Diagnostic Accuracy of Screening Contrast-enhanced Mammography for Women with Extremely Dense Breasts at Increased Risk of Breast Cancer.

Background Mammogram interpretation is challenging in female patients with extremely dense breasts (Breast Imaging Reporting and Data System [BI-RADS] category D), who have a higher breast cancer risk. Contrast-enhanced mammography (CEM) has recently emerged as a potential alternative; however, data regarding CEM utility in this subpopulation are limited. Purpose To evaluate the diagnostic performance of CEM for breast cancer screening in female patients with extremely dense breasts. Materials and Methods This retrospective single-institution study included consecutive CEM examinations in asymptomatic female patients with extremely dense breasts performed from December 2012 to March 2022. From CEM examinations, low-energy (LE) images were the equivalent of a two-dimensional full-field digital mammogram. Recombined images highlighting areas of contrast enhancement were constructed using a postprocessing algorithm. The sensitivity and specificity of LE images and CEM images (ie, including both LE and recombined images) were calculated and compared using the McNemar test. Results This study included 1299 screening CEM examinations (609 female patients; mean age, 50 years ± 9 [SD]). Sixteen screen-detected cancers were diagnosed, and two interval cancers occured. Five cancers were depicted at LE imaging and an additional 11 cancers were depicted at CEM (incremental cancer detection rate, 8.7 cancers per 1000 examinations). CEM sensitivity was 88.9% (16 of 18; 95% CI: 65.3, 98.6), which was higher than the LE examination sensitivity of 27.8% (five of 18; 95% CI: 9.7, 53.5) (P = .003). However, there was decreased CEM specificity (88.9%; 1108 of 1246; 95% CI: 87.0, 90.6) compared with LE imaging (specificity, 96.2%; 1199 of 1246; 95% CI: 95.0, 97.2) (P < .001). Compared with specificity at baseline, CEM specificity at follow-up improved to 90.7% (705 of 777; 95% CI: 88.5, 92.7; P = .01). Conclusion Compared with LE imaging, CEM showed higher sensitivity but lower specificity in female patients with extremely dense breasts, although specificity improved at follow-up. © RSNA, 2024 See also the editorial by Lobbes in this issue.

Performance of dual-energy subtraction in contrast-enhanced mammography for three different manufacturers: a phantom study.

BACKGROUND: Dual-energy subtraction (DES) imaging is critical in contrast-enhanced mammography (CEM), as the recombination of low-energy (LE) and high-energy (HE) images produces contrast enhancement while reducing anatomical noise. The study's purpose was to compare the performance of the DES algorithm among three different CEM systems using a commercial phantom. METHODS: A CIRS Model 022 phantom, designed for CEM, was acquired using all available automatic exposure modes (AECs) with three CEM systems from three different manufacturers (CEM1, CEM2, and CEM3). Three studies were acquired for each system/AEC mode to measure both radiation dose and image quality metrics, including estimation of measurement error. The mean glandular dose (MGD) calculated over the three acquisitions was used as the dosimetry index, while contrast-to-noise ratio (CNR) was obtained from LE and HE images and DES images and used as an image quality metric. RESULTS: On average, the CNR of LE images of CEM1 was 2.3 times higher than that of CEM2 and 2.7 times higher than that of CEM3. For HE images, the CNR of CEM1 was 2.7 and 3.5 times higher than that of CEM2 and CEM3, respectively. The CNR remained predominantly higher for CEM1 even when measured from DES images, followed by CEM2 and then CEM3. CEM1 delivered the lowest MGD (2.34 ± 0.03 mGy), followed by CEM3 (2.53 ± 0.02 mGy) in default AEC mode, and CEM2 (3.50 ± 0.05 mGy). The doses of CEM2 and CEM3 increased by 49.6% and 8.0% compared with CEM1, respectively. CONCLUSION: One system outperformed others in DES algorithms, providing higher CNR at lower doses. RELEVANCE STATEMENT: This phantom study highlighted the variability in performance among the DES algorithms used by different CEM systems, showing that these differences can be translated in terms of variations in contrast enhancement and radiation dose. KEY POINTS: DES images, obtained by recombining LE and HE images, have a major role in CEM. Differences in radiation dose among CEM systems were between 8.0% and 49.6%. One DES algorithm achieved superior technical performance, providing higher CNR values at a lower radiation dose.

EOSedge vs. standard digital radiography: image quality and dose exposure in hip-focused scanning.

OBJECTIVES: To compare the image quality and X-ray exposure dose from EOSedge versus standard digital radiography (SDR) focused on the hip for replacement or prosthesis revision surgery. MATERIALS & METHODS: This prospective single centre study included 97 patients between November 2022 and April 2023. For preoperative assessment, SDR radiographs from frontal pelvic and hip profile were indispensable. Often, surgeon requested spine radiograph from EOSedge, to which we added frontal pelvic and hip profile. A radiologist with 4 years' experience established a score based on European guidelines to evaluate the EOSedge versus SDR image quality. Quality scores (QS) were compared using paired Student's t test. The entrance skin dose (ESD) and dose area product (DAP) of the images were recorded. RESULTS: A total of 97 patients, including 49 women and 48 men (mean 68.10 years ± 13.28) were assessed. The QSEOSedge was significantly higher than the QSSDR, with differences of 1.34 ± 1.90 (p < 0.001) and 1.74 ± 1.90 (p < 0.001), respectively. The difference in total QS was 3.08 ± 3.48 (p < 0.001). The radiation delivered (ESD x DAP) by EOSedge was 29 times lower than that delivered by SDR for frontal pelvic imaging (0.35 ± 0.91 versus 10.20 ± 12.63; p < 0.001) and 17 times lower for hip profile imaging (0.41 ± 0.84 versus 6.91 ± 9.70; p < 0.001). CONCLUSION: This study highlighted the superior image quality of EOSedge images focused on the pelvis and hip compared to SDR, with significantly less radiation exposure.

Deep Learning-Based Reconstruction Algorithm With Lung Enhancement Filter for Chest CT: Effect on Image Quality and Ground Glass Nodule Sharpness.

OBJECTIVE: To assess the effect of a new lung enhancement filter combined with deep learning image reconstruction (DLIR) algorithm on image quality and ground-glass nodule (GGN) sharpness compared to hybrid iterative reconstruction or DLIR alone. MATERIALS AND METHODS: Five artificial spherical GGNs with various densities (-250, -350, -450, -550, and -630 Hounsfield units) and 10 mm in diameter were placed in a thorax anthropomorphic phantom. Four scans at four different radiation dose levels were performed using a 256-slice CT (Revolution Apex CT, GE Healthcare). Each scan was reconstructed using three different reconstruction algorithms: adaptive statistical iterative reconstruction-V at a level of 50% (AR50), Truefidelity (TF), which is a DLIR method, and TF with a lung enhancement filter (TF + Lu). Thus, 12 sets of reconstructed images were obtained and analyzed. Image noise, signal-to-noise ratio, and contrast-to-noise ratio were compared among the three reconstruction algorithms. Nodule sharpness was compared among the three reconstruction algorithms using the full-width at half-maximum value. Furthermore, subjective image quality analysis was performed. RESULTS: AR50 demonstrated the highest level of noise, which was decreased by using TF + Lu and TF alone (P = 0.001). TF + Lu significantly improved nodule sharpness at all radiation doses compared to TF alone (P = 0.001). The nodule sharpness of TF + Lu was similar to that of AR50. Using TF alone resulted in the lowest nodule sharpness. CONCLUSION: Adding a lung enhancement filter to DLIR (TF + Lu) significantly improved the nodule sharpness compared to DLIR alone (TF). TF + Lu can be an effective reconstruction technique to enhance image quality and GGN evaluation in ultralow-dose chest CT scans.

Improving the radiographic image analysis of the classic metaphyseal lesion via conditional diffusion models.

The classic metaphyseal lesion (CML) is a unique fracture highly specific for infant abuse. This fracture is often subtle in radiographic appearance and commonly occurs in the distal tibia. The development of an automated model that can accurately identify distal tibial radiographs with CMLs is important to assist radiologists in detecting these fractures. However, building such a model typically requires a large and diverse training dataset. To address this problem, we propose a novel diffusion model for data augmentation called masked conditional diffusion model (MaC-DM). In contrast to previous generative models, our approach produces a wide range of realistic-appearing synthetic images of distal tibial radiographs along with their associated segmentation masks. MaC-DM achieves this by incorporating weighted segmentation masks of the distal tibias and CML fracture sites as image conditions for guidance. The augmented images produced by MaC-DM significantly enhance the performance of various commonly used classification models, accurately distinguishing normal distal tibial radiographs from those with CMLs. Additionally, it substantially improves the performance of different segmentation models, accurately labeling areas of the CMLs on distal tibial radiographs. Furthermore, MaC-DM can control the size of the CML fracture in the augmented images.

A comparative analysis of deep learning and hybrid iterative reconstruction algorithms with contrast-enhancement-boost post-processing on the image quality of indirect computed tomography venography of the lower extremities.

PURPOSE: To examine whether there is a significant difference in image quality between the deep learning reconstruction (DLR [AiCE, Advanced Intelligent Clear-IQ Engine]) and hybrid iterative reconstruction (HIR [AIDR 3D, adaptive iterative dose reduction three dimensional]) algorithms on the conventional enhanced and CE-boost (contrast-enhancement-boost) images of indirect computed tomography venography (CTV) of lower extremities. MATERIALS AND METHODS: In this retrospective study, seventy patients who underwent CTV from June 2021 to October 2022 to assess deep vein thrombosis and varicose veins were included. Unenhanced and enhanced images were reconstructed for AIDR 3D and AiCE, AIDR 3D-boost and AiCE-boost images were obtained using subtraction software. Objective and subjective image qualities were assessed, and radiation doses were recorded. RESULTS: The CT values of the inferior vena cava (IVC), femoral vein ( FV), and popliteal vein (PV) in the CE-boost images were approximately 1.3 (1.31-1.36) times higher than in those of the enhanced images. There were no significant differences in mean CT values of IVC, FV, and PV between AIDR 3D and AiCE, AIDR 3D-boost and AiCE-boost images. Noise in AiCE, AiCE-boost images was significantly lower than in AIDR 3D and AIDR 3D-boost images ( P < 0.05). The SNR (signal-to-noise ratio), CNR (contrast-to-noise ratio), and subjective scores of AiCE-boost images were the highest among 4 groups, surpassing AiCE, AIDR 3D, and AIDR 3D-boost images (all P < 0.05). CONCLUSION: In indirect CTV of the lower extremities images, DLR with the CE-boost technique could decrease the image noise and improve the CT values, SNR, CNR, and subjective image scores. AiCE-boost images received the highest subjective image quality score and were more readily accepted by radiologists.

Scale-based landmark for internal organs assessments of common carp (Cyprinus carpio) during digital radiography and ultrasonography imaging.

Medical imaging techniques such as digital radiography and ultrasonography are non-invasive and provide precise results for examining internal organs and structures within fish. Their effectiveness can be further enhanced by using body parts like scales as markers for the organs beneath them. This study utilized the number of scales as landmarks in digital radiography and ultrasonography to non-invasively evaluate the muscles, bones, and images of internal and reproductive organs of common carp (Cyprinus carpio). Digital radiography was performed in the dorsoventral and lateral views of the fish, whereas ultrasonography was conducted in longitudinal and transverse views on sequence scale numbers with brightness and colour Doppler-modes. Digital radiography of the common carp revealed the whole-body morphology, including the bony parts from the head, pectoral fins, dorsal fins, pelvic fins, anal fins, and vertebrae to the tail that appeared radiopaque. Internal organs were also observed, with the swim bladder and heart appeared radiolucent, while the intestines, liver, testes, and ovaries appeared radiopaque. Ultrasonography in brightness mode displayed the digestive organs, reproductive organs, and muscle thickness. Additionally, colour Doppler mode demonstrated blood flow within the heart's ventricle.

Role of contrast-enhanced mammogram as an adjunct to tomosynthesis in evaluation of circumscribed breast lesions.

OBJECTIVES: We evaluated the role of contrast-enhanced mammogram (CEM) in the characterization of circumscribed lesions detected in digital breast tomosynthesis (DBT) and correlated with histopathology. METHODS: A retrospective study was done on 205 circumscribed breast masses detected with DBT and for whom CEM was done before core biopsy/excision biopsy. Morphology of lesion enhancement was noted at a 2-min CEM image and depending on the enhancement pattern, they were classified as benign, malignant, and indeterminate. Indeterminate lesions were further characterized by contrast kinetics at 8 min and divided into benign and malignant lesions. The results were correlated with histopathology reports. RESULTS: Among the 205 lesions, 158 were benign and 47 were malignant by histopathology. All 47 malignant cases were diagnosed as malignant (sensitivity-100%) and 150 out of 158 benign lesions were diagnosed as benign by CEM (specificity-95%). Eight benign lesions were reported as malignant (false positive) by CEM. Positive predictive value was 85.4% (47/55); negative predictive value (NPV) was 100% (150/150); accuracy was 96% (197/205). CONCLUSIONS: The high sensitivity of CEM in our study suggests that CEM helps in the early diagnosis of benign-looking circumscribed breast malignancies. The high NPV of CEM helps to avoid unnecessary biopsies and interventions in benign lesions. ADVANCES IN KNOWLEDGE: This study describes the contrast enhancement pattern of benign and malignant circumscribed breast lesions and thereby helps in the diagnosis of malignancy at an early stage. CEM is a promising adjunct tool since it offers functional imaging as a supplement to anatomical imaging by DBT.

Bedside diagnosis of silent aspiration using mobile dynamic digital radiography: a preliminary study.

PURPOSE: This study aimed to assess reliable options for bedside diagnosis of silent aspiration in the intensive care unit by examining the use of default grayscale images (DGI) obtained using a mobile, general-purpose, radiography system capable of dynamic digital radiography (M-DDR) and inverted grayscale images (IGI) of DGI. METHODS: This cohort study (exploratory and preliminary) involved 18 adult patients (mean age, 89.0 years) for whom a swallowing assessment request was received from their primary physicians. Fifty-six IGI videoclips were evaluated by three specialists using the penetration-aspiration scale (PAS), with the gold standard being the consensus reading of all three specialists. Another three speech-language pathologists (SLPs) assessed 56 DGI and IGI videoclips using the PAS. PAS scores 1 and 2 were classified as normal range, PAS scores 3-5 as pathological laryngeal penetration, and PAS scores 6-8 as aspiration. The correct rates with IGI and DGI were then determined, and the level of agreement of IGI and DGI evaluations was evaluated. RESULTS: The correct rate of all evaluators was 100% for normal range, 80-100% for pathological laryngeal penetration, and 83-100% for aspiration with IGI and 100% for normal range, 90% for pathological laryngeal penetration, and 83% for aspiration with DGI. The kappa coefficient for IGI and DGI showed almost complete agreement for abnormal conditions. CONCLUSION: Dynamic imaging of swallowing 2-5 ml of liquid using M-DDR performed for elderly patients at the bedside showed that aspiration assessments by SLPs obtained from DGI videos immediately after imaging are acceptable.

Impact of reducing iodinated intravenous contrast volume in brain CT on image diagnostic quality.

PURPOSE: To investigate whether reducing the volume of intravenous iodinated contrast material injected during brain computed tomography (CT) provides reliable and accurate imaging without compromising diagnostic accuracy. METHODS: This prospective study enrolled patients undergoing enhanced brain CT at a single tertiary hospital. Subjects who agreed to participate received a reduced dose of 60 ml contrast. The images were compared to an age and gender-matched control group who received the conventional 80 cc dose. Neuroradiologists assessed image quality and interpretation using a 5-point Likert scale with six specific domains. Based on ICC, inter-rater reliability was high at 0.873. Multiple linear regression predicted overall diagnostic accuracy based on contrast dose, age, and gender. Visual Grading Characteristics (VGC) analysis was also performed to quantify regional brain enhancement differences between the two contrast groups. RESULTS: The study included 47 patients in the 60 cc group and 55 in the 80 cc control group. The results showed the 80 cc group had significantly higher enhancement ratings compared to 60 cc for all six structures assessed. The differences between groups ranged from -0.241 to -0.433 (p < 0.001) on the 5-point scale.The VGC analysis confirmed significantly greater brain parenchymal enhancement in the 80 cc group compared to the 60 cc group. CONCLUSION: The findings indicate that reducing the intravenous iodinated contrast material volume during brain CT from 80 cc to 60 cc leads to a statistically significant reduction in image quality and diagnostic accuracy. Further research with larger cohorts is needed to confirm these findings and assess the clinical impact of these differences.

Contrast enhancement boost improves the image quality of CT angiography derived from 80-kVp cerebral CT perfusion data.

RATIONALE AND OBJECTIVE: To investigate the impact of the contrast enhancement boost (CE-boost) technique on the image quality of CT angiography (CTA) derived from 80-kVp cerebral CT perfusion (CTP) data, and to compare it with conventional CTApeak as well as other currently employed methods for enhancing CTA images, such as CTAtMIP and CTAtAve extracted from CTP. MATERIALS AND METHODS: The data of forty-seven patients who underwent CTP at 80 kVp were retrospectively collected. Four sets of images: CTApeak, CTAtMIP, CTAtAve, and CE-boost images. The CTApeak image represents the arterial phase at its peak value, captured as a single time point. CTAtMIP and CTAtAve are 4D CTA images that provide maximum density projection and average images from the three most prominent time points. CE-boost is a postprocessing technique used to enhance contrast in the arterial phase at its peak value. We compared the average CT value, standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of the internal carotid artery (ICA) and basilar artery (BA) among the four groups. Image quality was evaluated using a 5-point scale. RESULTS: The CE-boost demonstrated and CNR in the ICA and BA (all p < 0.001). Compared with the other three CTA reconstructed images, the CE-boost images had the best subjective image quality, with the highest scores of 4.77 ± 0.43 and 4.87 ± 0.34 for each reader (all p < 0.001). CONCLUSION: Compared with other currently used techniques,CE-boost enhances the image quality of CTA derived from 80-kVp CTP data, leading to improved visualization of intracranial arteries.

Evaluating the impact of delayed-phase imaging in Contrast-Enhanced Mammography on breast cancer staging: A comparative study of abbreviated versus complete protocol.

PURPOSE: Contrast-enhanced mammography (CEM) is an innovative imaging tool for breast cancer detection, involving intravenous injection of a contrast medium and the assessment of lesion enhancement in two phases: early and delayed. The aim of the study was to analyze the topographic concordance of lesions detected in the early- versus delayed phase acquisitions. MATERIALS AND METHODS: Approved by the Ethics Committee (No. 118/20), this prospective study included 100 women with histopathological confirmed breast neoplasia (B6) at the Radiodiagnostics Department of the Maggiore della Carità Hospital of Novara, Italy from May 1, 2021, to October 17, 2022. Participants underwent CEM examinations using a complete protocol, encompassing both early- and delayed image acquisitions. Three experienced radiologists blindly analyzed the CEM images for contrast enhancement to determine the topographic concordance of the identified lesions. Two readers assessed the complete study (protocol A), while one reader assessed the protocol without the delayed phase (protocol B). The average glandular dose (AGD) of the entire procedure was also evaluated. RESULTS: The analysis demonstrated high concordance among the three readers in the topographical identification of lesions within individual quadrants of both breasts, with a Cohen's κ > 0.75, except for the lower inner quadrant of the right breast and the retro-areolar region of the left breast. The mean whole AGD was 29.2 mGy. The mean AGD due to CEM amounted to 73% of the whole AGD (21.2 mGy). The AGD attributable to the delayed phase of CEM contributed to 36% of the whole AGD (10.5 mGy). CONCLUSIONS: As we found no significant discrepancy between the readings of the two protocols, we conclude that delayed-phase image acquisition in CEM does not provide essential diagnostic benefits for effective disease management. Instead, it contributes to unnecessary radiation exposure.

Background parenchymal enhancement on contrast-enhanced mammography: associations with breast density and patient's characteristics.

PURPOSE: To evaluate if background parenchymal enhancement (BPE) on contrast-enhanced mammography (CEM), graded according to the 2022 CEM-dedicated Breast Imaging Reporting and Data System (BI-RADS) lexicon, is associated with breast density, menopausal status, and age. METHODS: This bicentric retrospective analysis included CEM examinations performed for the work-up of suspicious mammographic findings. Three readers independently and blindly evaluated BPE on recombined CEM images and breast density on low-energy CEM images. Inter-reader reliability was estimated using Fleiss κ. Multivariable binary logistic regression was performed, dichotomising breast density and BPE as low (a/b BI-RADS categories, minimal/mild BPE) and high (c/d BI-RADS categories, moderate/marked BPE). RESULTS: A total of 200 women (median age 56.8 years, interquartile range 50.5-65.6, 140/200 in menopause) were included. Breast density was classified as a in 27/200 patients (13.5%), as b in 110/200 (55.0%), as c in 52/200 (26.0%), and as d in 11/200 (5.5%), with moderate inter-reader reliability (κ = 0.536; 95% confidence interval [CI] 0.482-0.590). BPE was minimal in 95/200 patients (47.5%), mild in 64/200 (32.0%), moderate in 25/200 (12.5%), marked in 16/200 (8.0%), with substantial inter-reader reliability (κ = 0.634; 95% CI 0.581-0.686). At multivariable logistic regression, premenopausal status and breast density were significant positive predictors of high BPE, with adjusted odds ratios of 6.120 (95% CI 1.847-20.281, p = 0.003) and 2.416 (95% CI 1.095-5.332, p = 0.029) respectively. CONCLUSION: BPE on CEM is associated with well-established breast cancer risk factors, being higher in women with higher breast density and premenopausal status.

A novel method to evaluate the transverse pedicle angles of the lower lumbar vertebrae using digital radiography.

To investigate a novel approach for establishing the transverse pedicle angle (TPA) of the lower lumbar spine using preoperative digital radiography (DR). Computed Tomography (CT) datasets of the lower lumbar were reconstructed using MIMICS 17.0 software and then imported into 3-matic software for surgical simulation and anatomical parameter measurement. A mathematical algorithm of TPA based on the Pythagorean theorem was established, and all obtained data were analyzed by SPSS software. The CT dataset from 66 samples was reconstructed as a digital model of the lower lumbar vertebrae (L3-L5), and the AP length/estimated lateral length for L3 between the right and left sides was statistically significant (P = 0.015, P = 0.005). The AP length of the right for L4 was smaller than that of the left after a paired t test was executed (P = 0.006). Both the width of the pedicle and the length of the pedicle (P2C1) were consistent with TPA (L3

Significant effect of image contrast enhancement on weld defect detection.

Weld defect inspection is an essential aspect of testing in industries field. From a human viewpoint, a manual inspection can make appropriate justification more difficult and lead to incorrect identification during weld defect detection. Weld defect inspection uses X-radiography testing, which is now mostly outdated. Recently, numerous researchers have utilized X-radiography digital images to inspect the defect. As a result, for error-free inspection, an autonomous weld detection and classification system are required. One of the most difficult issues in the field of image processing, particularly for enhancing image quality, is the issue of contrast variation and luminosity. Enhancement is carried out by adjusting the brightness of the dark or bright intensity to boost segmentation performance and image quality. To equalize contrast variation and luminosity, many different approaches have recently been put forth. In this research, a novel approach called Hybrid Statistical Enhancement (HSE), which is based on a direct strategy using statistical data, is proposed. The HSE method divided each pixel into three groups, the foreground, border, and problematic region, using the mean and standard deviation of a global and local neighborhood (luminosity and contrast). To illustrate the impact of the HSE method on the segmentation or detection stage, the datasets, specifically the weld defect image, were used. Bernsen and Otsu's methods are the two segmentation techniques utilized. The findings from the objective and visual elements demonstrated that the HSE approach might automatically improve segmentation output while effectively enhancing contrast variation and normalizing luminosity. In comparison to the Homomorphic Filter (HF) and Difference of Gaussian (DoG) approaches, the segmentation results for HSE images had the lowest result according to Misclassification Error (ME). After being applied to the HSE images during the segmentation stage, every quantitative result showed an increase. For example, accuracy increased from 64.171 to 84.964. In summary, the application of the HSE method has resulted in an effective and efficient outcome for background correction as well as improving the quality of images.

[Examination of Optimal Frequency Processing in X-ray Images for Occult Femoral Neck Fractures].

PURPOSE: Osteosclerotic images are known as an image appearance of occult femoral neck fractures in X-ray images. The aim of this study is to investigate frequency enhancement processing that improves the visibility of the osteosclerotic image. METHODS: We acquired three sclerotic bone images with different thicknesses, and self-made bone equivalent phantoms were set up on a pelvic phantom. The frequency processing type and high-density enhancement coefficients were applied to the X-ray images taken at RF-A(1.0, 2.0), C(2.0, 4.0), D(1.0), and H(2.0, 4.0). For the physical index, we compared the difference in signal values between the sclerotic and background normal bone. We evaluated the preference using Scheffé's paired comparison methods for the visual index. RESULTS: For the physical index, RF-C(4.0) had the most significant signal value difference for all 3 bone stiffness images. For the visual index, RF-C(4.0) showed the highest preference. CONCLUSION: Using frequency-enhanced processing, RF-C(4.0) was suggested to improve the visibility of the osteosclerosis image.