Evaluation of an automatic registration-based algorithm for direct measurement of volume change in tumors

Generating missing patient anatomy from partially acquired cone-beam computed tomography images using deep learning: a proof of concept

The patient setup technique currently in practice in most radiotherapy departments utilises on-couch cone-beam computed tomography (CBCT) imaging. Patients are positioned on the treatment couch using visual markers, followed by fine adjustments to the treatment couch position depending on the shift observed between the computed tomography (CT) image acquired for treatment planning and the CBCT image acquired immediately before commencing treatment. The field of view of CBCT images is limited to the size of the kV imager which leads to the acquisition of partial CBCT scans for lateralised tumors. The cone-beam geometry results in high amounts of streaking artifacts and in conjunction with limited anatomical information reduces the registration accuracy between planning CT and the CBCT image. This study proposes a methodology that can improve radiotherapy patient setup CBCT images by removing streaking artifacts and generating the missing patient anatomy with patient-specific precision. This research was split into two separate studies. In Study A, synthetic CBCT (sCBCT) data was created and used to train two machine learning models, one for removing streaking artifacts and the other for generating the missing patient anatomy. In Study B, planning CT and on-couch CBCT data from several patients was used to train a base model, from which a transfer of learning was performed using imagery from a single patient, producing a patient-specific model. The models developed for Study A performed well at removing streaking artifacts and generating the missing anatomy. The outputs yielded in Study B show that the model understands the individual patient and can generate the missing anatomy from partial CBCT datasets. The outputs generated demonstrate that there is utility in the proposed methodology which could improve the patient setup and ultimately lead to improving overall treatment quality.

Evaluation of tissue shrinkage after CT-guided microwave ablation in patients with liver malignancies using Jacobian determinant

PURPOSE

To assess short-term tissue shrinkage in patients with liver malignancies undergoing computed tomography (CT)-guided microwave ablation (MWA) using Jacobian determinant (JD).

MATERIALS AND METHODS

Twenty-nine patients with 29 hepatic malignancies (primary n = 24; metastases n = 5; median tumor diameter 18 mm) referred to CT-guided MWA (single position; 10 min, 100 W) were included in this retrospective IRB-approved study, after exclusion of five patients. Following segmentation of livers and tumors on pre-interventional images, segmentations were registered on post-interventional images. JD mapping was applied to quantify voxelwise tissue volume changes after MWA. Percentual volume changes were evaluated in the ablated tumor, a 5-cm tumor perimeter and in the whole liver and compared in different clinical conditions (tumor entity: primary vs. secondary; tumor location: subcapsular vs. non-subcapsular; tumor volume: >/<6 ml: cirrhosis: yes vs. no; prior chemotherapy: yes vs. no using Shapiro-Wilk, χ² and Wilcoxon rank sum tests, respectively (with p < 0.05 deemed significant).

RESULTS

Tissue volume change was 0.6% in the ablated tumor, 1.6% in the 5-cm perimeter and 0.3% in the whole liver. Shrinkage in the ablated tumor was pronounced in non-subcapsular located tumors, whereas tissue expansion was noted in subcapsular tumors (median -3.5 vs. 1.1%; p = 0.0195). Shrinkage in the whole liver was higher in tumor volumes >6ml, compared with smaller tumors, in which tissue expansion was noted (median -1.0 vs. 2.5%; p = 0.002). Other clinical conditions had no significant influence on the extent of tissue shrinkage (p > 0.05).

CONCLUSION

3D Jacobian analysis shows that hepatic tissue deformation following MWA is most pronounced in a 5-cm area surrounding the treated tumor. Tumor location and tumor volume may have an impact on the extent of tissue shrinkage which may affect estimation of the safety margin.

Liver DCE-MRI registration based on sparse recovery of contrast agent curves

PURPOSE

Dynamic contrast-enhanced MRI (DCE-MRI) registration is a challenging task because of the effect of remarkable intensity changes caused by contrast agent injections. Unrealistic deformation usually occurs by using traditional intensity-based algorithms. To alleviate the effect of contrast agent on registration, we proposed a DCE-MRI registration strategy and investigated the registration performance on the clinical DCE-MRI time series of liver.

METHOD

We reconstructed the time-intensity curves of the contrast agent through sparse representation with a predefined dictionary whose columns were the time-intensity curves with high correlations with respect to a preselected contrast agent curve. After reshaping 1D-reconstructed contrast agent time-intensity curves into a 4D contrast agent time series, we aligned the original time series to the reconstructed contrast agent time series through traditional free-form deformation (FFD) registration scheme combined with a residual complexity (RC) similarity and an iterative registration strategy. This study included the DCE-MRI time series of 20 patients with liver cancer.

RESULTS

Qualitatively, the time-cut images and subtraction images of different registration methods did not obviously differ. Quantitatively, the mean (standard deviation) of temporal intensity smoothness of all the patients achieved 54.910 (18.819), 54.609 (18.859), and 53.391 (19.031) in FFD RC, RDDR, Zhou et al.'s method and the proposed method, respectively. The mean (standard deviation) of changes in the lesion volume were 0.985 (0.041), 0.983 (0.041), 0.981 (0.046), and 0.989 (0.036) in FFD RC, RDDR, Zhou et al.'s method and the proposed method.

CONCLUSION

Our proposed method would be an effective registration strategy for DCE-MRI time series, and its performance was comparable with that of three advanced registration methods.

Evaluating the effectiveness of a single CT method for attenuation correction in stress-rest myocardial perfusion imaging with thallium-201 chloride SPECT

This study aimed to evaluate the effectiveness of a single computed tomography (CT) based attenuation correction method using thallium-201 chloride (²⁰¹TlCl) in stress-rest myocardial perfusion imaging (MPI). The data of 106 patients who underwent MPI with single photon emission computed tomography (SPECT) using ²⁰¹TlCl were retrospectively reviewed. MPI SPECT images were reconstructed using stress SPECT and stress CT (SI_O), rest SPECT and rest CT (RI_O), and rest SPECT and stress CT (RI_A). The accuracy of alignment between the SPECT and CT images was evaluated with normalized cross-correlation (NCC) and visual examination. The summed rest score (SRS) was used to evaluate hypoperfusion at rest; washout rate (WO) was used to assess ischemia; and left ventricular ejection fraction (LVEF) was used to evaluate the left ventricle (LV) function. There was no significant difference in NCC and visual evaluation in all three dimensions. The SRS of both RI_O and RI_A (7.5 ± 7.7 and 7.7 ± 7.6, respectively) did not differ significantly. However, SRSs of RI_O and RI_A showed a strong correlation (r = 0.98). The WO was 39.0 ± 0.98% for both RI_O and RI_A, with a strong correlation between the two values (r = 1.00). LVEF was 61.1 ± 17.4% for RI_O and 61.3 ± 17.4% for RI_A, and a strong correlation was observed between the two values (r = 1.00). In conclusion, the single CT-based attenuation correction method with ²⁰¹TlCl SPECT has an accuracy equivalent to that of the conventional two CT-based attenuation correction method.

Liver DCE-MRI Registration in Manifold Space Based on Robust Principal Component Analysis

A technical challenge in the registration of dynamic contrast-enhanced magnetic resonance (DCE-MR) imaging in the liver is intensity variations caused by contrast agents. Such variations lead to the failure of the traditional intensity-based registration method. To address this problem, a manifold-based registration framework for liver DCE-MR time series is proposed. We assume that liver DCE-MR time series are located on a low-dimensional manifold and determine intrinsic similarities between frames. Based on the obtained manifold, the large deformation of two dissimilar images can be decomposed into a series of small deformations between adjacent images on the manifold through gradual deformation of each frame to the template image along the geodesic path. Furthermore, manifold construction is important in automating the selection of the template image, which is an approximation of the geodesic mean. Robust principal component analysis is performed to separate motion components from intensity changes induced by contrast agents; the components caused by motion are used to guide registration in eliminating the effect of contrast enhancement. Visual inspection and quantitative assessment are further performed on clinical dataset registration. Experiments show that the proposed method effectively reduces movements while preserving the topology of contrast-enhancing structures and provides improved registration performance.

Deformable registration for quantifying longitudinal tumor changes during neoadjuvant chemotherapy

PURPOSE

To evaluate DRAMMS, an attribute-based deformable registration algorithm, compared to other intensity-based algorithms, for longitudinal breast MRI registration, and to show its applicability in quantifying tumor changes over the course of neoadjuvant chemotherapy.

METHODS

Breast magnetic resonance images from 14 women undergoing neoadjuvant chemotherapy were analyzed. The accuracy of DRAMMS versus five intensity-based deformable registration methods was evaluated based on 2,380 landmarks independently annotated by two experts, for the entire image volume, different image subregions, and patient subgroups. The registration method with the smallest landmark error was used to quantify tumor changes, by calculating the Jacobian determinant maps of the registration deformation.

RESULTS

DRAMMS had the smallest landmark errors (6.05 ± 4.86 mm), followed by the intensity-based methods CC-FFD (8.07 ± 3.86 mm), NMI-FFD (8.21 ± 3.81 mm), SSD-FFD (9.46 ± 4.55 mm), Demons (10.76 ± 6.01 mm), and Diffeomorphic Demons (10.82 ± 6.11 mm). Results show that registration accuracy also depends on tumor versus normal tissue regions and different patient subgroups.

CONCLUSIONS

The DRAMMS deformable registration method, driven by attribute-matching and mutual-saliency, can register longitudinal breast magnetic resonance images with a higher accuracy than several intensity-matching methods included in this article. As such, it could be valuable for more accurately quantifying heterogeneous tumor changes as a marker of response to treatment.

Image registration for quantitative parametric response mapping of cancer treatment response

Imaging biomarkers capable of early quantification of tumor response to therapy would provide an opportunity to individualize patient care. Image registration of longitudinal scans provides a method of detecting treatment associated changes within heterogeneous tumors by monitoring alterations in the quantitative value of individual voxels over time, which is unattainable by traditional volumetric-based histogram methods. The concepts involved in the use of image registration for tracking and quantifying breast cancer treatment response using parametric response mapping (PRM), a voxel-based analysis of diffusion-weighted magnetic resonance imaging (DW-MRI) scans, are presented. Application of PRM to breast tumor response detection is described, wherein robust registration solutions for tracking small changes in water diffusivity in breast tumors during therapy are required. Methodologies that employ simulations are presented for measuring expected statistical accuracy of PRM for response assessment. Test-retest clinical scans are used to yield estimates of system noise to indicate significant changes in voxel-based changes in water diffusivity. Overall, registration-based PRM image analysis provides significant opportunities for voxel-based image analysis to provide the required accuracy for early assessment of response to treatment in breast cancer patients receiving neoadjuvant chemotherapy.

Evaluation of the registration of temporal series of contrast-enhanced perfusion magnetic resonance 3D images of the liver

The registration of 2D and 3D images is one of the key tasks in medical image processing and analysis. Accurate registration is a crucial preprocessing step for many tasks; consequently, the evaluation of its accuracy becomes necessary. Unfortunately, this is a difficult task, especially when no golden pattern (true result) is available and when the signal values may have changed between successive images to be registered. This is the case this paper deals with: we have a series of 3D images, magnetic resonance images (MRI) of the liver and adjacent areas that have to be registered. They have been taken while a contrast is diffused through the liver tissue, so intensity of each observed point changes for two reasons: contrast diffusion/perfusion and deformation of the liver (due to body movement and breathing). In this paper, we introduce a new method to automatically compare two or more registration algorithms applied to the same case of a perfusion magnetic resonance dynamic image so that the best of them can be chosen when no ground truth is available. This is done by modeling the function that gives the intensity at a given point as a functional datum, and using statistical techniques to assess its change in comparison with other functions. An example of the application is shown by comparing two parametrizations of a B-spline based registration algorithm. The main result of the proposed method is a suggestive evidence to guide the physician in the process of selecting a registration algorithm, that recommends the algorithm of minimal complexity but still suitable for the case to be analyzed.