The z-sbDBA, a new concept for a dynamic sheet-based fluence field modulator in x-ray CT

Patient-specific radiation risk-based tube current modulation for diagnostic CT

PURPOSE

Modern CT scanners use automatic exposure control (AEC) techniques, such as tube current modulation (TCM), to reduce dose delivered to patients while maintaining image quality. In contrast to conventional approaches that minimize the tube current time product of the CT scan, referred to as mAsTCM in the following, we herein propose a new method referred to as riskTCM which aims at reducing the radiation risk to the patient by taking into account the specific radiation risk of every dose-sensitive organ.

METHODS

For current mAsTCM implementations, the mAs-product is used as a surrogate for the patient dose. Thus they do not take into account the varying dose sensitivity of different organs. Our riskTCM framework assumes that a coarse CT reconstruction, an organ segmentation and an estimation of the dose distribution can be provided in real time, e.g. by applying machine learning techniques. Using this information riskTCM determines a tube current curve that minimizes a patient risk measure, e.g. the effective dose, while keeping the image quality constant. We retrospectively applied riskTCM to 20 patients covering all relevant anatomical regions and tube voltages from 70 kV to 150 kV. The potential reduction of effective dose at same image noise is evaluated as a figure of merit and compared to mAsTCM and to a situation with a constant tube current referred to as noTCM.

RESULTS

Anatomical regions like the neck, thorax, abdomen and the pelvis benefit from the proposed riskTCM. On average, a reduction of effective dose of about 23 % for the thorax, 31 % for the abdomen, 24 % for the pelvis, and 27% for the neck have been evaluated compared to today's state-of-the-art mAsTCM. For the head, the resulting reduction of effective dose is lower, about 13 % on average compared to mAsTCM.

CONCLUSIONS

With a risk-minimizing tube current modulation, significant higher reduction of effective dose compared to mAs-minimizing tube current modulation is possible. This article is protected by copyright. All rights reserved.

Performance of a deep learning-based CT image denoising method: Generalizability over dose, reconstruction kernel and slice thickness

PURPOSE

Deep learning (DL) is rapidly finding applications in low-dose CT image denoising. While having the potential to improve image quality (IQ) over the filtered back projection method (FBP) and produce images quickly, performance generalizability of the data-driven DL methods is not fully understood yet. The main purpose of this work is to investigate the performance generalizability of a low-dose CT image denoising neural network in data acquired under different scan conditions, particularly relating to these three parameters: reconstruction kernel, slice thickness and dose (noise) level. A secondary goal is to identify any underlying data property associated with the CT scan settings that might help predict the generalizability of the denoising network.

METHODS

We select the residual encoder-decoder convolutional neural network (REDCNN) as an example of a low-dose CT image denoising technique in this work. To study how the network generalizes on the three imaging parameters, we grouped the CT volumes in the Low-Dose Grand Challenge (LDGC) data into three pairs of training datasets according to their imaging parameters, changing only one parameter in each pair. We trained REDCNN with them to obtain six denoising models. We test each denoising model on datasets of matching and mismatching parameters with respect to its training sets regarding dose, reconstruction kernel and slice thickness, respectively, to evaluate the denoising performance changes. Denoising performances are evaluated on patient scans, simulated phantom scans and physical phantom scans using IQ metrics including mean squared error (MSE), contrast-dependent modulation transfer function (MTF), pixel-level noise power spectrum (pNPS) and low-contrast lesion detectability (LCD).

RESULTS

REDCNN had larger MSE when the testing data was different from the training data in reconstruction kernel, but no significant MSE difference when varying slice thickness in the testing data. REDCNN trained with quarter-dose data had slightly worse MSE in denoising higher-dose images than that trained with mixed-dose data (17-80%). The MTF tests showed that REDCNN trained with the two reconstruction kernels and slice thicknesses yielded images of similar image resolution. However, REDCNN trained with mixed-dose data preserved the low-contrast resolution better compared to REDCNN trained with quarter-dose data. In the pNPS test, it was found that REDCNN trained with smooth-kernel data could not remove high-frequency noise in the test data of sharp kernel, possibly because the lack of high-frequency noise in the smooth-kernel data limited the ability of the trained model in removing high-frequency noise. Finally, in the LCD test, REDCNN improved the lesion detectability over the original FBP images regardless of whether the training and testing data had matching reconstruction kernels.

CONCLUSIONS

REDCNN is observed to be poorly generalizable between reconstruction kernels, more robust in denoising data of arbitrary dose levels when trained with mixed-dose data, and not highly sensitive to slice thickness. It is known that reconstruction kernel affects the in-plane pNPS shape of a CT image whereas slice thickness and dose level do not, so it is possible that the generalizability performance of this CT image denoising network highly correlates to the pNPS similarity between the testing and training data. This article is protected by copyright. All rights reserved.

On the potential of ROI imaging in x-ray CT - A comparison of novel dynamic beam attenuators with current technology

PURPOSE

In this work, we explore the potential of region-of-interest (ROI) imaging in x-ray computed tomography (CT). Using two dynamic beam attenuator (DBA) concepts for fluence field modulation (FFM) previously developed, we investigate and evaluate the potential dose savings in comparison with current FFM technology.

METHODS

ROI imaging is a special application of FFM where the bulk of x-ray radiation is propagated toward a certain anatomical target (ROI), specified by the imaging task, while the surrounding tissue is spared from radiation. We introduce a criterion suitable to quantitatively describe the balance between image quality inside an ROI and total radiation dose with respect to a given ROI imaging task. It accounts for the mean image variance at the ROI and the effective patient dose calculated from Monte Carlo simulations. The criterion is further used to compile task-specific DBA trajectories determining the primary x-ray fluence, and eventually used for comparing different FFM techniques, namely the sheet-based dynamic beam attenuator (sbDBA), the z-aligned sbDBA (z-sbDBA), and an adjustable static operation mode of the z-sbDBA. Furthermore, two static bowtie filters and the influence of tube current modulation (TCM) are included in the comparison.

RESULTS

Our findings demonstrate by simulations that the presented trajectory optimization method determines reasonable DBA trajectories. The influence of TCM is strongly depending on the imaging task. The narrow bowtie filter allows for dose reductions of about 10% compared to the regular bowtie filter in the considered ROI imaging tasks. The DBAs are shown to realize substantially larger dose reductions. In our cardiac imaging scenario, the DBAs can reduce the effective dose by about 30% (z-sbDBA) or 60% (sbDBA). We can further verify that the noise characteristics are not adversely affected by the DBAs.

CONCLUSION

Our research demonstrates that ROI imaging using the presented DBA concepts is a promising technique toward a more patient- and task-specific CT imaging requiring lower radiation dose. Both the sbDBA and the z-sbDBA are potential technical solutions for realizing ROI imaging in x-ray CT.