Simulation of scanner- and patient-specific low-dose CT imaging from existing CT images

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.

Weakly-supervised progressive denoising with unpaired CT images

Although low-dose CT imaging has attracted a great interest due to its reduced radiation risk to the patients, it suffers from severe and complex noise. Recent fully-supervised methods have shown impressive performances on CT denoising task. However, they require a huge amount of paired normal-dose and low-dose CT images, which is generally unavailable in real clinical practice. To address this problem, we propose a weakly-supervised denoising framework that generates paired original and noisier CT images from unpaired CT images using a physics-based noise model. Our denoising framework also includes a progressive denoising module that bypasses the challenges of mapping from low-dose to normal-dose CT images directly via progressively compensating the small noise gap. To quantitatively evaluate diagnostic image quality, we present the noise power spectrum and signal detection accuracy, which are well correlated with the visual inspection. The experimental results demonstrate that our method achieves remarkable performances, even superior to fully-supervised CT denoising with respect to the signal detectability. Moreover, our framework increases the flexibility in data collection, allowing us to utilize any unpaired data at any dose levels.

Reproducibility of an airway tapering measurement in computed tomography with application to bronchiectasis

We propose a pipeline to acquire a scalar tapering measurement from the carina to the most distal point of an individual airway visible on computed tomography (CT). We show the applicability of using tapering measurements on clinically acquired data by quantifying the reproducibility of the tapering measure. We generate a spline from the centerline of an airway to measure the area and arclength at contiguous intervals. The tapering measurement is the gradient of the linear regression between area in log space and arclength. The reproducibility of the measure was assessed by analyzing different radiation doses, voxel sizes, and reconstruction kernel on single timepoint and longitudinal CT scans and by evaluating the effect of airway bifurcations. Using 74 airways from 10 CT scans, we show a statistical difference, p=3.4×10−4, in tapering between healthy airways (n=35) and those affected by bronchiectasis (n=39). The difference between the mean of the two populations is 0.011  mm−1, and the difference between the medians of the two populations was 0.006  mm−1. The tapering measurement retained a 95% confidence interval of ±0.005  mm−1 in a simulated 25 mAs scan and retained a 95% confidence of ±0.005  mm−1 on simulated CTs up to 1.5 times the original voxel size. We have established an estimate of the precision of the tapering measurement and estimated the effect on precision of the simulated voxel size and CT scan dose. We recommend that the scanner calibration be undertaken with the phantoms as described, on the specific CT scanner, radiation dose, and reconstruction algorithm that are to be used in any quantitative studies.

Evaluation of an iterative model-based CT reconstruction algorithm by intra-patient comparison of standard and ultra-low-dose examinations

Background The explosive growth of computer tomography (CT) has led to a growing public health concern about patient and population radiation dose. A recently introduced technique for dose reduction, which can be combined with tube-current modulation, over-beam reduction, and organ-specific dose reduction, is iterative reconstruction (IR). Purpose To evaluate the quality, at different radiation dose levels, of three reconstruction algorithms for diagnostics of patients with proven liver metastases under tumor follow-up. Material and Methods A total of 40 thorax-abdomen-pelvis CT examinations acquired from 20 patients in a tumor follow-up were included. All patients were imaged using the standard-dose and a specific low-dose CT protocol. Reconstructed slices were generated by using three different reconstruction algorithms: a classical filtered back projection (FBP); a first-generation iterative noise-reduction algorithm (iDose4); and a next generation model-based IR algorithm (IMR). Results The overall detection of liver lesions tended to be higher with the IMR algorithm than with FBP or iDose4. The IMR dataset at standard dose yielded the highest overall detectability, while the low-dose FBP dataset showed the lowest detectability. For the low-dose protocols, a significantly improved detectability of the liver lesion can be reported compared to FBP or iDose⁴ ( P = 0.01). The radiation dose decreased by an approximate factor of 5 between the standard-dose and the low-dose protocol. Conclusion The latest generation of IR algorithms significantly improved the diagnostic image quality and provided virtually noise-free images for ultra-low-dose CT imaging.

Abdominal imaging dose in radiology and radiotherapy - Phantom point dose measurements, effective dose and secondary cancer risk

PURPOSE

To compare abdominal imaging dose from 3D imaging in radiology (standard/low-dose/dual-energy CT) and radiotherapy (planning CT, kV cone-beam CT (CBCT)).

METHODS

Dose was measured by thermoluminescent dosimeters (TLD's) placed at 86 positions in an anthropomorphic phantom. Point, organ and effective dose were assessed, and secondary cancer risk from imaging was estimated.

RESULTS

Overall dose and mean organ dose comparisons yield significantly lower dose for the optimized radiology protocols (dual-source and care kV), with an average dose of 0.34±0.01 mGy and 0.54±0.01 mGy (average ± standard deviation), respectively. Standard abdominal CT and planning CT involve considerably higher dose (13.58 ± 0.18 mGy and 18.78±0.27 mGy, respectively). The CBCT dose show a dose fall-off near the field edges. On average, dose is reduced as compared with the planning or standard CT (3.79 ± 0.21 mGy for 220° rotation and 7.76 ± 0.37 mGy for 360°), unless the high-quality setting is chosen (20.30 ± 0.96 mGy). The mean organ doses show a similar behavior, which translates to the estimated secondary cancer risk. The modelled risk is in the range between 0.4 cases per million patient years (PY) for the radiological scans dual-energy and care kV, and 300 cases per million PY for the high-quality CBCT setting.

CONCLUSIONS

Modern radiotherapy imaging techniques (while much lower in dose than radiotherapy), involve considerably more dose to the patient than modern radiology techniques. Given the frequency of radiotherapy imaging, a further reduction in radiotherapy imaging dose appears to be both desirable and technically feasible.