Effects of quantum noise in 4D-CT on deformable image registration and derived ventilation data

Continuum topological derivative - a novel application tool for denoising CT and MRI medical images

BACKGROUND

CT and MRI modalities are important diagnostics tools for exploring the anatomical and tissue properties, respectively of the human beings. Several advancements like HRCT, FLAIR and Propeller have advantages in diagnosing the diseases very accurately, but still have enough space for improvements due to the presence of inherent and instrument noises. In the case of CT and MRI, the quantum mottle and the Gaussian and Rayleigh noises, respectively are still present in their advanced modalities of imaging. This paper addresses the denoising problem with continuum topological derivative technique and proved its trustworthiness based on the comparative study with other traditional filtration methods such as spatial, adaptive, frequency and transformation techniques using measures like visual inspection and performance metrics.

METHODS

This research study focuses on identifying a novel method for denoising by testing different filters on HRCT (High-Resolution Computed Tomography) and MR (Magnetic Resonance) images. The images were acquired from the Image Art Radiological Scan Centre using the SOMATOM CT and SIGNA Explorer (operating at 1.5 Tesla) machines. To compare the performance of the proposed CTD (Continuum Topological Derivative) method, various filters were tested on both HRCT and MR images. The filters tested for comparison were Gaussian (2D convolution operator), Wiener (deconvolution operator), Laplacian and Laplacian diagonal (2nd order partial differential operator), Average, Minimum, and Median (ordinary spatial operators), PMAD (Anisotropic diffusion operator), Kuan (statistical operator), Frost (exponential convolution operator), and HAAR Wavelet (time-frequency operator). The purpose of the study was to evaluate the effectiveness of the CTD method in removing noise compared to the other filters. The performance metrics were analyzed to assess the diligence of noise removal achieved by the CTD method. The primary outcome of the study was the removal of quantum mottle noise in HRCT images, while the secondary outcome focused on removing Gaussian (foreground) and Rayleigh (background) noise in MR images. The study aimed to observe the dynamics of noise removal by examining the values of the performance metrics. In summary, this study aimed to assess the denoising ability of various filters in HRCT and MR images, with the CTD method being the proposed approach. The study evaluated the performance of each filter using specific metrics and compared the results to determine the effectiveness of the CTD method in removing noise from the images.

RESULTS

Based on the calculated performance metric values, it has been observed that the CTD method successfully removed quantum mottle noise in HRCT images and Gaussian as well as Rayleigh noise in MRI. This can be evidenced by the PSNR (Peak Signal-to-Noise Ratio) metric, which consistently exhibited values ranging from 50 to 65 for all the tested images. Additionally, the CTD method demonstrated remarkably low residual values, typically on the order of e-09, which is a distinctive characteristic across all the images. Furthermore, the performance metrics of the CTD method consistently outperformed those of the other tested methods. Consequently, the results of this study have significant implications for the quality, structural similarity, and contrast of HRCT and MR images, enabling clinicians to obtain finer details for diagnostic purposes.

CONCLUSION

Continuum topological derivative algorithm is found to be constructive in removing prominent noises in both CT and MRI images and can serve as a potential tool for recognition of anatomical details in case of diseased and normal ones. The results obtained from this research work are highly inspiring and offer great promise in obtaining accurate diagnostic information for critical cases such as Thoracic Cavity Carina, Brain SPI Globe Lens 4th Ventricle, Brain-Middle Cerebral Artery, Brain-Middle Cerebral Artery and neoplastic lesions. These findings lay the foundation for implementing the proposed CTD technique in routine clinical diagnosis.

Automatic organ completion with image stitching for personalized radiation dosimetry in CT examinations

PURPOSE

Computed tomography (CT) image-based patient-specific voxel-based dosimetry has difficulties complementing missing tissues for organs located partially inside or completely outside the image volume. Previous studies constructed patient-specific whole-body models by rescaling reference phantoms or extending regional CT images with manually adjusted phantoms. This study proposes a methodology for automatic organ completion of regional CT images for CT dosimetry using a stitching approach.

METHODS

Virtual clinical trials were performed by truncating whole-body CT images to generate virtual clinical chest and abdominopelvic CT images. Corresponding anchor images for each patient were selected according to sex and similarity of the axial length and water equivalent diameter of the virtual regional CT images. Automatic image stitching was performed by transformation initialization and iteration, while the stitched CT images and organ atlas were used in GPU-based Geant4 Monte Carlo simulations to generate a radiation dose map and absorbed organ dose. To evaluate the performance of the stitching model in radiation dosimetry, organ mass differences and Jaccard's coefficient of stitched and rescaled anchor images were calculated, and the radiation doses were compared among the corresponding values from the VirtualDose®, original whole-body CT, stitching model, regional CT, registration-based rescaling method, and WED-based rescaling method.

RESULTS

The anatomical accuracy of stitched images was significantly improved. For organs partially inside the image volume, organ dose estimation from the stitching model could be more accurate than that reported in previous studies. The absolute differences in effective dose from the stitched images were 6.55% and 4.81% for chest and abdominopelvic CT scans, respectively.

CONCLUSION

The proposed automatic stitching model partially complements organs inside or outside the CT scan range and provides more accurate anatomical representations for radiation dosimetry than traditional phantom rescaling methods.

Evaluating the accuracy of 4D-CT ventilation imaging: First comparison with Technegas SPECT ventilation

PURPOSE

Computed tomography ventilation imaging (CTVI) is a highly accessible functional lung imaging modality that can unlock the potential for functional avoidance in lung cancer radiation therapy. Previous attempts to validate CTVI against clinical ventilation single-photon emission computed tomography (V-SPECT) have been hindered by radioaerosol clumping artifacts. This work builds on those studies by performing the first comparison of CTVI with ^99m Tc-carbon ('Technegas'), a clinical V-SPECT modality featuring smaller radioaerosol particles with less clumping.

METHODS

Eleven lung cancer radiotherapy patients with early stage (T1/T2N0) disease received treatment planning four-dimensional CT (4DCT) scans paired with Technegas V/Q-SPECT/CT. For each patient, we applied three different CTVI methods. Two of these used deformable image registration (DIR) to quantify breathing-induced lung density changes (CTVI_DIR-HU ), or breathing-induced lung volume changes (CTVI_DIR-Jac ) between the 4DCT exhale/inhale phases. A third method calculated the regional product of air-tissue densities (CTVI_HU ) and did not involve DIR. Corresponding CTVI and V-SPECT scans were compared using the Dice similarity coefficient (DSC) for functional defect and nondefect regions, as well as the Spearman's correlation r computed over the whole lung. The DIR target registration error (TRE) was quantified using both manual and computer-selected anatomic landmarks.

RESULTS

Interestingly, the overall best performing method (CTVI_HU ) did not involve DIR. For nondefect regions, the CTVI_HU , CTVI_DIR-HU , and CTVI_DIR-Jac methods achieved mean DSC values of 0.69, 0.68, and 0.54, respectively. For defect regions, the respective DSC values were moderate: 0.39, 0.33, and 0.44. The Spearman r-values were generally weak: 0.26 for CTVI_HU , 0.18 for CTVI_DIR-HU , and -0.02 for CTVI_DIR-Jac . The spatial accuracy of CTVI was not significantly correlated with TRE, however the DIR accuracy itself was poor with TRE > 3.6 mm on average, potentially indicative of poor quality 4DCT. Q-SPECT scans achieved good correlations with V-SPECT (mean r > 0.6), suggesting that the image quality of Technegas V-SPECT was not a limiting factor in this study.

CONCLUSIONS

We performed a validation of CTVI using clinically available 4DCT and Technegas V/Q-SPECT for 11 lung cancer patients. The results reinforce earlier findings that the spatial accuracy of CTVI exhibits significant interpatient and intermethod variability. We propose that the most likely factor affecting CTVI accuracy was poor image quality of clinical 4DCT.

A Biomechanical Modeling Guided CBCT Estimation Technique

Two-dimensional-to-three-dimensional (2D-3D) deformation has emerged as a new technique to estimate cone-beam computed tomography (CBCT) images. The technique is based on deforming a prior high-quality 3D CT/CBCT image to form a new CBCT image, guided by limited-view 2D projections. The accuracy of this intensity-based technique, however, is often limited in low-contrast image regions with subtle intensity differences. The solved deformation vector fields (DVFs) can also be biomechanically unrealistic. To address these problems, we have developed a biomechanical modeling guided CBCT estimation technique (Bio-CBCT-est) by combining 2D-3D deformation with finite element analysis (FEA)-based biomechanical modeling of anatomical structures. Specifically, Bio-CBCT-est first extracts the 2D-3D deformation-generated displacement vectors at the high-contrast anatomical structure boundaries. The extracted surface deformation fields are subsequently used as the boundary conditions to drive structure-based FEA to correct and fine-tune the overall deformation fields, especially those at low-contrast regions within the structure. The resulting FEA-corrected deformation fields are then fed back into 2D-3D deformation to form an iterative loop, combining the benefits of intensity-based deformation and biomechanical modeling for CBCT estimation. Using eleven lung cancer patient cases, the accuracy of the Bio-CBCT-est technique has been compared to that of the 2D-3D deformation technique and the traditional CBCT reconstruction techniques. The accuracy was evaluated in the image domain, and also in the DVF domain through clinician-tracked lung landmarks.

Ventilation Series Similarity: A Study for Ventilation Calculation Using Deformable Image Registration and 4DCT to Avoid Motion Artifacts

The major problem with ventilation distribution calculations using DIR and 4DCT is the motion artifacts in 4DCT. Quite often not all phases would exhibit mushroom motion artifacts. If the ventilation series similarity is sufficiently robust, the ventilation distribution can be calculated using only the artifact-free phases. This study investigated the ventilation similarity among the data derived from different respiration phases. Fifteen lung cancer cases were analyzed. In each case, DIR was performed between the end-expiration phase and all other phases. Ventilation distributions were then calculated using the deformation matrices. The similarity was compared between the series ventilation distributions. The correlation between the majority phases was reasonably good, with average SCC values between 0.28 and 0.70 for the original data and 0.30 and 0.75 after smoothing. The better correlation between the neighboring phases, with average SCC values between 0.55 and 0.70 for the original data, revealed the nonlinear property of the dynamic ventilation. DSC analysis showed the same trend. To reduce the errors if motion artifacts are present, the phases without serious mushroom artifacts may be used. To minimize the effect of the nonlinearity in dynamic ventilation, the calculation phase should be chosen as close to the end-inspiration as possible.

A robust noise reduction technique for time resolved CT

PURPOSE

To develop a noise reduction method for time resolved CT data, especially those with significant patient motion.

METHODS

PArtial TEmporal Nonlocal (PATEN) means is a technique that uses the redundant information in time-resolved CT data to achieve noise reduction. In this method, partial temporal profiles are used to determine the similarity (or weight) between pixels, and the similarity search makes use of both spatial and temporal information, providing robustness to patient motion. The performance of the PATEN filter was qualitatively and quantitatively evaluated with nine cardiac CT patient data sets and five CT brain perfusion patient data sets. In cardiac CT, PATEN was applied to reduce noise primarily in the reduced-dose phases created with electrocardiographic (ECG) pulsing. CT number accuracy and noise reduction were evaluated in both full-dose phases and reduced-dose phases between filtered backprojection images and PATEN filtered images. In CT brain perfusion, simulated quarter dose data were obtained by adding noise to the raw data of a routine dose scan. PATEN was applied to the simulated low-dose images. Image noise, time-intensity profile accuracy, and perfusion parameter maps were compared among low-dose, low-dose+PATEN filter, and full-dose images. The noise reduction performance of PATEN was compared to a previously proposed noise reduction method, time-intensity profile similarity (TIPS) bilateral filtering.

RESULTS

In 4D cardiac CT, after PATEN filtering, the image noise in the reduced-dose phases was greatly reduced, making anatomical structures easier to identify. The mean decreases in noise values between the original and PATEN images were 11.0% and 53.8% for the full and reduced-dose phases of the cardiac cycle, respectively. TIPS could not achieve effective noise reduction. In CT brain perfusion, PATEN achieved a 55.8%-66.3% decrease in image noise in the low-dose images. The contrast to noise ratio in the axial images was increased and was comparable to the full-dose images. Differentiation of anatomical structure in the PATEN images and corresponding quantitative perfusion parameter maps were preferred by two neuroradiologists compared to the simulated low-dose and TIPS results. The mean perfusion parameters calculated from the PATEN images agreed with those determined from full-dose data to within 12% and 20% for normal and diseased regions.

CONCLUSIONS

In ECG-gated cardiac CT, where the dose had already been reduced by a factor of 5 in the reduced-dose phases, PATEN achieved a 53.8% noise reduction, which decreased the noise level in the reduced-dose phases close to that of the full-dose phases. In CT brain perfusion, a fourfold dose reduction was demonstrated to be achievable by PATEN filtering, which improved quantitative perfusion analysis. PATEN can be used to effectively reduce image noise to improve image quality, even when significant motion occurred between temporal samples.

Sensitivity of Image Features to Noise in Conventional and Respiratory-Gated PET/CT Images of Lung Cancer: Uncorrelated Noise Effects

The effect of noise on image features has yet to be studied in depth. Our objective was to explore how significantly image features are affected by the addition of uncorrelated noise to an image. The signal-to-noise ratio and noise power spectrum were calculated for a positron emission tomography/computed tomography scanner using a Ge-68 phantom. The conventional and respiratory-gated positron emission tomography/computed tomography images of 31 patients with lung cancer were retrospectively examined. Multiple sets of noise images were created for each original image by adding Gaussian noise of varying standard deviation equal to 2.5%, 4.0%, and 6.0% of the maximum intensity for positron emission tomography images and 10, 20, 50, 80, and 120 Hounsfield units for computed tomography images. Image features were extracted from all images, and percentage differences between the original image and the noise image feature values were calculated. These features were then categorized according to the noise sensitivity. The contour-dependent shape descriptors averaged below 4% difference in positron emission tomography and below 13% difference in computed tomography between noise and original images. Gray level size zone matrix features were the most sensitive to uncorrelated noise exhibiting average differences >200% for conventional and respiratory-gated images in computed tomography and 90% in positron emission tomography. Image feature differences increased as the noise level increased for shape, intensity, and gray-level co-occurrence matrix features in positron emission tomography and for gray-level co-occurrence matrix and gray-level size zone matrix features in conventional computed tomography. Investigators should be aware of the noise effects on image features.

Measuring interfraction and intrafraction lung function changes during radiation therapy using four-dimensional cone beam CT ventilation imaging

PURPOSE

Adaptive ventilation guided radiation therapy could minimize the irradiation of healthy lung based on repeat lung ventilation imaging (VI) during treatment. However the efficacy of adaptive ventilation guidance requires that interfraction (e.g., week-to-week), ventilation changes are not washed out by intrafraction (e.g., pre- and postfraction) changes, for example, due to patient breathing variability. The authors hypothesize that patients undergoing lung cancer radiation therapy exhibit larger interfraction ventilation changes compared to intrafraction function changes. To test this, the authors perform the first comparison of interfraction and intrafraction lung VI pairs using four-dimensional cone beam CT ventilation imaging (4D-CBCT VI), a novel technique for functional lung imaging.

METHODS

The authors analyzed a total of 215 4D-CBCT scans acquired for 19 locally advanced non-small cell lung cancer (LA-NSCLC) patients over 4-6 weeks of radiation therapy. This set of 215 scans was sorted into 56 interfraction pairs (including first day scans and each of treatment weeks 2, 4, and 6) and 78 intrafraction pairs (including pre/postfraction scans on the same-day), with some scans appearing in both sets. VIs were obtained from the Jacobian determinant of the transform between the 4D-CBCT end-exhale and end-inhale images after deformable image registration. All VIs were deformably registered to their corresponding planning CT and normalized to account for differences in breathing effort, thus facilitating image comparison in terms of (i) voxelwise Spearman correlations, (ii) mean image differences, and (iii) gamma pass rates for all interfraction and intrafraction VI pairs. For the side of the lung ipsilateral to the tumor, we applied two-sided t-tests to determine whether interfraction VI pairs were more different than intrafraction VI pairs.

RESULTS

The (mean ± standard deviation) Spearman correlation for interfraction VI pairs was r̄(Inter)=0.52±0.25, which was significantly lower than for intrafraction pairs (r̄(Intra)=0.67±0.20, p = 0.0002). Conversely, mean absolute ventilation differences were larger for interfraction pairs than for intrafraction pairs, with |ΔV̄(Inter)|=0.42±0.65 and |ΔV̄(Intra)|=0.32±0.53, respectively (p < 10(-15)). Applying a gamma analysis with ventilation/distance tolerance of 25%/10 mm, we observed mean pass rate of (69% ± 20%) for interfraction VIs, which was significantly lower compared to intrafraction pairs (80% ± 15%, with p ∼ 0.0003). Compared to the first day scans, all patients experienced at least one subsequent change in median ipsilateral ventilation ≥10%. Patients experienced both positive and negative ventilation changes throughout treatment, with the maximum change occurring at different weeks for different patients.

CONCLUSIONS

The authors' data support the hypothesis that interfraction ventilation changes are larger than intrafraction ventilation changes for LA-NSCLC patients over a course of conventional lung cancer radiation therapy. Longitudinal ventilation changes are observed to be highly patient-dependent, supporting a possible role for adaptive ventilation guidance based on repeat 4D-CBCT VIs. We anticipate that future improvement of 4D-CBCT image reconstruction algorithms will improve the capability of 4D-CBCT VI to resolve interfraction ventilation changes.

Evaluation of the tool "Reg Refine" for user-guided deformable image registration

“Reg Refine” is a tool available in the MIM Maestro v6.4.5 platform (www.mimsoftware.com) that allows the user to actively participate in the deformable image registration process. The purpose of this work was to evaluate the efficacy of this tool and investigate strategies for how to apply it effectively. This was done by performing DIR on two publicly available ground‐truth models, the Pixel‐based Breathing Thorax Model (POPI) for lung, and the Deformable Image Registration Evaluation Project (DIREP) for head and neck. Image noise matched in both magnitude and texture to clinical CBCT scans was also added to each model to simulate the use case of CBCT–CT alignment. For lung, the results showed Reg Refine effective at improving registration accuracy when controlled by an expert user within the context of large lung deformation. CBCT noise was also shown to have no effect on DIR performance while using the MIM algorithm for this site. For head and neck, the results showed CBCT noise to have a large effect on the accuracy of registration, specifically for low‐contrast structures such as the brainstem and parotid glands. In these cases, the Reg Refine tool was able to improve the registration accuracy when controlled by an expert user. Several strategies for how to achieve these results have been outlined to assist other users and provide feedback for developers of similar tools.

PACS number(s): 87.44.Qr, 87.57.nj, 87.57.c

Evaluation of the ΔV 4D CT ventilation calculation method using in vivo xenon CT ventilation data and comparison to other methods

Ventilation distribution calculation using 4D CT has shown promising potential in several clinical applications. This study evaluated the direct geometric ventilation calculation method, namely the ΔV method, with xenon-enhanced CT (XeCT) ventilation data from four sheep, and compared it with two other published meth-ods, the Jacobian and the Hounsfield unit (HU) methods. Spearman correlation coefficient (SCC) and Dice similarity coefficient (DSC) were used for the evaluation and comparison. The average SCC with one standard deviation was 0.44 ± 0.13 with a range between 0.29 and 0.61 between the XeCT and ΔV ventilation distributions. The average DSC value for lower 30% ventilation volumes between the XeCT and ΔV ventilation distributions was 0.55 ± 0.07 with a range between 0.48 and 0.63. Ventilation difference introduced by deformable image registration errors improved with smoothing. In conclusion, ventilation distributions generated using ΔV-4D CT and deformable image registration are in reasonably agreement with the in vivo XeCT measured ventilation distribution.