Low-dose x-ray CT simulation from an available higher-dose scan

Impacts of Radiation on Metabolism and Vascular Cell Senescence

Significance: This review investigates how radiation therapy (RT) increases the risk of delayed cardiovascular disease (CVD) in cancer survivors. Understanding the mechanisms underlying radiation-induced CVD is essential for developing targeted therapies to mitigate these effects and improve long-term outcomes for patients with cancer. Recent Advances: Recent studies have primarily focused on metabolic alterations induced by irradiation in various cancer cell types. However, there remains a significant knowledge gap regarding the role of chronic metabolic alterations in normal cells, particularly vascular cells, in the progression of CVD after RT. Critical Issues: This review centers on RT-induced metabolic alterations in vascular cells and their contribution to senescence accumulation and chronic inflammation across the vasculature post-RT. We discuss key metabolic pathways, including glycolysis, the tricarboxylic acid cycle, lipid metabolism, glutamine metabolism, and redox metabolism (nicotinamide adenine dinucleotide/Nicotinamide adenine dinucleotide (NADH) and nicotinamide adenine dinucleotide phosphate (NADP+)/NADPH). We further explore the roles of regulatory proteins such as p53, adenosine monophosphate-activated protein kinase, and mammalian target of rapamycin in driving these metabolic dysregulations. The review emphasizes the impact of immune-vascular crosstalk mediated by the senescence-associated secretory phenotype, which perpetuates metabolic dysfunction, enhances chronic inflammation, drives senescence accumulation, and causes vascular damage, ultimately contributing to cardiovascular pathogenesis. Future Directions: Future research should prioritize identifying therapeutic targets within these metabolic pathways or the immune-vascular interactions influenced by RT. Correcting metabolic dysfunction and reducing chronic inflammation through targeted therapies could significantly improve cardiovascular outcomes in cancer survivors. Antioxid. Redox Signal. 00, 000-000.

Dose distributions of proton therapy plans are robust against lowering the resolution of CTs combined with increasing noise

BACKGROUND

Treatment planning in radiation therapy (RT) is performed on image sets acquired with commercial x-ray computed tomography (CT) scanners. Considering an increased frequency of verification scans for adaptive RT and the advent of alternatives to x-ray CTs, there is a need to review the requirements for image sets used in RT planning.

PURPOSE

This study aims to derive the required image quality (IQ) for the computation of the dose distribution in proton therapy (PT) regarding spatial resolution and the combination of spatial resolution and noise. The knowledge gained is used to explore the potential for dose reduction in tomography-guided PT.

METHODS

Mathematical considerations indicate that the required spatial resolution for dose computation is on the scale of the set-up margins fed into the robust optimization. This hypothesis was tested by processing retrospectively 12 clinical PT cases, which reflect a variety of tumor localizations. Image sets were low-pass filtered and were made noisy in a generic manner. Dose distributions on the modified CT scans were computed with a Monte-Carlo dose engine. The similarity of these dose distributions with clinical ones was quantified with the gamma-index (1 mm/1%). The potential reduction of the x-ray exposure compared to the planning CT scan was estimated.

RESULTS

Dose distributions within the irradiated volume were robust against low-pass filtering of the CTs with kernels up to a full-width-at-half-maximum of 4 mm, that is, the gamma pass rate (1 mm/1%) was ≥ $\ge$ 98%. The limit of the filter width was 6 mm for brain tumors and 8 mm for targets in the abdomen. These pass rates remained approximately unchanged if a limited amount of noise was added to the CT image sets. The estimated potential reductions of the x-ray exposure were at least a factor of 20.

CONCLUSIONS

The requirements on IQ in terms of spatial resolution in combination with noise for computing the dose in PT are clearly lower than the IQ of current clinical planning. The results apply, for example, to ultra-low dose x-ray CTs, proton CTs with coarse spatial detection, and attenuation images from the joint reconstruction of time-of-flight PET scans.

Automated estimation of individualized organ-specific dose and noise from clinical CT scans

Objective. Radiation dose and diagnostic image quality are opposing constraints in x-ray computed tomography (CT). Conventional methods do not fully account for organ-level radiation dose and noise when considering radiation risk and clinical task. In this work, we develop a pipeline to generate individualized organ-specific dose and noise at desired dose levels from clinical CT scans.Approach. To estimate organ-specific dose and noise, we compute dose maps, noise maps at desired dose levels and organ segmentations. In our pipeline, dose maps are generated using Monte Carlo simulation. The noise map is obtained by scaling the inserted noise in synthetic low-dose emulation in order to avoid anatomical structures, where the scaling coefficients are empirically calibrated. Organ segmentations are generated by a deep learning-based method (TotalSegmentator). The proposed noise model is evaluated on a clinical dataset of 12 CT scans, a phantom dataset of 3 uniform phantom scans, and a cross-site dataset of 26 scans. The accuracy of deep learning-based segmentations for organ-level dose and noise estimates was tested using a dataset of 41 cases with expert segmentations of six organs: lungs, liver, kidneys, bladder, spleen, and pancreas.Main results. The empirical noise model performs well, with an average RMSE approximately 1.5 HU and an average relative RMSE approximately 5% across different dose levels. The segmentation from TotalSegmentator yielded a mean Dice score of 0.8597 across the six organs (max = 0.9315 in liver, min = 0.6855 in pancreas). The resulting error in organ-level dose and noise estimation was less than 2% for most organs.Significance. The proposed pipeline can output individualized organ-specific dose and noise estimates accurately for personalized protocol evaluation and optimization. It is fully automated and can be scalable to large clinical datasets. This pipeline can be used to optimize image quality for specific organs and thus clinical tasks, without adversely affecting overall radiation dose.

A practical method to simulate realistic reduced-exposure CT images by the addition of computationally generated noise

Computed tomography (CT) scanning protocols should be optimized to minimize the radiation dose necessary for imaging. The addition of computationally generated noise to the CT images facilitates dose reduction. The objective of this study was to develop a noise addition method that reproduces the complexity of the noise texture present in clinical images with directionality that varies over images according to the underlying anatomy, requiring only Digital Imaging and Communications in Medicine (DICOM) images as input data and commonly available phantoms for calibration. The developed method is based on the estimation of projection data by forward projection from images, the addition of Poisson noise, and the reconstruction of new images. The method was validated by applying it to images acquired from cylindrical and thoracic phantoms using source images with exposures up to 49 mAs and target images between 39 and 5 mAs. 2D noise spectra were derived for regions of interest in the generated low-dose images and compared with those from the scanner-acquired low-dose images. The root mean square difference between the standard deviations of noise was 4%, except for very low exposures in peripheral regions of the cylindrical phantom. The noise spectra from the corresponding regions of interest exhibited remarkable agreement, indicating that the complex nature of the noise was reproduced. A practical method for adding noise to CT images was presented, and the magnitudes of noise and spectral content were validated. This method may be used to optimize CT imaging.

An AI-Based Colonic Polyp Classifier for Colorectal Cancer Screening Using Low-Dose Abdominal CT

Among the non-invasive Colorectal cancer (CRC) screening approaches, Computed Tomography Colonography (CTC) and Virtual Colonoscopy (VC), are much more accurate. This work proposes an AI-based polyp detection framework for virtual colonoscopy (VC). Two main steps are addressed in this work: automatic segmentation to isolate the colon region from its background, and automatic polyp detection. Moreover, we evaluate the performance of the proposed framework on low-dose Computed Tomography (CT) scans. We build on our visualization approach, Fly-In (FI), which provides "filet"-like projections of the internal surface of the colon. The performance of the Fly-In approach confirms its ability with helping gastroenterologists, and it holds a great promise for combating CRC. In this work, these 2D projections of FI are fused with the 3D colon representation to generate new synthetic images. The synthetic images are used to train a RetinaNet model to detect polyps. The trained model has a 94% f1-score and 97% sensitivity. Furthermore, we study the effect of dose variation in CT scans on the performance of the the FI approach in polyp visualization. A simulation platform is developed for CTC visualization using FI, for regular CTC and low-dose CTC. This is accomplished using a novel AI restoration algorithm that enhances the Low-Dose CT images so that a 3D colon can be successfully reconstructed and visualized using the FI approach. Three senior board-certified radiologists evaluated the framework for the peak voltages of 30 KV, and the average relative sensitivities of the platform were 92%, whereas the 60 KV peak voltage produced average relative sensitivities of 99.5%.

Low-Dose Computed Tomography Scanning Protocols for Online Adaptive Proton Therapy of Head-and-Neck Cancers

PURPOSE

To evaluate the suitability of low-dose CT protocols for online plan adaptation of head-and-neck patients.

METHODS

We acquired CT scans of a head phantom with protocols corresponding to CT dose index volume CTDI_vol in the range of 4.2-165.9 mGy. The highest value corresponds to the standard protocol used for CT simulations of 10 head-and-neck patients included in the study. The minimum value corresponds to the lowest achievable tube current of the GE Discovery RT scanner used for the study. For each patient and each low-dose protocol, the noise relative to the standard protocol, derived from phantom images, was applied to a virtual CT (vCT). The vCT was obtained from a daily CBCT scan corresponding to the fraction with the largest anatomical changes. We ran an established adaptive workflow twice for each low-dose protocol using a high-quality daily vCT and the corresponding low-dose synthetic vCT. For a relative comparison of the adaptation efficacy, two adapted plans were recalculated in the high-quality vCT and evaluated with the contours obtained through deformable registration of the planning CT. We also evaluated the accuracy of dose calculation in low-dose CT volumes using the standard CT protocol as reference.

RESULTS

The maximum differences in D₉₈ between low-dose protocols and the standard protocol for the high-risk and low-risk CTV were found to be 0.6% and 0.3%, respectively. The difference in OAR sparing was up to 3%. The Dice similarity coefficient between propagated contours obtained with low-dose and standard protocols was above 0.982. The mean 2%/2 mm gamma pass rate for the lowest-dose image, using the standard protocol as reference, was found to be 99.99%.

CONCLUSION

The differences between low-dose protocols and the standard scanning protocol were marginal. Thus, low-dose CT protocols are suitable for online adaptive proton therapy of head-and-neck cancers. As such, considering scanning protocols used in our clinic, the imaging dose associated with online adaption of head-and-neck cancers treated with protons can be reduced by a factor of 40.

Low-dose CT allows for accurate proton therapy dose calculation and plan optimization

Objective. Protons offer a more conformal dose delivery compared to photons, yet they are sensitive to anatomical changes over the course of treatment. To minimize range uncertainties due to anatomical variations, a new CT acquisition at every treatment session would be paramount to enable daily dose calculation and subsequent plan adaptation. However, the series of CT scans results in an additional accumulated patient dose. Reducing CT radiation dose and thereby decreasing the potential risk of radiation exposure to patients is desirable, however, lowering the CT dose results in a lower signal-to-noise ratio and therefore in a reduced quality image. We hypothesized that the signal-to-noise ratio provided by conventional CT protocols is higher than needed for proton dose distribution estimation. In this study, we aim to investigate the effect of CT imaging dose reduction on proton therapy dose calculations and plan optimization. Approach. To verify our hypothesis, a CT dose reduction simulation tool has been developed and validated to simulate lower-dose CT scans from an existing standard-dose scan. The simulated lower-dose CTs were then used for proton dose calculation and plan optimization and the results were compared with those of the standard-dose scan. The same strategy was adopted to investigate the effect of CT dose reduction on water equivalent thickness (WET) calculation to quantify CT noise accumulation during integration along the beam. Main results. The similarity between the dose distributions acquired from the low-dose and standard-dose CTs was evaluated by the dose-volume histogram and the 3D Gamma analysis. The results on an anthropomorphic head phantom and three patient cases indicate that CT imaging dose reduction up to 90% does not have a significant effect on proton dose calculation and plan optimization. The relative error was employed to evaluate the similarity between WET maps and was found to be less than 1% after reducing the CT imaging dose by 90%. Significance. The results suggest the possibility of using low-dose CT for proton therapy dose estimation, since the dose distributions acquired from the standard-dose and low-dose CTs are clinically equivalent.

Low-dose MDCT: evaluation of the impact of systematic tube current reduction and sparse sampling on quantitative paraspinal muscle assessment

Background

Wasting disease entities like cachexia or sarcopenia are associated with a decreasing muscle mass and changing muscle composition. For valid and reliable disease detection and monitoring diagnostic techniques offering quantitative musculature assessment are needed. Multi-detector computed tomography (MDCT) is a broadly available imaging modality allowing for muscle composition analysis. A major disadvantage of using MDCT for muscle composition assessment is the radiation exposure. In this study we evaluated the performance of different methods of radiation dose reduction for paravertebral muscle composition assessment.

Methods

MDCT scans of eighteen subjects (6 males, age: 71.5±15.9 years, and 12 females, age: 71.0±8.9 years) were retrospectively simulated as if they were acquired at 50%, 10%, 5%, and 3% of the original X-ray tube current or number of projections (i.e., sparse sampling). Images were reconstructed with a statistical iterative reconstruction (SIR) algorithm. Paraspinal muscles (psoas and erector spinae muscles) at the level of L4 were segmented in the original-dose images. Segmentations were superimposed on all low-dose scans and muscle density (MD) extracted.

Results

Sparse sampling derived mean MD showed no significant changes (P=0.57 and P=0.22) down to 5% of the original projections in the erector spinae and psoas muscles, respectively. All virtually reduced tube current series showed significantly different (P>0.05) mean MD in the psoas and erector spinae muscles as compared to the original dose except for the images of 5% of the original tube current in the erector spinae muscle.

Conclusions

Our findings demonstrated the possibility of considerable radiation dose reduction using MDCT scans for assessing the composition of the paravertebral musculature. The sparse sampling approach seems to be promising and a potentially superior technique for dose reduction as compared to tube current reduction.

Low-dose CT with metal artifact reduction in arthroplasty imaging: a cadaveric and clinical study

OBJECTIVE

To determine whether a simulated low-dose metal artifact reduction (MAR) CT technique is comparable with a clinical dose MAR technique for shoulder arthroplasty evaluation.

MATERIALS AND METHODS

Two shoulder arthroplasties in cadavers and 25 shoulder arthroplasties in patients were scanned using a clinical dose (140 kVp, 300 qrmAs); cadavers were also scanned at half dose (140 kVp, 150 qrmAs). Images were reconstructed using a MAR CT algorithm at full dose and a noise-insertion algorithm simulating 50% dose reduction. For the actual and simulated half-dose cadaver scans, differences in SD for regions of interest were assessed, and streak artifact near the arthroplasty was graded by 3 blinded readers. Simulated half-dose scans were compared with full-dose scans in patients by measuring differences in implant position and by comparing readers' grades of periprosthetic osteolysis and muscle atrophy.

RESULTS

The mean difference in SD between actual and simulated half-dose methods was 2.42 HU (95% CI [1.4, 3.4]). No differences in streak artifact grades were seen in 13/18 (72.2%) comparisons in cadavers. In patients, differences in implant position measurements were within 1° or 1 mm in 149/150 (99.3%) measurements. The inter-reader agreement rates were nearly identical when readers were using full-dose (77.3% [232/300] for osteolysis and 76.9% [173/225] for muscle atrophy) and simulated half-dose (76.7% [920/1200] for osteolysis and 74.0% [666/900] for muscle atrophy) scans.

CONCLUSION

A simulated half-dose MAR CT technique is comparable both quantitatively and qualitatively with a standard-dose technique for shoulder arthroplasty evaluation, demonstrating that this technique could be used to reduce dose in arthroplasty imaging.

Potentials and caveats of AI in hybrid imaging

State-of-the-art patient management frequently mandates the investigation of both anatomy and physiology of the patients. Hybrid imaging modalities such as the PET/MRI, PET/CT and SPECT/CT have the ability to provide both structural and functional information of the investigated tissues in a single examination. With the introduction of such advanced hardware fusion, new problems arise such as the exceedingly large amount of multi-modality data that requires novel approaches of how to extract a maximum of clinical information from large sets of multi-dimensional imaging data. Artificial intelligence (AI) has emerged as one of the leading technologies that has shown promise in facilitating highly integrative analysis of multi-parametric data. Specifically, the usefulness of AI algorithms in the medical imaging field has been heavily investigated in the realms of (1) image acquisition and reconstruction, (2) post-processing and (3) data mining and modelling. Here, we aim to provide an overview of the challenges encountered in hybrid imaging and discuss how AI algorithms can facilitate potential solutions. In addition, we highlight the pitfalls and challenges in using advanced AI algorithms in the context of hybrid imaging and provide suggestions for building robust AI solutions that enable reproducible and transparent research.