Patient-specific stopping power calibration for proton therapy planning based on single-detector proton radiography.

A simple robust optimizer has been developed that can produce patient-specific calibration curves to convert x-ray computed tomography (CT) numbers to relative stopping powers (HU-RSPs) for proton therapy treatment planning. The difference between a digitally reconstructed radiograph water-equivalent path length (DRRWEPL) map through the x-ray CT dataset and a proton radiograph (set as the ground truth) is minimized by optimizing the HU-RSP calibration curve. The function of the optimizer is validated with synthetic datasets that contain no noise and its robustness is shown against CT noise. Application of the procedure is then demonstrated on a plastic and a real tissue phantom, with proton radiographs produced using a single detector. The mean errors using generic/optimized calibration curves between the DRRWEPL map and the proton radiograph were 1.8/0.4% for a plastic phantom and -2.1/ - 0.2% for a real tissue phantom. It was then demonstrated that these optimized calibration curves offer a better prediction of the water equivalent path length at a therapeutic depth. We believe that these promising results are suggestive that a single proton radiograph could be used to generate a patient-specific calibration curve as part of the current proton treatment planning workflow

Statistically Deformable 2D/3D Registration for Estimating Post-operative Cup Orientation from a Single Standard AP X-ray Radiograph

The widely used procedure of estimating post-operative cup orientation based on a single standard AP X-ray radiograph is known inaccurate, largely due to the wide variability in individual pelvic orientation relative to X-ray plate. CT-based 2D/3D rigid image registration methods have been developed to measure post-operative cup orientation. Although encouraging results have been reported, their extensive usage in clinical routine is still limited. This may be explained by their requirement of having a CT study of the patient at some point during treatment, which is not available for vast majority of Total Hip Arthroplasty procedures performed nowadays. To address this limitation, this article proposes a statistically deformable 2D/3D registration approach for estimating post-operative cup orientation. No CT study of the patient is required any more. Compared to ground truths established from post-operative CT images, the cup orientations measured by the present technique in a cadaver experiment showed differences of 1.7±1.4° for anteversion and difference of 1.5±1.5° for inclination. When the present technique was evaluated on patients' datasets, differences of 2.2±1.3° and differences of 2.0±0.8° were found for the anteversion and the inclination, respectively. The experimental results, though still preliminary, demonstrated the efficacy of the present approac

Simultaneous object detection and segmentation for patient‐specific markerless lung tumor tracking in simulated radiographs with deep learning

Background Real-time tumor tracking is one motion management method to address motion-induced uncertainty. To date, fiducial markers are often required to reliably track lung tumors with X-ray imaging, which carries risks of complications and leads to prolonged treatment time. A markerless tracking approach is thus desirable. Deep learning-based approaches have shown promise for markerless tracking, but systematic evaluation and procedures to investigate applicability in individual cases are missing. Moreover, few efforts have been made to provide bounding box prediction and mask segmentation simultaneously, which could allow either rigid or deformable multi-leaf collimator tracking. Purpose The purpose of this study was to implement a deep learning-based markerless lung tumor tracking model exploiting patient-specific training which outputs both a bounding box and a mask segmentation simultaneously. We also aimed to compare the two kinds of predictions and to implement a specific procedure to understand the feasibility of markerless tracking on individual cases. Methods We first trained a Retina U-Net baseline model on digitally reconstructed radiographs (DRRs) generated from a public dataset containing 875 CT scans and corresponding lung nodule annotations. Afterwards, we used an independent cohort of 97 lung patients to develop a patient-specific refinement procedure. In order to determine the optimal hyperparameters for automatic patient-specific training, we selected 13 patients for validation where the baseline model predicted a bounding box on planning CT (PCT)-DRR with intersection over union (IoU) with the ground-truth higher than 0.7. The final test set contained the remaining 84 patients with varying PCT-DRR IoU. For each testing patient, the baseline model was refined on the PCT-DRR to generate a patient-specific model, which was then tested on a separate 10-phase 4DCT-DRR to mimic the intrafraction motion during treatment. A template matching algorithm served as benchmark model. The testing results were evaluated by four metrics: the center of mass (COM) error and the Dice similarity coefficient (DSC) for segmentation masks, and the center of box (COB) error and the DSC for bounding box detections. Performance was compared to the benchmark model including statistical testing for significance. Results A PCT-DRR IoU value of 0.2 was shown to be the threshold dividing inconsistent (68%) and consistent (100%) success (defined as mean bounding box DSC > 0.6) of PS models on 4DCT-DRRs. Thirty-seven out of the eighty-four testing cases had a PCT-DRR IoU above 0.2. For these 37 cases, the mean COM error was 2.6 mm, the mean segmentation DSC was 0.78, the mean COB error was 2.7 mm, and the mean box DSC was 0.83. Including the validation cases, the model was applicable to 50 out of 97 patients when using the PCT-DRR IoU threshold of 0.2. The inference time per frame was 170 ms. The model outperformed the benchmark model on all metrics, and the comparison was significant (p 0.2 cases, but not over the undifferentiated 84 testing cases. Conclusions The implemented patient-specific refinement approach based on a pre-trained baseline model was shown to be applicable to markerless tumor tracking in simulated radiographs for lung cases

Improved human observer performance in digital reconstructed radiograph verification in head and neck cancer radiotherapy.

Purpose: Digitally reconstructed radiographs (DRRs) are routinely used as an a priori reference for setup correction in radiotherapy. The spatial resolution of DRRs may be improved to reduce setup error in fractionated radiotherapy treatment protocols. The influence of finer CT slice thickness reconstruction (STR) and resultant increased resolution DRRs on physician setup accuracy was prospectively evaluated. Methods: Four head and neck patient CT-simulation images were acquired and used to create DRR cohorts by varying STRs at 0.5, 1, 2, 2.5, and 3 mm. DRRs were displaced relative to a fixed isocenter using 0–5 mm random shifts in the three cardinal axes. Physician observers reviewed DRRs of varying STRs and displacements and then aligned reference and test DRRs replicating daily KV imaging workflow. A total of 1,064 images were reviewed by four blinded physicians. Observer errors were analyzed using nonparametric statistics (Friedman’s test) to determine whether STR cohorts had detectably different displacement profiles. Post hoc bootstrap resampling was applied to evaluate potential generalizability. Results: The observer-based trial revealed a statistically significant difference between cohort means for observer displacement vector error (p = 0.02) and for Z-axis (p < 0.01). Bootstrap analysis suggests a 15% gain in isocenter translational setup error with reduction of STR from 3 mm to ≤2 mm, though interobserver variance was a larger feature than STR-associated measurement variance. Conclusions: Higher resolution DRRs generated using finer CT scan STR resulted in improved observer performance at shift detection and could decrease operator-dependent geometric error. Ideally, CT STRs ≤2 mm should be utilized for DRR generation in the head and break neck

Optimizing computed tomography : quality assurance, radiation dose and contrast media

Computed tomography (CT) is an important modality in radiology; it enables imaging of the inside of patients without superimposed anatomy. The radiation dose and quality of a CT image are highly dependent on the CT scanner, the scan settings and, if applicable, the timing and dosage of the intravenous contrast media (CM). The aim of this Thesis was to develop tools and insights that help maximize the value of examinations for patients undergoing CT and to reduce its cost in terms of radiation and CM dose. The Thesis consists of five studies. The first paper was on quality control (QC) of CT, which is the foundation for a radiology clinic: it provides trust that the equipment functions as expected. A new method of performing routine QCs was proposed where the concept of key performance indicators (KPI) was introduced, together with a semi-automatic process allowing for daily QCs. During the time of the study, multiple deviations were discovered that would have been difficult to detect using traditional QCs. Performing QCs more frequently facilitates more extensive trend analysis. The second paper was on automatic tube current modulation (ATCM). A phantom and a method for the characterization of ATCM were developed. These allowed for a characterization of CT scanners from the four main CT vendors in Sweden, summarized in four extensive tables showing how the ATCM responds to changes in scan parameters. More specifically, the tables present how changes in scan settings of the localizer radiograph (LR), scan settings of the acquisition, reconstruction parameters and patient miscentering affect the ATCM. The third paper was on radiation dose estimation uncertainties coupled to the patient table. In most commercial radiation dose estimation software packages for CT, the patient table is not included. That effect was previously unknown but could be shown using Monte Carlo (MC) calculations of CT scans performed with and without the patient table. It was shown that by not including the effect from the patient table in radiation dose estimations, the radiation doses are overestimated by 5% to 23%, depending on the scan mode. The fourth paper evaluated whether the standard LR can be replaced by a low-dose spiral scan, a so-called synthetic LR (SLR). Such an SLR can potentially improve ATCM, CM dosage and CT planning. Radiation doses were estimated using MC, the image quality was compared in a prospective study of ten patients and the impact of miscentering was investigated with a phantom measurement of water equivalent diameters. It was shown that the radiation doses and the image quality of SLR and LRs were similar. Estimated water equivalent diameters were more consistent when calculated from the low-dose spiral scan compared to the LRs. It was concluded that it is feasible to replace the traditional LR with an SLR for CT scan planning. The fifth paper was a continued investigation of the low-dose spiral scan, but with focus on intravenous CM dosage planning. Altogether, 238 patients who had undergone PET/CT and ii for whom body metrics (height and weight) had been acquired were retrospectively analyzed, the CT number enhancement of the liver was measured, and body volumes of muscle and fat were calculated using the attenuation correction CT (low-dose spiral scan). Multiple linear regressions showed that for CM dose planning, the body volumes of muscle and fat are better to use than body weight. However, the adjusted R2 values of all the investigated models were low, indicating that responses to CM dosage are complex and require more research. In this PhD Thesis, tools and insights were developed to improve the imaging stability of the CT scan by developing semi-automatic QC protocols and techniques to better estimate patient size and shape potentially reducing variation in image quality, radiation dose and CM enhancement among patients

Three-Dimensional Biplanar Reconstruction of the Scoliotic Spine for Standard Clinical Setup

Tese de Doutoramento. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto. 201

Weight-bearing cone beam CT scans in the foot and ankle

The 3D anatomical complexity of the foot and ankle and the importance of weight-bearing in diagnosis have required the combination of conventional radiographs and medical CT. Conventional plain radiographs (XR) have demonstrated substantial limitations such as perspective, rotational and fan distortion, as well as poor reproducibility of radiographic installations. Conventional CT produces high levels of radiation exposure and does not offer weight-bearing capabilities. The literature investigating biometrics based on 2D XR has inherent limitations due to the technology itself and thereby can focus only on whether measurements are reproducible, when the real question is whether the radiographs are. Low dose weight-bearing cone beam CT (WBCT) combines 3D and weight-bearing as well as 'built in' reliability validated through industry-standardized processes during production and clinical use (quality assurance testing). Research is accumulating to validate measurements based on traditional 2D techniques, and new 3D biometrics are being described and tested. Time- and cost-efficient use in medical imaging will require the use of automatic measurements. Merging WBCT and clinical data will offer new perspectives in terms of research with the help of modern data analysis techniques