Breast Phantom Images Research Articles

Detection of malignant breast tissue using SAR observation with microwave imaging and convolutional neural network

The research paper introduces a deep learning approach for distinguishing between benign and malignant breast tissues using Ultra Wide Band (UWB) microwave imaging. A multi-resonant monopole microstrip patch antenna was crafted and placed on a female breast phantom model containing a tumor. The phantom’s dielectric properties were adjusted to mimic those of benign and malignant tissues. The Specific Absorption Rate (SAR) was analyzed by rotating the antenna at various angles throughout the phantom’s trajectory. Images capturing the SAR distribution over the breast phantom at different antenna resonances were obtained. The popular Convolutional Neural Network (CNN) based AlexNet model was employed for autonomous feature learning from SAR images. To address the challenge of overfitting with a limited dataset, the study utilized image augmentation and transfer learning methods. The proposed model achieved an impressive 98.59% accuracy in classifying benign and malignant breast phantom images, surpassing the performance of three other CNN models—VggNet16, GoogleNet, and ResNet. Notably, this microwave imaging system based on SAR images detected tumors as small as 1 mm with an accuracy of 97.08% outperforming existing malignancy screening techniques.

Enhancing Image Contrast in Breast USCT Reflection Imaging Using Coherence Factor Beamforming

Abstract Breast cancer is the most common cancer worldwide, and early screening is essential. Existing breast ultrasound is affordable and convenient, but its 2D image quality is poor, and it heavily relies on the doctor’s skills. The novel breast ultrasound computed tomography (USCT) can use a ring array probe to implement hundreds of single transducer emission for full-view focusing synthetic aperture reflection imaging, thereby obtaining high-resolution 3D images. However, single transducer emission results in low acoustic intensity, which further leads to low signal-to-noise ratio and low contrast of reflection images. To improve image contrast, this study incorporated beamforming with coherence factor (CF) into reflection imaging using ring transducers, promising to produce high-contrast USCT reflection images. For evaluation, we developed a USCT prototype with 2048 elements and conducted phantom experiments. We conducted phantom experiments. Results showed that the contrast achieved by the coherence factor method of the breast phantom image is improved from 24.54 dB to 63.18 dB, and the artifacts are significantly suppressed. It can be seen that the CF method could significantly improve the contrast of the breast USCT reflection image.

Comparative analysis of cone‐beam breast computed tomography and digital breast tomosynthesis for breast cancer diagnosis: A comprehensive study on reconstruction algorithms

AbstractBreast cancer (BC) is the most commonly diagnosed non‐skin cancer in women. To achieve early and accurate diagnosis, three‐dimensional (3D) cone‐beam breast computed tomography (CBBCT) and digital breast tomosynthesis (DBT) modalities are used. Importantly, the comparison of reconstruction accuracy of both CBBCT and DBT has rarely been investigated, thus constituting a research gap. This study systematically investigated the performances of the CBBCT and DBT for different reconstruction algorithms using both BR3D breast phantom and breast images. We acquired clinical breast images and scanned the BR3D phantom for additional breast images. These acquired images were used to simulate projection data using predefined CBBCT and DBT geometries. The simulated projections were reconstructed using five different reconstruction algorithms. To evaluate the reconstruction accuracy, we calculated average image quality assessment (IQA) indices, including peak signal‐to‐noise ratio (PSNR), structural similarity index (SSIM), root mean square error (RMSE), and others, across different algorithms and modalities. The pooled PSNR, SSIM, and RMSE for DBT and CBBCT images are (31.6265 ± 0.8725), (0.9353 ± 0.0077), and (0.0270 ± 0.0025) and (29.7007 ± 0.9249), (0.9136 ± 0.0130), and (0.0342 ± 0.0040), which implies that the overall IQA indices of DBT are superior to CBBCT; therefore, DBT tends to reveal more BC detectability as the diagnosis outcome would largely depend on good quality images. The results show that DBT gives an improved result for all algorithms compared to CBBCT, although further experimental trials may be needed to establish the findings fully. The findings suggest that using DBT may enhance the accuracy of BC diagnosis compared to CBBCT due to its superior image quality in clinical practice, emphasizing the importance of selecting optimal reconstruction algorithms for improved diagnostic outcomes.

Superficial Dosimetry Study of the Frequency of Bolus Using in Volumetric Modulated Arc Therapy after Modified Radical Mastectomy.

To investigate the effect of various frequencies of bolus use on the superficial dose of volumetric modulated arc therapy after modified radical mastectomy for breast cancer. Based on the computed tomography images of a female anthropomorphic breast phantom, a 0.5 cm silicone-based 3D-printed bolus was created. Nine points evenly distributed on the breast skin were selected for assessing the skin dose, and a volume of subcutaneous lymphatic drainage of the breast (noted as ROI2-3) was delineated for assessing the chest wall dose. The treatment plans with and without bolus (plan_wb and plan_nb) were separately designed using the prescription of 50 Gy in 25 fractions following the standard dose constraints of the adjacent organ at risk. To characterize the accuracy of treatment planning system (TPS) dose calculations, the doses of the nine points were measured five times by thermoluminescence dosimeters (TLDs) and then were compared with the TPS calculated dose. Compared with Plan_nb (144.46 ± 10.32 cGy), the breast skin dose for plan_wb (208.75 ± 4.55 cGy) was significantly increased (t = -18.56, P < 0.001). The deviation of skin dose was smaller for Plan_wb, and the uniformity was significantly improved. The calculated value of TPS was in good agreement with the measured value of TLD, and the maximum deviation was within 5%. Skin and ROI2-3 doses were significantly increased with increasing frequencies of bolus applications. The mean dose of the breast skin and ROI2-3 for 15 and 23 times bolus applications were 45.33 Gy, 50.88 Gy and 50.36 Gy, 52.39 Gy, respectively. 3D printing bolus can improve the radiation dose and the accuracy of the planned dose. Setting Plan_wb to 15 times for T1-3N+ breast cancer patients and 23 times for T4N+ breast cancer patients can meet the clinical need. Quantitative analysis of the bolus application frequency for different tumor stages can provide a reference for clinical practice.

A general framework for the assessment of scatter correction techniques in digital mammography

Scattered radiation negatively impacts radiographic imaging, with particular regard to mammography. In clinical practice, anti-scatter grids are exploited for this purpose; however, anti-scatter grids may also degrade the image quality, since they remove part of the useful primary radiation with the consequent increase of the dose to be administered to the patient. A suitable digital scatter correction method could tackle the limits imposed by such grids with a great impact on diagnosis. The main contribution of this study is the development of a general framework for the assessment of digital scatter correction techniques in mammography. To this aim, the formation process of both primary and scattered image is described on the basis of a systems-theory approach. Through a simulation of the radiological process, a reference model of the primary image is obtained and used as ground truth to compare the intensities of images obtained by applying a deconvolution-based digital scattering correction technique. Then, an experimental case study on breast phantom images is carried out to assess the scatter correction using different Point Spread Functions (PSFs) (Gaussian and Hyperbolic) with varying parameters values. A central issue was the identification of a spatially variant PSF to model the scattered radiation. The results demonstrate that the proposed approach enables the assessment and the comparison of different PSF kernels employed for scatter correction; in particular, our procedure shows that rather low relative errors are obtained ([−0.5;0.5]) for both the PSFs tested and that Gaussian ones are more sensitive to variations in their parameters.

A multispectral transmission image cluster analysis method based on “Terrace compression Method” and window function

Multispectral transmission imaging has a great potential for the early screening of breast cancer due to the low cost, safety and ease in operation. Accurate detection of heterogeneity is important for the diagnosis of breast disease. The low contrast and unclear heterogeneity boundaries of transmission images can lead to difficulties in recognition and segmentation. Therefore, we propose a clustering segmentation method of multispectral transmission images based on “terrace compression method” and window transformation. The images are preprocessed by the frame accumulation to improve the signal-to-noise ratio. After that, the “terrace compression method” is used to compress the images nonlinearly, to reduce data redundancy and then to improve the edge information of heterogeneities. Afterwards, the window function is used to eliminate the redundant information of the background and to reduce the influence of background noise on clustering. Finally, the processed images at each wavelength are transformed into multidimensional data for cluster analysis. The multispectral transmission images of breast phantom are acquired for experimental validation. Then, compared the method with common clustering segmentation methods (including K-means, K-means++, Mean-shift, Gaussian Mixture). The results showed that this processing method can effectively segment and classify the three heterogeneities in the breast phantom. Among these methods, the method proposed in the paper has the best segmentation and classification results for the three types of heterogeneities in the breast phantom. The Dice coefficients of all the heterogeneities segmentation reached more than 0.84 and increased by a maximum of 1.08 times as compared to the common clustering methods. The applications of the terrace compression method and the grayscale window transformation improved the effect of image clustering segmentation.

Evaluating denoising performances of basic filters in the detection of microcalcifications on mammogram images

This research evaluates the denoising abilities of some image-processing filters used in facilitating the early detection of microcalcifications in breast tissues. The mean, median and Gaussian filters were employed to denoise mammogram images of microcalcification breast phantoms of various densities. The performances of the filters were assessed by evaluating the mean squared error (MSE), peak signal-to-noise ratio (PSNR), and signal-to-noise ratio (SNR). All experiments were carried out on MATLAB R2020a platform. The results revealed that the Gaussian filter recorded optimal performance in denoising images with all 3 types of added noises compared to the mean and median filters. The PSNR value of the heterogeneous phantom (PVAL/H) was superior to those of the less dense (PVAL/E), dense (PVAL), and extremely dense (PVAL/G) phantoms for all the tested filters. The results of this work agree with the high contrast recorded by the original image of PVAL/H.

Evaluation of physical properties and image of polyvinyl chloride as breast tissue equivalence for dual-modality (mammography and ultrasound).

TMP is gradually becoming a fundamental element for quality assurance and control in ionizing and non-ionizing radiation imaging modalities as well as in the development of different techniques. This study aimed to evaluate and obtain polyvinyl chloride tissue mimicking material for dual-modality breast phantoms in mammography and ultrasound. Breast tissue equivalence was evaluated based on X-ray attenuation properties, speed of sound, attenuation, and acoustic impedance. There are six samples of PVC-plasticizer material with variations of PVC concentration and additives. The evaluation of X-ray attenuation was carried out using mammography from 23 to 35kV, while the acoustic properties were assessed with mode A ultrasound and a transducer frequency of 5MHz. A breast phantom was created from TMP material with tissue equivalence and was then evaluated using mammography as well as ultrasound to analyze its image quality. The results showed that samples A (PVC 5%, DOP 95%), B (PVC 7%, DOP 93%), C (PVC 10%, DOP 90%), E (PVC 7%, DOP 90%, graphite 3%), and F (PVC 7%, DOP 90%, silicone oil 3%) have the closest equivalent to the ACR breast phantom material with a different range of 0.01-1.39 in the 23-35kV range. Based on the evaluation of the acoustic properties of ultrasound, A had high similarity to fat tissue with a difference of 0.03 (dB cm- 1MHz- 1) and 0.07 (106kg m- 2s- 1), while B was close to the glandular tissue with a difference of 9.2m s- 1. Multilayer breast phantom images' results showed gray levels in mammography and ultrasound modalities. Therefore, this study succeeded in establishing TMP material for mammography and ultrasound. It can also be used for simple quality assurance and control programs.

Deep learning denoising of digital breast tomosynthesis: Observer performance study of the effect on detection of microcalcifications in breast phantom images.

The noise in digital breast tomosynthesis (DBT) includes x-ray quantum noise and detector readout noise. The total radiation dose of a DBT scan is kept at about the level of a digital mammogram but the detector noise is increased due to acquisition of multiple projections. The high noise can degrade the detectability of subtle lesions, specifically microcalcifications (MCs). We previously developed a deep-learning-based denoiser to improve the image quality of DBT. In the current study, we conducted an observer performance study with breast radiologists to investigate the feasibility of using deep-learning-based denoising to improve the detection of MCs in DBT. We have a modular breast phantom set containing seven 1-cm-thick heterogeneous 50% adipose/50% fibroglandular slabs custom-made by CIRS, Inc. (Norfolk, VA). We made six 5-cm-thick breast phantoms embedded with 144 simulated MC clusters of four nominal speck sizes (0.125-0.150, 0.150-0.180, 0.180-0.212, 0.212-0.250mm) at random locations. The phantoms were imaged with a GE Pristina DBT system using the automatic standard (STD) mode. The phantoms were also imaged with the STD+ mode that increased the average glandular dose by 54% to be used as a reference condition for comparison of radiologists' reading. Our previously trained and validated denoiser was deployed to the STD images to obtain a denoised DBT set (dnSTD). Seven breast radiologists participated as readers to detect the MCs in the DBT volumes of the six phantoms under the three conditions (STD, STD+, dnSTD), totaling 18 DBT volumes. Each radiologist read all the 18 DBT volumes sequentially, which were arranged in a different order for each reader in a counter-balanced manner to minimize any potential reading order effects. They marked the location of each detected MC cluster and provided a conspicuity rating and their confidence level for the perceived cluster. The visual grading characteristics (VGC) analysis was used to compare the conspicuity ratings and the confidence levels of the radiologists for the detection of MCs. The average sensitivities over all MC speck sizes were 65.3%, 73.2%, and 72.3%, respectively, for the radiologists reading the STD, dnSTD, and STD+ volumes. The sensitivity for dnSTD was significantly higher than that for STD (p<0.005, two-tailed Wilcoxon signed rank test) and comparable to that for STD+. The average false positive rates were 3.9±4.6, 2.8±3.7, and 2.7±3.9 marks per DBT volume, respectively, for reading the STD, dnSTD, and STD+ images but the difference between dnSTD and STD or STD+ did not reach statistical significance. The overall conspicuity ratings and confidence levels by VGC analysis for dnSTD were significantly higher than those for both STD and STD+ (p≤0.001). The critical alpha value for significance was adjusted to be 0.025 with Bonferroni correction. This observer study using breast phantom images showed that deep-learning-based denoising has the potential to improve the detection of MCs in noisy DBT images and increase radiologists' confidence in differentiating noise from MCs without increasing radiation dose. Further studies are needed to evaluate the generalizability of these results to the wide range of DBTs from human subjects and patient populations in clinical settings.

Evaluation of silicone rubber-lead shield's effectiveness in protecting the breast during thoracic CT

Radiation of thoracic computed tomography (CT) involves the breast although it is not considered an organ of interest. According to the International Commission on Radiological Protection (ICRP) No. 103, the breast is an organ with a high level of sensitivity when interacting with x-rays, increasing the potential risk of breast cancer. Therefore, the radiation dose must be optimized while maintaining image quality. The dose optimization can be accomplished using a radiation shield. This study aims to determine the effect of silicone rubber (SR)-lead (Pb) in various thicknesses as an alternative protective material limiting dose and preserving the image quality of the breast in thoracic CT. SR-Pb was made from SR and Pb by a simple method. The SR-Pb had thicknesses of 3, 6, 9, and 12 mm. The breast dose was measured using a CT dose profiler on the surface of the breast phantom. The CT number and the noise level of the resulting image were determined quantitatively. The dose without the radiation shield was 5.4 mGy. The doses measured using shielding with thicknesses of 3, 6, 9, and 12 mm were 5.2, 4.5, 4.3, and 3.3 mGy, respectively. Radiation shielding with a thickness of 12 mm reduced breast surface dose by up to 38%. The CT numbers and noise levels for the left and right breast phantom images were almost the same as those ​​without radiation shields indicating there were only slight artifacts in the image. Therefore, SR-Pb is considered a good shielding material which can be pplied in a clinical setting by placing it directly on the breast surface for dose optimization.

An Innovative Concept of a 3D-Coded Aperture Imaging System Proposed for Early Breast Cancer Detection.

Coded Aperture (CA) imaging has recently been used in nuclear medicine, but still, there is no commercial SPECT imaging camera based on CA for cancer detection. The literature is rich in examples of using the CA for planar and thin 3D imaging. However, thick 3D reconstruction is still challenging for small lesion detection. This paper presents the results of mosaic modified uniformly redundant array (MURA) mask/antimask CA combined with a maximum-likelihood expectation-maximization (MLEM) algorithm. The MLEM is an iterative algorithm applied to a mosaic MURA CA mask/antimask for 3D anthropomorphic breast phantom reconstruction, slice by slice. The difference between the mask and the antimask suppresses the background noise to enhance the quality of reconstructed images. Furthermore, all reconstructed slices are stacked to form a 3D breast phantom image from single-projection data. The results of phantom reconstruction with 8 mm, 6 mm, 4 mm, and 3 mm lesions are presented. Moreover, the proposed SPECT imaging camera can reconstruct a 3D breast phantom from single-projection data of the patient’s scanning. To assess the quality of lesions in the reconstructed images, the contrast-to-background ratio (CBR), the peak signal-to-noise ratio (PSNR) and mean square error (MSE) were measured.

The regression detectability index RDI for mammography images of breast phantoms with calcification-like objects and anatomical background

Currently, quality assurance measurements in mammography are performed on unprocessed images. For diagnosis, however, radiologists are provided with processed images. This image processing is optimised for images of human anatomy and therefore does not always perform satisfactorily with technical phantoms. To overcome this problem, it may be possible to use anthropomorphic phantoms reflecting the anatomic structure of the human breast in place of technical phantoms when carrying out task-specific quality assessment using model observers. However, the use of model observers is hampered by the fact that a large number of images needs to be acquired. A recently published novel observer called the regression detectability index (RDI) needs significantly fewer images, but requires the background of the images to be flat. Therefore, to be able to apply the RDI to images of anthropomorphic phantoms, the anatomic background needs to be removed. For this, a procedure in which the anatomical structures are fitted by thin plate spline (TPS) interpolation has been developed. When the object to be detected is small, such as a calcification-like lesion, it is shown that the anatomic background can be removed successfully by subtracting the TPS interpolation, which makes the background-free image accessible to the RDI. We have compared the detectability obtained by the RDI with TPS background subtraction to results of the channelized Hotelling observer (CHO) and human observers. With the RDI, results for the detectability can be obtained using 75% fewer images compared to the CHO, while the same uncertainty of is achieved. Furthermore, the correlation of (RDI) with the results of human observers is at least as good as that of (CHO) with human observers.

Dosimetric analysis of the effects of a temporary tissue expander on the radiotherapy technique.

The current study aimed to evaluate the dose effect of a temporary tissue expander (TTE) according to the radiotherapy technique for breast cancer patients. Computed tomography images of a 3D-printed breast phantom with a TTE were acquired for dosimetric analysis. For dose measurement during 180cGy of radiotherapy, 13 EBT3 films were attached to the TTE while including the metal port area. Treatment planning was performed for three-dimensional conformal radiotherapy (3DCRT), field-in-field radiotherapy, intensity-modulated radiotherapy (IMRT), and volumetric modulated arc therapy (VMAT) while considering whether a bolus was used and whether artifacts were corrected. The difference at each point between the measured mean and calculated doses was analyzed to determine the association with the radiotherapy techniques. The effect of the metal port on the radiation dose was associated with the treatment technique. The dose difference between the measured and calculated doses was 6.8% (191.6cGy vs. 179.5cGy) for cases treated with 3DCRT with bolus and artifact correction. The dose difference for cases treated with VMAT and 3DCRT without bolus and with artifact correction was 5.8% (190.2cGy vs. 179.8cGy) and 5.6% (193.3cGy vs. 183.1cGy), respectively. IMRT with the bolus showed a minimum difference of 0.3% (180.7cGy vs. 181.3cGy). The presence of the metal port within the TTE in radiation fields resulted to insignificant increased dose differences according to the treatment technique. Future studies should assess whether this dose difference could affect clinical outcomes.

Effects of kV, filtration, dose, and object size on soft tissue and iodine contrast in dedicated breast CT.

Clinical use of dedicated breast computed tomography (bCT) requires relatively short scan times necessitating systems with high frame rates. This in turn impacts the x-ray tube operating range. We characterize the effects of tube voltage, beam filtration, dose, and object size on contrast and noise properties related to soft tissue and iodine contrast agents as a way to optimize imaging protocols for soft tissue and iodine contrast at high frame rates. This study design uses the signal-difference-to-noise ratio (SDNR), noise-equivalent quanta (NEQ), and detectability (d´) as measures of imaging performance for a prototype breast CT scanner that utilizes a pulsed x-ray tube (with a 4ms pulse width) at 43.5fps acquisition rate. We assess a range of kV, filtration, breast phantom size, and mean glandular dose (MGD). Performance measures are estimated from images of adipose-equivalent breast phantoms machined to have a representative size and shape of small, medium, and large breasts. Water (glandular tissue equivalent) and iodine contrast (5mg/ml) were used to fill two cylindrical wells in the phantoms. Air kerma levels required for obtaining an MGD of 6mGy ranged from 7.1 to 9.1mGy and are reported across all kV, filtration, and breast phantom sizes. However, at 50kV, the thick filters (0.3mm of Cu or Gd) exceeded the maximum available mA of the x-ray generator, and hence, these conditions were excluded from subsequent analysis. There was a strong positive association between measurements of SDNR and d' (R2 >0.97) within the range of parameters investigated in this work. A significant decrease in soft tissue SDNR was observed for increasing phantom size and increasing kV with a maximum SDNR at 50kV with 0.2mm Cu or 0.2mm Gd filtration. For iodine contrast SDNR, a significant decrease was observed with increasing phantom size, but a decrease in SDNR for increasing kV was only observed for 70kV (50 and 60kV were not significantly different). Thicker Gd filtration (0.3mm Gd) resulted in a significant increase in iodine SDNR and decrease in soft tissue SDNR but requires significantly more tube current to deliver the same MGD. The choice of 60kV with 0.2mm Gd filtration provides a good trade-off for maximizing both soft tissue and iodine contrast. This scanning technique takes advantage of the ~50keV Gd k-edge to produce contrast and can be achieved within operating range of the x-ray generator used in this work. Imaging at 60kV allows for a greater range in dose delivered to the large breast sizes when uniform image quality is desired across all breast sizes. While imaging performance metrics (i.e., detectability index and SDNR) were shown to be strongly correlated, the methodologies presented in this work for the estimation of NEQ (and subsequently d') provides a meaningful description of the spatial resolution and noise characteristics of this prototype bCT system across a range of beam quality, dose, and object sizes.

Multimodal Breast Phantoms for Microwave, Ultrasound, Mammography, Magnetic Resonance and Computed Tomography Imaging.

The aim of this work was to develop multimodal anthropomorphic breast phantoms suitable for evaluating the imaging performance of a recently-introduced Microwave Imaging (MWI) technique in comparison to the established diagnostic imaging modalities of Magnetic Resonance Imaging (MRI), Ultrasound (US), mammography and Computed Tomography (CT). MWI is an emerging technique with significant potential to supplement established imaging techniques to improve diagnostic confidence for breast cancer detection. To date, numerical simulations have been used to assess the different MWI scanning and image reconstruction algorithms in current use, while only a few clinical trials have been conducted. To bridge the gap between the numerical simulation environment and a more realistic diagnostic scenario, anthropomorphic phantoms which mimic breast tissues in terms of their heterogeneity, anatomy, morphology, and mechanical and dielectric characteristics, may be used. Key in this regard is achieving realism in the imaging appearance of the different healthy and pathologic tissue types for each of the modalities, taking into consideration the differing imaging and contrast mechanisms for each modality. Suitable phantoms can thus be used by radiologists to correlate image findings between the emerging MWI technique and the more familiar images generated by the conventional modalities. Two phantoms were developed in this study, representing difficult-to-image and easy-to-image patients: the former contained a complex boundary between the mammary fat and fibroglandular tissues, extracted from real patient MRI datasets, while the latter contained a simpler and less morphologically accurate interface. Both phantoms were otherwise identical, with tissue-mimicking materials (TMMs) developed to mimic skin, subcutaneous fat, fibroglandular tissue, tumor and pectoral muscle. The phantoms’ construction used non-toxic materials, and they were inexpensive and relatively easy to manufacture. Both phantoms were scanned using conventional modalities (MRI, US, mammography and CT) and a recently introduced MWI radar detection procedure called in-coherent Multiple Signal Classification (I-MUSIC). Clinically realistic artifact-free images of the anthropomorphic breast phantoms were obtained using the conventional imaging techniques as well as the emerging technique of MWI.

Phase‐based window function and CD‐DMAS beamforming for microwave breast cancer detection

Multistatic, radar-based, microwave breast cancer detection provides more information in a shorter period of time than the monostatic approach. However, artefact removal is harder in the multistatic case due to antenna coupling. In this study, a multi-step imaging system is proposed for the multistatic approach. The first step is responsible for artefact removal, and it includes signals grouping, windowing, filtering, and merging. The second step utilises combined doubled delay-multiply-and-sum beamforming, and produces the breast images. To test the proposed system, a heterogeneous breast phantom having two tumours was simulated and illuminated by ultra-wideband signals in multistatic scan. The simulation results show the success of the proposed system in both eliminating the artefact and clutter in the breast phantom image.

Optimization of the difference-of-Gaussian channel sets for the channelized Hotelling observer.

The channelized-Hotelling observer (CHO) was investigated as a surrogate of human observers in task-based image quality assessment. The CHO with difference-of-Gaussian (DoG) channels has shown potential for the prediction of human detection performance in digital mammography (DM) images. However, the DoG channels employ parameters that describe the shape of each channel. The selection of these parameters influences the performance of the DoG CHO and needs further investigation. The detection performance of the DoG CHO was calculated and correlated with the detection performance of three humans who evaluated DM images in 2-alternative forced-choice experiments. A set of DM images of an anthropomorphic breast phantom with and without calcification-like signals was acquired at four different dose levels. For each dose level, 200 square regions-of-interest (ROIs) with and without signal were extracted. Signal detectability was assessed on ROI basis using the CHO with various DoG channel parameters and it was compared to that of the human observers. It was found that varying these DoG parameter values affects the correlation ( ) of the CHO with human observers for the detection task investigated. In conclusion, it appears that the the optimal DoG channel sets that maximize the prediction ability of the CHO might be dependent on the type of background and signal of ROIs investigated.

A 16‐modified antipodal Vivaldi antenna array for microwave‐based breast tumor imaging applications

AbstractIn this article, an improved method is introduced for enhancing the gain and directivity of a modified antipodal Vivaldi antenna, which is suitable for detecting malignant cells in the breast through microwave imaging. By slotting on the fins of the antenna with the addition of parasitic elliptical patch makes the antenna radiation more directive with more gain at the lower band range. The operating fractional bandwidth of this proposed Vivaldi antenna is 120% (2.50‐11 GHz) with compact dimension and directive radiation pattern with 7.20 dBi highest gain. The antenna time‐domain performance and the near‐field directivity (NFD) are also observed. Effective microwave breast phantom imaging system with an array of 16 antipodal antennas is designed where one antenna works as a transmitter and rest of the antenna works as a receiver in turn. The imaging performance is investigated with tumor inside the breast phantom using the Microwave Radar‐Based Imaging Toolbox open source software. Detection of tumor tissue inside breast phantom has been identified by analyzing the modified antipodal Vivaldi antennas' backscattered signal.

Can a channelized Hotelling observer assess image quality in acquired mammographic images of an anthropomorphic breast phantom including image processing?

To study the feasibility of a channelized Hotelling observer (CHO) to predict human observer performance in detecting calcification-like signals in mammography images of an anthropomorphic breast phantom, as part of a quality control (QC) framework. A prototype anthropomorphic breast phantom with inserted gold disks of 0.25 mm diameter was imaged with two different digital mammography x-ray systems at four different dose levels. Regions of interest (ROIs) were extracted from the acquired processed and unprocessed images, signal-present and signal-absent. The ROIs were evaluated by a CHO using four different formulations of the difference of Gaussian (DoG) channel sets. Three human observers scored the ROIs in a two-alternative forced-choice experiment. We compared the human and the CHO performance on the simple task to detect calcification-like disks in ROIs with and without postprocessing. The proportion of correct responses of the human reader (PCH ) and the CHO (PCCHO ) was calculated and the correlation between the two was analyzed using a mixed-effect regression model. To address the signal location uncertainty, the impact of shifting the DoG channel sets in all directions up to two pixels was evaluated. Correlation results including the goodness of fit (r2 ) of PCH and PCCHO for all different parameters were evaluated. Subanalysis by system yielded strong correlations between PCH and PCCHO , with r2 between PCH and PCCHO was found to be between 0.926 and 0.958 for the unshifted and between 0.759 and 0.938 for the shifted channel sets, respectively. However, the linear fit suggested a slight system dependence. PCCHO with shifted channel sets increased CHO performance but the correlation with humans was decreased. These correlations were not considerably affected by of the DoG channel set used. There is potential for the CHO to be used in QC for the evaluation of detectability of calcification-like signals. The CHO can predict the PC of humans in images of calcification-like signals of two different systems. However, a global model to be used for all systems requires further investigation.

Breast Phantom Imaging Using Iteratively Corrected Coherence Factor Delay and Sum

In this paper, a system for microwave breast tumor detection is presented using iteratively corrected coherence factor delay and sum (CF-DAS) algorithm. CF-DAS is data independent, which makes it stable in a noisy environment. However, data adaptive techniques have made significant progress by enhancing the image quality in microwave tomography. Thus, a novel data adaptive iterative variant of CF-DAS is proposed in this paper to produce stable and accurate images. The microwave imaging (MI) system contains a rotatable array of nine modified antipodal Vivaldi antennas in a circular arrangement, an array-mounting stand based on the stepper motor, the flexible phantom mounting podium, a control system for RF switching of the transceivers, and signal processing unit based on personal computer involved in the reconstruction of the image. The impedance bandwidth of the modified antenna is recorded from 2.5 to 11 GHz with stable directional radiation pattern. For performing the transmission and reception of the microwave signals, an SP8T nine port RF switch is used ranging from 2.5 to 8.0 GHz, and the switching is controlled by MATLAB software. Several low-cost lab-based homogenous and heterogeneous phantoms containing the dielectric property of human breast and tumor tissue are prepared to test the system efficiency. Since typical data independent radar-based techniques are ill-equipped for multiple reflection scenarios, an iteratively corrected variant of CF-DAS algorithm is used for processing the recorded backscattered signals to reconstruct the image of the breast phantom and to identify the existence and locate the area of the multiple breast tumors. The proposed method achieves more than 10-dB improvement over conventional CF-DAS in terms of signal to mean ratio for four different phantoms measured in this study.