The effect of different image reconstruction techniques on pre-clinical quantitative imaging and dual-energy CT

Filtered back projection vs. iterative reconstruction for CBCT: effects on image noise and processing time

OBJECTIVES

To assess the effect of standard filtered back projection (FBP) and iterative reconstruction (IR) methods on CBCT image noise and processing time (PT), acquired with various acquisition parameters with and without metal artefact reduction (MAR).

METHODS

CBCT scans using the Midmark EIOS unit of a human mandible embedded in soft tissue equivalent material with and without the presence of an implant at mandibular first molar region were acquired at various acquisition settings (milliamperages [4mA-14mA], FOV [5 × 5, 6 × 8, 9 × 10 cm], and resolutions [low, standard, high] and reconstructed using standard FBP and IR, and with and without MAR. The processing time was recorded for each reconstruction. ImageJ was used to analyze specific axial images. Radial transaxial fiducial lines were created relative to the implant site. Standard deviations of the gray density values (image noise) were calculated at fixed distances on the fiducial lines on the buccal and lingual aspects at specific axial levels, and mean values for FBP and IR were compared using paired t-tests. Significance was defined as p < 0.05.

RESULTS

The overall mean for image noise (± SD) for FBP was 198.65 ± 55.58 and 99.84 ± 16.28 for IR. IR significantly decreased image noise compared to FBP at all acquisition parameters (p < 0.05). Noise reduction among different scanning protocols ranged between 29.7% (5 × 5 cm FOV) and 58.1% (5mA). IR increased processing time by an average of 35.1 s.

CONCLUSIONS

IR significantly reduces CBCT image noise compared to standard FBP without substantially increasing processing time.

11β-HSD1 determines the extent of muscle atrophy in a model of acute exacerbation of COPD

Muscle atrophy is an extrapulmonary complication of acute exacerbations (AE) in chronic obstructive pulmonary disease (COPD). The endogenous production and therapeutic application of glucocorticoids (GCs) have been implicated as drivers of muscle loss in AE-COPD. The enzyme 11 β-hydroxysteroid dehydrogenase 1 (11β-HSD1) activates GCs and contributes toward GC-induced muscle wasting. To explore the potential of 11βHSD1 inhibition to prevent muscle wasting here, the objective of this study was to ascertain the contribution of endogenous GC activation and amplification by 11βHSD1 in skeletal muscle wasting during AE-COPD. Emphysema was induced by intratracheal (IT) instillation of elastase to model COPD in WT and 11βHSD1/KO mice, followed by vehicle or IT-LPS administration to mimic AE. µCT scans were obtained prior and at study endpoint 48 h following IT-LPS, to assess emphysema development and muscle mass changes, respectively. Plasma cytokine and GC profiles were determined by ELISA. In vitro, myonuclear accretion and cellular response to plasma and GCs were determined in C2C12 and human primary myotubes. Muscle wasting was exacerbated in LPS-11βHSD1/KO animals compared with WT controls. RT-qPCR and western blot analysis showed elevated catabolic and suppressed anabolic pathways in muscle of LPS-11βHSD1/KO animals relative to WTs. Plasma corticosterone levels were higher in LPS-11βHSD1/KO animals, whereas C2C12 myotubes treated with LPS-11βHSD1/KO plasma or exogenous GCs displayed reduced myonuclear accretion relative to WT counterparts. This study reveals that 11β-HSD1 inhibition aggravates muscle wasting in a model of AE-COPD, suggesting that therapeutic inhibition of 11β-HSD1 may not be appropriate to prevent muscle wasting in this setting.

Dual energy CT for a small animal radiation research platform using an empirical dual energy calibration

Objective. Dual energy computed tomography (DECT) has been shown to provide additional image information compared to conventional CT and has been used in clinical routine for several years. The objective of this work is to present a DECT implementation for a Small Animal Radiation Research Platform (SARRP) and to verify it with a quantitative analysis of a material phantom and a qualitative analysis with an ex-vivo mouse measurement. Approach. For dual energy imaging, two different spectra are required, but commercial small animal irradiators are usually not optimized for DECT. We present a method that enables dual energy imaging on a SARRP with sequential scanning and an Empirical Dual Energy Calibration (EDEC). EDEC does not require the exact knowledge of spectra and attenuation coefficients; instead, it is based on a calibration. Due to the SARRP geometry and reconstruction algorithm, the calibration is done using an artificial CT image based on measured values. The calibration yields coefficients to convert the measured images into material decomposed images. Main results. To analyze the method quantitatively, the electron density and the effective atomic number of a material phantom were calculated and compared with theoretical values. The electron density showed a maximum deviation from the theoretical values of less than 5% and the atomic number of slightly more than 6%. For use in mice, DECT is particularly useful in distinguishing iodine contrast agent from bone. A material decomposition of an ex-vivo mouse with iodine contrast agent was material decomposed to show that bone and iodine can be distinguished and iodine-corrected images can be calculated. Significance. DECT is capable of calculating electron density images and effective atomic number images, which are appropriate parameters for quantitative analysis. Furthermore, virtual monochromatic images can be obtained for a better differentiation of materials, especially bone and iodine contrast agent.

Deep Learning Based Automated Orthotopic Lung Tumor Segmentation in Whole-Body Mouse CT-Scans

Lung cancer is the leading cause of cancer related deaths worldwide. The development of orthotopic mouse models of lung cancer, which recapitulates the disease more realistically compared to the widely used subcutaneous tumor models, is expected to critically aid the development of novel therapies to battle lung cancer or related comorbidities such as cachexia. However, follow-up of tumor take, tumor growth and detection of therapeutic effects is difficult, time consuming and requires a vast number of animals in orthotopic models. Here, we describe a solution for the fully automatic segmentation and quantification of orthotopic lung tumor volume and mass in whole-body mouse computed tomography (CT) scans. The goal is to drastically enhance the efficiency of the research process by replacing time-consuming manual procedures with fast, automated ones. A deep learning algorithm was trained on 60 unique manually delineated lung tumors and evaluated by four-fold cross validation. Quantitative performance metrics demonstrated high accuracy and robustness of the deep learning algorithm for automated tumor volume analyses (mean dice similarity coefficient of 0.80), and superior processing time (69 times faster) compared to manual segmentation. Moreover, manual delineations of the tumor volume by three independent annotators was sensitive to bias in human interpretation while the algorithm was less vulnerable to bias. In addition, we showed that besides longitudinal quantification of tumor development, the deep learning algorithm can also be used in parallel with the previously published method for muscle mass quantification and to optimize the experimental design reducing the number of animals needed in preclinical studies. In conclusion, we implemented a method for fast and highly accurate tumor quantification with minimal operator involvement in data analysis. This deep learning algorithm provides a helpful tool for the noninvasive detection and analysis of tumor take, tumor growth and therapeutic effects in mouse orthotopic lung cancer models.

New image-restoration method using a simultaneous algebraic reconstruction technique: comparison with the Richardson-Lucy algorithm

We developed a new image-restoration method that incorporates the point spread function (PSF) into the simultaneous algebraic reconstruction technique (SART-PSF). Additionally, through simulation studies, we investigated the usefulness of the method in comparison with the Richardson-Lucy (RL) algorithm. In the simulation studies, degraded images were generated by convolving magnetic resonance imaging-based brain images with PSF and adding Gaussian or Poisson noise to them to simulate various noise levels. The effects of the number of iterations N, noise, and PSF error on the processed images were quantitatively evaluated using the percent root mean square error (PRMSE) and mean structural similarity index (mSSIM). After applying the SART-PSF to images degraded using Gaussian noise, the PRMSE value and increase thereof, when N was increased, were smaller than those when using the RL algorithm. The mSSIM value was higher and its decrease upon increasing N was smaller than that of the RL algorithm. When Poisson noise was assumed, the differences in PRMSE and mSSIM between both methods were smaller than those when Gaussian noise was assumed. When the PSF error was negative, its effect on PRMSE and mSSIM was similar for both methods. However, when it was positive, the deterioration of these parameters for the SART-PSF was less than that for the RL algorithm in both the Gaussian and Poisson noise cases. The results suggest that the SART-PSF is more robust against noise and a PSF error than the RL algorithm and, thus, can be used as an alternative to the RL algorithm.

Evaluation of a Novel Liquid Fiducial Marker, BioXmark®, for Small Animal Image-Guided Radiotherapy Applications

BioXmark^® (Nanovi A/S, Denmark) is a novel fiducial marker based on a liquid, iodine-based and non-metallic formulation. BioXmark^® has been clinically validated and reverse translated to preclinical models to improve cone-beam CT (CBCT) target delineation in small animal image-guided radiotherapy (SAIGRT). However, in phantom image analysis and in vivo evaluation of radiobiological response after the injection of BioXmark^® are yet to be reported. In phantom measurements were performed to compare CBCT imaging artefacts with solid fiducials and determine optimum imaging parameters for BioXmark^®. In vivo stability of BioXmark^® was assessed over a 5-month period, and the impact of BioXmark^® on in vivo tumour response from single-fraction and fractionated X-ray exposures was investigated in a subcutaneous syngeneic tumour model. BioXmark^® was stable, well tolerated and detectable on CBCT at volumes ≤10 µL. Our data showed imaging artefacts reduced by up to 84% and 89% compared to polymer and gold fiducial markers, respectively. BioXmark^® was shown to have no significant impact on tumour growth in control animals, but changes were observed in irradiated animals injected with BioXmark^® due to alterations in dose calculations induced by the sharp contrast enhancement. BioXmark^® is superior to solid fiducials with reduced imaging artefacts on CBCT. With minimal impact on the tumour growth delay, BioXmark^® can be implemented in SAIGRT to improve target delineation and reduce set-up errors.

Single-source dual-energy computed tomography for the detection of bone marrow lesions: impact of iterative reconstruction and algorithms

PURPOSE

To compare the diagnostic performance of different reconstruction algorithms of single-source dual-energy computed tomography (DECT) for the detection of bone marrow lesions (BML) in patients with vertebral compression fracture using MRI as the standard of reference.

MATERIAL AND METHODS

Seventeen patients with an age over 50 who underwent single-source DECT of the spine were included. The raw data (RD) were reconstructed using filtered back-projection (FBP) and iterative reconstruction (IR) with three iteration levels (IR1-IR3). Bone marrow images were generated using a three-material decomposition (3MD) and a two-material decomposition (2MD) algorithm and an RD-based approach. Three blinded readers scored the images for image quality and the presence of bone marrow lesions (BML). Only vertebrae with height loss were included. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. The different reconstructions were compared using Dunn's multiple comparison test.

RESULTS

Thirty-nine vertebrae were included. IR(1-3) showed superior sensitivity (87.5%) compared to FBP (75%) using 3MD but was comparable to RD (83.3%). All 2MD images were inferior (sensitivity < 38%). The image quality score was significantly higher for 3MD-IR(1-3) compared to 3MD-FBP (p < 0.0001) and all 2MD data sets (p < 0.03). This pattern was also supported by the SNR and CNR measurements. RD showed no significant improvement compared to IR.

CONCLUSION

The image quality of bone marrow images acquired with DECT can be improved by using IR compared with FBP. RD-based reconstruction does not offer significant improvement over image data-based reconstruction. 2MD algorithms are not suitable for BML detection.

Status and innovations in pre-treatment CT imaging for proton therapy

Pre-treatment CT imaging is a topic of growing importance in particle therapy. Improvements in the accuracy of stopping-power prediction are demanded to allow for a dose conformality that is not inferior to state-of-the-art image-guided photon therapy. Although range uncertainty has been kept practically constant over the last decades, recent technological and methodological developments, like the clinical application of dual-energy CT, have been introduced or arise at least on the horizon to improve the accuracy and precision of range prediction. This review gives an overview of the current status, summarizes the innovations in dual-energy CT and its potential impact on the field as well as potential alternative technologies for stopping-power prediction.

Automated CT-derived skeletal muscle mass determination in lower hind limbs of mice using a 3D U-Net deep learning network

The loss of skeletal muscle mass is recognized as a complication of several chronic diseases and is associated with increased mortality and a decreased quality of life. Relevant and reliable animal models in which muscle wasting can be monitored noninvasively over time are instrumental to investigate and develop new therapies. In this work, we developed a fully automatic deep learning algorithm for segmentation of micro cone beam computed tomography images of the lower limb muscle complex in mice and subsequent muscle mass calculation. A deep learning algorithm was trained on manually segmented data from 32 mice. Muscle wet mass measurements were obtained from 47 mice and served as a data set for model validation and reverse model validation. The automatic algorithm performance was ~150 times faster than manual segmentation. Reverse validation of the algorithm showed high quantitative metrics (i.e., a Dice similarity coefficient of 0.93, a Hausdorff distance of 0.4 mm, and a center of mass displacement of 0.1 mm), substantiating the robustness and accuracy of the model. A high correlation (R² = 0.92) was obtained between the computed tomography-derived muscle mass measurements and the muscle wet masses. Longitudinal follow-up revealed time-dependent changes in muscle mass that separated control from lung tumor-bearing mice, which was confirmed as cachexia. In conclusion, this deep learning model for automated assessment of the lower limb muscle complex provides highly accurate noninvasive longitudinal evaluation of skeletal muscle mass. Furthermore, it facilitates the workflow and increases the amount of data derived from mouse studies while reducing the animal numbers.NEW & NOTEWORTHY This deep learning application enables highly accurate noninvasive longitudinal evaluation of skeletal muscle mass changes in mice with minimal requirement for operator involvement in the data analysis. It provides a unique opportunity to increase and analyze the amount of data derived from animal studies automatically while reducing animal numbers and analytical workload.