition potentially suitable for the MR-Linac was developed to generate MRI-derived surrogate signals and build accurate respiratory motion models with the generalized framework for lung cancer patients. The developed models and the MCSR images were thoroughly evaluated for lung cancer patients scanned on an MR-Linac. The results showed that respiratory motion models built with the generalized framework and minimal training data generally produced median errors within the MCSR voxel size of 2 mm, throughout the whole 3D thoracic field-of-view and over the expected lung MRIgRT treatment times

Improved correction for the tissue fraction effect in lung PET/CT imaging

Recently, there has been an increased interest in imaging different pulmonary disorders using PET techniques. Previous work has shown, for static PET/CT, that air content in the lung influences reconstructed image values and that it is vital to correct for this 'tissue fraction effect' (TFE). In this paper, we extend this work to include the blood component and also investigate the TFE in dynamic imaging. CT imaging and PET kinetic modelling are used to determine fractional air and blood voxel volumes in six patients with idiopathic pulmonary fibrosis. These values are used to illustrate best and worst case scenarios when interpreting images without correcting for the TFE. In addition, the fractional volumes were used to determine correction factors for the SUV and the kinetic parameters. These were then applied to the patient images. The kinetic parameters K1 and Ki along with the static parameter SUV were all found to be affected by the TFE with both air and blood providing a significant contribution to the errors. Without corrections, errors range from 34-80% in the best case and 29-96% in the worst case. In the patient data, without correcting for the TFE, regions of high density (fibrosis) appeared to have a higher uptake than lower density (normal appearing tissue), however this was reversed after air and blood correction. The proposed correction methods are vital for quantitative and relative accuracy. Without these corrections, images may be misinterpreted

Synthesis of Realistic Simultaneous Positron Emission Tomography and Magnetic Resonance Imaging Data

The investigation of the performance of different positron emission tomography (PET) reconstruction and motion compensation methods requires accurate and realistic representation of the anatomy and motion trajectories as observed in real subjects during acquisitions. The generation of well-controlled clinical datasets is difficult due to the many different clinical protocols, scanner specifications, patient sizes, and physiological variations. Alternatively, computational phantoms can be used to generate large data sets for different disease states, providing a ground truth. Several studies use registration of dynamic images to derive voxel deformations to create moving computational phantoms. These phantoms together with simulation software generate raw data. This paper proposes a method for the synthesis of dynamic PET data using a fast analytic method. This is achieved by incorporating realistic models of respiratory motion into a numerical phantom to generate datasets with continuous and variable motion with magnetic resonance imaging (MRI)-derived motion modeling and high resolution MRI images. In this paper, data sets for two different clinical traces are presented, ¹⁸F-FDG and ⁶⁸Ga-PSMA. This approach incorporates realistic models of respiratory motion to generate temporally and spatially correlated MRI and PET data sets, as those expected to be obtained from simultaneous PET-MRI acquisitions

On the investigation of a novel x-ray imaging techniques in radiation oncology

Radiation therapy is indicated for nearly 50% of cancer patients in Australia. Radiation therapy requires accurate delivery of ionising radiation to the neoplastic tissue and pre-treatment in situ x-ray imaging plays an important role in meeting treatment accuracy requirements. Four dimensional cone-beam computed tomography (4D CBCT) is one such pre-treatment imaging technique that can help to visualise tumour target motion due to breathing at the time of radiation treatment delivery. Measuring and characterising the target motion can help to ensure highly accurate therapeutic x-ray beam delivery. In this thesis, a novel pre-treatment x-ray imaging technique, called Respiratory Triggered 4D cone-beam Computed Tomography (RT 4D CBCT), is conceived and investigated. Specifically, the aim of this work is to progress the 4D CBCT imaging technology by investigating the use of a patient’s breathing signal to improve and optimise the use of imaging radiation in 4D CBCT to facilitate the accurate delivery of radiation therapy. These investigations are presented in three main studies: 1. Introduction to the concept of respiratory triggered four dimensional conebeam computed tomography. 2. A simulation study exploring the behaviour of RT 4D CBCT using patientmeasured respiratory data. 3. The experimental realisation of RT 4D CBCT working in a real-time acquisitions setting. The major finding from this work is that RT 4D CBCT can provide target motion information with a 50% reduction in the x-ray imaging dose applied to the patient

Optimization strategies for respiratory motion management in stereotactic body radiation therapy

Various challenges arise during the treatment of lung tumors with stereotactic body radiation therapy (SBRT), which is a form of hypofractionated high precision conformal radiation therapy delivered to small targets. The dose is applied in only a few fractions and respiratory organ and tumor motion is a source of uncertainty additional to interfractional set-up errors. Respiratory organ and tumor motion is highly patient-specific and it affects the whole radiotherapy treatment chain. In this thesis, motion management techniques for SBRT are evaluated and improved in a clinical setting. A clinical need for improvement has been present at the LMU university hospital for each issue addressed in this thesis: Initially, the usage of respiratory correlated computed tomography (4DCT), which is vital for SBRT treatment, was seen as impractical and prone to uncertainties in the data reconstruction in its current form. Therefore, the 4DCT reconstruction workflow has been improved to minimize these potential error sources. Secondly, treatment planning for tumors affected by respiratory motion was evaluated and subsequently improved. Finally, the treatment technique of respiratory gating was implemented at the clinic, which led to the need of evaluating the respiratory gating characteristics of the novel system configuration. At first, the 4DCT reconstruction workflow used in clinical practice was investigated, as in the presence of respiratory motion the knowledge of tumor position over time is essential in SBRT treatments. Using 4DCT, the full motion range of the individual tumor can be determined. However, certain 4DCT reconstruction methods can under- or overestimate tumor motion due to limitations in the data acquisition scheme and due to the incorrect sorting of certain X-ray computed tomography (CT) image slices into different respiratory phases. As the regular clinical workflow of cycle-based sorting (CBS) without maximum inspiration detection (and therefore no clear starting point for the individual breathing cycles) seemed to be affected by these potential errors, the usage of CBS with correct maximum detection and another sorting algorithm of the respiration states, so-called local amplitude-based sorting (LAS), both have been implemented for a reduction of image artifacts and improved 4DCT quality. The three phase binning algorithms have been investigated in a phantom study (using 10 different breathing waveforms) and in a patient study (with 10 different patients). The mis-representation of the tumor volume was reduced in both implemented sorting algorithms compared to the previously used CBS approach (without correct maximum detection) in the phantom and the patient study. The clinical recommendation was the use of CBS with improved maximum detection, as too many manual interventions would be needed for the LAS workflow. Secondly, a combination of the actual patient breathing trace during treatment, the log files generated by the linear accelerator (LINAC), and Monte Carlo (MC) four-dimensional (4D) dose calculations for each individual fraction was implemented as a 4D dose evaluation tool. This workflow was tested in a clinical environment for SBRT treatment planning on multiple CT datasets featuring: a native free-breathing 3DCT, an average intensity projection (AIP) as well as a maximum intensity projection (MIP), both obtained from the patient's 4DCT, and density overrides (DOs) in a 3DCT. This study has been carried out for 5 SBRT patients for three-dimensional conformal radiation therapy (3D-CRT) and volumetric modulated arc therapy (VMAT) treatment plans. The dose has been recalculated on each 4DCT breathing phase according the the patient's breathing waveform and accumulated to the gross tumor volume (GTV) at the end-of-exhale (EOE) breathing phase using deformable image registration. Even though the least differences in planned and recalculated dose were found for AIP and MIP treatment planning, the results indicate a strong dependency on individual tumor motion due to the variability of breathing motion in general, and on tumor size. The combination of the patient's individual breathing trace during each SBRT fraction with 4D MC dose calculation based on the LINAC log file information leads to a good approximation of actual dose delivery. Finally, in order to ensure precise and accurate treatment for respiratory gating techniques, the technical characteristics of the LINAC in combination with a breathing motion monitoring system as s surrogate for tumor motion have to be identified. The dose delivery accuracy and the latency of a surface imaging system in connection with a modern medical LINAC were investigated using a dynamic breathing motion phantom. The dosimetric evaluation has been carried out using a static 2D-diode array. The measurement of the dose difference between gated and ungated radiation delivery was found to be below 1% (for clinical relevant gating levels of about 30%). The beam-on latency, or time delay, determined using radiographic films was found to be up to 851 ms±100 ms. With these known parameters, an adjustment of the pre-selected gating level or the internal target volume (ITV) margins could be made. With the highly patient-specific character of respiratory motion, lung SBRT faces many additional challenges besides the specific issues addressed in this thesis. However, the findings of this thesis have improved clinical workflows at the Department of Radiation Oncology of the LMU University hospital. In a future perspective, a workflow using evaluation of the actual 4D dose in combination with accurate 4DCT image acquisition and specialized treatment delivery (such as respiratory gating) has the potential for a safe further reduction of treatment margins and increased sparing of organs-at-risk (OARs) in SBRT without compromising tumor dose targeting accuracy

Technical note: Towards more realistic 4DCT(MRI) numerical lung phantoms.

BACKGROUND Numerical 4D phantoms, together with associated ground truth motion, offer a flexible and comprehensive data set for realistic simulations in radiotherapy and radiology in target sites affected by respiratory motion. PURPOSE We present an openly available upgrade to previously reported methods for generating realistic 4DCT lung numerical phantoms, which now incorporate respiratory ribcage motion and improved lung density representation throughout the breathing cycle. METHODS Density information of reference CTs, toget her with motion from multiple breathing cycle 4DMRIs have been combined to generate synthetic 4DCTs (4DCT(MRI)s). Inter-subject correspondence between the CT and MRI anatomy was first established via deformable image registration (DIR) of binary masks of the lungs and ribcage. Ribcage and lung motions were extracted independently from the 4DMRIs using DIR and applied to the corresponding locations in the CT after post-processing to preserve sliding organ motion. In addition, based on the Jacobian determinant of the resulting deformation vector fields, lung densities were scaled on a voxel-wise basis to more accurately represent changes in local lung density. For validating this process, synthetic 4DCTs, referred to as 4DCT(CT)s, were compared to the originating 4DCTs using motion extracted from the latter, and the dosimetric impact of the new features of ribcage motion and density correction were analyzed using pencil beam scanned proton 4D dose calculations. RESULTS Lung density scaling led to a reduction of maximum mean lung Hounsfield units (HU) differences from 45 to 12 HU when comparing simulated 4DCT(CT)s to their originating 4DCTs. Comparing 4D dose distributions calculated on the enhanced 4DCT(CT)s to those on the original 4DCTs yielded 2%/2 mm gamma pass rates above 97% with an average improvement of 1.4% compared to previously reported phantoms. CONCLUSIONS A previously reported 4DCT(MRI) workflow has been successfully improved and the resulting numerical phantoms exhibit more accurate lung density representations and realistic ribcage motion