Grating-based x-ray dark-field CT for lung cancer diagnosis in mice

BACKGROUND

The low absorption of x-rays in lung tissue and the poor resolution of conventional computed tomography (CT) limits its use to detect lung disease. However, x-ray dark-field imaging can sense the scattered x-rays deflected by the structures being imaged. This technique can facilitate the detection of small alveolar lesions that would be difficult to detect with conventional CT. Therefore, it may provide an alternative imaging modality to diagnose lung disease at an early stage.

METHODS

Eight mice were inoculated with lung cancers simultaneously. Each time two mice were scanned using a grating-based dark-field CT on days 4, 8, 12, and 16 after the introduction of the cancer cells. The detectability index was calculated between nodules and healthy parenchyma for both attenuation and dark-field modalities. High-resolution micro-CT and pathological examinations were used to crosscheck and validate our results. Paired t-test was used for comparing the ability of dark-field and attenuation modalities in pulmonary nodule detection.

RESULTS

The nodules were shown as a signal decrease in the dark-field modality and a signal increase in the attenuation modality. The number of nodules increased from day 8 to day 16, indicating disease progression. The detectability indices of dark-field modality were higher than those of attenuation modality (p = 0.025).

CONCLUSIONS

Compared with the standard attenuation CT, the dark-field CT improved the detection of lung nodules.

RELEVANCE STATEMENT

Dark-field CT has a higher detectability index than conventional attenuation CT in lung nodule detection. This technique could improve the early diagnosis of lung cancer.

KEY POINTS

• Lung cancer progression was observed using x-ray dark-field CT. • Dark-field modality complements with attenuation modality in lung nodule detection. • Dark-field modality showed a detectability index higher than that attenuation in nodule detection.

Model-based deep CNN-regularized reconstruction for digital breast tomosynthesis with a task-based CNN image assessment approach

Objective. Digital breast tomosynthesis (DBT) is a quasi-three-dimensional breast imaging modality that improves breast cancer screening and diagnosis because it reduces fibroglandular tissue overlap compared with 2D mammography. However, DBT suffers from noise and blur problems that can lower the detectability of subtle signs of cancers such as microcalcifications (MCs). Our goal is to improve the image quality of DBT in terms of image noise and MC conspicuity.Approach. We proposed a model-based deep convolutional neural network (deep CNN or DCNN) regularized reconstruction (MDR) for DBT. It combined a model-based iterative reconstruction (MBIR) method that models the detector blur and correlated noise of the DBT system and the learning-based DCNN denoiser using the regularization-by-denoising framework. To facilitate the task-based image quality assessment, we also proposed two DCNN tools for image evaluation: a noise estimator (CNN-NE) trained to estimate the root-mean-square (RMS) noise of the images, and an MC classifier (CNN-MC) as a DCNN model observer to evaluate the detectability of clustered MCs in human subject DBTs.Main results. We demonstrated the efficacies of CNN-NE and CNN-MC on a set of physical phantom DBTs. The MDR method achieved low RMS noise and the highest detection area under the receiver operating characteristic curve (AUC) rankings evaluated by CNN-NE and CNN-MC among the reconstruction methods studied on an independent test set of human subject DBTs.Significance. The CNN-NE and CNN-MC may serve as a cost-effective surrogate for human observers to provide task-specific metrics for image quality comparisons. The proposed reconstruction method shows the promise of combining physics-based MBIR and learning-based DCNNs for DBT image reconstruction, which may potentially lead to lower dose and higher sensitivity and specificity for MC detection in breast cancer screening and diagnosis.

X-ray imaging detector for radiological applications adapted to the context and requirements of low- and middle-income countries

This paper describes the development of a novel medical x-ray imaging system adapted to the needs and constraints of low- and middle-income countries. The developed system is based on an indirect conversion chain: a scintillator plate produces visible light when excited by the x rays, and then, a calibrated multi-camera architecture converts the visible light from the scintillator into a set of digital images. The partial images are then unwarped, enhanced, and stitched through parallel field programmable gate array processing units and specialized software. All the detector components were carefully selected focusing on optimizing the system's image quality, robustness, cost-effectiveness, and capability to work in harsh tropical environments. With this aim, different customized and commercial components were characterized. The resulting detector can generate high quality medical diagnostic images with detective quantum efficiency levels up to 60% (@2.34 μGy), even under harsh environments, i.e., 60 °C and 98% humidity.

Performance of Spectral Photon-Counting Coronary CT Angiography and Comparison with Energy-Integrating-Detector CT: Objective Assessment with Model Observer

AIMS

To evaluate spectral photon-counting CT's (SPCCT) objective image quality characteristics in vitro, compared with standard-of-care energy-integrating-detector (EID) CT.

METHODS

We scanned a thorax phantom with a coronary artery module at 10 mGy on a prototype SPCCT and a clinical dual-layer EID-CT under various conditions of simulated patient size (small, medium, and large). We used filtered back-projection with a soft-tissue kernel. We assessed noise and contrast-dependent spatial resolution with noise power spectra (NPS) and target transfer functions (TTF), respectively. Detectability indices (d') of simulated non-calcified and lipid-rich atherosclerotic plaques were computed using the non-pre-whitening with eye filter model observer.

RESULTS

SPCCT provided lower noise magnitude (9-38% lower NPS amplitude) and higher noise frequency peaks (sharper noise texture). Furthermore, SPCCT provided consistently higher spatial resolution (30-33% better TTF10). In the detectability analysis, SPCCT outperformed EID-CT in all investigated conditions, providing superior d'. SPCCT reached almost perfect detectability (AUC ≈ 95%) for simulated 0.5-mm-thick non-calcified plaques (for large-sized patients), whereas EID-CT had lower d' (AUC ≈ 75%). For lipid-rich atherosclerotic plaques, SPCCT achieved 85% AUC vs. 77.5% with EID-CT.

CONCLUSIONS

SPCCT outperformed EID-CT in detecting simulated coronary atherosclerosis and might enhance diagnostic accuracy by providing lower noise magnitude, markedly improved spatial resolution, and superior lipid core detectability.

A novel method to assess the spatiotemporal image quality in fluoroscopy

Objectives. The planar formulation of the noise equivalent quanta (NEQ) and detective quantum efficiency (DQE) used to assess the image quality of projection images does not deal with the influence of temporal resolution on signal blurring and image noise. These metrics require correction factors based on temporal resolution when used for dynamic imaging systems such as fluoroscopy. Additionally, the standard NEQ and detector DQE are determined on pre-processed images in scatter-free conditions for effective energies produced by additional aluminium or copper filters that are not representative of clinical fluoroscopic procedures. In this work, we developed a method to measure 'frame NEQ' and 'frame system DQE' which include the temporal frequency bandwidth and consider the anti-scatter grid, the detector and the image processing procedures for beam qualities with scatter fractions representative of clinical use.Approach. We used a solid water phantom to simulate a patient and a thin copper disc to measure the spatial resolution. The copper disc, set in uniform rectilinear motion in the image plane, assessed the temporal resolution. These new metrics were tested on two fluoroscopy systems, a C-arm and a floor-mounted cardiology, for multiple parameters: phantom thicknesses from 5 to 20 cm, frame rates from 3 to 30 fps, spatial and temporal image processing of different weights.Main results.The frame NEQ correctly described the image quality for different scatter conditions, temporal resolutions and image processing techniques. The frame system DQE varied between 0.38 and 0.65 within the different beam and scatter conditions, and correctly mitigated the influence of spatial and temporal image processing.Significance.This study introduces and validates an unbiased formulation of in-plane NEQ and system DQE to assess the spatiotemporal image quality of fluoroscopy systems.

Assessment of task-based image quality for abdominal CT protocols linked with national diagnostic reference levels

Objectives

To assess task-based image quality for two abdominal protocols on various CT scanners. To establish a relationship between diagnostic reference levels (DRLs) and task-based image quality.

Methods

A protocol for the detection of focal liver lesions was used to scan an anthropomorphic abdominal phantom containing 8- and 5-mm low-contrast (20 HU) spheres at five CTDI_vol levels (4, 8, 12, 16, and 20 mGy) on 12 CTs. Another phantom with high-contrast calcium targets (200 HU) was scanned at 2, 4, 6, 10, and 15 mGy using a renal stones protocol on the same CTs. To assess the detectability, a channelized Hotelling observer was used for low-contrast targets and a non-prewhitening observer with an eye filter was used for high contrast targets. The area under the ROC curve and signal to noise ratio were used as figures of merit.

Results

For the detection of 8-mm spheres, the image quality reached a high level (mean AUC over all CTs higher than 0.95) at 11 mGy. For the detection of 5-mm spheres, the AUC never reached a high level of image quality. Variability between CTs was found, especially at low dose levels. For the search of renal stones, the AUC was nearly maximal even for the lowest dose level.

Conclusions

Comparable task-based image quality cannot be reached at the same dose level on all CT scanners. This variability implies the need for scanner-specific dose optimization.

Key Points

• There is an image quality variability for subtle low-contrast lesion detection in the clinically used dose range.

• Diagnostic reference levels were linked with task-based image quality metrics.

• There is a need for specific dose optimization for each CT scanner and clinical protocol.

Detective quantum efficiency of intensified CMOS cameras for Cherenkov imaging in radiotherapy

In this study the metric of detective quantum efficiency (DQE) was applied to Cherenkov imaging systems for the first time, and results were compared for different detector hardware, gain levels and with imaging processing for noise suppression. Intensified complementary metal oxide semiconductor cameras using different image intensifier designs (Gen3 and Gen2+) were used to image Cherenkov emission from a tissue phantom in order to measure the modulation transfer function (MTF) and noise power spectrum (NPS) of the systems. These parameters were used to calculate the DQE for varying acquisition settings and image processing steps. MTF curves indicated that the Gen3 system had superior contrast transfer and spatial resolution than the Gen2+ system, with [Formula: see text] values of 0.52 mm-1 and 0.31 mm-1, respectively. With median filtering for noise suppression, these values decreased to 0.50 mm-1 and 0.26 mm-1. The maximum NPS values for the Gen3 and Gen2+ systems at high gain were 1.3 × 106 mm2 and 9.1 × 104 mm2 respectively, representing a 14x decrease in noise power for the Gen2+ system. Both systems exhibited increased NPS intensity with increasing gain, while median filtering lowered the NPS. The DQE of each system increased with increasing gain, and at the maximum gain levels the Gen3 system had a low-frequency DQE of 0.31%, while the Gen2+ system had a value of 1.44%. However, at a higher frequency of 0.4 mm-1, these values became 0.54% and 0.03%. Filtering improved DQE for the Gen3 system and reduced DQE for the Gen2+ system and had a mix of detrimental and beneficial qualitative effects by decreasing the spatial resolution and sharpness but also substantially lowering noise. This methodology for DQE measurement allowed for quantitative comparison between Cherenkov imaging cameras and improvements to their sensitivity, and yielded the first formal assessment of Cherenkov image formation efficiency.

Task-Based Model Observer Assessment of A Partial Model-Based Iterative Reconstruction Algorithm in Thoracic Oncologic Multidetector CT

To investigate the impact of a partial model-based iterative reconstruction (ASiR-V) on image quality in thoracic oncologic multidetector computed tomography (MDCT), using human and mathematical model observers. Twenty cancer patients examined with regular-dose thoracic-abdominal-pelvic MDCT were retrospectively included. Thoracic images reconstructed using a sharp kernel and filtered back-projection (reference) or ASiR-V (0-100%, 20% increments; follow-up) were analysed by three thoracic radiologists. Advanced quantitative physical metrics, including detectability indexes of simulated 4-mm-diameter solid non-calcified nodules and ground-glass opacities, were computed at regular and reduced doses using a custom-designed phantom. All three radiologists preferred higher ASiR-V levels (best = 80%). Increasing ASiR-V substantially decreased noise magnitude, with slight changes in noise texture. For high-contrast objects, changing the ASiR-V level had no major effect on spatial resolution; whereas for lower-contrast objects, increasing ASiR-V substantially decreased spatial resolution, more markedly at reduced dose. For both high- and lower-contrast pulmonary lesions, detectability remained excellent, regardless of ASiR-V and dose levels, and increased significantly with increasing ASiR-V levels (all p < 0.001). While high ASiR-V levels (80%) are recommended to detect solid non-calcified nodules and ground-glass opacities in regular-dose thoracic oncologic MDCT, care must be taken because, for lower-contrast pulmonary lesions, high ASiR-V levels slightly change noise texture and substantially decrease spatial resolution, more markedly at reduced dose.