Method for a detailed measurement of image intensity nonuniformity in magnetic resonance imaging

In magnetic resonance imaging (MRI), the MR signal intensity can vary spatially and this spatial variation is usually referred to as MR intensity nonuniformity. Although the main source of intensity nonuniformity arises from B1 inhomogeneity of the coil acting as a receiver and/or transmitter, geometric distortion also alters the MR signal intensity. It is useful on some occasions to have these two different sources be separately measured and analyzed. In this paper, we present a practical method for a detailed measurement of the MR intensity nonuniformity. This method is based on the same three-dimensional geometric phantom that was recently developed for a complete measurement of the geometric distortion in MR systems. In this paper, the contribution to the intensity nonuniformity from the geometric distortion can be estimated and thus, it provides a mechanism for estimation of the intensity nonuniformity that reflects solely the spatial characteristics arising from B1. Additionally, a comprehensive scheme for characterization of the intensity nonuniformity based on the new measurement method is proposed. To demonstrate the method, the intensity nonuniformity in a 1.5 T Sonata MR system was measured and is used to illustrate the main features of the method.     

Measurements of MTF and SNR(f) using a subtraction method in MRI

A method was developed for accurate measurement of the modulation transfer function (MTF) and signal-to-noise ratio in the spatial frequency domain (SNR(f)) of magnetic resonance images (MRI). The MTF was calculated from the complex images of a line object which were obtained by the subtraction of two separately acquired data sets of a specially designed phantom with a sliding sheet. Moreover, the SNR(f) was calculated from the MTF and Wiener spectrum, both of which were determined using the same phantom configuration. The MTFs and SNR(f)s in the conventional spin-echo (SE) and turbo SE, in which the effective echo time was set to the first echo, were evaluated by changing the T2 of the phantom and the echo train length. The MTFs in the positive and negative frequencies indicated the effect of the k-space trajectory for each pulse sequence. SNR(f)s gave spatial frequency information that was not obtained with conventional methods. In this method, the influence of image nonuniformity and unwanted artefacts (edge and ghost) could be eliminated. An analysis of the MTF and the SNR in the spatial frequency domain provides additional information for the assessment of image quality in MRI.     

CT and MRI derived source localization error in a custom prostate phantom using automated image coregistration.

Dosimetric evaluation of completed brachytherapy implant procedures is crucial in developing proper technique. Additionally, accurate dosimetry may be useful in predicting the success of an implant. Accurate definition of the prostate gland and localization of the implanted radioactive sources are critical to attain meaningful dosimetric data. MRI is recognized as a superior imaging modality in delineating the prostate gland. More importantly, MRI can be used for source localization in postimplant prostates. However, the MRI derived source localization error bears further investigation. We present a useful tool in determining the source localization error as well as permitting the fusion, or coregistration, of selected data from multiple imaging modalities. We constructed a custom prostate phantom of hydrocolloid material precisely implanted with I-125 seeds. We obtained CT, the accepted modality, and MRI scans of the phantom. Subsequently, we developed an automated algorithm that employs a sequential translation of data sets to initially maximize coregistration and minimize error between data sets. This was followed by a noniterative solution for the necessary rotation transformation matrix using the Orthogonal Procrustes Solution. We applied this algorithm to CT and MRI scans of the custom phantom. CT derived source locations had source localization errors of 1.59 mm +/- 0.64. MRI derived source locations produced similar results (1.67 mm +/- 0.76). These errors may be attributed to the image digitization process.     

Dose and image quality for a cone-beam C-arm CT system

We assess dose and image quality of a state-of-the-art angiographic C-arm system (Axiom Artis dTA, Siemens Medical Solutions, Forchheim, Germany) for three-dimensional neuro-imaging at various dose levels and tube voltages and an associated measurement method. Unlike conventional CT, the beam length covers the entire phantom, hence, the concept of computed tomography dose index (CTDI) is not the metric of choice, and one can revert to conventional dosimetry methods by directly measuring the dose at various points using a small ion chamber. This method allows us to define and compute a new dose metric that is appropriate for a direct comparison with the familiar CTDIw of conventional CT. A perception study involving the CATPHAN 600 indicates that one can expect to see at least the 9 mm inset with 0.5% nominal contrast at the recommended head-scan dose (60 mGy) when using tube voltages ranging from 70 kVp to 125 kVp. When analyzing the impact of tube voltage on image quality at a fixed dose, we found that lower tube voltages gave improved low contrast detectability for small-diameter objects. The relationships between kVp, image noise, dose, and contrast perception are discussed.     

Automatic on-line electronic portal image analysis with a wavelet-based edge detector

A fully automatic method for on-line electronic portal image analysis is proposed. The method uses multiscale edge detection with wavelets for both the field outline and the anatomical structures. An algorithm to extract and combine the information from different scales has been developed. The edges from the portal image are aligned with the edges from the reference image using chamfer matching. The reference is the first portal image of each treatment. The matching is applied first to the field and subsequently to the anatomy. The setup deviations are quantified as the displacement of the anatomical structures relative to the radiation beam boundaries. The performance of the algorithm was investigated for portal images with different contrast and noise level. The automatic analysis was used first to detect simulated displacements. Then the automatic procedure was tested on anterior-posterior and lateral portal images of a pelvic phantom. In both sets of tests the differences between the measured and the actual shifts were used to quantify the performance. Finally we applied the automatic procedure to clinical images of pelvic and lung regions. The output of the procedure was compared with the results of a manual match performed by a trained operator. The errors for the phantom tests were small: average standard deviation of 0.39 mm and 0.26 degrees and absolute mean error of 0.31 mm and 0.2 degrees were obtained. In the clinical cases average standard deviations of 1.32 mm and 0.6 degrees were found. The average absolute mean errors were 1.09 mm and 0.39 degrees. Failures were registered in 2% of the phantom tests and in 3% of the clinical cases. The algorithm execution is approximately 5 s on a 168 MHz Sun Ultra 2 workstation. The automatic analysis tool is considered to be a very useful tool for on-line setup corrections.     

PET/CT image registration: preliminary tests for its application to clinical dosimetry in radiotherapy

The quality of dosimetry in radiotherapy treatment requires the accurate delimitation of the gross tumor volume. This can be achieved by complementing the anatomical detail provided by CT images through fusion with other imaging modalities that provide additional metabolic and physiological information. Therefore, use of multiple imaging modalities for radiotherapy treatment planning requires an accurate image registration method. This work describes tests carried out on a Discovery LS positron emission/computed tomography (PET/CT) system by General Electric Medical Systems (GEMS), for its later use to obtain images to delimit the target in radiotherapy treatment. Several phantoms have been used to verify image correlation, in combination with fiducial markers, which were used as a system of external landmarks. We analyzed the geometrical accuracy of two different fusion methods with the images obtained with these phantoms. We first studied the fusion method used by the PET/CT system by GEMS (hardware fusion) on the basis that there is satisfactory coincidence between the reconstruction centers in CT and PET systems; and secondly the fiducial fusion, a registration method, by means of least-squares fitting algorithm of a landmark points system. The study concluded with the verification of the centroid position of some phantom components in both imaging modalities. Centroids were estimated through a calculation similar to center-of-mass, weighted by the value of the CT number and the uptake intensity in PET. The mean deviations found for the hardware fusion method were: deltax/ +/-sigma = 3.3 mm +/- 1.0 mm and /deltax/ +/-sigma = 3.6 mm +/- 1.0 mm. These values were substantially improved upon applying fiducial fusion based on external landmark points: /deltax/ +/-sigma = 0.7 mm +/- 0.8 mm and /deltax/ +/-sigma = 0.3 mm 1.7 mm. We also noted that differences found for each of the fusion methods were similar for both the axial and helical CT image acquisition protocols.     

Digital mammography image simulation using Monte Carlo

Monte Carlo simulations of digital images of the contrast detail phantom and the ACR phantom are presented for two different x-ray digital mammography modalities: a synchrotron mammography system and a next-generation scanning slot clinical system. A combination of variance reduction methods made it possible to simulate accurate images using real pixel dimensions within reasonable computation times. The complete method of image simulation, including a simple detector response model, a simple noise model, and the incorporation of system effects (MTF), is presented. The simulated images of the phantoms show good agreement with images measured on the two systems.     

Comparison of conventional, model-based quantitative planar, and quantitative SPECT image processing methods for organ activity estimation using In-111 agents

Accurate quantification of organ radionuclide uptake is important for patient-specific dosimetry. The quantitative accuracy from conventional conjugate view methods is limited by overlap of projections from different organs and background activity, and attenuation and scatter. In this work, we propose and validate a quantitative planar (QPlanar) processing method based on maximum likelihood (ML) estimation of organ activities using 3D organ VOIs and a projector that models the image degrading effects. Both a physical phantom experiment and Monte Carlo simulation (MCS) studies were used to evaluate the new method. In these studies, the accuracies and precisions of organ activity estimates for the QPlanar method were compared with those from conventional planar (CPlanar) processing methods with various corrections for scatter, attenuation and organ overlap, and a quantitative SPECT (QSPECT) processing method. Experimental planar and SPECT projections and registered CT data from an RSD Torso phantom were obtained using a GE Millenium VH/Hawkeye system. The MCS data were obtained from the 3D NCAT phantom with organ activity distributions that modelled the uptake of (111)In ibritumomab tiuxetan. The simulations were performed using parameters appropriate for the same system used in the RSD torso phantom experiment. The organ activity estimates obtained from the CPlanar, QPlanar and QSPECT methods from both experiments were compared. From the results of the MCS experiment, even with ideal organ overlap correction and background subtraction, CPlanar methods provided limited quantitative accuracy. The QPlanar method with accurate modelling of the physical factors increased the quantitative accuracy at the cost of requiring estimates of the organ VOIs in 3D. The accuracy of QPlanar approached that of QSPECT, but required much less acquisition and computation time. Similar results were obtained from the physical phantom experiment. We conclude that the QPlanar method, based on 3D organ VOIs and accurate models of the projection process, provided a substantial increase in accuracy of organ activity estimates from planar images compared to CPlanar processing and had accuracy approaching that of QSPECT.     

Enhanced bone structural analysis through pQCT image preprocessing

Several factors, including preprocessing of the image, can affect the reliability of pQCT-measured bone traits, such as cortical area and trabecular density. Using repeated scans of four different liquid phantoms and repeated in vivo scans of distal tibiae from 25 subjects, the performance of two novel preprocessing methods, based on the down-sampling of grayscale intensity histogram and the statistical approximation of image data, was compared to 3 × 3 and 5 × 5 median filtering. According to phantom measurements, the signal to noise ratio in the raw pQCT images (XCT 3000) was low (～20 dB) which posed a challenge for preprocessing. Concerning the cortical analysis, the reliability coefficient (R) was 67% for the raw image and increased to 94–97% after preprocessing without apparent preference for any method. Concerning the trabecular density, the R-values were already high (～99%) in the raw images leaving virtually no room for improvement. However, some coarse structural patterns could be seen in the preprocessed images in contrast to a disperse distribution of density levels in the raw image. In conclusion, preprocessing cannot suppress the high noise level to the extent that the analysis of mean trabecular density is essentially improved, whereas preprocessing can enhance cortical bone analysis and also facilitate coarse structural analyses of the trabecular region.     

Multispectral magnetic resonance image analysis

Multiecho magnetic resonance (MR) scanning produces tomographic images with approximately equal morphologic information but varying gray scales at the same anatomic level. Multispectral image classification techniques, originally developed for satellite imaging, have recently been applied to MR tissue characterization. Statistical assessment of multispectral tissue classification techniques has been used to select the most promising of several alternative methods. MR examinations of the head and body, obtained with a 0.35, 0.5, or 1.5T imager, comprised data sets with at least two pulse sequences yielding three images at each anatomical level: (1) TR = 0.3 sec, TE = 30 msec, (2) TR = 1.5, TE = 30, (3) TR = 1.5, TE = 120. Normal and pathological images have been analyzed using multispectral analysis and image classification. MR image data are first subjected to radiometric and geometric corrections to reduce error resulting from (1) instrumental variations in data acquisition, (2) image noise, and (3) misregistration. Training regions of interest (ROI) are outlined in areas of normal (gray and white matter, CSF) and pathological tissue. Statistics are extracted from these ROIs and classification maps generated using table lookup, minimum distance to means, maximum likelihood, and cluster analysis. These synthetic maps are then compared pixel by pixel with manually prepared classification maps of the same MR images. Using these methods, the authors have found that: (1) both supervised and unsupervised classification techniques yielded theme maps (class maps) which demonstrated tissue characteristic signatures and (2) tissue classification errors found in computer-generated theme maps were due to subtle gray scale changes present in the original MR data sets arising from radiometric inhomogeneity and spatial nonuniformity.     

Using an external gating signal to estimate noise in PET with an emphasis on tracer avid tumors

The purpose of this study is to establish and validate a methodology for estimating the standard deviation of voxels with large activity concentrations within a PET image using replicate imaging that is immediately available for use in the clinic. To do this, ensembles of voxels in the averaged replicate images were compared to the corresponding ensembles in images derived from summed sinograms. In addition, the replicate imaging noise estimate was compared to a noise estimate based on an ensemble of voxels within a region. To make this comparison two phantoms were used. The first phantom was a seven-chamber phantom constructed of 1 liter plastic bottles. Each chamber of this phantom was filled with a different activity concentration relative to the lowest activity concentration with ratios of 1:1, 1:1, 2:1, 2:1, 4:1, 8:1 and 16:1. The second phantom was a GE Well-Counter phantom. These phantoms were imaged and reconstructed on a GE DSTE PET/CT scanner with 2D and 3D reprojection filtered backprojection (FBP), and with 2D- and 3D-ordered subset expectation maximization (OSEM). A series of tests were applied to the resulting images that showed that the region and replicate imaging methods for estimating standard deviation were equivalent for backprojection reconstructions. Furthermore, the noise properties of the FBP algorithms allowed scaling the replicate estimates of the standard deviation by a factor of 1/square root N, where N is the number of replicate images, to obtain the standard deviation of the full data image. This was not the case for OSEM image reconstruction. Due to nonlinearity of the OSEM algorithm, the noise is shown to be both position and activity concentration dependent in such a way that no simple scaling factor can be used to extrapolate noise as a function of counts. The use of the Well-Counter phantom contributed to the development of a heuristic extrapolation of the noise as a function of radius in FBP. In addition, the signal-to-noise ratio for high uptake objects was confirmed to be higher with backprojection image reconstruction methods. These techniques were applied to several patient data sets acquired in either 2D or 3D mode, with (18)F (FLT and FDG). Images of the standard deviation and signal-to-noise ratios were constructed and the standard deviations of the tumors' uptake were determined. Finally, a radial noise extrapolation relationship deduced in this paper was applied to patient data.     

Using combined x-ray and MR imaging for prostate I-125 post-implant dosimetry: phantom validation and preliminary patient work

Post-implantation dosimetry is an important element of permanent prostate brachytherapy. This process relies on accurate localization of implanted seeds relative to the surrounding organs. Localization is commonly achieved using CT images, which provide suboptimal prostate delineation. On MR images, conversely, prostate visualization is excellent but seed localization is imprecise due to distortion and susceptibility artefacts. This paper presents a method based on fused MR and x-ray images acquired consecutively in a combined x-ray and MRI interventional suite. The method does not rely on any explicit registration step but on a combination of system calibration and tracking. A purpose-built phantom was imaged using MRI and x-rays, and the images were successfully registered. The same protocol was applied to three patients where combining soft tissue information from MRI with stereoscopic seed identification from x-ray imaging facilitated post-implant dosimetry. This technique has the potential to improve on dosimetry using either CT or MR alone.     

The influence of patient centering on CT dose and image noise

Although x-ray intensity shaping filters (bowtie filters) have been used since the introduction of some of the earliest CT scanner models, the clinical implications on dose and noise are not well understood. To achieve the intended dose and noise advantage requires the patient to be centered in the scan field of view. In this study we explore the implications of patient centering in clinical practice. We scanned various size and shape phantoms on a GE LightSpeed VCT scanner using each available source filter with the phantom centers positioned at 0, 3, and 6 cm below the center of rotation (isocenter). Surface doses were measured along with image noise over a large image region. Regression models of surface dose and noise were generated as a function of phantom size and centering error. Methods were also developed to determine the amount of miscentering using a scout scan projection radiograph (SPR). These models were then used to retrospectively evaluate 273 adult body patients for clinical implications. When miscentered by 3 and 6 cm, the surface dose on a 32 cm CTDI phantom increased by 18% and 41% while image noise also increased by 6% and 22%. The retrospective analysis of adult body scout SPR scans shows that 46% of patients were miscentered in elevation by 20-60 mm with a mean position 23 mm below the center of rotation (isocenter). The analysis indicated a surface dose penalty of up to 140% with a mean dose penalty of 33% assuming that tube current is increased to compensate for the increased noise due to miscentering. Clinical image quality and dose efficiency can be improved on scanners with bowtie filters if care is exercised when positioning patients. Automatically providing patient specific centering and scan parameter selection information can help the technologist improve workflow, achieve more consistent image quality and reduce patient dose.     

Effect of x-ray filtration on dose and image performance of CT scanners

X-ray source filtration as a means to reduce patient dose while maintaining image quality was investigated for CT scanners. The CT values, their variances for various materials, and the surface dose to a cylindrical phantom were calculated for different filter thicknesses and composition as well as for different tube potentials. Thermoluminescent dosimetry indicated that the maximum dose could be predicted by calculation with an accuracy of 10% (+/- 2 s.d.). The product of the variance of the CT values times surface dose was used to establish the appropriate thickness and composition of the filter, a figure of merit that was independent of dose and noise when the sole source of noise was Poisson statistics. This analysis indicated that source filter materials with an atomic number from 29 to 40 are optimum, and if aluminum is used, the minimum thickness, at 120 kVp, should be 4 mm.     

DoseLab软件检测CT图像噪声的程序改进及应用分析

目的:对DoseLab软件进行程序改进,增加检测CT图像噪声的功能,对改进的程序进行测试分析。方法:首先,通过使用圆的内接多边形顶点位置计算公式,得到圆内接正三十二边形顶点坐标值。然后,在DoseLab软件Catphan 504模体CTP486模块的图像分析程序中,添加一个正三十二边形的感兴趣区（ROI）,用于检测CT图像噪声。选取2018年每月由西门子CT模拟机日常质量检测（DQC）程序得到的水模体两个层面（S3和S4）的CT图像,对DoseLab改进程序进行测试。对DoseLab改进程序和DQC程序得到的CT图像噪声数据,进行统计分析和比较研究。结果:根据公式计算得到了半径4 cm圆的内接正三十二边形的32个顶点的坐标值,该多边形ROI的面积为49.94 cm2。计算DoseLab改进程序和DQC程序得到的CT图像噪声的差异（ΔN）。在120 kV情形,S3和S4层的ΔN值分别为（0.06±0.07） HU和（0.03±0.09） HU;在140 kV情形,S3和S4层的ΔN值分别为（0.10±0.09） HU和（0.08±0.09） HU。结论:通过添加正三十二边形ROI得到的DoseLab改进程序,可以自动分析水模体和Catphan模体,得到CT图像噪声数据。     

Integral test phantom for dosimetric quality assurance of image guided and intensity modulated stereotactic radiotherapy

The objective of this work is to develop a dosimetric phantom quality assurance (QA) of linear accelerators capable of cone-beam CT (CBCT) image guided and intensity-modulated radiotherapy (IG-IMRT). This phantom is to be used in an integral test to quantify in real-time both the performance of the image guidance and the dose delivery systems in terms of dose localization. The prototype IG-IMRT QA phantom consisted of a cylindrical imaging phantom (CatPhan) combined with an array of 11 radiation diodes mounted on a 10 cm diameter disk, oriented perpendicular to the phantom axis. Basic diode response characterization was performed for 6 and 18 MV photons. The diode response was compared to planning system calculations in the open and penumbrae regions of simple and complex beam arrangements. The clinical use of the QA phantom was illustrated in an integral test of an IG-IMRT treatment designed for a clinical spinal radiosurgery case. The sensitivity of the phantom to multileaf collimator (MLC) calibration and setup errors in the clinical setting was assessed by introducing errors in the IMRT plan or by displacing the phantom. The diodes offered good response linearity and long-term reproducibility for both 6 and 18 MV. Axial dosimetry of coplanar beams (in a plane containing the beam axes) was made possible with the nearly isoplanatic response of the diodes over 360 degrees of gantry (usually within +/-1%). For single beam geometry, errors in phantom placement as small as 0.5 mm could be accurately detected (in gradient > or = 1% /mm). In clinical setting, MLC systematic errors of 1 mm on a single MLC bank introduced in the IMRT plan were easily detectable with the QA phantom. The QA phantom demonstrated also sufficient sensitivity for the detection of setup errors as small as 1 mm for the IMRT delivery. These results demonstrated that the prototype can accurately and efficiently verify the entire IG-IMRT process. This tool, in conjunction with image guidance capabilities has the potential to streamline this QA process and improve the level of performance of image guided and intensity modulated radiotherapy.     

Convolutional image reconstruction for quantitative single photon emission computed tomography

Two image reconstruction algorithms have been investigated. They are based on filtered backprojection, and are useful when the tissue attenuation is considered to be uniform in the object. The first method uses a weighted backprojection, the weighting factor being determined in such a way that the photon attenuation is compensated with low noise propagation. The parameters involved in the convolution kernel and the correction function were determined by a computer iteration program. The second method, which is a simplified version of the first, uses conventional backprojection, and takes a shorter computation time than the first method. The statistical noise of an image can be minimised by suitable positioning of the coordinate origin for the reconstruction. The theory of the two methods, their performance on statistical noise and some results of mathematical and experimental phantom studies are described.     

Experimental investigation of the dose and image quality characteristics of a digital mammography imaging system

Our purpose in this study was to investigate the image quality and absorbed dose characteristics of a digital mammography imaging system with a CsI scintillator, and to identify an optimal x-ray tube voltage for imaging simulated masses in an average size breast with 50% glandularity. Images were taken of an ACR accreditation phantom using a LORAD digital mammography system with a Mo target and a Mo filter. In one experiment, exposures were performed at 80 mAs with x-ray tube voltages varying between 24 and 34 kVp. In a second experiment, the x-ray tube voltage was kept constant at 28 kVp and the technique factor was varied between 5 and 500 mAs. The average glandular dose at each x-ray tube voltage was determined from measurements of entrance skin exposure and x-ray beam half-value layer. Image contrast was measured as the fractional digital signal intensity difference for the image of a 4 mm thick acrylic disk. Image noise was obtained from the standard deviation in a uniformly exposed region of interest expressed as a fraction of the background intensity. The measured digital signal intensity was proportional to the mAs and to the kVp5.8. Image contrast was independent of mAs, and dropped by 21% when the x-ray tube voltage increased from 24 to 34 kVp. At a constant x-ray tube voltage, image noise was shown to be approximately proportional to (mAs)(-05), which permits the image contrast to noise ratio (CNR) to be modified by changing the mAs. At 80 mAs, increasing the x-ray tube voltage from 24 to 34 kVp increased the CNR by 78%, and increased the average glandular dose by 285%. At a constant lesion CNR, the lowest average glandular dose value occurred at 27.3 kVp. Increasing or decreasing the x-ray tube voltage by 2.3 kVp from the optimum kVp increased the average glandular dose values by 5%. These results show that imaging simulated masses in a 4.2 cm compressed breast at approximately 27 kVp with a Mo/Mo target/filter results in the lowest average glandular dose.     

MRI图像的小波融合处理

背景：小波图像融合是将两幅图像融合在一起,以获取对同一场景的更为精确、全面、可靠的图像描述。 目的：用小波变换图像融合技术融合MRI脑梗死图像,以恢复缺损图像。 方法：图像融合的主要机制是利用二维小波分析法对MRI脑梗死图像进行小波分解,并对高低频信号采用多种融合方式进行融合。通过对比不同融合方式后的效果图,找出最适合本部位MRI图像的融合方法。 结果与结论：不同方式的融合技术能成功修复不同的缺损部位,多种融合方式的合适组合能完全修复多处缺失部位。对于文中给出的MRI脑梗死图像,采用最小值融合方式的融合效果最好。提示使用二维小波分析法处理医学图像,简便快捷,能有效改善图像的视觉效果,辅助临床诊断。     

Effect of area x-ray beam equalization on image quality and dose in digital mammography

In mammography, thick or dense breast regions persistently suffer from reduced contrast-to-noise ratio (CNR) because of degraded contrast from large scatter intensities and relatively high noise. Area x-ray beam equalization can improve image quality by increasing the x-ray exposure to under-penetrated regions without increasing the exposure to other breast regions. Optimal equalization parameters with respect to image quality and patient dose were determined through computer simulations and validated with experimental observations on a step phantom and an anthropomorphic breast phantom. Three parameters important in equalization digital mammography were considered: attenuator material (Z = 13-92), beam energy (22-34 kVp) and equalization level. A Mo/Mo digital mammography system was used for image acquisition. A prototype 16 x 16 piston driven equalization system was used for preparing patient-specific equalization masks. Simulation studies showed that a molybdenum attenuator and an equalization level of 20 were optimal for improving contrast, CNR and figure of merit (FOM = CNR2/dose). Experimental measurements using these parameters showed significant improvements in contrast, CNR and FOM. Moreover, equalized images of a breast phantom showed improved image quality. These results indicate that area beam equalization can improve image quality in digital mammography.