Picture Perfect: The Status of Image Quality in Prostate MRI

Magnetic resonance imaging is the gold standard imaging modality for the diagnosis of prostate cancer (PCa). Image quality is a fundamental prerequisite for the ability to detect clinically significant disease. In this critical review, we separate the issue of image quality into quality improvement and quality assessment. Beginning with the evolution of technical recommendations for scan acquisition, we investigate the role of patient preparation, scanner factors, and more advanced sequences, including those featuring Artificial Intelligence (AI), in determining image quality. As means of quality appraisal, the published literature on scoring systems (including the Prostate Imaging Quality score), is evaluated. Finally, the application of AI and teaching courses as ways to facilitate quality assessment are discussed, encouraging the implementation of future image quality initiatives along the PCa diagnostic and monitoring pathway. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

On-eye centration of soft contact lenses

PURPOSE

To evaluate the relative positions of modern soft contact lenses (SCLs) relative to the limbus/cornea and the pupil.

METHODS

Sixty images of the anterior eyes of 101 subjects were acquired over 10 s while participants fixated the centre of the camera lens located 33 cm in front of the eye in a well-lit (300 lux) clinic. Custom validated image analysis software was used to locate the boundaries of the contact lenses, pupils and corneas (limbus). Horizontal and vertical relative positions of the contact lens, pupil and limbus were calculated from the fitted boundaries.

RESULTS

The mean (standard deviation) pupil and corneal diameters for all subjects were 3.84 mm, (0.83) and 11.97 mm (0.48), respectively. The mean [95% confidence interval] pupil centre was located 0.28 mm [0.26, 0.30] nasally and 0.07 mm [0.05, 0.10] superiorly to the corneal centre. Consistent with clinical observations, the contact lenses centred accurately relative to the corneal centre both nasally 0.04 mm [0.01, 0.07] and inferiorly -0.01 mm [-0.06, 0.03]. However, regardless of the eye, the contact lens was significantly (p < 0.001) decentred relative to the pupil centre both temporally -0.23 mm [-0.26, -0.20] and inferiorly -0.08 mm [-0.12, -0.04]. Decentration magnitudes were significantly correlated between the right and left eyes.

CONCLUSIONS

Spherical SCLs centred well on the cornea but temporally and inferiorly from the primary line of sight (pupil centre), due to the differences in the location of the pupil and corneal centres. Contrary to some previous reports, there was no evidence that lens optics or material affected lens centration significantly.

Task-based automatic keV selection: leveraging routine virtual monoenergetic imaging for dose reduction on clinical photon-counting detector CT. Phys Med Biol 2024;69:115029. [PMID: 38648795 DOI: 10.1088/1361-6560/ad41b3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Objective. Photon-counting detector (PCD) CT enables routine virtual-monoenergetic image (VMI) reconstruction. We evaluated the performance of an automatic VMI energy level (keV) selection tool on a clinical PCD-CT system in comparison to an automatic tube potential (kV) selection tool from an energy-integrating-detector (EID) CT system from the same manufacturer.Approach.Four torso-shaped phantoms (20-50 cm width) containing iodine (2, 5, and 10 mg cc-1) and calcium (100 mg cc-1) were scanned on PCD-CT and EID-CT. Dose optimization techniques, task-based VMI energy level and tube-potential selection on PCD-CT (CARE keV) and task-based tube potential selection on EID-CT (CARE kV), were enabled. CT numbers, image noise, and dose-normalized contrast-to-noise ratio (CNRd) were compared.Main results. PCD-CT produced task-specific VMIs at 70, 65, 60, and 55 keV for non-contrast, bone, soft tissue with contrast, and vascular settings, respectively. A 120 kV tube potential was automatically selected on PCD-CT for all scans. In comparison, EID-CT used x-ray tube potentials from 80 to 150 kV based on imaging task and phantom size. PCD-CT achieved consistent dose reduction at 9%, 21% and 39% for bone, soft tissue with contrast, and vascular tasks relative to the non-contrast task, independent of phantom size. On EID-CT, dose reduction factor for contrast tasks relative to the non-contrast task ranged from a 65% decrease (vascular task, 70 kV, 20 cm phantom) to a 21% increase (soft tissue with contrast task, 150 kV, 50 cm phantom) due to size-specific tube potential adaptation. PCD-CT CNRdwas equivalent to or higher than those of EID-CT for all tasks and phantom sizes, except for the vascular task with 20 cm phantom, where 70 kV EID-CT CNRdoutperformed 55 keV PCD-CT images.Significance. PCD-CT produced more consistent CT numbers compared to EID-CT due to standardized VMI output, which greatly benefits standardization efforts and facilitates radiation dose reduction.

[Validation of Optimal Imaging Conditions for Coronary Computed Tomography Angiography Using High-definition Mode and Deep Learning Image Reconstruction Algorithm]

PURPOSE

To verify the optimal imaging conditions for coronary computed tomography angiography (CCTA) examinations when using high-definition (HD) mode and deep learning image reconstruction (DLIR) in combination.

METHOD

A chest phantom and an in-house phantom using 3D printer were scanned with a 256-row detector CT scanner. The scan parameters were as follows - acquisition mode: ON (HD mode) and OFF (normal resolution [NR] mode), rotation time: 0.28 s/rotation, beam coverage width: 160 mm, and the radiation dose was adjusted based on CT-AEC. Image reconstruction was performed using ASiR-V (Hybrid-IR), TrueFidelity Image (DLIR), and HD-Standard (HD mode) and Standard (NR mode) reconstruction kernels. The task-based transfer function (TTF) and noise power spectrum (NPS) were measured for image evaluation, and the detectability index (d') was calculated. Visual evaluation was also performed on an in-house coronary phantom.

RESULT

The in-plane TTF was better for the HD mode than for the NR mode, while the z-axis TTF was lower for DLIR than for Hybrid-IR. The NPS values in the high-frequency region were higher for the HD mode compared to those for the NR mode, and the NPS was lower for DLIR than for Hybrid-IR. The combination of HD mode and DLIR showed the best value for in-plane d', whereas the combination of NR mode and DLIR showed the best value for z-axis d'. In the visual evaluation, the combination of NR mode and DLIR showed the best values from a noise index of 45 HU.

CONCLUSION

The optimal combination of HD mode and DLIR depends on the image noise level, and the combination of NR mode and DLIR was the best imaging condition under noisy conditions.

Protocol optimization for functional cardiac CT imaging using noise emulation in the raw data domain

BACKGROUND

Four-dimensional (4D) wide coverage computed tomography (CT) is an effective imaging modality for measuring the mechanical function of the myocardium. However, repeated CT measurement across a number of heartbeats is still a concern.

PURPOSE

A projection-domain noise emulation method is presented to generate accurate low-dose (mA modulated) 4D cardiac CT scans from high-dose scans, enabling protocol optimization to deliver sufficient image quality for functional cardiac analysis while using a dose level that is as low as reasonably achievable (ALARA).

METHODS

Given a targeted low-dose mA modulation curve, the proposed noise emulation method injects both quantum and electronic noise of proper magnitude and correlation to the high-dose data in projection domain. A spatially varying (i.e., channel-dependent) detector gain term as well as its calibration method were proposed to further improve the noise emulation accuracy. To determine the ALARA dose threshold, a straightforward projection domain image quality (IQ) metric was proposed that is based on the number of projection rays that do not fall under the non-linear region of the detector response. Experiments were performed to validate the noise emulation method with both phantom and clinical data in terms of visual similarity, contrast-to-noise ratio (CNR), and noise-power spectrum (NPS).

RESULTS

For both phantom and clinical data, the low-dose emulated images exhibited similar noise magnitude (CNR difference within 2%), artifacts, and texture to that of the real low-dose images. The proposed channel-dependent detector gain term resulted in additional increase in emulation accuracy. Using the proposed IQ metric, recommended kVp and mA settings were calculated for low dose 4D Cardiac CT acquisitions for patients of different sizes.

CONCLUSIONS

A detailed method to estimate system-dependent parameters for a raw-data based low dose emulation framework was described. The method produced realistic noise levels, artifacts, and texture with phantom and clinical studies. The proposed low-dose emulation method can be used to prospectively select patient-specific minimal-dose protocols for functional cardiac CT.

Image quality evaluation of a new high-performance ring-gantry cone-beam computed tomography imager

Objective. Newer cone-beam computed tomography (CBCT) imaging systems offer reconstruction algorithms including metal artifact reduction (MAR) and extended field-of-view (eFoV) techniques to improve image quality. In this study a new CBCT imager, the new Varian HyperSight CBCT, is compared to fan-beam CT and two CBCT imagers installed in a ring-gantry and C-arm linear accelerator, respectively.Approach. The image quality was assessed for HyperSight CBCT which uses new hardware, including a large-size flat panel detector, and improved image reconstruction algorithms. The decrease of metal artifacts was quantified (structural similarity index measure (SSIM) and root-mean-squared error (RMSE)) when applying MAR reconstruction and iterative reconstruction for a dental and spine region using a head-and-neck phantom. The geometry and CT number accuracy of the eFoV reconstruction was evaluated outside the standard field-of-view (sFoV) on a large 3D-printed chest phantom. Phantom size dependency of CT numbers was evaluated on three cylindrical phantoms of increasing diameter. Signal-to-noise and contrast-to-noise were quantified on an abdominal phantom.Main results. In phantoms with streak artifacts, MAR showed comparable results for HyperSight CBCT and CT, with MAR increasing the SSIM (0.97-0.99) and decreasing the RMSE (62-55 HU) compared to iterative reconstruction without MAR. In addition, HyperSight CBCT showed better geometrical accuracy in the eFoV than CT (Jaccard Conformity Index increase of 0.02-0.03). However, the CT number accuracy outside the sFoV was lower than for CT. The maximum CT number variation between different phantom sizes was lower for the HyperSight CBCT imager (∼100 HU) compared to the two other CBCT imagers (∼200 HU), but not fully comparable to CT (∼50 HU).Significance. This study demonstrated the imaging performance of the new HyperSight CBCT imager and the potential of applying this CBCT system in more advanced scenarios by comparing the quality against fan-beam CT.

Comparative analysis of the image quality and diagnostic performance of the zooming technique with diffusion-weighted imaging using different b-values for thyroid papillary carcinomas and benign nodules

Objective

To compare image quality and diagnostic performance using different b-values for the zooming technique with diffusion-weighted imaging (ZOOMit-DWI) in thyroid nodules.

Materials and methods

A total of 51 benign thyroid nodules and 50 thyroid papillary carcinomas were included. ZOOMit-DWI was performed with b-values of 0, 500, 1000, 1500 and 2000 s/mm2. The sharpness was evaluated as subjective index. The signal intensity ratio (SIR), signal-to-noise ratio (SNR) and apparent diffusion coefficient (ADC) were measured as objective indices. Pairwise comparisons were performed among the different b-value groups using the Friedman test. A receiver operating characteristic curve of the ADC value was used to evaluate diagnostic performance. The DeLong test was used to compare diagnostic effectiveness among the different b-value groups.

Results

In both the papillary carcinoma group (P = 0.670) and the benign nodule group (P = 0.185), the sharpness of nodules was similar between b-values of 1000 s/mm2and 1500 s/mm2. In the papillary carcinoma group, the SIRnodule was statistically higher in DWI images with a b-value of 1500 s/mm2than in DWI images with b-values of 500 s/mm2(P = 0.004), 1000 s/mm2(P = 0.002), and 2000 s/mm2(P = 0.003). When the b-values were 1500 s/mm2(P = 0.008) and 2000 s/mm2(P = 0.009), the SIRnodule significantly differed between the papillary carcinoma group and the benign nodule group. When b = 500 s/mm2, the ADC had an AUC of 0.888. When b = 1000 s/mm2, the ADC had an AUC of 0.881. When b = 1500 s/mm2, the ADC had an AUC of 0.896. When b = 2000 s/mm2, the ADC had an AUC of 0.871. The DeLong test showed comparable diagnostic effectiveness among the different b-value groups except for between b-values of 2000 s/mm2and 1500 s/mm2, with a b-value of 2000 s/mm2showing lower effectiveness.

Conclusion

This study suggests that 1500 s/mm2may be a suitable b-value to differentiate benign and malignant thyroid nodules in ZOOMit-DWI images, which yielded better image quality.

Investigation of a potential upstream harmonization based on image appearance matching to improve radiomics features robustness: a phantom study

Objective. Radiomics is a promising valuable analysis tool consisting in extracting quantitative information from medical images. However, the extracted radiomics features are too sensitive to variations in used image acquisition and reconstruction parameters. This limited robustness hinders the generalizable validity of radiomics-assisted models. Our aim is to investigate a possible harmonization strategy based on matching image quality to improve feature robustness.Approach.We acquired CT scans of a phantom with two scanners across different dose levels and percentages of Iterative Reconstruction algorithms. The detectability index was used as a comprehensive task-based image quality metric. A statistical analysis based on the Intraclass Correlation Coefficient was performed to determine if matching image quality/appearance could enhance the robustness of radiomics features extracted from the phantom images. Additionally, an Artificial Neural Network was trained on these features to automatically classify the scanner used for image acquisition.Main results.We found that the ICC of the features across protocols providing a similar detectability index improves with respect to the ICC of the features across protocols providing a different detectability index. This improvement was particularly noticeable in features relevant for distinguishing between scanners.Significance.This preliminary study demonstrates that a harmonization based on image quality/appearance matching could improve radiomics features robustness and heterogeneous protocols can be used to obtain a similar image appearance in terms of the detectability index. Thus protocols with a lower dose level could be selected to reduce the amount of radiation dose delivered to the patient and simultaneously obtain a more robust quantitative analysis.

Evaluation of a retinal oximetry image quality indicator in patients with cataract

PURPOSE

To evaluate a new automated retinal oximetry image quality indicator with cataract as a clinical model.

METHODS

Sixty-one eyes in 61 patients were imaged by the Oxymap T1 Retinal Oximeter at baseline and 25 eyes were also examined 3 weeks after cataract surgery. Image quality (0-10 on a continuous scale) was compared with standardized AREDS cataract grading and Pentacam lens densitometry. Associations with retinal oximetry measurements and visual acuity were examined.

RESULTS

Image quality correlated with total, nuclear and posterior subcapsular cataract grades (ANOVA, p < 0.05), tended to be associated with lens densitometry and it improved from 4.3 ± 1.4 to 5.7 ± 1.0 (p < 0.05) after cataract surgery. Very low image quality, below 3, led to vessel detection failure in retinal oximetry images. Higher image qualities were linearly associated with higher measured retinal oxygen saturations (r = 0.52 in arteries and r = 0.46 in veins; p < 0.001).

CONCLUSION

Retinal oximetry image quality deteriorated with increasing cataract density and improved after cataract surgery, supporting its use as a measure of optical clarity. The numerical quality indicator demonstrated a threshold below which images of poor optical quality should be discarded. Image quality affects the estimates of retinal oximetry parameters and should therefore be included in future analyses.

Human Observer Sensitivity to Temporal Noise During B-Mode Ultrasound Scanning: Characterization and Imaging Implications

This work measures temporal signal-to-noise ratio (SNR) thresholds that indicate when random noise during ultrasound scanning becomes imperceptible to expert human observers. Visible noise compromises image quality and can potentially lead to non-diagnostic scans. Noise can arise from both stable acoustic sources (clutter) or randomly varying electronic sources (temporal noise). Extensive engineering effort has focused on decreasing noise in both of these categories. In this work, an observer study with five practicing sonographers was performed to assess sonographer sensitivity to temporal noise in ultrasound cine clips. Understanding the conditions where temporal noise is no longer visible during ultrasound imaging can inform engineering efforts seeking to minimize the impact this noise has on image quality. The sonographers were presented with paired temporal noise-free and noise-added simulated speckle cine clips and asked to select the noise-added clips. The degree of motion in the imaging target was found to have a significant effect on the SNR levels where noise was perceived, while changing imaging frequency had little impact. At realistic in vivo motion levels, temporal noise was not perceived in cine clips at and above 28 dB SNR. In a case study presented here, the potential of adaptive intensity adjustment based on this noise perception threshold is validated in a fetal imaging scenario. This study demonstrates how noise perception thresholds can be applied to help design or tune ultrasound systems for different imaging tasks and noise conditions.

Fully automatic online geometric calibration for non-circular cone-beam CT orbits using fiducials with unknown placement

BACKGROUND

Cone-beam CT (CBCT) with non-circular scanning orbits can improve image quality for 3D intraoperative image guidance. However, geometric calibration of such scans can be challenging. Existing methods typically require a prior image, specialized phantoms, presumed repeatable orbits, or long computation time.

PURPOSE

We propose a novel fully automatic online geometric calibration algorithm that does not require prior knowledge of fiducial configuration. The algorithm is fast, accurate, and can accommodate arbitrary scanning orbits and fiducial configurations.

METHODS

The algorithm uses an automatic initialization process to eliminate human intervention in fiducial localization and an iterative refinement process to ensure robustness and accuracy. We provide a detailed explanation and implementation of the proposed algorithm. Physical experiments on a lab test bench and a clinical robotic C-arm scanner were conducted to evaluate spatial resolution performance and robustness under realistic constraints.

RESULTS

Qualitative and quantitative results from the physical experiments demonstrate high accuracy, efficiency, and robustness of the proposed method. The spatial resolution performance matched that of our existing benchmark method, which used a 3D-2D registration-based geometric calibration algorithm.

CONCLUSIONS

We have demonstrated an automatic online geometric calibration method that delivers high spatial resolution and robustness performance. This methodology enables arbitrary scan trajectories and should facilitate translation of such acquisition methods in a clinical setting.

The Teledermatology Experience: Cost Savings and Image Quality Control

Introduction: Teledermatology adoption continues to increase, in part, spurred by the COVID-19 pandemic. This study analyzes the utility and cost savings of a store-and-forward teledermatology consultative system within the Veterans Health Administration (VA). Methods: Retrospective cohort of 4,493 patients across 14 remote sites in Tennessee and Kentucky from May 2017 through August 2019. The study measured the agreement between the teledermatology diagnoses and follow-up face-to-face clinic evaluations as well as the cost effectiveness of the teledermatology program over the study period. Results: Fifty-four percent of patients were recommended for face-to-face appointment for biopsy or further evaluation. Most patients, 80.5% received their face-to-face care by a VA dermatologist. There was a high level of concordance between teledermatologist and clinic dermatologist for pre-malignant and malignant cutaneous conditions. Veterans were seen faster at a VA clinic compared with a community dermatology site. Image quality improved as photographers incorporated teledermatologist feedback. From a cost perspective, teledermatology saved the VA system $1,076,000 in community care costs. Discussion: Teledermatology is a useful diagnostic tool within the VA system providing Veteran care at a cost savings.

AAPM Task Group Report 261: Comprehensive quality control methodology and management of dental and maxillofacial cone beam computed tomography (CBCT) systems

Cone-beam computed tomography (CBCT) systems specifically designed and manufactured for dental, maxillofacial imaging (MFI) and otolaryngology (OLR) applications have been commercially available in the United States since 2001 and have been in widespread clinical use since. Until recently, there has been a lack of professional guidance available for medical physicists about how to assess and evaluate the performance of these systems and about the establishment and management of quality control (QC) programs. The owners and users of dental CBCT systems may have only a rudimentary understanding of this technology, including how it differs from conventional multidetector CT (MDCT) in terms of acceptable radiation safety practices. Dental CBCT systems differ from MDCT in several ways and these differences are described. This report provides guidance to medical physicists and serves as a basis for stakeholders to make informed decisions regarding how to manage and develop a QC program for dental CBCT systems. It is important that a medical physicist with experience in dental CBCT serves as a resource on this technology and the associated radiation protection best practices. The medical physicist should be involved at the pre-installation stage to ensure that a CBCT room configuration allows for a safe and efficient workflow and that structural shielding, if needed, is designed into the architectural plans. Acceptance testing of new installations should include assessment of mechanical alignment of patient positioning lasers and x-ray beam collimation and benchmarking of essential image quality performance parameters such as image uniformity, noise, contrast-to-noise ratio (CNR), spatial resolution, and artifacts. Several approaches for quantifying radiation output from these systems are described, including simply measuring the incident air-kerma (Kair) at the entrance surface of the image receptor. These measurements are to be repeated at least annually as part of routine QC by the medical physicist. QC programs for dental CBCT, at least in the United States, are often driven by state regulations, accreditation program requirements, or manufacturer recommendations.

Improvement of 2D cine image quality using 3D priors and cycle generative adversarial network for low field MRI-guided radiation therapy

BACKGROUND

Cine magnetic resonance (MR) images have been used for real-time MR guided radiation therapy (MRgRT). However, the onboard MR systems with low-field strength face the problem of limited image quality.

PURPOSE

To improve the quality of cine MR images in MRgRT using prior image information provided by the patient planning and positioning MR images.

METHODS

This study employed MR images from 18 pancreatic cancer patients who received MR-guided stereotactic body radiation therapy. Planning 3D MR images were acquired during the patient simulation, and positioning 3D MR images and 2D sagittal cine MR images were acquired before and during the beam delivery, respectively. A deep learning-based framework consisting of two cycle generative adversarial networks (CycleGAN), Denoising CycleGAN and Enhancement CycleGAN, was developed to establish the mapping between the 3D and 2D MR images. The Denoising CycleGAN was trained to first denoise the cine images using the time domain cine image series, and the Enhancement CycleGAN was trained to enhance the spatial resolution and contrast by taking advantage of the prior image information from the planning and positioning images. The denoising performance was assessed by signal-to-noise ratio (SNR), structural similarity index measure, peak SNR, blind/reference-less image spatial quality evaluator (BRISQUE), natural image quality evaluator, and perception-based image quality evaluator scores. The quality enhancement performance was assessed by the BRISQUE and physician visual scores. In addition, the target contouring was evaluated on the original and processed images.

RESULTS

Significant differences were found for all evaluation metrics after Denoising CycleGAN processing. The BRISQUE and visual scores were also significantly improved after sequential Denoising and Enhancement CycleGAN processing. In target contouring evaluation, Dice similarity coefficient, centroid distance, Hausdorff distance, and average surface distance values were significantly improved on the enhanced images. The whole processing time was within 20 ms for a typical input image size of 512 × 512.

CONCLUSION

Taking advantage of the prior high-quality positioning and planning MR images, the deep learning-based framework enhanced the cine MR image quality significantly, leading to improved accuracy in automatic target contouring. With the merits of both high computational efficiency and considerable image quality enhancement, the proposed method may hold important clinical implication for real-time MRgRT.

Image quality characterization of an ultra-high-speed kilovoltage cone-beam computed tomography imaging system on an O-ring linear accelerator

PURPOSE

The quality of on-board imaging systems, including cone-beam computed tomography (CBCT), plays a vital role in image-guided radiation therapy (IGRT) and adaptive radiotherapy. Recently, there has been an upgrade of the CBCT systems fused in the O-ring linear accelerators called HyperSight, featuring a high imaging performance. As the characterization of a new imaging system is essential, we evaluated the image quality of the HyperSight system by comparing it with Halcyon 3.0 CBCT and providing benchmark data for routine imaging quality assurance.

METHODS

The HyperSight features ultra-fast scan time, a larger kilovoltage (kV) detector, a more substantial kV tube, and an advanced reconstruction algorithm. Imaging protocols in the two modes of operation, treatment mode with IGRT and the CBCT for planning (CBCTp) mode were evaluated and compared with Halcyon 3.0 CBCT. Image quality metrics, including spatial resolution, contrast resolution, uniformity, noise, computed tomography (CT) number linearity, and calibration error, were assessed using a Catphan and an electron density phantom and analyzed with TotalQA software.

RESULTS

HyperSight demonstrated substantial improvements in contrast-to-noise ratio and noise in both IGRT and CBCTp modes compared to Halcyon 3.0 CBCT. CT number calibration error of HyperSight CBCTp mode (1.06%) closely matches that of a full CT scanner (0.72%), making it suitable for adaptive planning. In addition, the advanced hardware of HyperSight, such as ultra-fast scan time (5.9 s) or 2.5 times larger heat unit capacity, enhanced the clinical efficiency in our experience.

CONCLUSIONS

HyperSight represented a significant advancement in CBCT imaging. With its image quality, CT number accuracy, and ultra-fast scans, HyperSight has a potential to transform patient care and treatment outcomes. The enhanced scan speed and image quality of HyperSight are expected to significantly improve the quality and efficiency of treatment, particularly benefiting patients.

Evaluating the relationship between magnetic resonance image quality metrics and deep learning-based segmentation accuracy of brain tumors

BACKGROUND

Magnetic resonance imaging (MRI) scans are known to suffer from a variety of acquisition artifacts as well as equipment-based variations that impact image appearance and segmentation performance. It is still unclear whether a direct relationship exists between magnetic resonance (MR) image quality metrics (IQMs) (e.g., signal-to-noise, contrast-to-noise) and segmentation accuracy.

PURPOSE

Deep learning (DL) approaches have shown significant promise for automated segmentation of brain tumors on MRI but depend on the quality of input training images. We sought to evaluate the relationship between IQMs of input training images and DL-based brain tumor segmentation accuracy toward developing more generalizable models for multi-institutional data.

METHODS

We trained a 3D DenseNet model on the BraTS 2020 cohorts for segmentation of tumor subregions enhancing tumor (ET), peritumoral edematous, and necrotic and non-ET on MRI; with performance quantified via a 5-fold cross-validated Dice coefficient. MRI scans were evaluated through the open-source quality control tool MRQy, to yield 13 IQMs per scan. The Pearson correlation coefficient was computed between whole tumor (WT) dice values and IQM measures in the training cohorts to identify quality measures most correlated with segmentation performance. Each selected IQM was used to group MRI scans as "better" quality (BQ) or "worse" quality (WQ), via relative thresholding. Segmentation performance was re-evaluated for the DenseNet model when (i) training on BQ MRI images with validation on WQ images, as well as (ii) training on WQ images, and validation on BQ images. Trends were further validated on independent test sets derived from the BraTS 2021 training cohorts.

RESULTS

For this study, multimodal MRI scans from the BraTS 2020 training cohorts were used to train the segmentation model and validated on independent test sets derived from the BraTS 2021 cohort. Among the selected IQMs, models trained on BQ images based on inhomogeneity measurements (coefficient of variance, coefficient of joint variation, coefficient of variation of the foreground patch) and the models trained on WQ images based on noise measurement peak signal-to-noise ratio (SNR) yielded significantly improved tumor segmentation accuracy compared to their inverse models.

CONCLUSIONS

Our results suggest that a significant correlation may exist between specific MR IQMs and DenseNet-based brain tumor segmentation performance. The selection of MRI scans for model training based on IQMs may yield more accurate and generalizable models in unseen validation.

An automated technique for global noise level measurement in CT image with a conjunction of image gradient

Automated assessment of noise level in clinical computed tomography (CT) images is a crucial technique for evaluating and ensuring the quality of these images. There are various factors that can impact CT image noise, such as statistical noise, electronic noise, structure noise, texture noise, artifact noise, etc. In this study, a method was developed to measure the global noise index (GNI) in clinical CT scans due to the fluctuation of x-ray quanta. Initially, a noise map is generated by sliding a 10 × 10 pixel for calculating Hounsfield unit (HU) standard deviation and the noise map is further combined with the gradient magnitude map. By employing Boolean operation, pixels with high gradients are excluded from the noise histogram generated with the noise map. By comparing the shape of the noise histogram from this method with Christianson's tissue-type global noise measurement algorithm, it was observed that the noise histogram computed in anthropomorphic phantoms had a similar shape with a close GNI value. In patient CT images, excluding the HU deviation due the structure change demonstrated to have consistent GNI values across the entire CT scan range with high heterogeneous tissue compared to the GNI values using Christianson's tissue-type method. The proposed GNI was evaluated in phantom scans and was found to be capable of comparing scan protocols between different scanners. The variation of GNI when using different reconstruction kernels in clinical CT images demonstrated a similar relationship between noise level and kernel sharpness as observed in uniform phantom: sharper kernel resulted in noisier images. This indicated that GNI was a suitable index for estimating the noise level in clinical CT images with either a smooth or grainy appearance. The study's results suggested that the algorithm can be effectively utilized to screen the noise level for a better CT image quality control.

Second-Order Motion-Compensated Echo-Planar Cardiac Diffusion-Weighted MRI: Usefulness of Compressed Sensitivity Encoding

BACKGROUND

Cardiac diffusion-weighted imaging (DWI) using second-order motion-compensated spin echo (M2C) can provide noninvasive in-vivo microstructural assessment, but limited by relatively low signal-to-noise ratio (SNR). Echo-planar imaging (EPI) with compressed sensitivity encoding (EPICS) could address these issues.

PURPOSE

To combine M2C DWI and EPCIS (M2C EPICS DWI), and compare image quality for M2C DWI.

STUDY TYPE

Prospective.

POPULATION

Ten ex-vivo hearts, 10 healthy volunteers (females, 5 [50%]; mean ± SD of age, 25 ± 4 years), and 12 patients with diseased hearts (female, 1 [8.3%]; mean ± SD of age, 44 ± 16 years; including coronary artery heart disease, congenital heart disease, dilated cardiomyopathy, amyloidosis, and myocarditis).

FIELD STRENGTH/SEQUENCE

3-T, M2C EPICS DWI, and M2C DWI.

ASSESSMENT

The apparent SNR (aSNR) and the rating scores were used to evaluate and compared image quality of all three groups. The aSNR was calculated usingaSNR = Mean intensity myocardium / Standard deviation myocardium $$ \mathrm{aSNR}={\mathrm{Mean}\ \mathrm{intensity}}_{\mathrm{myocardium}}/{\mathrm{Standard}\ \mathrm{deviation}}_{\mathrm{myocardium}} $$ , and the myocardium was segmented manually. Three observers independently rated subjective image quality using a 5-point Likert scale.

STATISTICAL TESTS

Bland-Altman analysis and paired t-tests. The threshold for statistical significance was set at P < 0.05.

RESULTS

In healthy volunteers, the aSNR with a b-value of 450 s/mm2 acquired by M2C EPICS DWI was significantly higher than M2C DWI at in-plane resolutions of 3.0 × 3.0, 2.5 × 2.5, and 2.0 × 2.0 mm2. In patients with diseased hearts, the aSNR ofM2C EPICS DWI was also significantly higher than that for M2C DWI (bias of M2C EPICS-M2C = 1.999, 95% limits of agreement, 0.362 to 3.636; mean ± SD, 7.80 ± 1.37 vs. 5.80 ± 0.81). The ADC values of M2C EPICS was significantly higher than M2C DWI in in-vivo hearts. Over 80% of the images with rating scores for M2C EPICS DWI were higher than M2C DWI in in-vivo hearts.

DATA CONCLUSION

Cardiac imaging by M2C EPICS DWI may demonstrate better overall image quality and higher aSNR than M2C DWI.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 1.

Deep learning-based reconstruction enhances image quality and improves diagnosis in magnetic resonance imaging of the shoulder joint

Background

Accelerated magnetic resonance imaging sequences reconstructed using the vendor-provided Recon deep learning algorithm (DL-MRI) were found to be more likely than conventional magnetic resonance imaging (MRI) sequences to detect subacromial (SbA) bursal thickening. However, the extent of this thickening was not extensively explored. This study aimed to compare the image quality of DL-MRI with conventional MRI sequences reconstructed via conventional pipelines (Conventional-MRI) for shoulder examinations and evaluate the effectiveness of DL-MRI in accurately demonstrating the degree of SbA bursal and subcoracoid (SC) bursal thickening.

Methods

This prospective cross-sectional study enrolled 41 patients with chronic shoulder pain who underwent 3-T MRI (including both Conventional-MRI and accelerated MRI sequences) of the shoulder between December 2022 and April 2023. Each protocol consisted of oblique axial, coronal, and sagittal images, including proton density-weighted imaging (PDWI) with fat suppression (FS) and oblique coronal T1-weighted imaging (T1WI) with FS. The image quality and degree of artifacts were assessed using a 5-point Likert scale for both Conventional-MRI and DL-MRI. Additionally, the degree of SbA and SC bursal thickening, as well as the relative signal-to-noise ratio (rSNR) and relative contrast-to-noise ratio (rCNR) were analyzed using the paired Wilcoxon test. Statistical significance was defined as P<0.05.

Results

The utilization of accelerated sequences resulted in a remarkable 54.7% reduction in total scan time. Overall, DL-MRI exhibited superior image quality scores and fewer artifacts compared to Conventional-MRI. Specifically, DL-MRI demonstrated significantly higher measurements of SC bursal thickenings in the oblique sagittal PDWI sequence compared to Conventional-MRI [3.92 (2.83, 5.82) vs. 3.74 (2.92, 5.96) mm, P=0.028]. Moreover, DL-MRI exhibited higher detection of SbA bursal thickenings in the oblique coronal PDWI sequence [2.61 (1.85, 3.46) vs. 2.48 (1.84, 3.25) mm], with a notable trend towards significant differences (P=0.071). Furthermore, the rSNRs of the muscle in DL-MRI images were significantly higher than those in Conventional-MRI images across most sequences (P<0.001). However, the rSNRs of bone on Conventional-MRI of oblique axial and oblique coronal PDWI sequences showed adverse results [oblique axial: 1.000 (1.000, 1.000) vs. 0.444 (0.367, 0.523); and oblique coronal: 1.000 (1.000, 1.000) vs. 0.460 (0.387, 0.631); all P<0.001]. Additionally, all DL-MRI images exhibited significantly greater rSNRs and rCNRs compared to accelerated MRI sequences reconstructed using traditional pipelines (P<0.001).

Conclusions

In conclusion, the utilization of DL-MRI enhances image quality and improves diagnostic capabilities, making it a promising alternative to Conventional-MRI for shoulder imaging.

Image quality and technical limitations in emergency department cardiac point-of-care ultrasound: A retrospective cohort study

OBJECTIVE

To assess the image quality and common technical limitations seen on cardiac point-of-care ultrasound (POCUS) performed and archived in a single New Zealand ED.

METHODS

A retrospective cohort study of clinically indicated cardiac POCUS, archived from 1 October 2019 to 20 May 2020. Archived examinations were retrospectively reviewed by an ED POCUS expert, and an expert cardiac sonographer to determine diagnostic image quality, technical limitations present and opportunities for image quality improvement. Image quality of credentialed examinations was compared to uncredentialed examinations and examinations that were undocumented in the medical record.

RESULTS

A total of 211 cardiac POCUS examinations were included. The impact of image quality on diagnostic interpretation was only documented in <2% of examinations. There was no difference in median global image quality scores for uncredentialed and credentialed examinations (8.5 vs 9, P = 0.55) and median score for undocumented examinations (5.5) was lower than credentialed examinations (P < 0.01). Common technical limitations identified were off-axis imaging and artefacts limiting image quality.

CONCLUSION

In the present study of clinically indicated cardiac POCUS, low image quality was common but the impact of image quality on diagnostic interpretation was very rarely documented in the medical record. Local quality assurance and training should be directed at credentialed and uncredentialed clinicians including strategies to improve off-axis imaging and managing artefacts where possible. Standardised documentation of image quality that may impact diagnostic accuracy should be encouraged.

Automatic image quality evaluation in digital radiography using for-processing and for-presentation images

PURPOSE

To investigate the impact of digital image post-processing algorithms on various image quality (IQ) metrics of radiographic images under different exposure conditions.

METHODS

A custom-made phantom constructed according to the instructions given in the IAEA Human Health Series No.39 publication was used, along with the respective software that automatically calculates various IQ metrics. Images with various exposure parameters were acquired with a digital radiography unit, which for each acquisition produces two images: one for-processing (raw) and one for-presentation (clinical). Various examination protocols were used, which incorporate diverse post-processing algorithms. The IQ metrics' values (IQ-scores) obtained were analyzed to investigate the effects of increasing incident air kerma (IAK) on the image receptor, tube potential (kVp), additional filtration, and examination protocol on image quality, and the differences between image type (raw or clinical).

RESULTS

The IQ-scores were consistent for repeated identical exposures for both raw and clinical images. The effect that changes in exposure parameters and examination protocol had on IQ-scores were different depending on the IQ metric and image type. The expected positive effect that increasing IAK and decreasing tube potential should have on IQ was clearly exhibited in two IQ metrics only, the signal difference-to-noise-ratio (SDNR) and the detectability index (d'), for both image types. No effect of additional filtration on any of the IQ metrics was detected on images of either type. An interesting finding of the study was that for all different image acquisition selections the d' scores were larger in raw images, whereas the other IQ metrics were larger in clinical images for most of the cases.

CONCLUSIONS

Since IQ-scores of raw and their respective clinical images may be largely different, the same type of image should be consistently used for monitoring IQ constancy and when results from different X-ray systems are compared.

Objective image quality assurance in cone-beam CT: Test methods, analysis, and workflow in longitudinal studies

BACKGROUND

Standards for image quality evaluation in multi-detector CT (MDCT) and cone-beam CT (CBCT) are evolving to keep pace with technological advances. A clear need is emerging for methods that facilitate rigorous quality assurance (QA) with up-to-date metrology and streamlined workflow suitable to a range of MDCT and CBCT systems.

PURPOSE

To evaluate the feasibility and workflow associated with image quality (IQ) assessment in longitudinal studies for MDCT and CBCT with a single test phantom and semiautomated analysis of objective, quantitative IQ metrology.

METHODS

A test phantom (CorgiTM Phantom, The Phantom Lab, Greenwich, New York, USA) was used in monthly IQ testing over the course of 1 year for three MDCT scanners (one of which presented helical and volumetric scan modes) and four CBCT scanners. Semiautomated software analyzed image uniformity, linearity, contrast, noise, contrast-to-noise ratio (CNR), 3D noise-power spectrum (NPS), modulation transfer function (MTF) in axial and oblique directions, and cone-beam artifact magnitude. The workflow was evaluated using methods adapted from systems/industrial engineering, including value stream process modeling (VSPM), standard work layout (SWL), and standard work control charts (SWCT) to quantify and optimize test methodology in routine practice. The completeness and consistency of DICOM data from each system was also evaluated.

RESULTS

Quantitative IQ metrology provided valuable insight in longitudinal quality assurance (QA), with metrics such as NPS and MTF providing insight on root cause for various forms of system failure-for example, detector calibration and geometric calibration. Monthly constancy testing showed variations in IQ test metrics owing to system performance as well as phantom setup and provided initial estimates of upper and lower control limits appropriate to QA action levels. Rigorous evaluation of QA workflow identified methods to reduce total cycle time to ∼10 min for each system-viz., use of a single phantom configuration appropriate to all scanners and Head or Body scan protocols. Numerous gaps in the completeness and consistency of DICOM data were observed for CBCT systems.

CONCLUSION

An IQ phantom and test methodology was found to be suitable to QA of MDCT and CBCT systems with streamlined workflow appropriate to busy clinical settings.

A comparative study of low voltage, low contrast cerebral computed tomography angiography with iterative reconstruction and conventional cerebral computed tomography angiography

BACKGROUND

Cerebral computed tomography angiography (CTA) has revolutionized the diagnosis of neurovascular emergencies. Strategies to reduce radiation, a concern for cancer, involve tube voltage and current reduction but with increased noise and inferior image quality. Hence, the objective of the study was to evaluate the quality of images obtained through low-dose radiation and low-contrast volume CTA with an iterative reconstruction (IR) technique versus standard CTA without IR.

METHODS

This prospective trial involved 100 adults requiring cerebral CTA for cerebrovascular diseases. They were split into two groups: one with 120 kVp tube voltage and 80 mL contrast using filtered back projection, and the other with 80 kVp and 30 mL contrast with IR. Evaluation criteria included attenuation values, signal-to-noise ratio, contrast-to-noise ratio, and subjective assessments.

RESULTS

Compared to 120 kVp, 80 kVp showed higher vessel attenuation in the internal (272.91 ± 30.59 vs 405.52 ± 53.08; p < .001) and middle cerebral artery (247.55 ± 29.84 vs 372.55 ± 49.02; p < .001) regions. Brain parenchymal attenuation at the centrum semiovale was lower with 80 kVp (29.12 ± 1.87 vs 24.78 ± 2.94; p < .001), accompanied by higher noise. Signal-to-noise ratio (p < .001) and contrast-to-noise ratio (p < .05) were lower at 80 kVp. Image quality didn't significantly differ, and radiation exposure reduced significantly by 70% in the 80 kVp group, suggesting its diagnostic feasibility.

CONCLUSIONS

The 80 kVp protocol for CTA of the cerebral vessels combined with lower contrast volume produces images with similar image quality with significant radiation effective dose and total iodine dose reduction. The 80 kVp protocol holds significant promise for replacing the standard 120 kVp protocol in cerebral CTA.

Thin slice photon-counting CT coronary angiography compared to conventional CT: Objective image quality and clinical radiation dose assessment

BACKGROUND

Photon-counting CT (PCCT) is the next-generation CT scanner that enables improved spatial resolution and spectral imaging. For full spectral processing, higher tube voltages compared to conventional CT are necessary to achieve the required spectral separation. This generated interest in the potential influence of thin slice high tube voltage PCCT on overall image quality and consequently on radiation dose.

PURPOSE

This study first evaluated tube voltages and radiation doses applied in patients who underwent coronary CT angiography with PCCT and energy-integrating detector CT (EID-CT). Next, image quality of PCCT and EID-CT was objectively evaluated in a phantom study simulating different patient sizes at these tube voltages and radiation doses.

METHODS

We conducted a retrospective analysis of clinical doses of patients scanned on a conventional and PCCT system. Average patient water equivalent diameters for different tube voltages were extracted from the dose reports for both EID-CT and PCCT. A conical phantom made of polyethylene with multiple diameters (26/31/36 cm) representing different patient sizes and containing an iodine insert was scanned with a EID-CT scanner using tube voltages and phantom diameters that match the patient scans and characteristics. Next, phantom scans were made with PCCT at a fixed tube voltage of 120 kV and with CTDIVOL values and phantom diameters identical to the EID-CT scans. Clinical image reconstructions at 0.6 mm slice thickness for conventional CT were compared to PCCT images with 0.4 mm slice thickness. Image quality was quantified using the detectability index (d'), which estimated the visibility of a 3 mm diameter contrast-enhanced coronary artery by considering noise, contrast, resolution, and human visual perception. Alongside d', noise, contrast and resolution were also individually assessed. In addition, the influence of various kernels (Bv40/Bv44/Bv48/Bv56), quantum iterative reconstruction strengths (QIR, 3/4) and monoenergetic levels (40/45/50/55 keV) for PCCT on d' was investigated.

RESULTS

In this study, 143 patients were included: 47 were scanned on PCCT (120 kV) and the remaining on EID-CT (74 small-sized at 70 kV, 18 medium-sized at 80 kV and four large-sized at 90 kV). EID-CT showed 7%-17% higher d' than PCCT with Bv40 kernel and strength four for small/medium patients. Lower monoenergetic images (40 keV) helped mitigate the difference to 1%-6%. For large patients, PCCT's detectability was up to 31% higher than EID-CT. PCCT has thinner slices but similar noise levels for similar reconstruction parameters. The noise increased with lower keV levels in PCCT (≈30% increase), but higher QIR strengths reduced noise. PCCT's iodine contrast was stable across patient sizes, while EID-CT had 33% less contrast in large patients than in small-sized patients.

CONCLUSION

At 120 kV, thin slice PCCT enables CCTA in phantom scans representing large patients without raising radiation dose or affecting vessel detectability. However, higher doses are needed for small and medium-sized patients to obtain a similar image quality as in EID-CT. The alternative of using lower mono-energetic levels requires further evaluation in clinical practice.

Image quality evaluation in deep-learning-based CT noise reduction using virtual imaging trial methods: Contrast-dependent spatial resolution

BACKGROUND

Deep-learning-based image reconstruction and noise reduction methods (DLIR) have been increasingly deployed in clinical CT. Accurate image quality assessment of these methods is challenging as the performance measured using physical phantoms may not represent the true performance of DLIR in patients since DLIR is trained mostly on patient images.

PURPOSE

In this work, we aim to develop a patient-data-based virtual imaging trial framework and, as a first application, use it to measure the spatial resolution properties of a DLIR method.

METHODS

The patient-data-based virtual imaging trial framework consists of five steps: (1) insertion of lesions into projection domain data using the acquisition geometry of the patient exam to simulate different lesion characteristics; (2) insertion of noise into projection domain data using a realistic photon statistical model of the CT system to simulate different dose levels; (3) creation of DLIR-processed images from projection or image data; (4) creation of ensembles of DLIR-processed patient images from a large number of noise and lesion realizations; and (5) evaluation of image quality using ensemble DLIR images. This framework was applied to measure the spatial resolution of a ResNet based deep convolutional neural network (DCNN) trained on patient images. Lesions in a cylindrical shape and different contrast levels (-500, -100, -50, -20, -10 HU) were inserted to the lower right lobe of the liver in a patient case. Multiple dose levels were simulated (50%, 25%, 12.5%). Each lesion and dose condition had 600 noise realizations. Multiple reconstruction and denoising methods were used on all the noise realizations, including the original filtered-backprojection (FBP), iterative reconstruction (IR), and the DCNN method with three different strength setting (DCNN-weak, DCNN-medium, and DCNN-strong). Mean lesion signal was calculated by performing ensemble averaging of all the noise realizations for each lesion and dose condition and then subtracting the lesion-present images from the lesion absent images. Modulation transfer functions (MTFs) both in-plane and along the z-axis were calculated based on the mean lesion signals. The standard deviations of MTFs at each condition were estimated with bootstrapping: randomly sampling (with replacement) all the DLIR/FBP/IR images from the ensemble data (600 samples) at each condition. The impact of varying lesion contrast, dose levels, and denoising strengths were evaluated. Statistical analysis with paired t-test was used to compare the z-axis and in-plane spatial resolution of five algorithms for five different contrasts and three dose levels.

RESULTS

The in-plane and z-axis spatial resolution degradation of DCNN becomes more severe as the contrast or radiation dose decreased, or DCNN denoising strength increased. In comparison with FBP, a 59.5% and 4.1% reduction of in-plane and z-axis MTF (in terms of spatial frequencies at 50% MTF), respectively, was observed at low contrast (-10 HU) for DCNN with the highest denoising strength at 25% routine dose level. When the dose level reduces from 50% to 12.5% of routine dose, the in-plane and z-axis MTFs reduces from 92.1% to 76.3%, and from 98.9% to 95.5%, respectively, at contrast of -100 HU, using FBP as the reference. For most conditions of contrasts and dose levels, significant differences were found among the five algorithms, with the following relationship in both in-plane and cross-plane spatial resolution: FBP > DCNN-Weak > IR > DCNN-Medium > DCNN-Strong. The spatial resolution difference among algorithms decreases at higher contrast or dose levels.

CONCLUSIONS

A patient-data-based virtual imaging trial framework was developed and applied to measuring the spatial resolution properties of a DCNN noise reduction method at different contrast and dose levels using real patient data. As with other non-linear image reconstruction and post-processing techniques, the evaluated DCNN method degraded the in-plane and z-axis spatial resolution at lower contrast levels, lower radiation dose, and higher denoising strength.

Improving image quality consistency and diagnostic accuracy in lower extremity CT angiography using a split-bolus contrast injection protocol

OBJECTIVES

To evaluate the clinical value of using a split-bolus contrast injection protocol in improving image quality consistency and diagnostic accuracy in lower extremity CT angiography (CTA).

METHODS

Fifty (mean age, 66 ± 12 years) and 39 (mean age, 66 ± 11 years) patients underwent CTA in the lower extremity arteries using split-bolus and fixed-bolus injection schemes, respectively. The objective and subjective image quality of the 2 groups were compared and the diagnostic efficacy for the degree of vessel stenosis was compared using digital subtraction angiography as the gold standard. A P < .05 was considered statistically significant.

RESULTS

In comparison with the fixed-bolus scheme, the split-bolus scheme greatly improved the consistency of image quality of the low extremities by significantly increasing the arterial enhancement (337.87 ± 64.67HU vs. 254.74 ± 71.58HU, P < .001), signal-to-noise ratio (22.58 ± 11.64 vs. 7.14 ± 1.98, P < .001), and contrast-to-noise ratio (37.21 ± 10.46 vs. 31.10 ± 15.40, P = .041) in the infrapopliteal segment. The subjective image quality was better (P < .001) and the diagnostic accuracy was higher in the split-bolus group than in the fixed-bolus group (96.00% vs. 91.67%, P < .05, for diagnosing >50% stenosis, and 97.00% vs. 89.10%, P < .05, for diagnosing occlusion) for the infrapopliteal segment arteries.

CONCLUSIONS

Compared with the fixed-bolus injection scheme, the split-bolus injection scheme improves the image quality consistency and diagnostic accuracy especially for the infrapopliteal segment arteries in lower extremity CTA.

ADVANCES IN KNOWLEDGE

(1) The split-bolus injection scheme of CTA of the lower extremity arteries improves the overall image quality, uniformity of contrast enhancement. (2) Compared with the fixed-bolus injection scheme, the split-bolus injection scheme especially improves the infrapopliteal segment arteries image quality and diagnostic efficacy.

In Situ Loading and Time-Resolved Synchrotron-Based Phase Contrast Tomography for the Mechanical Investigation of Connective Knee Tissues: A Proof-of-Concept Study

Articular cartilage and meniscus transfer and distribute mechanical loads in the knee joint. Degeneration of these connective tissues occurs during the progression of knee osteoarthritis, which affects their composition, microstructure, and mechanical properties. A deeper understanding of disease progression can be obtained by studying them simultaneously. Time-resolved synchrotron-based X-ray phase-contrast tomography (SR-PhC-µCT) allows to capture the tissue dynamics. This proof-of-concept study presents a rheometer setup for simultaneous in situ unconfined compression and SR-PhC-µCT of connective knee tissues. The microstructural response of bovine cartilage (n = 16) and meniscus (n = 4) samples under axial continuously increased strain, or two steps of 15% strain (stress-relaxation) is studied. The chondrocyte distribution in cartilage and the collagen fiber orientation in the meniscus are assessed. Variations in chondrocyte density reveal an increase in the top 40% of the sample during loading, compared to the lower half. Meniscus collagen fibers reorient perpendicular to the loading direction during compression and partially redisperse during relaxation. Radiation damage, image repeatability, and image quality assessments show little to no effects on the results. In conclusion, this approach is highly promising for future studies of human knee tissues to understand their microstructure, mechanical response, and progression in degenerative diseases.

[Effect of Gonadal Protection on Image Quality in Frontal Hip Radiography]

PURPOSE

In recent years, there has been a growing movement in Western countries toward the abolition of gonadal protection during radiography. The reasons for this recommendation are that there are few reports of increased risk of genetic effects, that the ovarian dose is not due to direct X-rays but due to internally scattered X-rays that cannot be shielded, and that the presence of gonadal protection may adversely affect the automatic exposure control mechanism and may mask important findings. In addition, the gonadal protection is a large high absorber of X-rays, and its presence in the irradiation field may have some effect on image quality, but the effect of the gonadal protection on image quality has not been clarified. In addition, after the abolition of gonadal protection, the optimal irradiation field setting is expected to become even more important to avoid unnecessary exposure. In this study, we investigated the effect of gonadal protection on image quality in frontal hip radiographs of adults with different radiation qualities and clarified the image quality under conditions in which the irradiation field is appropriately narrowed.

METHOD

Frontal hip radiographs were taken using a human phantom as the subject, and the image quality of the femoral head was evaluated. Two irradiation fields were used: (a) 14×17 inch field and (b) an appropriate field (11.6×15 inch) that does not impair the reference line and image information necessary for reading hip joint images. The imaging tube voltage was set at 70 kV, and conditions for adding a copper filter were also considered. The incident surface air kerma was set to 1.25 mGy. The incident surface dose at this time was sufficiently lower than the diagnostic reference level (2.5 mGy) in Japan and was judged to be appropriate for imaging using an indirect conversion flat panel detector. The image quality evaluation item was the signal difference to noise ratio (SdNR) including scatterers.

RESULT

The SdNR decreased by 4.6% when a gonadal shield was placed, indicating that the gonadal shield reduced image quality. When the irradiation field size was appropriately narrowed down, SdNR slightly increased or decreased depending on the quality of the imaging material, but the change was small compared to the change in SdNR with and without the gonadal protection shield.

CONCLUSION

The results of this study confirm that the elimination of gonadal protection in hip radiography has significant advantages, such as reducing unnecessary X-ray exposure while ensuring image quality, when the irradiation field is set appropriately.

Imaging of hypodense gliotic lesions in photon counting computed tomography using virtual monoenergetic images

OBJECTIVES

Glioses appear as hypodense lesions in non-contrast CT examinations of the head. Photon counting CT (PCCT) enables the calculation of virtual monoenergetic images (VMI). The aim of this study is to investigate in which VMI hypodense gliotic lesions can be delineated best.

MATERIALS AND METHODS

35 patients with an MRI-confirmed gliotic lesion and a non-contrast PCCT of the head were retrospectively included. All available VMI from 40 keV to 190 keV were calculated. In a quantitative analysis, conventional image quality parameters were calculated, in particular the contrast-to-noise ratio (CNR) of the hypodense lesion compared to the white matter. In a qualitative analysis, selected VMI were rated by experienced radiologists.

RESULTS

The absolute maximum of CNR was 8.12 ± 5.64 in the VMI 134 keV, in post hoc testing, there were significant differences in comparison to VMI with keV ≤110 and keV ≥180 (corrected p < .05). In the qualitative analysis, there were only very slight differences in the rating of the VMI with 66 keV, 80 keV, 100 keV, and 134 keV with overall low agreement between the readers.

CONCLUSIONS

The quantitative superiority of VMI 134 keV for the delineation of hypodense gliotic lesions did not translate into a superiority in the qualitative analysis. Therefore, it remains uncertain if the reconstruction of a high keV VMIs for the detection of hypodense gliotic lesions is useful in everyday clinical practice. However, more studies, are necessary to further assess this issue.

Deep Convolutional Neural Networks Provide Motion Grading for High-Resolution Peripheral Quantitative Computed Tomography of the Scaphoid

In vivo high-resolution peripheral quantitative computed tomography (HR-pQCT) studies on bone characteristics are limited, partly due to the lack of standardized and objective techniques to describe motion artifacts responsible for lower-quality images. This study investigates the ability of such deep-learning techniques to assess image quality in HR-pQCT datasets of human scaphoids. In total, 1451 stacks of 482 scaphoid images from 53 patients, each with up to six follow-ups within one year, and each with one non-displaced fractured and one contralateral intact scaphoid, were independently graded by three observers using a visual grading scale for motion artifacts. A 3D-CNN was used to assess image quality. The accuracy of the 3D-CNN to assess the image quality compared to the mean results of three skilled operators was between 92% and 96%. The 3D-CNN classifier reached an ROC-AUC score of 0.94. The average assessment time for one scaphoid was 2.5 s. This study demonstrates that a deep-learning approach for rating radiological image quality provides objective assessments of motion grading for the scaphoid with a high accuracy and a short assessment time. In the future, such a 3D-CNN approach can be used as a resource-saving and cost-effective tool to classify the image quality of HR-pQCT datasets in a reliable, reproducible and objective way.

Noise-Optimized CBCT Imaging of Temporomandibular Joints-The Impact of AI on Image Quality

Background: Temporomandibular joint disorder (TMD) is a common medical condition. Cone beam computed tomography (CBCT) is effective in assessing TMD-related bone changes, but image noise may impair diagnosis. Emerging deep learning reconstruction algorithms (DLRs) could minimize noise and improve CBCT image clarity. This study compares standard and deep learning-enhanced CBCT images for image quality in detecting osteoarthritis-related degeneration in TMJs (temporomandibular joints). This study analyzed CBCT images of patients with suspected temporomandibular joint degenerative joint disease (TMJ DJD). Methods: The DLM reconstructions were performed with ClariCT.AI software. Image quality was evaluated objectively via CNR in target areas and subjectively by two experts using a five-point scale. Both readers also assessed TMJ DJD lesions. The study involved 50 patients with a mean age of 28.29 years. Results: Objective analysis revealed a significantly better image quality in DLM reconstructions (CNR levels; p < 0.001). Subjective assessment showed high inter-reader agreement (κ = 0.805) but no significant difference in image quality between the reconstruction types (p = 0.055). Lesion counts were not significantly correlated with the reconstruction type (p > 0.05). Conclusions: The analyzed DLM reconstruction notably enhanced the objective image quality in TMJ CBCT images but did not significantly alter the subjective quality or DJD lesion diagnosis. However, the readers favored DLM images, indicating the potential for better TMD diagnosis with CBCT, meriting more study.

Channelized hotelling observer-based low-contrast detectability on the ACR CT accreditation phantom: Part II. Repeatability study

BACKGROUND

Objective and quantitative evaluation for low-contrast detectability that correlates with human observer performance is lacking for routine CT quality control testing. Channelized Hotelling observer (CHO) is considered a strong candidate to fill the need but has long been deemed impractical to implement due to its requirement of a large number of repeated scans in order to provide accurate and precise estimates of index of detectability (d'). In our previous work, we optimized a CHO model observer on the American College of Radiology (ACR) CT accreditation phantom and achieved accurate measurement of d' with only 1-3 repeat scans.

PURPOSE

In this work, we aim to validate the repeatability of the proposed CHO-based low-contrast evaluation on four scanner models using the ACR CT accreditation phantom.

METHODS

The repeatability test was performed on four different scanners from two major CT manufacturers: Siemens Force and Alpha; Canon Prism and Prime SP. An ACR CT phantom was scanned 10 times, each time after repositioning of the phantom. For each repositioning, 3 repeated scans were acquired at 24, 12, and 6 mGy on all four scanner models. CHO was applied at the measured dose levels for different low-contrast object sizes (4-6 mm). The CHO was also applied to images created using deep learning-based reconstructions on Canon Prism and to four different scan/reconstruction modes on the Siemens Alpha, a photon-counting-detector (PCD)-CT. The repeatability was evaluated by the probability that a measurement would fall within the ±15% tolerance (P<15% ).

RESULTS

With the CHO setting optimized for the ACR phantom and the use of 3 repeated scans and 9 non-overlapping slices per scan, the CHO measurement could provide high repeatability with P<15% of 98.8%-99.9% at 12 mGy with IR reconstruction on all four scanners. On scanner A, P<15% were 91.5%-99.9% at the three dose levels and for all three object sizes while the numbers were 93.6%-99.998% on scanner B. P<15% were 96.5%-97.2% for the two deep learning reconstructions and 97.0%-99.97% for the four scan/reconstruction modes on the PCD-CT.

CONCLUSION

The CHO provided highly repeatable measurements with over 95% probability that a CHO measurement would lie within the ±15% tolerance for most of the dose levels and object sizes on the ACR phantom. The repeatability was maintained when the CHO was applied to images created with a commercial deep learning-based reconstruction and various scan/reconstruction modes on a PCD-CT. This study demonstrates that practical implementation of CHO for routine quality control and performance evaluation is feasible.

Technical note: Preprocessing of portal images to improve image quality of VMAT-CT

BACKGROUND

The concept of volumetric modulated arc therapy-computed tomography (VMAT-CT) was proposed more than a decade ago. However, its application has been very limited mainly due to the poor image quality. More specifically, the blurred areas in electronic portal imaging device (EPID) images collected during VMAT heavily degrade the image quality of VMAT-CT.

PURPOSE

The goal of this study was to propose systematic methods to preprocess EPID images and improve the image quality of VMAT-CT.

METHODS

Online region-based active contour method was introduced to binarize portal images. Multi-leaf collimator (MLC) motion modeling was developed to remove the MLC motion blur. Outlier filtering was then applied to replace the remaining artifacts with plausible data. To assess the impact of these preprocessing methods on the image quality of VMAT-CT, 44 clinical VMAT plans for several treatment sites (lung, esophagus, and head & neck) were delivered to a Rando phantom, and several real-patient cases were also acquired. VMAT-CT reconstruction was attempted for all the cases, and image quality was evaluated.

RESULTS

All three preprocessing methods could effectively remove the blurred edges of EPID images. The combined preprocessing methods not only saved VMAT-CT from distortions and artifacts, but also increased the percentage of VMAT plans that can be reconstructed.

CONCLUSIONS

The systematic preprocessing of portal images improves the image quality of VMAT-CT significantly, and facilitates the application of VMAT-CT as an effective image guidance tool.

Effects of body part thickness on low-contrast detail detection and radiation dose during adult chest radiography

INTRODUCTION

Differences in patient size often provide challenges for radiographers, particularly when choosing the optimum acquisition parameters to obtain radiographs with acceptable image quality (IQ) for diagnosis. This study aimed to assess the effect of body part thickness on IQ in terms of low-contrast detail (LCD) detection and radiation dose when undertaking adult chest radiography (CXR).

METHODS

This investigation made use of a contrast detail (CD) phantom. Polymethyl methacrylate (PMMA) was utilised to approximate varied body part thicknesses (9, 11, 15 and 17 cm) simulating underweight, standard, overweight and obese patients, respectively. Different tube potentials were tested against a fixed 180 cm source to image distance (SID) and automatic exposure control (AEC). IQ was analysed using bespoke software thus providing an image quality figure inverse (IQFinv ) value which represents LCD detectability. Dose area product (DAP) was utilised to represent the radiation dose.

RESULTS

IQFinv values decreased statistically (P = 0.0001) with increasing phantom size across all tube potentials studied. The highest IQFinv values were obtained at 80 kVp for all phantom thicknesses (2.29, 2.02, 1.8 and 1.65, respectively). Radiation dose increased statistically (P = 0.0001) again with increasing phantom thicknesses.

CONCLUSION

Our findings demonstrate that lower tube potentials provide the highest IQFinv scores for various body part thicknesses. This is not consistent with professional practice because radiographers frequently raise the tube potential with increased part thickness. Higher tube potentials did result in radiation dose reductions. Establishing a balance between dose and IQ, which must be acceptable for diagnosis, can prevent the patient from receiving unnecessary additional radiation dose.

Reducing windmill artifacts in clinical spiral CT using a deep learning-based projection raw data upsampling: Method and robustness evaluation

BACKGROUND

Multislice spiral computed tomography (MSCT) requires an interpolation between adjacent detector rows during backprojection. Not satisfying the Nyquist sampling condition along the z-axis results in aliasing effects, also known as windmill artifacts. These image distortions are characterized by bright streaks diverging from high contrast structures.

PURPOSE

The z-flying focal spot (zFFS) is a well-established hardware-based solution that aims to double the sampling rate in longitudinal direction and therefore reduce aliasing artifacts. However, given the technical complexity of the zFFS, this work proposes a deep learning-based approach as an alternative solution.

METHODS

We propose a supervised learning approach to perform a mapping between input projections and the corresponding rows required for double sampling in the z-direction. We present a comprehensive evaluation using both a clinical dataset obtained using raw data from 40 real patient scans acquired with zFFS and a synthetic dataset consisting of 100 simulated spiral scans using a phantom specifically designed for our problem. For the clinical dataset, we utilized 32 scans as training set and 8 scans as validation set, whereas for the synthetic dataset, we used 80 scans for training and 20 scans for validation purposes. Both qualitative and quantitative assessments are conducted on a test set consisting of nine real patient scans and six phantom measurements to validate the performance of our approach. A simulation study was performed to investigate the robustness against different scan configurations in terms of detector collimation and pitch value.

RESULTS

In the quantitative comparison based on clinical patient scans from the test set, all network configurations show an improvement in the root mean square error (RMSE) of approximately 20% compared to neglecting the doubled longitudinal sampling by the zFFS. The results of the qualitative analysis indicate that both clinical and synthetic training data can reduce windmill artifacts through the application of a correspondingly trained network. Together with the qualitative results from the test set phantom measurements it is emphasized that a training of our method with synthetic data resulted in superior performance in windmill artifact reduction.

CONCLUSIONS

Deep learning-based raw data interpolation has the potential to enhance the sampling in z-direction and thus minimize aliasing effects, as it is the case with the zFFS. Especially a training with synthetic data showed promising results. While it may not outperform zFFS, our method represents a beneficial solution for CT scanners lacking the necessary hardware components for zFFS.

Deep learning-accelerated image reconstruction in MRI of the orbit to shorten acquisition time and enhance image quality

BACKGROUND AND PURPOSE

This study explores the use of deep learning (DL) techniques in MRI of the orbit to enhance imaging. Standard protocols, although detailed, have lengthy acquisition times. We investigate DL-based methods for T2-weighted and T1-weighted, fat-saturated, contrast-enhanced turbo spin echo (TSE) sequences, aiming to improve image quality, reduce acquisition time, minimize artifacts, and enhance diagnostic confidence in orbital imaging.

METHODS

In a 3-Tesla MRI study of 50 patients evaluating orbital diseases from March to July 2023, conventional (TSES ) and DL TSE sequences (TSEDL ) were used. Two neuroradiologists independently assessed the image datasets for image quality, diagnostic confidence, noise levels, artifacts, and image sharpness using a randomized and blinded 4-point Likert scale.

RESULTS

TSEDL significantly reduced image noise and artifacts, enhanced image sharpness, and decreased scan time, outperforming TSES (p < .05). TSEDL showed superior overall image quality and diagnostic confidence, with relevant findings effectively detected in both DL-based and conventional images. In 94% of cases, readers preferred accelerated imaging.

CONCLUSION

The study proved that using DL for MRI image reconstruction in orbital scans significantly cut acquisition time by 69%. This approach also enhanced image quality, reduced image noise, sharpened images, and boosted diagnostic confidence.

A spiculated mass target model for clinical image quality control in digital mammography

OBJECTIVES

Quality assurance of breast imaging has a long history of using test objects to optimize and follow up imaging devices. In particular, the evaluation of new techniques benefits from suitable test objects. The applicability of a phantom consisting of spiculated masses to assess image quality and its dependence on dose in flat field digital mammography (FFDM) and digital breast tomosynthesis systems (DBT) is investigated.

METHODS

Two spiculated masses in five different sizes each were created from a database of clinical tumour models. The masses were produced using 3D printing and embedded into a cuboid phantom. Image quality is determined by the number of spicules identified by human observers.

RESULTS

The results suggest that the effect of dose on spicule detection is limited especially in cases with smaller objects and probably hidden by the inter-reader variability. Here, an average relative inter-reader variation of the counted number of 31% was found (maximum 83%). The mean relative intra-reader variability was found to be 17%. In DBT, sufficiently good results were obtained only for the largest masses.

CONCLUSIONS

It is possible to integrate spiculated masses into a cuboid phantom. It is easy to print and should allow a direct and prompt evaluation of the quality status of the device by counting visible spicules. Human readout presented the major uncertainty in this study, indicating that automated readout may improve the reproducibility and consistency of the results considerably.

ADVANCES IN KNOWLEDGE

A cuboid phantom including clinical objects as spiculated lesion models for visual assessing the image quality in FFDM and DBT was developed and is introduced in this work. The evaluation of image quality works best with the two larger masses with 21 spicules.

Impact of cone-beam computed tomography artifacts on dose calculation accuracy for lung cancer

BACKGROUND

To implement image-guided adaptive radiotherapy (IGART), many studies investigated dose calculations on cone-beam computed tomography (CBCT). A high HU accuracy is crucial for a high dose calculation accuracy and many imaging sites showed satisfactory results. It has been shown that the dose calculation accuracy for lung cancer lags behind.

PURPOSE

To examine why the dose calculation accuracy for lung is insufficient, the relative effects of the field-of-view (FOV), breathing motion, and scatter on dose calculation accuracy were studied.

METHODS

A framework was built to simulate CBCT scans for lung cancer patients by forward projecting repeat CT (rCT) scans for two scan geometries: small (SFOV) and medium FOV (MFOV). Breathing motion was modeled by applying a 4D deformation vector field to the mid-position rCT. Scatter was modeled by Monte-Carlo simulations with/without an anti-scatter grid (ASG). Simulated projections were reconstructed using filtered back-projection with/without scatter correction. In case of the SFOV, the CBCT images were patched with the planning CT scan in axial direction. The treatment plan was recalculated on the rCT and simulated CBCT. The mean Hounsfield unit (HU) difference (ΔHUmean ), the structural similarity index measure (SSIM), and γ metrics were calculated for the CBCT datasets of various imaging settings.

RESULTS

The differences in HU, SSIM and dose calculation accuracy for CBCTs with and without breathing motion were negligible (mean ΔHUmean  = 6.4 vs. 13.7, mean SSIM = 0.941 vs. 0.957, mean γ (ref = MFOV) = 0.75). The SFOV resulted in a lower HU (mean ΔHUmean  = -9.2 vs. 13.7) and SSIM (mean SSIM = 0.912 vs. 0.957), and therefore in dose differences compared to the MFOV (mean γ = 1.22). Scatter led to considerable discrepancies in all metrics. Adding only the ASG improved the results more than only applying a scatter correction algorithm. Combining ASG and scatter correction algorithm resulted in an even higher dose calculation accuracy.

CONCLUSIONS

Scatter and FOV are the main contributors to dose inaccuracies and motion has only a minor effect on dose calculation accuracy. Therefore, utilizing an appropriate scatter correction and FOV is important to achieve sufficient dose calculation accuracy to facilitate IGART for lung.

A challenge and solution for automatic thin slice thickness measurements on images of the Catphan phantom

Purpose. The use of the Hough transform for angle detection is quite accurate for relatively wide slice thickness. However, the Hough transform fails to accurately detect the angle for thin slice thickness. This study proposes a method for automatically measuring the thickness of thin slices on images of a Catphan phantom.Methods. In the proposed method, the angle of the phantom's orientation was determined based on the relative coordinates of the four hole objects in the phantom. After the angles of the wires were determined, the profiles of pixel values across the wire objects were constructed. Finally, their full widths at half maximum (FWHMs) were determined and multiplied bytan23° to obtain the slice thicknesses of the images. The results of the proposed method were compared to a previous method, which used the Hough transform to obtain the phantom's orientation. We used slice thicknesses ranging from 0.8 mm to 5.0 mm, and phantom angles from 0° to 10°.Results. Our proposed method detected the angle of the phantom accurately for thin slices, whereas a previous method did not accurately detect the angle. The results of the slice thickness using this current method were slightly higher (within 7.9%) compared to the previous method. However, the results of the two methods did not differ significantly (p-value > 0.05). Using different angles, the current method detected all the angles more accurately. Again, the slice thicknesses were not significantly different from the previous method (p-value > 0.05).Conclusion. The proposed method for measuring the thickness of thin slices in an image of a Catphan phantom, based on the relative coordinates of the four hole objects in the phantom, outperformed a previous method based on the Hough transform.

Standardization of a CT Protocol for Imaging Patients with Suspected COVID-19-A RACOON Project

CT protocols that diagnose COVID-19 vary in regard to the associated radiation exposure and the desired image quality (IQ). This study aims to evaluate CT protocols of hospitals participating in the RACOON (Radiological Cooperative Network) project, consolidating CT protocols to provide recommendations and strategies for future pandemics. In this retrospective study, CT acquisitions of COVID-19 patients scanned between March 2020 and October 2020 (RACOON phase 1) were included, and all non-contrast protocols were evaluated. For this purpose, CT protocol parameters, IQ ratings, radiation exposure (CTDIvol), and central patient diameters were sampled. Eventually, the data from 14 sites and 534 CT acquisitions were analyzed. IQ was rated good for 81% of the evaluated examinations. Motion, beam-hardening artefacts, or image noise were reasons for a suboptimal IQ. The tube potential ranged between 80 and 140 kVp, with the majority between 100 and 120 kVp. CTDIvol was 3.7 ± 3.4 mGy. Most healthcare facilities included did not have a specific non-contrast CT protocol. Furthermore, CT protocols for chest imaging varied in their settings and radiation exposure. In future, it will be necessary to make recommendations regarding the required IQ and protocol parameters for the majority of CT scanners to enable comparable IQ as well as radiation exposure for different sites but identical diagnostic questions.

B1 Power Modification for Amide Proton Transfer Imaging in Parotid Glands: A Strategy for Image Quality Accommodation and Evaluation of Tumor Detection Feasibility

BACKGROUND

In the application of APTw protocols for evaluating tumors and parotid glands, inhomogeneity and hyperintensity artifacts have remained an obstacle. This study aimed to improve APTw imaging quality and evaluate the feasibility of difference B1 values to detect parotid tumors.

METHODS

A total of 31 patients received three APTw sequences to acquire 32 lesions and 30 parotid glands (one patient had lesions on both sides). Patients received T2WI and 3D turbo-spin-echo (TSE) APTw imaging on a 3.0 T scanner for three sequences (B1 = 2 μT, 1 μT, and 0.7 μT in APTw 1, 2, and 3, respectively). APTw image quality was evaluated using four-point Likert scales in terms of integrity and hyperintensity artifacts. Image quality was compared between the three sequences. An evaluable group and a trustable group were obtained for APTmean value comparison.

RESULTS

Tumors in both APT2 and APT3 had fewer hyperintensity artifacts than in APT1. With B1 values decreasing, tumors had less integrity in APTw imaging. APTmean values of tumors were higher than parotid glands in traditional APT1 sequence though not significant, while the APTmean subtraction value was significantly different.

CONCLUSIONS

Applying a lower B1 value could remove hyperintensity but could also compromise its integrity. Combing different APTw sequences might increase the feasibility of tumor detection.

Optimization of Thermal Control Design for Aerial Reflective Opto-Mechanical Structure

To improve the adaptability of aerial reflective opto-mechanical structures (mainly including the primary mirror and secondary mirror) to low-temperature environments, typically below -40 °C, an optimized thermal control design, which includes passive insulation and temperature-negative feedback-variable power zone active heating, is proposed. Firstly, the relationship between conventional heating methods and the axial/radial temperature differences of mirrors with different shapes is analyzed. Based on the heat transfer analyses, it is pointed out that optimized thermal control design is necessary to ensure the temperature uniformity of the fused silica mirror, taking into account the temperature level when the aerial electro-optics system is working in low-temperature environments. By adjusting the input voltage based on the measured temperature, the heating power of the subregion is changed accordingly, so as to locally increase or decrease the temperature of the mirrors. The thermal control scheme ensures that the average temperature of the mirror fluctuates slowly and slightly around 20 °C. At the same time, the temperature differences within a mirror and between the primary mirror and the secondary mirror can be controlled within 5 °C. Thereby, the resolution of EO decreases by no more than 11.4%.

Ultra-low-dose photon-counting CT of paranasal sinus: an in vivo comparison of radiation dose and image quality to cone-beam CT

PURPOSE

This study investigated the differences in subjective and objective image parameters as well as dose exposure of photon-counting CT (PCCT) compared to cone-beam CT (CBCT) in paranasal sinus imaging for the assessment of rhinosinusitis and sinonasal anatomy.

METHODS

This single-centre retrospective study included 100 patients, who underwent either clinically indicated PCCT or CBCT of the paranasal sinus. Two blinded experienced ENT radiologists graded image quality and delineation of specific anatomical structures on a 5-point Likert scale. In addition, contrast-to-noise ratio (CNR) and applied radiation doses were compared among both techniques.

RESULTS

Image quality and delineation of bone structures in paranasal sinus PCCT was subjectively rated superior by both readers compared to CBCT (P < .001). CNR was significantly higher for photon-counting CT (P < .001). Mean effective dose for PCCT examinations was significantly lower than for CBCT (0.038 mSv ± 0.009 vs. 0.14 mSv ± 0.011; P < .001).

CONCLUSION

In a performance comparison of PCCT and a modern CBCT scanner in paranasal sinus imaging, we demonstrated that first-use PCCT in clinical routine provides higher subjective image quality accompanied by higher CNR at close to a quarter of the dose exposure compared to CBCT.

Evaluating Image Quality and Radiation Dose in Low-Dose Thoraco-Abdominal CT Angiography with a Tin Filter for Patients with Aortic Disease

Background: We aimed to compared radiation exposure and image quality between tin-filter-based and standard dose thoraco-abdominal computed tomography angiography (TACTA) protocols, aiming to address a gap in the existing literature. Methods: In this retrospective study, ninety consecutive patients undergoing TACTA were included. Of these, 45 followed a routine standard-dose protocol (ST100kV), and 45 underwent a low-dose protocol with a tin filter (TF100kV). Radiation metrics were compared. The signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and figure of merit (FOM) were calculated for the thoracic and abdominal aorta and right common iliac artery. Two independent readers assessed the image noise, image contrast, sharpness, and subjective image quality. Results: The mean dose for the TF100kV group was significantly lower (DLP 128.25 ± 18.18 mGy*cm vs. 662.75 ± 181.29, p < 0.001; CTDIvol 1.83 ± 0.25 mGy vs. 9.28 ± 2.17, p = 0.001), with an effective dose close to 2.3 mSv (2.31 ± 0.33 mSv; p < 0.001). The TF100kV group demonstrated greater dose efficiency (FOM, thoracic aorta: 36.70 ± 22.77 vs. 13.96 ± 13.18 mSv-1, p < 0.001) compared to the ST100kV group. Conclusions: Dedicated low-dose TACTA using a tin filter can significantly reduce the radiation dose while maintaining sufficient diagnostic image quality.

An unusual artifact observed on screening mammography in a patient with an LVAD

PURPOSE

Screening mammography and digital breast tomosynthesis consist of high-resolution x-ray images to identify findings that are potentially indicative of breast cancer, enabling early detection and reduction of breast cancer mortality. Imaging artifacts can occasionally occur, sometimes due to patient-related medical devices. Because of continuous evolution of new technologies, there is potential for novel artifacts to be encountered. In this technical note, we report an unusual artifact in the screening mammogram of a patient with an Abbott HeartMate 3 left ventricular assist device (LVAD).

METHODS

A 72-year-old patient with a HeartMate 3 LVAD presented to our breast imaging facility for a standard screening exam with digital breast tomosynthesis (Selenia Dimensions, Hologic Inc., Bedford, MA) and synthetic 2D images (C-view, Hologic Inc., Bedford, MA).

RESULTS

Linear artifacts oriented in the anteroposterior dimension demonstrating a spatial periodicity of ∼1.4 mm were seen on all left breast images, whereas concurrent right breast images did not demonstrate any artifacts. Repeat attempts using two identical digital breast tomosynthesis units demonstrated the same artifacts. No other exam at our imaging center that day demonstrated any such artifacts. Mammogram exams performed on this patient prior to her LVAD placement did not exhibit any similar artifacts.

CONCLUSION

Findings support the patient's LVAD as the underlying source of linear artifacts observed on left breast images, particularly given the proximity of the LVAD to the left breast. With the number of patients receiving LVAD placement on the rise, as well as increasing median survival rates status post LVAD implantation, recognition of this LVAD related artifact on mammography may be important.

Synergizing photon-counting CT with deep learning: potential enhancements in medical imaging

This review article highlights the potential of integrating photon-counting computed tomography (CT) and deep learning algorithms in medical imaging to enhance diagnostic accuracy, improve image quality, and reduce radiation exposure. The use of photon-counting CT provides superior image quality, reduced radiation dose, and material decomposition capabilities, while deep learning algorithms excel in automating image analysis and improving diagnostic accuracy. The integration of these technologies can lead to enhanced material decomposition and classification, spectral image analysis, predictive modeling for individualized medicine, workflow optimization, and radiation dose management. However, data requirements, computational resources, and regulatory and ethical concerns remain challenges that need to be addressed to fully realize the potential of this technology. The fusion of photon-counting CT and deep learning algorithms is poised to revolutionize medical imaging and transform patient care.

Edge Enhancement Optimization in Flexible Endoscopic Images to the Perception of Ear, Nose and Throat Professionals

OBJECTIVES

Digital endoscopes are connected to a video processor that applies various operations to process the image. One of those operations is edge enhancement that sharpens the image. The purpose of this study was to (1) quantify the level of edge enhancement, (2) measure the effect on sharpness and image noise, and (3) study the influence of edge enhancement on image quality perceived by ENT professionals.

METHODS

Three digital flexible endoscopic systems were included. The level of edge enhancement and the influence on sharpness and noise were measured in vitro, while systematically varying the levels of edge enhancement. In vivo images were captured at identical levels of one healthy larynx. Each series of in vivo images was presented to 39 ENT professionals according to a forced pairwise comparison test, to select the image with the best image quality for diagnostic purposes. The numbers of votes were converted to a psychometric scale of just noticeable differences (JND) according to the Thurstone V model.

RESULTS

The maximum level of edge enhancement varied per endoscopic system and ranged from 0.8 to 1.2. Edge enhancement increased sharpness and noise. Images with edge enhancement were unanimously preferred to images without edge enhancement. The quality difference with respect to zero edge enhancement reaches an optimum at levels between 0.7 and 0.9.

CONCLUSION

Edge enhancement has a major impact on sharpness, noise, and the resulting perceived image quality. We conclude that ENT professionals benefit from this video processing and should verify if their equipment is optimally configured.

LEVEL OF EVIDENCE

NA Laryngoscope, 134:842-847, 2024.

Pediatric evaluations for deep learning CT denoising

BACKGROUND

Deep learning (DL) CT denoising models have the potential to improve image quality for lower radiation dose exams. These models are generally trained with large quantities of adult patient image data. However, CT, and increasingly DL denoising methods, are used in both adult and pediatric populations. Pediatric body habitus and size can differ significantly from adults and vary dramatically from newborns to adolescents. Ensuring that pediatric subgroups of different body sizes are not disadvantaged by DL methods requires evaluations capable of assessing performance in each subgroup.

PURPOSE

To assess DL CT denoising in pediatric and adult-sized patients, we built a framework of computer simulated image quality (IQ) control phantoms and evaluation methodology.

METHODS

The computer simulated IQ phantoms in the framework featured pediatric-sized versions of standard CatPhan 600 and MITA-LCD phantoms with a range of diameters matching the mean effective diameters of pediatric patients ranging from newborns to 18 years old. These phantoms were used in simulating CT images that were then inputs for a DL denoiser to evaluate performance in different sized patients. Adult CT test images were simulated using standard-sized phantoms scanned with adult scan protocols. Pediatric CT test images were simulated with pediatric-sized phantoms and adjusted pediatric protocols. The framework's evaluation methodology consisted of denoising both adult and pediatric test images then assessing changes in image quality, including noise, image sharpness, CT number accuracy, and low contrast detectability. To demonstrate the use of the framework, a REDCNN denoising model trained on adult patient images was evaluated. To validate that the DL model performance measured with the proposed pediatric IQ phantoms was representative of performance in more realistic patient anatomy, anthropomorphic pediatric XCAT phantoms of the same age range were also used to compare noise reduction performance.

RESULTS

Using the proposed pediatric-sized IQ phantom framework, size differences between adult and pediatric-sized phantoms were observed to substantially influence the adult trained DL denoising model's performance. When applied to adult images, the DL model achieved a 60% reduction in noise standard deviation without substantial loss in sharpness in mid or high spatial frequencies. However, in smaller phantoms the denoising performance dropped due to different image noise textures resulting from the smaller field of view (FOV) between adult and pediatric protocols. In the validation study, noise reduction trends in the pediatric-sized IQ phantoms were found to be consistent with those found in anthropomorphic phantoms.

CONCLUSION

We developed a framework of using pediatric-sized IQ phantoms for pediatric subgroup evaluation of DL denoising models. Using the framework, we found the performance of an adult trained DL denoiser did not generalize well in the smaller diameter phantoms corresponding to younger pediatric patient sizes. Our work suggests noise texture differences from FOV changes between adult and pediatric protocols can contribute to poor generalizability in DL denoising and that the proposed framework is an effective means to identify these performance disparities for a given model.

Image quality assessment and feasibility of three-dimensional amide proton transfer-weighted imaging for hepatocellular carcinoma

Background

With the continuous innovation of magnetic resonance imaging (MRI) hardware and software technology, amide proton transfer-weighted (APTw) imaging has been applied in liver cancer. However, to our knowledge, no study has evaluated the feasibility of a three-dimensional amide proton transfer-weighted (3D-APTw) imaging sequence for hepatocellular carcinoma (HCC). This study thus aimed to conduct an image quality assessment of 3D-APTw for HCC and to explore its feasibility.

Methods

3D-APTw MRI examinations were completed in 134 patients with clinically suspected HCC. According to the uniformity of APTw signal in the liver and within the lesion and the proportion of artifact and missing signal regions, APTw images were subjectively scored using a 5-point scale. The scanning success rate of liver APTw imaging was calculated as the ratio of the number of cases with a quality assurance measurement of more than 3 to the total number of HCC cases. The intra- and interobserver quality assurance measurements for APTw images were compared via the Kappa consistency test. Within the HCC cases with a minimum image quality threshold of 3 points, the APT values of HCC and the liver parenchyma, signal-to-noise ratio of APT-weighted images (SNRAPTw), and contrast-to-noise ratio of HCC (CNRHCC) were measured by two observers. The intra- and interobserver agreement was assessed using the intraclass correlation coefficient (ICC). The differences in APT values between HCC and liver parenchyma was determined using the Mann-Whitney test.

Results

Sixty-six HCC cases with a quality assurance measurement of APTw imaging were included in the final analysis, and the calculated success rate was 70.21% (66/94). The subjective APT image quality scores of the two observers were consistent (3.66±1.18, 3.50±1.19, and 3.68±1.18), and no intergroup or intragroup statistical differences were found (P=0.594, and P=0.091), but the consistency of inter- and intraobserver was not as satisfactory (κ=0.594 and κ=0.580). The APT values in HCC lesion were significantly higher than those in liver parenchyma (2.73%±0.91% vs. 1.62%±0.55%; P<0.001). The APT values in HCC showed favorable intra- and interobserver consistency between the two observers (ICC =0.808 and ICC =0.853); the APT values in liver parenchyma, SNRAPTw, and CNRHCC values had moderate intraobserver consistency (ICC =0.578, ICC =0.568, and ICC =0.508) and interobserver consistency (ICC =0.599, ICC =0.199, and ICC =0.650). The coefficients of variation of the APTw values in the HCC lesion and in liver parenchyma were 33.4% and 34.4%, respectively. The SNRAPTw and CNRHCC were 30.75±18.74 and 3.56±3.19, with a coefficient of variation of 60.9% and 74.9%, respectively.

Conclusions

Liver 3D-APTw imaging was preliminarily demonstrated to be clinically feasible for evaluating HCC.

Application of deep learning image reconstruction-high algorithm in one-stop coronary and carotid-cerebrovascular CT angiography with low radiation and contrast doses

Background

For patients with suspected simultaneous coronary and cerebrovascular atherosclerosis, conventional single-site computed tomography angiography (CTA) for both sites can result in nonnegligible radiation and contrast agent dose. The purpose of this study was to validate the feasibility of one-stop coronary and carotid-cerebrovascular CTA (C&CC-CTA) with a "double-low" (low radiation and contrast) dose protocol reconstructed with deep learning image reconstruction with high setting (DLIR-H) algorithm.

Methods

From February 2018 to January 2019, 60 patients referred to C&CC-CTA simultaneously in West China Hospital were recruited in this prospective cohort study. By random assignment, patients were divided into two groups: double-low dose group (n=30) used 80 kVp and 24 mgI/kg/s contrast dose with images reconstructed using DLIR-H; and routine-dose group (n=30) used 100 kVp and 32 mgI/kg/s contrast dose with images reconstructed using 50% adaptive statistical iterative reconstruction-V (ASIR-V50%). Radiation and contrast doses, subjective image quality score, CT attenuation values, noise, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were measured and compared between the groups.

Results

The DLIR-H group used 30% less contrast dose (35.80±4.85 vs. 51.13±6.91 mL) and 48% less overall radiation dose (1.00±0.09 vs. 1.91±0.42 mSv) than the ASIR-V50% group (both P<0.001). There was no statistically significant difference on subjective quality score between the two groups (C-CTA: 4.38±0.67 vs. 4.17±0.81, P=0.337 and CC-CTA: 4.18±0.87 vs. 4.08±0.79, P=0.604). For coronary CTA, lower background noise (18.93±1.43 vs. 22.86±3.75 HU) was reached in DLIR-H group, and SNR and CNR at all assessed branches were significantly increased compared to ASIR-V50% group (all P<0.05), except SNR of left anterior descending (P>0.05). For carotid-cerebrovascular CTA, DLIR-H group was comparable in background noise (19.25±1.42 vs. 20.23±2.40 HU), SNR and CNR at all assessed branches with ASIR-V50% group (all P>0.05).

Conclusions

The "double-low" dose one-stop C&CC-CTA with DLIR-H obtained higher image quality compared with the routine-dose protocol with ASIR-V50% while achieving 48% and 30% reduction in radiation and contrast dose, respectively.