Use In Radiotherapy Treatment Planning Research Articles

Artificial intelligence based auto-contouring solutions for use in radiotherapy treatment planning of head and neck cancer

BackgroundManual contouring is time-consuming and subjective. Thus, auto-segmentation methods, which can be deployed in the existing workflow, are needed. The objective of this study was to assess the feasibility of Limbus AI and AI Rad Companion auto-contours for head and neck treatment planning. MethodsHead and neck patients treated with RapidArc were selected retrospectively. The manual contours on the planning CT were used as reference. Geometric analysis of the auto-contours was performed using several evaluation metrics such as the Dice Similarity Coefficient (DSC) and the Mean Distance to Conformity (MDC). Dosimetric analysis was performed by recalculating the original plan on the auto-contours and comparing Dose Volume Histogram (DVH) metrics to the original plan. Results and discussionBoth AI tools tend to underestimate the volumes of brainstem and cord. For brainstem and parotids, median DSC values were ≥ 0.8. For all auto-contours, median MDC values were ∼ 3–6 mm. Median differences were found of up to ±7 % in DVH points on the auto-contours relative to the planning CT contours, but these were not statistically-significant. ConclusionThe auto-contours could be used as a starting point to assist the clinician with the manual contouring of structures on the planning and re-scanning planning CT.

An evaluation of an energy independent CT reconstruction algorithm for use in radiotherapy treatment planning.

Radiotherapy treatment planning relies upon density information provided by CT for accurate dose calculations. Hounsfield units (HUs) are converted to electron/physical density via an energy dependant calibration curve. Multiple curves are required to make full use of the available accelerating potentials (kVp). The curves are bi-linear with a discontinuity occurring at soft-tissue densities. The commercial algorithm, DirectDensityTM (Siemens Healthcare GmbH), constructs a single calibration curve covering all available kVp. This enables the optimisation of the CT image quality, e.g. in terms of contrast, or the reduction of the imaging dose, whilst rendering the radiotherapy treatment dose calculation robust to the energy used to acquire the CT image. We report our investigations on the clinical utilisation of the DirectDensityTM algorithm for radiotherapy treatments, by using all accelerating potentials, i.e. from 70 kVp up to 140 kVp, available at our CT treatment simulator, in contrast to previous studies that were limited to accelerating potentials spanning a subset of the available kVp. The DirectDensityTM (DD) reconstruction algorithm available on a SOMATOM go.Open Pro CT scanner (Siemens Healthineers) was evaluated using the RayStation v. 9 treatment planning system (RaySearch Laboratories, Stockholm, Sweden) and a CIRS Model 002LFC IMRT Thorax Phantom (SunNuclear, Melbourne, FL), which was imaged at all available kVp with clinical protocols corresponding to various anatomical sites. The DD images were compared to those with the standard reconstruction algorithm acquired only at 120 kVp, as per our routine clinical practice. The effect of increasing kVp on HU is investigated for relevant tissue substitutes. In addition, a dosimetric comparison is performed for a VMAT plan technique with 6 MV X-rays using retrospective patient CT data sets representing four anatomical sites (pelvis, thorax, brain and "head and neck") with five patients for each site. The original dose distributions were calculated on images acquired at 120 kVp using the standard clinical iterative reconstruction (Qr40) and compared with dose distributions recalculated on images reconstructed with the new DD (Sm40) algorithm. The maximum difference for radiotherapy doses calculated using images of the phantom reconstructed with Qr40 (120 kVp) or DD (all available kVp) was 0.73%. The patient plans on the anatomically representative sites studied here showed a mean PTV dose difference of -0.2% (s.d. 0.7) for D99%, -0.4% (s.d. 0.4%) for D50% and -0.3% (s.d. 0.4%) for D2%. Incidentally, we found a previously unreported decrease in HU, mostly notable for bone type inserts (~34 HU (cortical bone)), at 110 kVp for the DD reconstructed images. The effect was not noted for the standard Qr40 reconstructions. DD has a minimal dosimetric impact in the dose calculations for radiotherapy treatments and could be implemented with existing clinical workflows. Attention should be paid to the HU values for images acquired at 110 kVp (DD algorithm), which warrants further investigation. This is the first paper where DD was evaluated at all available kVp, leading to the incidental discovery of abnormal HU values at 110 kVp for this algorithm.

TU‐G‐211‐01: Uptake Volume Histograms: A Novel Avenue towards Delineation of Biological Target Volumes (BTV) in Radiotherapy

Purpose: To investigate the feasibility of decomposition of differential uptake volume histograms (UVH) derived from FDG‐PET and CT data for uncovering tumor sub‐volumes as a novel approach for defining biological target volumes (BTV) for use in radiotherapy treatment planning. Methods: For a cohort of 27 histopathologically proven non‐small cell lung carcinoma (NSCLC) patients, background uptake values were sampled within contra‐lateral healthy lung over PET slices containing tumor and then scaled by the ratio of tissue densities between healthy lung and tumor derived from CT data. Signal‐to‐background uptake values within volumes of interest encompassing the tumor were scored from which differential uptake volume histograms were constructed. These were subsequently decomposed into the minimum number of analytical functions that yielded acceptable net fits, as assessed by chî2 values. Results: Based on the assumption that each function used to decompose the UVH may correspond to a single sub‐volume comprising the volume of interest sampled, at least four sub‐volumes consistently evolved for our patient population. Furthermore, if crossing points between adjacent functions are interpreted as threshold values that differentiate sub‐volumes, average threshold values between the four sub‐volumes were found to be 0.80±0.21, 1.56±0.48, and 2.96±1.04 for adenocarcinomas, 0.89±0.48, 1.60±0.40, and 2.85±0.75 for large cell carcinomas, and 0.84±0.31, 1.70±0.58, and 3.72±1.68 for squamous cell carcinomas. Conclusions: Our study suggests that FDG‐based PET data could be used to identify biological sub‐volumes within tumor in NSCLC patients. Significant fluctuations in threshold values throughout the patient cohort can be explained as a consequence of large variability in physiological status of the tumor volume for each patient at the time of the PET/CT scan. This further suggests that BTV threshold values may be rather patient‐specific, and could be determined by creation and curve fitting of differential uptake volume histograms on a patient‐specific basis.

Evaluation of MRI Distortion Correction for Use in Radiotherapy Treatment Planning

Aims: The aim of this work was to investigate the distortion correction feature of a dedicated Siemens Espree 1.5T MRI scanner for use in radiotherapy treatment planning. This application of MRI requires a high degree of spatial accuracy to allow fusion of CT and MRI images to improve tumour delineation. Commercially available phantoms did not satisfy requirements as they did not allow testing over a sufficiently large field of view, and needed repositioning between scans to image different planes.

Complexity and accuracy of image registration methods in SPECT-guided radiation therapy

The use of functional imaging in radiotherapy treatment (RT) planning requires accurate co-registration of functional imaging scans to CT scans. We evaluated six methods of image registration for use in SPECT-guided radiotherapy treatment planning. Methods varied in complexity from 3D affine transform based on control points to diffeomorphic demons and level set non-rigid registration. Ten lung cancer patients underwent perfusion SPECT-scans prior to their radiotherapy. CT images from a hybrid SPECT/CT scanner were registered to a planning CT, and then the same transformation was applied to the SPECT images. According to registration evaluation measures computed based on the intensity difference between the registered CT images or based on target registration error, non-rigid registrations provided a higher degree of accuracy than rigid methods. However, due to the irregularities in some of the obtained deformation fields, warping the SPECT using these fields may result in unacceptable changes to the SPECT intensity distribution that would preclude use in RT planning. Moreover, the differences between intensity histograms in the original and registered SPECT image sets were the largest for diffeomorphic demons and level set methods. In conclusion, the use of intensity-based validation measures alone is not sufficient for SPECT/CT registration for RTTP. It was also found that the proper evaluation of image registration requires the use of several accuracy metrics.

On the Estimation of the Location of the Hippocampus in the Context of Hippocampal Avoidance Whole Brain Radiotherapy Treatment Planning

We demonstrate a technique for estimating the location of the hippocampus in MRI and CT images for use in radiotherapy treatment planning, using both rigid and contour based deformable image registration. The automatically generated contours can be subsequently modified for a given patient. By mapping the hippocampi from several patients into a template image set, a population-based average hippocampal atlas was generated. Approximate hippocampal contours can be automatically generated in a given image set by mapping this atlas onto it. The performance and accuracy of several atlases generated in different ways was tested on 10 MRI images and 7 CT images. Auto-contouring based on deformable registration significantly outperformed that based on rigid registration alone, with an average Dice similarity score of 0.62 (range .40-.76) for methods utilizing deformation. Comparable results were achieved in auto-contouring CT images when deformable registration was used, demonstrating that the methodology is robust with respect to imaging modality.

O-(2-(18F)Fluorethyl)-L-Tyrosine Compared to L-(Methyl-11C) Methionine in Positron Emissions Tomography for Tumor Volume Delineation of Gliomas and Metastases

Purpose/Objective: For delineation of gross tumor volume (GTV) in recurrent and previously treated brain tumors conventional diagnostic procedures like computed tomography and MRI are often not sufficient. L-(methyl-11C) methionine positron emissions tomography (MET-PET) was shown to improve the visualization of tumor extension in brain tumors. However the short physical half-life of cabon-11 necessitates an on-site cyclotron for MET-PET examinations. Aim of this study was the evaluation of O-(2-(18F)fluorethyl)-L-tyrosine (18F-FET), a tracer with a longer physical half live of 110 minutes compared to 11C-MET, for its use in radiotherapy treatment planning of gliomas and brain metastases.

Reproducibility of geometric distortion in magnetic resonance imaging based on phantom studies

Background and purpose: Image distortion is one of the major drawbacks of magnetic resonance (MR) imaging for use in radiotherapy treatment planning (RTTP). In this study, the reproducibility of MR imaging distortion was evaluated by repeated phantom measurements. Materials and methods: A grid-pattern acrylic phantom was scanned with a 0.2-Tesla permanent magnetic unit. We repeated a series of scans three times to evaluate the reproducibility of the distortion. In each series, co-ordinates at 432 intersections of the grid were measured for both T1- and T2-weighted spin-echo (SE) pulse sequences. Positional displacements and their variations at the intersections were calculated. Results: Averages of the displacements were distributed between 1.58 and 1.74 mm, and maximum values (MAX) between 12.6 and 15.0 mm. Within 120 mm of the image center, the average values ranged from 0.73 to 0.80 mm, and from 3.4 to 5.0 mm for MAX. The absolute values of the positional variations among three series were distributed between 0.41 and 0.88 mm for average values, and between 1.4 and 4.5 mm for MAX. Conclusions: The positional variations were mostly within 3 pixels, and most of the positional displacements within the radius of 120 mm of the image center were 2 mm or less. Therefore, it will be possible to use this MR system in RTTP under limited situations, although careful applications are required for RTTP of the body. The development of a computer program to correct image distortion is expected.

Analysis and correction of geometric distortions in 1.5 T magnetic resonance images for use in radiotherapy treatment planning

The aim of this study is to investigate and correct for machine- and object-related distortions in magnetic resonance images for use in radiotherapy treatment planning. Patients with brain tumours underwent magnetic resonance imaging (MRI) in the radiotherapy position with the head fixed by a plastic cast in a Perspex localization frame. The imaging experiments were performed on a 1.5 T whole body MRI scanner with 3 mT m-1 maximum gradient capability. Image distortions, caused by static magnetic field inhomogeneity, were studied by varying the direction of the read-out gradient. For purposes of accuracy assessment, external and internal landmarks were indicated. Tubes attached to the cast and in the localization frame served as external landmarks. In the midsagittal plane the brain-sinus sphenoidalis interface, the pituitary gland-sinus sphenoidalis interface, the sphenoid bone and the corpora of the cervical vertebra served as internal landmarks. Landmark displacements as observed in the reversed read-out gradient experiments were analysed with respect to the contributions of machine-related static magnetic field inhomogeneity and susceptibility and chemical shift artifacts. The machine-related static magnetic field inhomogeneity in the midsagittal plane was determined from measurements on a grid phantom. Distortions due to chemical shift effects were estimated for bone marrow containing structures such as the sphenoid bone and the corpora of the cervical vertebra using the values obtained from the literature. Susceptibility-induced magnetic field perturbations are caused by the patient and the localization frame. Magnetic field perturbations were calculated for a typical patient dataset. The midsagittal head image was converted into a susceptibility distribution by segmenting the image into water-equivalent tissues and air; also the Perspex localization frame was included in the susceptibility distribution. Given the susceptibility distribution, the magnetic field was calculated by numerically solving the Maxwell equations for a magnetostatic field. Results were shown as magnetic field perturbations and corresponding spatial distortions of internal and external landmarks. The midsagittal head images were corrected for the machine imperfections (gradient non-linearity and static magnetic field inhomogeneity). The locations of the external landmarks in the frame were also corrected for susceptibility artifacts. The efficacy of the corrections was evaluated for these external landmarks in the localization frame with known geometry. In this study at 1.5 T with read-out gradient strength of 3 mT m-1, machine-related, chemical shift and susceptibility-induced static magnetic held inhomogeneity were of the same order, resulting in spatial distortions between -2 and 2 mm with only negative values for the chemical shift effect. Both the patient and the localization frame proved to perturb the magnetic field. The field perturbations were shown to be additive. In total, static magnetic field inhomogeneity led to spatial distortions ranging from -2 to 4 mm in the direction of the read-out gradient. Non-linearity of the gradients resulted in spatial distortions ranging from -3.5 to 0.5 mm. After correction for the machine imperfections and susceptibility artifacts, the geometric accuracy of the landmarks in the localization frame was better than 1.3 mm.

A simulator-based CT system for radiotherapy treatment planning.

The extensive academic and commercial development of CT in the last decade has resulted in the emergence of sophisticated and expensive instruments capable of carrying out whole-body section scans at high spatial and density resolutions. Where such equipment is installed, it is normally in heavy demand for diagnostic work, and, as a result, only a small proportion of patients who might benefit from a CT scan for the purposes of radiotherapy treatment planning can be accommodated. A need persists, therefore, for less costly and more convenient alternative forms of CT scanner for use in radiotherapy treatment planning, where one of the requirements is merely the delineation of large anatomical features, such as the lungs and spine. High spatial and density resolution are not essential. The clear advantages of using an existing piece of treatment planning equipment, the radiotherapy simulator, to obtain low-resolution cross-sectional images, have been pointed out by a number of authors. Webb and Leach (1980)...