A vessel bifurcation liver CT landmark pair dataset for evaluating deformable image registration algorithms

PURPOSE

Evaluating deformable image registration (DIR) algorithms is vital for enhancing algorithm performance and gaining clinical acceptance. However, there is a notable lack of dependable DIR benchmark datasets for assessing DIR performance except for lung images. To address this gap, we aim to introduce our comprehensive liver computed tomography (CT) DIR landmark dataset library. This dataset is designed for efficient and quantitative evaluation of various DIR methods for liver CTs, paving the way for more accurate and reliable image registration techniques.

ACQUISITION AND VALIDATION METHODS

Forty CT liver image pairs were acquired from several publicly available image archives and authors' institutions under institutional review board (IRB) approval. The images were processed with a semi-automatic procedure to generate landmark pairs: (1) for each case, liver vessels were automatically segmented on one image; (2) landmarks were automatically detected at vessel bifurcations; (3) corresponding landmarks in the second image were placed using two deformable image registration methods to avoid algorithm-specific biases; (4) a comprehensive validation process based on quantitative evaluation and manual assessment was applied to reject outliers and ensure the landmarks' positional accuracy. This workflow resulted in an average of ∼56 landmark pairs per image pair, comprising a total of 2220 landmarks for 40 cases. The general landmarking accuracy of this procedure was evaluated using digital phantoms and manual landmark placement. The landmark pair target registration errors (TRE) on digital phantoms were 0.37 ± 0.26 and 0.55 ± 0.34 mm respectively for the two selected DIR algorithms used in our workflow, with 97% of landmark pairs having TREs below 1.5 mm. The distances from the calculated landmarks to the averaged manual placement were 1.27 ± 0.79 mm.

DATA FORMAT AND USAGE NOTES

All data, including image files and landmark information, are publicly available at Zenodo (https://zenodo.org/records/13738577). Instructions for using our data can be found on our GitHub page at https://github.com/deshanyang/Liver-DIR-QA.

POTENTIAL APPLICATIONS

The landmark dataset generated in this work is the first collection of large-scale liver CT DIR landmarks prepared on real patient images. This dataset can provide researchers with a dense set of ground truth benchmarks for the quantitative evaluation of DIR algorithms within the liver.

Intraoperative liver deformation and organ motion caused by ventilation, laparotomy, and pneumoperitoneum in a porcine model for image-guided liver surgery

BACKGROUND

Image-guidance promises to make complex situations in liver interventions safer. Clinical success is limited by intraoperative organ motion due to ventilation and surgical manipulation. The aim was to assess influence of different ventilatory and operative states on liver motion in an experimental model.

METHODS

Liver motion due to ventilation (expiration, middle, and full inspiration) and operative state (native, laparotomy, and pneumoperitoneum) was assessed in a live porcine model (n = 10). Computed tomography (CT)-scans were taken for each pig for each possible combination of factors. Liver motion was measured by the vectors between predefined landmarks along the hepatic vein tree between CT scans after image segmentation.

RESULTS

Liver position changed significantly with ventilation. Peripheral regions of the liver showed significantly higher motion (maximal Euclidean motion 17.9 ± 2.7 mm) than central regions (maximal Euclidean motion 12.6 ± 2.1 mm, p < 0.001) across all operative states. The total average motion measured 11.6 ± 0.7 mm (p < 0.001). Between the operative states, the position of the liver changed the most from native state to pneumoperitoneum (14.6 ± 0.9 mm, p < 0.001). From native state to laparotomy comparatively, the displacement averaged 9.8 ± 1.2 mm (p < 0.001). With pneumoperitoneum, the breath-dependent liver motion was significantly reduced when compared to other modalities. Liver motion due to ventilation was 7.7 ± 0.6 mm during pneumoperitoneum, 13.9 ± 1.1 mm with laparotomy, and 13.5 ± 1.4 mm in the native state (p < 0.001 in all cases).

CONCLUSIONS

Ventilation and application of pneumoperitoneum caused significant changes in liver position. Liver motion was reduced but clearly measurable during pneumoperitoneum. Intraoperative guidance/navigation systems should therefore account for ventilation and intraoperative changes of liver position and peripheral deformation.

Review and recommendations on deformable image registration uncertainties for radiotherapy applications

Deformable image registration (DIR) is a versatile tool used in many applications in radiotherapy (RT). DIR algorithms have been implemented in many commercial treatment planning systems providing accessible and easy-to-use solutions. However, the geometric uncertainty of DIR can be large and difficult to quantify, resulting in barriers to clinical practice. Currently, there is no agreement in the RT community on how to quantify these uncertainties and determine thresholds that distinguish a good DIR result from a poor one. This review summarises the current literature on sources of DIR uncertainties and their impact on RT applications. Recommendations are provided on how to handle these uncertainties for patient-specific use, commissioning, and research. Recommendations are also provided for developers and vendors to help users to understand DIR uncertainties and make the application of DIR in RT safer and more reliable.

[Status Quo of Surgical Navigation]

Surgical navigation, also referred to as computer-assisted or image-guided surgery, is a technique that employs a variety of methods - such as 3D imaging, tracking systems, specialised software, and robotics to support surgeons during surgical interventions. These emerging technologies aim not only to enhance the accuracy and precision of surgical procedures, but also to enable less invasive approaches, with the objective of reducing complications and improving operative outcomes for patients. By harnessing the integration of emerging digital technologies, surgical navigation holds the promise of assisting complex procedures across various medical disciplines. In recent years, the field of surgical navigation has witnessed significant advances. Abdominal surgical navigation, particularly endoscopy, laparoscopic, and robot-assisted surgery, is currently undergoing a phase of rapid evolution. Emphases include image-guided navigation, instrument tracking, and the potential integration of augmented and mixed reality (AR, MR). This article will comprehensively delve into the latest developments in surgical navigation, spanning state-of-the-art intraoperative technologies like hyperspectral and fluorescent imaging, to the integration of preoperative radiological imaging within the intraoperative setting.

Hepatic fibrosis grading with extracellular volume fraction from iodine mapping in spectral liver CT

PURPOSE

To determine whether hepatic extracellular volume fraction (ECV) obtained from iodine density map (ECV-iodine) can be used to estimate hepatic fibrosis grade and to compare performance with ECV measured using Hounsfield units (ECV-HU).

METHODS

From December 2016 to March 2019, patients who underwent liver resection or biopsy within four weeks after spectral liver CT were included. ECV-iodine and ECV-HU were calculated using the equilibrium phase. Within each of these, comparison of ECVs was made for different fibrosis grades (F0 - 1 vs. F2 - 3 vs. F4) and also for patients with compensated and decompensated cirrhosis. The diagnostic performance of ECVs in detecting clinically significant fibrosis (≥ F2) and cirrhosis (F4) was assessed using ROC analysis.

RESULTS

A total of 144 patients (men = 98, mean age 58.1 ± 11.5 years) were included. The ECV-iodine value was significantly higher in cirrhosis (33.6 ± 6.8 %) than those with F0 - 1 (25.0 ± 3.7 %) or F2 - 3 (28.3 ± 3.4 %, P < 0.001 for all). It was significantly higher in decompensated cirrhosis than those with compensated cirrhosis (36.5 ± 7.2 % vs. 30.7 ± 5.0 %, respectively; P < 0.001). The AUC of ECV-iodine was 0.82 for detecting F2 or above (cut-off value, > 26.9 %) and 0.81 for detecting cirrhosis (cut-off value, > 29 %). ECV-iodine had a significantly higher AUC than ECV-HU for detecting F2 or above (AUC: 0.69, P <  0.001) and cirrhosis (AUC: 0.74, P =  0.04).

CONCLUSIONS

ECV-iodine from spectral CT was able to detect clinically significant hepatic fibrosis and cirrhosis.

Prediction of Local Tumor Progression after Radiofrequency Ablation (RFA) of Hepatocellular Carcinoma by Assessment of Ablative Margin Using Pre-RFA MRI and Post-RFA CT Registration

Objective

To evaluate the clinical impact of using registration software for ablative margin assessment on pre-radiofrequency ablation (RFA) magnetic resonance imaging (MRI) and post-RFA computed tomography (CT) compared with the conventional side-by-side MR-CT visual comparison.

Materials and Methods

In this Institutional Review Board-approved prospective study, 68 patients with 88 hepatocellulcar carcinomas (HCCs) who had undergone pre-RFA MRI were enrolled. Informed consent was obtained from all patients. Pre-RFA MRI and post-RFA CT images were analyzed to evaluate the presence of a sufficient safety margin (≥ 3 mm) in two separate sessions using either side-by-side visual comparison or non-rigid registration software. Patients with an insufficient ablative margin on either one or both methods underwent additional treatment depending on the technical feasibility and patient's condition. Then, ablative margins were re-assessed using both methods. Local tumor progression (LTP) rates were compared between the sufficient and insufficient margin groups in each method.

Results

The two methods showed 14.8% (13/88) discordance in estimating sufficient ablative margins. On registration software-assisted inspection, patients with insufficient ablative margins showed a significantly higher 5-year LTP rate than those with sufficient ablative margins (66.7% vs. 27.0%, p = 0.004). However, classification by visual inspection alone did not reveal a significant difference in 5-year LTP between the two groups (28.6% vs. 30.5%, p = 0.79).

Conclusion

Registration software provided better ablative margin assessment than did visual inspection in patients with HCCs who had undergone pre-RFA MRI and post-RFA CT for prediction of LTP after RFA and may provide more precise risk stratification of those who are treated with RFA.

An evaluation of systematic errors on marker-based registration of computed tomography and magnetic resonance images of the liver

We demonstrated a general method to evaluate systematic errors related to Magnetic Resonance (MR) imaging sequences in marker-based co-registration of MR and Computed Tomography (CT) images, and investigated the effect of MR image quality in the co-registration process using clinical MR and CT protocols for stereotactic ablative body radiotherapy (SABR) planning of the liver. Small systematic errors (under 1.6 mm) were detected, unlikely to be a clinical risk to liver SABR. The least favourable marker configuration was found to be a co-planar arrangement parallel to the transaxial image plane.

Use of image registration and fusion algorithms and techniques in radiotherapy: Report of the AAPM Radiation Therapy Committee Task Group No. 132

Image registration and fusion algorithms exist in almost every software system that creates or uses images in radiotherapy. Most treatment planning systems support some form of image registration and fusion to allow the use of multimodality and time-series image data and even anatomical atlases to assist in target volume and normal tissue delineation. Treatment delivery systems perform registration and fusion between the planning images and the in-room images acquired during the treatment to assist patient positioning. Advanced applications are beginning to support daily dose assessment and enable adaptive radiotherapy using image registration and fusion to propagate contours and accumulate dose between image data taken over the course of therapy to provide up-to-date estimates of anatomical changes and delivered dose. This information aids in the detection of anatomical and functional changes that might elicit changes in the treatment plan or prescription. As the output of the image registration process is always used as the input of another process for planning or delivery, it is important to understand and communicate the uncertainty associated with the software in general and the result of a specific registration. Unfortunately, there is no standard mathematical formalism to perform this for real-world situations where noise, distortion, and complex anatomical variations can occur. Validation of the software systems performance is also complicated by the lack of documentation available from commercial systems leading to use of these systems in undesirable 'black-box' fashion. In view of this situation and the central role that image registration and fusion play in treatment planning and delivery, the Therapy Physics Committee of the American Association of Physicists in Medicine commissioned Task Group 132 to review current approaches and solutions for image registration (both rigid and deformable) in radiotherapy and to provide recommendations for quality assurance and quality control of these clinical processes.

Deformable image registration for tissues with large displacements

Image registration for internal organs and soft tissues is considered extremely challenging due to organ shifts and tissue deformation caused by patients' movements such as respiration and repositioning. In our previous work, we proposed a fast registration method for deformable tissues with small rotations. We extend our method to deformable registration of soft tissues with large displacements. We analyzed the deformation field of the liver by decomposing the deformation into shift, rotation, and pure deformation components and concluded that in many clinical cases, the liver deformation contains large rotations and small deformations. This analysis justified the use of linear elastic theory in our image registration method. We also proposed a region-based neuro-fuzzy transformation model to seamlessly stitch together local affine and local rigid models in different regions. We have performed the experiments on a liver MRI image set and showed the effectiveness of the proposed registration method. We have also compared the performance of the proposed method with the previous method on tissues with large rotations and showed that the proposed method outperformed the previous method when dealing with the combination of pure deformation and large rotations. Validation results show that we can achieve a target registration error of [Formula: see text] and an average centerline distance error of [Formula: see text]. The proposed technique has the potential to significantly improve registration capabilities and the quality of intraoperative image guidance. To the best of our knowledge, this is the first time that the complex displacement of the liver is explicitly separated into local pure deformation and rigid motion.

Improving anatomical mapping of complexly deformed anatomy for external beam radiotherapy and brachytherapy dose accumulation in cervical cancer

PURPOSE

In the treatment of cervical cancer, large anatomical deformations, caused by, e.g., tumor shrinkage, bladder and rectum filling changes, organ sliding, and the presence of the brachytherapy (BT) applicator, prohibit the accumulation of external beam radiotherapy (EBRT) and BT dose distributions. This work proposes a structure-wise registration with vector field integration (SW+VF) to map the largely deformed anatomies between EBRT and BT, paving the way for 3D dose accumulation between EBRT and BT.

METHODS

T2w-MRIs acquired before EBRT and as a part of the MRI-guided BT procedure for 12 cervical cancer patients, along with the manual delineations of the bladder, cervix-uterus, and rectum-sigmoid, were used for this study. A rigid transformation was used to align the bony anatomy in the MRIs. The proposed SW+VF method starts by automatically segmenting features in the area surrounding the delineated organs. Then, each organ and feature pair is registered independently using a feature-based nonrigid registration algorithm developed in-house. Additionally, a background transformation is calculated to account for areas far from all organs and features. In order to obtain one transformation that can be used for dose accumulation, the organ-based, feature-based, and the background transformations are combined into one vector field using a weighted sum, where the contribution of each transformation can be directly controlled by its extent of influence (scope size). The optimal scope sizes for organ-based and feature-based transformations were found by an exhaustive analysis. The anatomical correctness of the mapping was independently validated by measuring the residual distances after transformation for delineated structures inside the cervix-uterus (inner anatomical correctness), and for anatomical landmarks outside the organs in the surrounding region (outer anatomical correctness). The results of the proposed method were compared with the results of the rigid transformation and nonrigid registration of all structures together (AST).

RESULTS

The rigid transformation achieved a good global alignment (mean outer anatomical correctness of 4.3 mm) but failed to align the deformed organs (mean inner anatomical correctness of 22.4 mm). Conversely, the AST registration produced a reasonable alignment for the organs (6.3 mm) but not for the surrounding region (16.9 mm). SW+VF registration achieved the best results for both regions (3.5 and 3.4 mm for the inner and outer anatomical correctness, respectively). All differences were significant (p < 0.02, Wilcoxon rank sum test). Additionally, optimization of the scope sizes determined that the method was robust for a large range of scope size values.

CONCLUSIONS

The novel SW+VF method improved the mapping of large and complex deformations observed between EBRT and BT for cervical cancer patients. Future studies that quantify the mapping error in terms of dose errors are required to test the clinical applicability of dose accumulation by the SW+VF method.

A new method to validate thoracic CT-CT deformable image registration using auto-segmented 3D anatomical landmarks

BACKGROUND

Deformable image registrations are prone to errors in aligning reliable anatomically features. Consequently, identification of registration inaccuracies is important. Particularly thoracic three-dimensional (3D) computed tomography (CT)-CT image registration is challenging due to lack of contrast in lung tissue. This study aims for validation of thoracic CT-CT image registration using auto-segmented anatomically landmarks.

MATERIAL AND METHODS

Five lymphoma patients were CT scanned three times within a period of 18 months, with the initial CT defined as the reference scan. For each patient the two successive CT scans were registered to the reference CT using three different image registration algorithms (Demons, B-spline and Affine). The image registrations were evaluated using auto-segmented anatomical landmarks (bronchial branch points) and Dice Similarity Coefficients (DSC). Deviation of corresponding bronchial landmarks were used to quantify inaccuracies in respect of both misalignment and geometric location within lungs.

RESULTS

The median bronchial branch point deviations were 1.6, 1.1 and 4.2 (mm) for the three tested algorithms (Demons, B-spline and Affine). The maximum deviations (> 15 mm) were found within both Demons and B-spline image registrations. In the upper part of the lungs the median deviation of 1.7 (mm) was significantly different (p < 0.02) relative to the median deviations of 2.0 (mm), found in the middle and lower parts of the lungs. The DSC revealed similar registration discrepancies among the three tested algorithms, with DSC values of 0.96, 0.97 and 0.91, for respectively Demons, B-spline and the Affine algorithms.

CONCLUSION

Bronchial branch points were found useful to validate thoracic CT-CT image registration. Bronchial branch points identified local registration errors > 15 mm in both Demons and B-spline deformable algorithms.

The effectiveness of gadolinium MRI to improve target delineation for radiotherapy in hepatocellular carcinoma: a comparative study of rigid image registration techniques

To achieve consistent target delineation in radiotherapy for hepatocellular carcinoma (HCC), image registration between simulation CT and diagnostic MRI was explored. Twenty patients with advanced HCC were included. The median interval between MRI and CT was 11 days. CT was obtained with shallow free breathing and MRI at exhale phase. On each CT and MRI, the liver and the gross target volume (GTV) were drawn. A rigid image registration was taken according to point information of vascular bifurcation (Method[A]) and pixel information of volume of interest only including the periphery of the liver (Method[B]) and manually drawn liver (Method[C]). In nine cases with an indefinite GTV on CT, a virtual sphere was generated at the epicenter of the GTV. The GTV from CT (VGTV[CT]) and MRI (VGTV[MR]) and the expanded GTV from MRI (V+GTV[MR]) considering geometrical registration error were defined. The underestimation (uncovered V[CT] by V[MR]) and the overestimation (excessive V[MR] by V[CT]) were calculated. Through a paired T-test, the difference between image registration techniques was analyzed. For method[A], the underestimation rates of VGTV[MR] and V+GTV[MR] were 16.4 ± 8.9% and 3.2 ± 3.7%, and the overestimation rates were 16.6 ± 8.7% and 28.4 ± 10.3%, respectively. For VGTV[MR] and V+GTV[MR], the underestimation rates and overestimation rates of method[A] were better than method[C]. The underestimation rates and overestimation rates of the VGTV[MR] were better in method[B] than method[C]. By image registration and additional margin, about 97% of HCC could be covered. Method[A] or method[B] could be recommended according to physician preference.

A strategy for multimodal deformable image registration to integrate PET/MR into radiotherapy treatment planning

BACKGROUND

Combined positron emission tomography (PET)/magnetic resonance imaging (MRI) is highly promising for biologically individualized radiotherapy (RT). Hence, the purpose of this work was to develop an accurate and robust registration strategy to integrate combined PET/MR data into RT treatment planning. Material and methods. Eight patient datasets consisting of an FDG PET/computed tomography (CT) and a subsequently acquired PET/MR of the head and neck (HN) region were available. Registration strategies were developed based on CT and MR data only, whereas the PET components were fused with the resulting deformation field. Following a rigid registration, deformable registration was performed with a transform parametrized by B-splines. Three different optimization metrics were investigated: global mutual information (GMI), GMI combined with a bending energy penalty (BEP) for regularization (GMI+ BEP) and localized mutual information with BEP (LMI+ BEP). Different quantitative registration quality measures were developed, including volumetric overlap and mean distance measures for structures segmented on CT and MR as well as anatomical landmark distances. Moreover, the local registration quality in the tumor region was assessed by the normalized cross correlation (NCC) of the two PET datasets.

RESULTS

LMI+ BEP yielded the most robust and accurate registration results. For GMI, GMI+ BEP and LMI+ BEP, mean landmark distances (standard deviations) were 23.9 mm (15.5 mm), 4.8 mm (4.0 mm) and 3.0 mm (1.0 mm), and mean NCC values (standard deviations) were 0.29 (0.29), 0.84 (0.14) and 0.88 (0.06), respectively.

CONCLUSION

Accurate and robust multimodal deformable image registration of CT and MR in the HN region can be performed using a B-spline parametrized transform and LMI+ BEP as optimization metric. With this strategy, biologically individualized RT based on combined PET/MRI in terms of dose painting is possible.

Reirradiation and stereotactic radiotherapy for tumors in the lung: dose summation and toxicity

PURPOSE

To assess the accumulated dose and the toxicity after reirradiation for tumors in the lung using non-rigid registration.

MATERIAL AND METHODS

Twenty patients with a tumor in the lung were reirradiated with or after stereotactic radiotherapy. The summed dose distribution was calculated using non-rigid registration. All doses were recalculated to an equivalent dose of 2 Gy per fraction (EQD2). The median follow-up time was 12 months (range 2-52).

RESULTS

The median Dmax of the lung in the summed plans was 363 Gy3 (range 123-590). The median accumulated V20 of the lungs was 15.2%. Seven patients had in the heart and the trachea an accumulated dose ≥70 Gy3, with a median D(max) of the heart of 115 Gy3 and 89 Gy3 for the trachea. Eight patients had in the esophagus an accumulated dose ≥70 Gy3, with a median accumulated dose of 85 Gy3. No grade 3-5 toxicity was observed.

CONCLUSION

Reirradiation of the lung with or after stereotactic radiotherapy is feasible to a median Dmax of 363 Gy3 to the lung, as low toxicity was observed.