2.02 - Physical Parameters of Image Quality

Contrast-enhanced (CE) breast imaging involves the injection contrast agents (i.e., iodine) to increase conspicuity of malignant lesions. CE imaging may be used in conjunction with digital mammography (DM) or digital breast tomosynthesis (DBT) and has shown promise in improving diagnostic specificity. Both CE-DM and CE-DBT techniques require optimization as clinical diagnostic tools. Physical factors including x-ray spectra, subtraction technique, and the signal from iodine contrast, must be considered to provide the greatest object detectability and image quality. We developed a cascaded linear system model (CLSM) for the optimization of CE-DM and CE-DBT employing dual energy (DE) subtraction or temporal (TE) subtraction. We have previously developed a CLSM for DBT implemented with an a-Se flat panel imager (FPI) and filtered backprojection (FBP) reconstruction algorithm. The model is used to track image quality metrics - modulation transfer function (MTF) and noise power spectrum (NPS) - at each stage of the imaging chain. In this study, the CLSM is extended for CE breast imaging. The effect of x-ray spectrum (varied by changing tube potential and the filter) and DE and TE subtraction techniques on breast structural noise was measured was studied and included as a deterministic source of noise in the CLSM. From the two-dimensional (2D) and three-dimensional (3D) MTF and NPS, the ideal observer signal-to-noise ratio (SNR), also known as the detectability index (d'), may be calculated. Using d' as a FOM, we discuss the optimization of CE imaging for the task of iodinated contrast object detection within structured backgrounds. Increasing x-ray energy was determined to decrease the magnitude of structural noise and not its correlation. By performing DE subtraction, the magnitude of the structural noise was further reduced at the expense of increased stochastic (quantum and electronic) noise. TE subtraction exhibited essentially no residual structural noise at the expense of increased quantum noise, even over that of the DE case. For DE subtraction, optimization of dose weighting to the HE view (fh ) results in the minimization of quantum noise. Both subtraction weighting factor (wSub ) and the iodine contrast signal were dependent on the LE and HE x-ray spectra. To best detect a 5 mm Gaussian lesion with 5 mg/ml of iodine within a 4 cm thick breast, it was found that the high energy (HE) view should be acquired with a tube potential of 47 kVp (W/Ti spectrum) and the low energy (LE) view with a potential of 23 kVp (W/Rh spectrum). Due to the complete removal of structural noise, TE subtraction produced much higher d' than DE subtraction both as a function of mean glandular dose and iodine concentration. We have shown the effect of increasing x-ray energy as well as projection domain subtraction on breast structural noise. Further, we have exhibited the utility of the CLSM for DE and TE subtraction CE imaging in the optimization of imaging parameters such as x-ray energy, fh , and wSub as well as guiding the understanding of their effects on image contrast and noise.

REACT (Relaxation-Enhanced Angiography without ContrasT) is a reliable non-contrast magnetic resonance angiography for imaging of the thoracic aorta but remains time-consuming. This study evaluates acceleration of image acquisition using compressed sensing and parallel imaging (Compressed SENSE, CS) combined with deep learning-based image reconstruction (CS-AI). In this prospective single-center study, 40 volunteers underwent ECG- and navigator-triggered 3D REACT at 3T using CS acceleration factor 4 (CS4; reference standard) and 8 (CS8). CS8 data were reconstructed with standard and CS-AI methods (CS8-AI). Two radiologists measured aortic diameters, rated subjective image quality and performed pairwise comparisons. Additionally, objective image quality metrics were calculated. Median scan time was reduced by 44% (CS4: 8:41min; CS8/CS8-AI: 4:52min). All techniques showed excellent agreement in aortic diameter measurements (mean differences<0.2mm; P>0.999). CS8-AI demonstrated reduced mean absolute deviation from CS4 compared to CS8 (0.67 vs. 0.77mm; P=0.003), and measurement variance was 40-50% lower with CS8-AI than with CS8 (inter-/intrarater: P<0.001), and comparable to CS4. CS8 showed significantly lower subjective image quality scores than CS4 (3.70[3.33-4.00] vs. 4.25[3.90-4.50]; P<0.001), while CS8-AI showed comparable or higher scores (4.40[4.00-4.70]; P=0.076). Forced-choice comparisons favored CS4 over CS8 (90% vs. 2.5%; P<0.001), but no preference was observed between CS4 and CS8-AI (42.5% vs. 37.5%; P>0.999). Objective metrics predominantly confirmed the subjective results. Deep learning-based reconstruction enables the acquisition of REACT of the thoracic aorta in less than five minutes while preserving high image quality and maintaining excellent measurement reproducibility.

Breast cancer remains a leading cause of cancer-related morbidity and mortality worldwide. Early detection of breast cancer using high-quality mammography is essential for improving prognosis and treatment outcomes. Digital breast tomosynthesis with synthesized two-dimensional mammography (SM) reduces dose and tissue overlap; however, its comprehensive physical evaluations remain limited. This study quantitatively compared the image quality of conventional digital mammography (DM) and SM acquired using the AMULET Innovality System. Polymethyl methacrylate, American College of Radiology, and contrast-detail (CD) mammography phantoms were imaged 10 times in both DM and SM modes. Image quality metrics, including signal-to-noise ratio (SNR) maps, noise power spectrum (NPS), contrast-to-noise ratio (CNR), modulation transfer function (MTF), and CD curves, were evaluated. DM demonstrated higher SNR uniformity and suppressed low- and high-frequency noise, and the CNR values were 1.3-2 times greater than those of SM across all object sizes. MTF analysis showed superior resolution in DM (MTF(f50), 4.0 cycles/mm) compared with that of the SM (2.5 cycles/mm). CD curve analysis confirmed the better detectability of fine structures in DM, with values of 134.9 for DM and 56.7 for SM. Quantitative phantom-based evaluation demonstrated that DM showed superior performance to SM in several physical image quality metrics, including SNR uniformity, noise characteristics, spatial resolution, and contrast-detail detectability. These findings indicate systematic differences in physical image quality between DM and SM; however, further observer and clinical studies are required to clarify how these differences translate to diagnostic performance in clinical practice.

The increased development and production of Computed Tomography (CT) scanner technology has expanded patient access to advanced and affordable medical imaging technologies but has also introduced sources of variability in the clinical imaging landscape, which may influence patient care. This study examines the impact of intra-scanner and inter-scanner variability on image quality and quantitative imaging tasks, with a focus on the detectability index (d') as a measure of patient-specific task performance. We evaluated 813 clinical phantom image sets from the COPDGene study, aggregated by CT scanner make, model, and acquisition and reconstruction protocol. Each phantom image set was assessed for image quality metrics, including the Noise Power Spectrum (NPS) and in-plane Modulation Transfer Function (MTF). The d' index was calculated for 12 hypothetical lesion detection tasks, emulating clinically relevant lung and liver lesions of varying sizes and contrast levels. Qualitatively, analysis showed intra-scanner variability in NPS and MTF curves measured for identical acquisition and reconstruction settings. Inter-scanner comparisons demonstrated variability in d' measurements across different scanner makes and models, of similar acquisition and reconstruction settings. The study showed an intra-scanner variability of up to 13.7% and an inter-scanner variability of up to 19.3% in the d' index. These findings emphasize the need for considering scanner variability in patient-centered care and indicate that CT technology may influence the reliability of imaging tasks. The results of this study further motivate the development of virtual scanner models to better model and mitigate the variability observed in the clinical imaging landscape.

The effect of instrumental spherical aberration has been examined through the construction of telescopes with approximately 1, 2, and 3 waves of aberration. Contrast sensitivity and resolution results have been obtained, as well as information on the preferred focus in the presence of aberration. The results show that an appropriately defined integral of the instrumental modulation transfer function (MTF), called MTFa, is the best candidate among objective image quality metrics for describing the observed degradation in performance as well as the preferred focus. For pupil diameters less than approximately 3 mm the results support the assertion that the monochromatic aberrations of a normal eye are of little importance in visual instrument optical design and testing.

For image quality assessment, the availability of diverse databases is vital for the development and evaluation of image quality metrics. Existing databases have played an important role in promoting the understanding of various types of distortion and in the evaluation of image quality metrics. However, a comprehensive representation of optical aberrations and their impact on image quality is lacking. This paper addresses this gap by introducing a novel image quality database that focuses on optical aberrations. We conduct a subjective experiment to capture human perceptual responses on a set of images with optical aberrations. We then test the performance of selected objective image quality metrics to assess these aberrations. This approach not only ensures the relevance of our database to real-world scenarios but also contributes to ensuring the performance of the selected image quality metrics. The database is available for download at https: // www. ntnu. edu/ colourlab/ software .

Amorphous silicon (a-Si:H) flat-panel (FP) imaging systems have recently become commercially available for both chest and mammographic imaging applications. It has been shown that this new detector technology offers better image quality and various operational advantages over the computed radiography (CR) which to date has been the most widely implemented and used digital radiography technique. However, most image quality measurements reported on flat-panel systems have been performed on prototype systems in laboratories while those for CR systems were typically independently performed and reported on in separate studies. To directly compare the two technologies, we have measured the image properties for a commercial amorphous silicon/cesium iodide [a-Si:H/CsI(Tl)] flat-panel based digital chest system and a commercial CR system under clinical imaging conditions. In this paper, measurements of image quality metrics, including the modulation transfer functions (MTFs), noise power spectra (NPSs), and detective quantum efficiencies (DQEs), for the FP and CR systems are presented and compared. Methods and issues related to these measurements are discussed. The results show that the flat-panel system has slightly lower MTF but significantly higher DQEs than the CR system. The DQEs of the flat-panel system were found to increase with the exposure while those of the CR system decrease slightly with the exposure.

Many technical updates have been made in multi-detector CT. To evaluate image quality and radiation dose of high-pitch second- and third-generation dual-source chest CT angiography and to assess the effects of different levels of advanced modeled iterative reconstruction (ADMIRE) in newborns and children. Chest CT angiography (70 kVp) was performed in 42 children (age 158 ± 267 days, range 1-1,194 days). We evaluated subjective and objective image quality, and radiation dose with filtered back projection (FBP) and different strength levels of ADMIRE. For comparison were 42 matched controls examined with a second-generation 128-slice dual-source CT-scanner (80 kVp). ADMIRE demonstrated improved objective and subjective image quality (P < .01). Mean signal/noise, contrast/noise and subjective image quality were 11.9, 10.0 and 1.9, respectively, for the 80 kVp mode and 11.2, 10.0 and 1.9 for the 70 kVp mode. With ADMIRE, the corresponding values for the 70 kVp mode were 13.7, 12.1 and 1.4 at strength level 2 and 17.6, 15.6 and 1.2 at strength level 4. Mean CTDIvol, DLP and effective dose were significantly lower with the 70-kVp mode (0.31 mGy, 5.33 mGy*cm, 0.36 mSv) compared to the 80-kVp mode (0.46 mGy, 9.17 mGy*cm, 0.62 mSv; P < .01). The third-generation dual-source CT at 70 kVp provided good objective and subjective image quality at lower radiation exposure. ADMIRE improved objective and subjective image quality.

For practicing medical physicists, CT image quality assessment is, in general, limited to the ones required by ACR and based on the axial images, such as CT number uniformity, image noise, contrast‐to‐noise ratio (CNR), and high contrast resolution. However, with more advanced technologies introduced by CT vendors, just assessing these basic image quality metrics may not be enough. Noise power spectrum (NPS), characterizing the signal noise component at each spatial frequency, is a powerful tool to predict the CT performance related to lesion detectability and noise texture. The quality of coronal and sagittal reformatted images is, in a large part, determined by the slice profile. How to accurately assess the slice profile is important for clinical medical physicists but not well recognized.Automatic tube current modulation (ATCM) has been adopted by major CT manufacturers, to maintain image quality across the scan range. The way how ATMC works varies significantly among vendors. A “standard” method is needed to evaluate the performance of this critical dose‐saving and image quality improvement technique. As dual energy CT becomes more readily available for routine clinical use, the need of image quality assessment metrics unique to the dual energy CT mode is becoming urgent.The session will facilitate the discussion of NPS and its application in the evaluation of FBP and iterative reconstruction. This session will also present the methods to measure the slice profile and characterize the performance of ATCM. The last part of this session will focus on the physics of CT dual energy mode, as well as unique image quality metrics to evaluate the performance of the dual energy mode.Learning Objectives: Be familiar with procedures on how to correctly compute CT noise power spectrum Understand why basic image quality metrics used for FBP may not be sufficient to characterize the performance of advanced iterative reconstruction Understand CT slice profile and how to accurately measure it Be familiar with ATCM from major CT vendors and how to evaluate its performance Understand different hardware approaches for dual energy CT Be familiar with image quality metrics relevant to dual energy mode Schmidt: Employee of Siemens HealthcareFan: Employee of GE Healthcare

Image quality metrics have become more and more popular in the image processing community. However, so far, no one has been able to define an image quality metric well correlated with the percept for overall image quality. One of the causes is that image quality is multi-dimensional and complex. One approach to bridge the gap between perceived and calculated image quality is to reduce the complexity of image quality, by breaking the overall quality into a set of quality attributes. In our research we have presented a set of quality attributes built on existing attributes from the literature. The six proposed quality attributes are: sharpness, color, lightness, artifacts, contrast, and physical. This set keeps the dimensionality to a minimum. An experiment validated the quality attributes as suitable for image quality evaluation. The process of applying image quality metrics to printed images is not straightforward, because image quality metrics require a digital input. A framework has been developed for this process, which includes scanning the print to get a digital copy, image registration, and the application of image quality metrics. With quality attributes for the evaluation of image quality and a framework for applying image quality metrics, a selection of suitable image quality metrics for the different quality attributes has been carried out. Each of the quality attributes has been investigated, and an experimental analysis carried out to find the most suitable image quality metrics for the given quality attributes. For the sharpness attributes the Structural SIMilarity index (SSIM) by Wang et al. (2004) is the the most suitable, and for the other attributes further evaluation is required.

With widespread use of screening digital mammography, efficient storage of the vast amounts of data has become a challenge. While lossless image compression causes no risk to the interpretation of the data, it does not allow for high compression rates. Lossy compression and the associated higher compression ratios are therefore more desirable. The U.S. Food and Drug Administration (FDA) currently interprets the Mammography Quality Standards Act as prohibiting lossy compression of digital mammograms for primary image interpretation, image retention, or transfer to the patient or her designated recipient. Previous work has used reader studies to determine proper usage criteria for evaluating lossy image compression in mammography, and utilized different measures and metrics to characterize medical image quality. The drawback of such studies is that they rely on a threshold on compression ratio as the fundamental criterion for preserving the quality of images. However, compression ratio is not a useful indicator of image quality. On the other hand, many objective image quality metrics (IQMs) have shown excellent performance for natural image content for consumer electronic applications. In this paper, we create a new synthetic mammogram database with several unique features. We compare and characterize the impact of image compression on several clinically relevant image attributes such as perceived contrast and mass appearance for different kinds of masses. We plan to use this database to develop a new objective IQM for measuring the quality of compressed mammographic images to help determine the allowed maximum compression for different kinds of breasts and masses in terms of visual and diagnostic quality.

Finding an objective image quality metric that matches the subjective quality has always been a challenging task. We propose a new full reference image quality metric based on features extracted from Convolutional Neural Networks (CNNs). Using a pre-trained AlexNet model, we extract feature maps of the test and reference images at multiple layers, and compare their feature similarity at each layer. Such similarity scores are then pooled across layers to obtain an overall quality value. Experimental results on four state-of-the-art databases show that our metric is either on par or outperforms 10 other state-of-the-art metrics, demonstrating that CNN features at multiple levels are superior to handcrafted features used in most image quality metrics in capturing aspects that matter for discriminative perception.

Purpose:To ascertain and implement the maximum level of adaptive statistical iterative reconstruction (ASiR) that can be used to establish dose reduced pediatric head CT protocols.Methods:Noise power spectrum (NPS), modulation transfer function (MTF), and qualitative visual assessment were used as image quality metrics to calculate image quality based on filtered back projection (FBP) and ASiR reconstruction. The effect on image quality as a function of level of ASiR implementation and tube current reduction was analyzed by comparing image noise magnitude and frequency, and spatial resolution with similar measured image quality for FBP. Relative patient dose reduction was estimated using both CTDIvol and effective dose calculated using pediatric sized physical anthropomorphic phantoms. Dose reduced protocols were investigated for CT head examinations (i.e. brain, orbits, sinus, maxillary and temporal bones) by implementing ASiR and lowering CT tube current.Results:An analysis of the effects on noise texture from implementation of statistical iterative reconstruction determined that the level of ASiR that correlated closest with FBP for the GE soft reconstruction kernel (for both 100 and 120 kVp) was determined to be 30%, and for the GE standard reconstruction kernel at 100 kVp was 20% ASiR. However, implementation of 60% ASiR (GE soft reconstruction kernel) and 30% ASiR (GE standard reconstruction kernel) led to an average relative reduction in CTDIvol by 40% (range: 30%‐47%) and effective dose by 36% (range: 12%‐54%) while maintaining the same overall noise magnitude and improving the CNR of the image as compared to FBP. A coarser noise texture was visualized at 60% ASiR.Conclusion:The data in this study are provided to allow customized selection of ASiR implementation relating to acceptable tolerance for change in noise texture appearance in reconstructed images, and level of relative dose reduction for a pediatric population undergoing head CT examination.

This article quantitatively analyzes the impact of bit errors in digitized RF data on ultrasound image quality. The quality of B-mode images in both linear array and phased array imaging is evaluated by means of three objective image quality metrics: peak signal-to-noise ratio, structural similarity index, and contrast-to-noise ratio, when bit errors are introduced to the RF data with different bit-error rates (BERs). The effectiveness of coding schemes for forward error detection and correction to improve the image quality is also studied. The results show that ultrasound imaging is inherently resilient to high BER. The image quality suffers unnoticeable degradation for BER lower than 1E-6. Simple 1-bit parity coding with 9% added redundancy helps to retain similar image quality for BER up to 1E-4, and Hamming coding with 33.3% added redundancy allows the BER to increase to 1E-3. These results can serve as a guideline in the datalink design for ultrasound probes with in-probe receive digitization. With much more relaxed BER requirements than in typical datalinks, the design can be optimized by allowing fewer cables with higher data rate per cable or lower power consumption with the same cable count.

Getting to grips with quantitative image quality measurements for QA of digital mammography (DM) systems