Deformable versus rigid registration of PET/CT images for radiation treatment planning of head and neck and lung cancer patients: a retrospective dosimetric comparison

Structure-specific rigid dose accumulation dosimetric analysis of ablative stereotactic MRI-guided adaptive radiation therapy in ultracentral lung lesions

BACKGROUND

Definitive local therapy with stereotactic ablative radiation therapy (SABR) for ultracentral lung lesions is associated with a high risk of toxicity, including treatment related death. Stereotactic MR-guided adaptive radiation therapy (SMART) can overcome many of the challenges associated with SABR treatment of ultracentral lesions.

METHODS

We retrospectively identified 14 consecutive patients who received SMART to ultracentral lung lesions from 10/2019 to 01/2021. Patients had a median distance from the proximal bronchial tree (PBT) of 0.38 cm. Tumors were most often lung primary (64.3%) and HILUS group A (85.7%). A structure-specific rigid registration approach was used for cumulative dose analysis. Kaplan-Meier log-rank analysis was used for clinical outcome data and the Wilcoxon Signed Rank test was used for dosimetric data.

RESULTS

Here we show that SMART dosimetric improvements in favor of delivered plans over predicted non-adapted plans for PBT, with improvements in proximal bronchial tree DMax of 5.7 Gy (p = 0.002) and gross tumor 100% prescription coverage of 7.3% (p = 0.002). The mean estimated follow-up is 17.2 months and 2-year local control and local failure free survival rates are 92.9% and 85.7%, respectively. There are no grade ≥ 3 toxicities.

CONCLUSIONS

SMART has dosimetric advantages and excellent clinical outcomes for ultracentral lung tumors. Daily plan adaptation reliably improves target coverage while simultaneously reducing doses to the proximal airways. These results further characterize the therapeutic window improvements for SMART. Structure-specific rigid dose accumulation dosimetric analysis provides insights that elucidate the dosimetric advantages of SMART more so than per fractional analysis alone.

Xenon-Enhanced Ventilation Computed Tomography for Functional Lung Avoidance Radiation Therapy in Patients With Lung Cancer

PURPOSE

This phase 2 trial aimed to determine whether xenon-enhanced ventilation computed tomography (XeCT)-guided functional-lung-avoidance radiation therapy could reduce the radiation pneumonitis (RP) rate in patients with lung cancer undergoing definitive chemoradiation therapy.

METHODS AND MATERIALS

Functional lung ventilation was measured via pulmonary function testing (PFT) and XeCT. A standard plan (SP) without reference to XeCT and a functional-lung-avoidance plan (fAP) optimized for lowering the radiation dose to the functional lung at the guidance of XeCT were designed. Dosimetric parameters and predicted RP risks modeled by biological evaluation were compared between the 2 plans in a treatment planning system (TPS). All patients received the approved fAP. The primary endpoint was the rate of grade ≥2 RP, and the secondary endpoints were the survival outcomes. The study hypothesis was that fAP could reduce the rate of grade ≥2 RP to 12% compared with a 30% historical rate.

RESULTS

Thirty-six patients were evaluated. Xenon-enhanced total functional lung volumes positively correlated with PFT ventilation parameters (forced vital capacity, P = .012; forced expiratory volume in 1 second, P = .035), whereas they were not correlated with the diffusion capacity parameter. We observed a 17% rate of grade ≥2 RP (6 of 36 patients), which was significantly different (P = .040) compared with the historical control. Compared with the SP, the fAP significantly spared the total ventilated lung, leading to a reduction in predicted grade ≥2 RP (P = .001) by TPS biological evaluation. The median follow-up was 15.2 months. The 1-year local control (LC), disseminated failure-free survival (DFFS), and overall survival (OS) rates were 88%, 66%, and 91%, respectively. The median LC and OS were not reached, and the median DFFS was 24.0 months (95% confidence interval, 15.7-32.3 months).

CONCLUSIONS

This report of XeCT-guided functional-lung-avoidance radiation therapy provided evidence showing its feasibility in clinical practice. Its benefit should be assessed in a broader multicenter trial setting.

Intra- and interobserver assessments of intestinal wall thickness and segmentations from transverse sections of feline abdominal ultrasound images

Measurements of intestinal wall thicknesses from ultrasound imaging (US) are routinely used to support diagnoses of intestinal disorders in cats, however published studies describing observer agreement are currently lacking. The aim of this retrospective, observer agreement study was to quantify inter- and intraobserver repeatability and agreement in the measurement of intestinal wall layer thicknesses and the segmentation of transverse sections of small intestines in US images of 20 cats. Intestinal wall layer thickness measurements of the mucosa, submucosa, muscularis, serosa layer, and total thickness of these layers were performed on five cats with small cell epitheliotropic lymphoma, five with inflammatory bowel disease, and 10 with other conditions. Thickness measurements and the segmentation encompassing the serosa layer were obtained from five observers four times non-sequentially. The average standard deviation in thickness measurements (95% confidence interval) in the mucosa, submucosa, muscularis, serosa, and total thickness were 0.35 (0.07-0.95), 0.24 (0.07-0.52), 0.22 (0.06-0.49), 0.20 (0.05-0.49), and 0.57 (0.11-1.60) mm, respectively. The average intraclass correlation coefficients, which estimates the degree of consistency in thickness measurements and segmentation areas for each observer, ranged from 0.355 to 0.870 and 0.850 to 0.993, respectively. The interclass correlation coefficient, which estimates the degree of consistency when measuring a thickness or segmentation area over all observers ranged from 0.115 to 0.753, and 0.811 to 0.902, respectively. The overall average Dice Coefficient, which estimates the extent of overlap of the segmentations for all observers was 0.957 (0.933 to 0.972). Our results suggest segmentations of small intestines have a higher interobserver agreement than measurements of intestinal wall thicknesses.

Please Place Your Seat in the Full Upright Position: A Technical Framework for Landing Upright Radiation Therapy in the 21st Century

Delivering radiotherapy to patients in an upright position can allow for increased patient comfort, reduction in normal tissue irradiation, or reduction of machine size and complexity. This paper gives an overview of the requirements for the delivery of contemporary arc and modulated radiation therapy to upright patients. We explore i) patient positioning and immobilization, ii) simulation imaging, iii) treatment planning and iv) online setup and image guidance. Treatment chairs have been designed to reproducibly position seated patients for treatment and can be augmented by several existing immobilisation systems or promising emerging technologies such as soft robotics. There are few solutions for acquiring CT images for upright patients, however, cone beam computed tomography (CBCT) scans of upright patients can be produced using the imaging capabilities of standard Linacs combined with an additional patient rotation device. While these images will require corrections to make them appropriate for treatment planning, several methods indicate the viability of this approach. Treatment planning is largely unchanged apart from translating gantry rotation to patient rotation, allowing for a fixed beam with a patient rotating relative to it. Rotation can be provided by a turntable during treatment delivery. Imaging the patient with the same machinery as used in treatment could be advantageous for online plan adaption. While the current focus is using clinical linacs in existing facilities, developments in this area could also extend to lower-cost and mobile linacs and heavy ion therapy.

Machine Learning Applications for Head and Neck Imaging

The head and neck (HN) consists of a large number of vital anatomic structures within a compact area. Imaging plays a central role in the diagnosis and management of major disorders affecting the HN. This article reviews the recent applications of machine learning (ML) in HN imaging with a focus on deep learning approaches. It categorizes ML applications in HN imaging into deep learning and traditional ML applications and provides examples of each category. It also discusses the main challenges facing the successful deployment of ML-based applications in the clinical setting and provides suggestions for addressing these challenges.

Comparison of the Gross Target Volumes Based on Diagnostic PET/CT for Primary Esophageal Cancer

Background

Clinically, many esophageal cancer patients who planned for radiation therapy have already undergone diagnostic Positron-emission tomography/computed tomography (PET/CT) imaging, but it remains unclear whether these imaging results can be used to delineate the gross target volume (GTV) of the primary tumor for thoracic esophageal cancer (EC).

Methods

Seventy-two patients diagnosed with thoracic EC had undergone prior PET/CT for diagnosis and three-dimensional CT (3DCT) for simulation. The GTV_3D was contoured on the 3DCT image without referencing the PET/CT image. The GTV_PET-ref was contoured on the 3DCT image referencing the PET/CT image. The GTV_PET-reg was contoured on the deformed registration image derived from 3DCT and PET/CT. Differences in the position, volume, length, conformity index (CI), and degree of inclusion (DI) among the target volumes were determined.

Results

The centroid distance in the three directions between two different GTVs showed no significant difference (P > 0.05). No significant difference was found among the groups in the tumor volume (P > 0.05). The median DI values of the GTV_PET-reg and GTV_PET-ref in the GTV_3D were 0.82 and 0.86, respectively (P = 0.006). The median CI values of the GTV_3D in the GTV_PET-reg and GTV_PET-ref were 0.68 and 0.72, respectively (P = 0.006).

Conclusions

PET/CT can be used to optimize the definition of the target volume in EC. However, no significant difference was found between the GTVs delineated based on visual referencing or deformable registration whether using the volume or position. So, in the absence of planning PET–CT images, it is also feasible to delineate the GTV of primary thoracic EC with reference to the diagnostic PET–CT image.

Deforming to Best Practice: Key considerations for deformable image registration in radiotherapy

Image registration is a process that underlies many new techniques in radiation oncology - from multimodal imaging and contour propagation in treatment planning to dose accumulation throughout treatment. Deformable image registration (DIR) is a subset of image registration subject to high levels of complexity in process and validation. A need for local guidance to assist in high-quality utilisation and best practice was identified within the Australian community, leading to collaborative activity and workshops. This report communicates the current limitations and best practice advice from early adopters to help guide those implementing DIR in the clinic at this early stage. They are based on the state of image registration applications in radiotherapy in Australia and New Zealand (ANZ), and consensus discussions made at the 'Deforming to Best Practice' workshops in 2018. The current status of clinical application use cases is presented, including multimodal imaging, automatic segmentation, adaptive radiotherapy, retreatment, dose accumulation and response assessment, along with uptake, accuracy and limitations. Key areas of concern and preliminary suggestions for commissioning, quality assurance, education and training, and the use of automation are also reported. Many questions remain, and the radiotherapy community will benefit from continued research in this area. However, DIR is available to clinics and this report is intended to aid departments using or about to use DIR tools now.

Multimodal imaging in radiotherapy: Focus on adaptive therapy and quality control

Improved computer resources in radiation oncology department have greatly facilitated the integration of multimodal imaging into the workflow of radiation therapy. Nowadays, physicians have highly informative imaging modalities of the anatomical region to be treated. These images contribute to the targeting accuracy with the current treatment device, impacting both segmentation or patient's positioning. Additionally, in a constant effort to deliver personalized care, many teams seek to confirm the benefits of adaptive radiotherapy. The published works highlight the importance of registration algorithms, particularly those of elastic or deformable registration necessary to take into account the anatomical evolutions of the patients during the course of their therapy. These algorithms, often considered as "black boxes", tend to be better controlled and understood by physicists and physicians thanks to the generalization of evaluation and validation methods. Given the still significant development of medical imaging techniques, it is foreseeable that multimodal registration needs require more efficient algorithms well integrated within the flow of data.

Comparison of rigid and deformable image registration for nasopharyngeal carcinoma radiotherapy planning with diagnostic position PET/CT

PURPOSE

This observer study aimed to compare rigid image registration (RIR) with deformable image registration (DIR) for diagnostic position (DP) positron emission tomography/computed tomography (PET/CT) images in the delineation of gross tumor volumes (GTVs) in nasopharyngeal carcinoma (NPC) radiotherapy planning.

MATERIALS AND METHODS

Four radiation oncologists individually delineated the GTVs, GTV_RIR, and GTV_DIR, on planning CT (pCT) images registered with DP-PET/CT images using RIR and B-spline-based DIR, respectively. Reference GTVs were independently delineated by all radiation oncologists using radiotherapy position (RP)-PET/CT images. DP- and RP-PET/CT images for 14 patients with NPC were acquired using early and delayed scans, respectively. Dice's similarity coefficient (DSC), mean distance to agreement, and volume agreement with reference GTVs were compared by considering the interobserver variability in reference contours.

RESULTS

The average DSCs for GTV_RIR and GTV_DIR were 0.77 and 0.77, which were acceptable for GTV delineation. There were no statistically significant differences between GTV_RIR and GTV_DIR in all evaluation indexes (p > 0.05). Furthermore, the correlation between neck flexion angle differences and GTV accuracy was not statistically significant (p > 0.05).

CONCLUSION

RIR was a feasible choice compared with the B-spline-based DIR in GTV delineation for NPC under variations of neck flexion angle.

Fully automated convolutional neural network-based affine algorithm improves liver registration and lesion co-localization on hepatobiliary phase T1-weighted MR images

BACKGROUND

Liver alignment between series/exams is challenged by dynamic morphology or variability in patient positioning or motion. Image registration can improve image interpretation and lesion co-localization. We assessed the performance of a convolutional neural network algorithm to register cross-sectional liver imaging series and compared its performance to manual image registration.

METHODS

Three hundred fourteen patients, including internal and external datasets, who underwent gadoxetate disodium-enhanced magnetic resonance imaging for clinical care from 2011 to 2018, were retrospectively selected. Automated registration was applied to all 2,663 within-patient series pairs derived from these datasets. Additionally, 100 within-patient series pairs from the internal dataset were independently manually registered by expert readers. Liver overlap, image correlation, and intra-observation distances for manual versus automated registrations were compared using paired t tests. Influence of patient demographics, imaging characteristics, and liver uptake function was evaluated using univariate and multivariate mixed models.

RESULTS

Compared to the manual, automated registration produced significantly lower intra-observation distance (p < 0.001) and higher liver overlap and image correlation (p < 0.001). Intra-exam automated registration achieved 0.88 mean liver overlap and 0.44 mean image correlation for the internal dataset and 0.91 and 0.41, respectively, for the external dataset. For inter-exam registration, mean overlap was 0.81 and image correlation 0.41. Older age, female sex, greater inter-series time interval, differing uptake, and greater voxel size differences independently reduced automated registration performance (p ≤ 0.020).

CONCLUSION

A fully automated algorithm accurately registered the liver within and between examinations, yielding better liver and focal observation co-localization compared to manual registration.

Deep Learning Deformation Initialization for Rapid Groupwise Registration of Inhomogeneous Image Populations

Groupwise image registration tackles biases that can potentially arise from inappropriate template selection. It typically involves simultaneous registration of a cohort of images to a common space that is not specified a priori. Existing groupwise registration methods are computationally complex and are only effective for image populations without large anatomical variations. In this paper, we propose a deep learning framework to rapidly estimate large deformations between images to significantly reduce structural variability. Specifically, we employ a multi-level graph coarsening method to agglomerate similar images into clusters, each represented by an exemplar image. We then use a deep learning framework to predict the initial deformations between images. Warping with the estimated deformations brings the images closer in the image manifold and their alignment can be further refined using conventional groupwise registration algorithms. We evaluated the effectiveness of our method in groupwise registration of MR brain images and compared it against state-of-the-art groupwise registration methods. Experimental results indicate that deformation initialization enables groupwise registration to converge significantly faster with competitive accuracy, therefore facilitates large-scale imaging studies.

Challenges and Solutions in Multimodal Medical Image Subregion Detection and Registration

BACKGROUND

The automatic detection of common subregions and registration in multimodal functional and structural images is challenging. This article gives an overview of multimodal image registration and the developments and technical issues with automatic detection and registration of subregions of interest in multimodal images.

DISCUSSION

The available knowledge about subregion detection and registration in multimodal images are described in detail. Besides the provision of compact knowledge on subregion detection and registration, the challenges and proposed solutions are also discussed.

CONCLUSION

This article provides research guidelines for the development of automatic detection and registration of subregions of interest in functional and structural images with high accuracy and efficiency.

Comparison of rigid and deformable registration through the respiratory phases of four-dimensional computed tomography image data sets for radiotherapy after breast-conserving surgery

BACKGROUND

The aim of this study was to compare the geometric differences in gross tumor volume (GTV) and surgical clips propagated by rigid image registration (RIR) and deformable image registration (DIR) using a four-dimensional computed tomography (4DCT) image data set for patients treated with boost irradiation or accelerated partial breast irradiation after breast-conserving surgery (BCS).

METHODS

The 4DCT data sets of 44 patients who had undergone BCS were acquired. GTV and selected clips were manually delineated on end-inhalation phase (CT0) and end-exhalation phase (CT50) images of 4DCT data sets. Subsequently, the GTV and selected clips from CT0 images were transformed and propagated to CT50 images using RIR and DIR, respectively. The geometric differences in GTV and surgical clips from DIR were compared with those of RIR.

RESULTS

The mean Dice similarity coefficient (DSC) index was 0.860 ± 0.042 for RIR and 0.870 ± 0.040 for DIR for GTV (P = .000). The three-dimensional distance to the center of mass (COM) of the GTV from RIR was longer than that from DIR (1.22 mm and 1.10 mm, respectively, P = .000). Moreover, in the anterior-posterior direction, displacements from RIR were significantly greater than those from DIR for both GTV (0.70 mm and 0.50 mm, respectively) and selected clips (upper clip, 0.45 mm vs 0.20 mm; inner clip, 0.55 mm vs 0.30 mm; outer clip, 0.40 mm vs 0.20 mm; lower clip, 0.50 mm vs 0.25 mm) (P = .000). However, in the left-right and superior-inferior directions, there were no significant displacement differences between RIR and DIR for GTV and the selected clips (all P > .050).

CONCLUSION

DIR can improve the overlap for GTV registration from CT0 to CT50 images from 4DCT scanning. Furthermore, DIR is superior to RIR in reflecting the displacement of GTV and selected clips in the anterior-posterior direction induced by respiratory movement.

Comparative evaluation of target volumes defined by deformable and rigid registration of diagnostic PET/CT to planning CT in primary esophageal cancer

BACKGROUND

To evaluate the geometrical differences of target volumes propagated by deformable image registration (DIR) and rigid image registration (RIR) to assist target volume delineation between diagnostic Positron emission tomography/computed tomography (PET/CT) and planning CT for primary esophageal cancer (EC).

METHODS

Twenty-five patients with EC sequentially underwent a diagnostic F-fluorodeoxyglucose (F-FDG) PET/CT scan and planning CT simulation. Only 19 patients with maximum standardized uptake value (SUVmax) ≥ 2.0 of the primary volume were available. Gross tumor volumes (GTVs) were delineated using CT and PET display settings. The PET/CT images were then registered with planning CT using MIM software. Subsequently, the PET and CT contours were propagated by RIR and DIR to planning CT. The properties of these volumes were compared.

RESULTS

When GTVCT delineated on CT of PET/CT after both RIR and DIR was compared with GTV contoured on planning CT, significant improvements using DIR were observed in the volume, displacements of the center of mass (COM) in the 3-dimensional (3D) direction, and Dice similarity coefficient (DSC) (P = 0.003; 0.006; 0.014). Although similar improvements were not observed for the same comparison using DIR for propagated PET contours from diagnostic PET/CT to planning CT (P > 0.05), for DSC and displacements of COM in the 3D direction of PET contours, the DIR resulted in the improved volume of a large percentage of patients (73.7%; 68.45%; 63.2%) compared with RIR. For diagnostic CT-based contours or PET contours at SUV2.5 propagated by DIR with planning CT, the DSC and displacements of COM in 3D directions in the distal segment were significantly improved compared to the upper and middle segments (P > 0.05).

CONCLUSION

We observed a trend that deformable registration might improve the overlap for gross target volumes from diagnostic PET/CT to planning CT. The distal EC might benefit more from DIR.

Image fusion and navigation platforms for percutaneous image-guided interventions

Image-guided interventional procedures, particularly image guided biopsy and ablation, serve an important role in the care of the oncology patient. The need for tumor genomic and proteomic profiling, early tumor response assessment and confirmation of early recurrence are common scenarios that may necessitate successful biopsies of targets, including those that are small, anatomically unfavorable or inconspicuous. As image-guided ablation is increasingly incorporated into interventional oncology practice, similar obstacles are posed for the ablation of technically challenging tumor targets. Navigation tools, including image fusion and device tracking, can enable abdominal interventionalists to more accurately target challenging biopsy and ablation targets. Image fusion technologies enable multimodality fusion and real-time co-displays of US, CT, MRI, and PET/CT data, with navigational technologies including electromagnetic tracking, robotic, cone beam CT, optical, and laser guidance of interventional devices. Image fusion and navigational platform technology is reviewed in this article, including the results of studies implementing their use for interventional procedures. Pre-clinical and clinical experiences to date suggest these technologies have the potential to reduce procedure risk, time, and radiation dose to both the patient and the operator, with a valuable role to play for complex image-guided interventions.

Robust Intratumor Partitioning to Identify High-Risk Subregions in Lung Cancer: A Pilot Study

PURPOSE

To develop an intratumor partitioning framework for identifying high-risk subregions from (18)F-fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) imaging and to test whether tumor burden associated with the high-risk subregions is prognostic of outcomes in lung cancer.

METHODS AND MATERIALS

In this institutional review board-approved retrospective study, we analyzed the pretreatment FDG-PET and CT scans of 44 lung cancer patients treated with radiation therapy. A novel, intratumor partitioning method was developed, based on a 2-stage clustering process: first at the patient level, each tumor was over-segmented into many superpixels by k-means clustering of integrated PET and CT images; next, tumor subregions were identified by merging previously defined superpixels via population-level hierarchical clustering. The volume associated with each of the subregions was evaluated using Kaplan-Meier analysis regarding its prognostic capability in predicting overall survival (OS) and out-of-field progression (OFP).

RESULTS

Three spatially distinct subregions were identified within each tumor that were highly robust to uncertainty in PET/CT co-registration. Among these, the volume of the most metabolically active and metabolically heterogeneous solid component of the tumor was predictive of OS and OFP on the entire cohort, with a concordance index or CI of 0.66-0.67. When restricting the analysis to patients with stage III disease (n=32), the same subregion achieved an even higher CI of 0.75 (hazard ratio 3.93, log-rank P=.002) for predicting OS, and a CI of 0.76 (hazard ratio 4.84, log-rank P=.002) for predicting OFP. In comparison, conventional imaging markers, including tumor volume, maximum standardized uptake value, and metabolic tumor volume using threshold of 50% standardized uptake value maximum, were not predictive of OS or OFP, with CI mostly below 0.60 (log-rank P>.05).

CONCLUSION

We propose a robust intratumor partitioning method to identify clinically relevant, high-risk subregions in lung cancer. We envision that this approach will be applicable to identifying useful imaging biomarkers in many cancer types.

Geometrical differences in target volumes based on 18F-fluorodeoxyglucose positron emission tomography/computed tomography and four-dimensional computed tomography maximum intensity projection images of primary thoracic esophageal cancer

The objective of the study was to compare geometrical differences of target volumes based on four-dimensional computed tomography (4DCT) maximum intensity projection (MIP) and 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) images of primary thoracic esophageal cancer for radiation treatment. Twenty-one patients with thoracic esophageal cancer sequentially underwent contrast-enhanced three-dimensional computed tomography (3DCT), 4DCT, and 18F-FDG PET/CT thoracic simulation scans during normal free breathing. The internal gross target volume defined as IGTVMIP was obtained by contouring on MIP images. The gross target volumes based on PET/CT images (GTVPET ) were determined with nine different standardized uptake value (SUV) thresholds and manual contouring: SUV≥2.0, 2.5, 3.0, 3.5 (SUVn); ≥20%, 25%, 30%, 35%, 40% of the maximum (percentages of SUVmax, SUVn%). The differences in volume ratio (VR), conformity index (CI), and degree of inclusion (DI) between IGTVMIP and GTVPET were investigated. The mean centroid distance between GTVPET and IGTVMIP ranged from 4.98 mm to 6.53 mm. The VR ranged from 0.37 to 1.34, being significantly (P<0.05) closest to 1 at SUV2.5 (0.94), SUV20% (1.07), or manual contouring (1.10). The mean CI ranged from 0.34 to 0.58, being significantly closest to 1 (P<0.05) at SUV2.0 (0.55), SUV2.5 (0.56), SUV20% (0.56), SUV25% (0.53), or manual contouring (0.58). The mean DI of GTVPET in IGTVMIP ranged from 0.61 to 0.91, and the mean DI of IGTVMIP in GTVPET ranged from 0.34 to 0.86. The SUV threshold setting of SUV2.5, SUV20% or manual contouring yields the best tumor VR and CI with internal-gross target volume contoured on MIP of 4DCT dataset, but 3DPET/CT and 4DCT MIP could not replace each other for motion encompassing target volume delineation for radiation treatment.