Clutter noise reduction in B-Mode image through mapping and clustering signal energy for better cyst classification

In ultrasound imaging using plane-wave (PW) techniques, image quality and contrast often suffer, especially when examining anechoic structures. Traditional beamforming methods like Delay-and-Sum or coherent PW compounding face limitations in balancing resolution and frame rate, which can result in suboptimal diagnosticaccuracy. This study aims to introduce a modular beamforming pipeline that overcomes these challenges and enhances PW image quality. By dividing the beamforming process into two modules: a multi-attention U-Net based model for capturing complex dependencies in time-delayed data and a super-resolution model for scaling up to the original B-mode image grid. We seek to improve PW image quality and modularity in the ultrasound imagingprocess. We implemented a modular beamforming approach, comprising a multi-attention U-Net model and a super-resolution model. We conducted experiments using simulated, experimental, and in vivo data from the PICMUS dataset to evaluate the performance of our pipeline against conventional methods such as PW1, PW9, and U-Net. Key metrics assessed included contrast-to-noise ratio (CNR), contrast ratio (CR), generalized Contrast-to-Noise Ratio (gCNR), andresolution. Our model demonstrated superior performance across all metrics. In simulated data, the model achieved a 0.99 improvement in CNR, a 3.5 dB increase in CR, and 18% in gCNR compared to PW1. Experimental data showed a 0.7 enhancement in CNR and a 8.6 dB improvement in CR and 24% increase in gCNR when compared to PW1. In vivo data also revealed significant improvements, with a 1.9 dB increase in CR and a 0.15 enhancement in CNR over PW1. The enhanced performance in the anechoic cyst region underscores the model's effectiveness in improving imagequality. The proposed modular beamforming approach offers significant advantages, including adaptability and improved image quality, despite the complexity of managing two models concurrently. The pipeline's flexibility in frame rate and image quality allows for customization based on specific clinical applications, making it a promising alternative to traditionalmethods.

To develop a model incorporating dynamic contrast material-enhanced (DCE) and diffusion-weighted (DW) magnetic resonance (MR) imaging features to differentiate high-nuclear-grade (HNG) from non-HNG ductal carcinoma in situ (DCIS) in vivo. This HIPAA-compliant study was approved by the institutional review board and requirement for informed consent was waived. A total of 55 pure DCIS lesions (19 HNG, 36 non-HNG) in 52 women who underwent breast MR imaging at 1.5 T with both DCE and DW imaging (b = 0 and 600 sec/mm(2)) were retrospectively reviewed. The following lesion characteristics were recorded or measured: DCE morphology, DCE maximum lesion size, peak initial enhancement at 90 seconds, worst-curve delayed enhancement kinetics, apparent diffusion coefficient (ADC), contrast-to-noise ratio (CNR) at DW imaging with b values of 0 and 600 sec/mm(2), and T2 signal effects (measured with CNR at b = 0 sec/mm(2)). Univariate and stepwise multivariate logistic regression modeling was performed to identify MR imaging features that optimally discriminated HNG from non-HNG DCIS. Discriminative abilities of models were compared by using the area under the receiver operating characteristic curve (AUC). HNG lesions exhibited larger mean maximum lesion size (P = .02) and lower mean CNR for images with b value of 600 sec/mm(2) (P = .004), allowing discrimination of HNG from non-HNG DCIS (AUC = 0.71 for maximum lesion size, AUC = 0.70 for CNR at b = 600 sec/mm(2)). Differences in CNR for images with b value of 0 sec/mm(2) (P = .025) without corresponding differences in ADC values were observed between HNG and non-HNG lesions. Peak initial enhancement was the only kinetic variable to approach significance (P = .05). No differences in lesion morphology (P = .11) or worst-curve delayed enhancement kinetics (P = .97) were observed. A multivariate model combining CNR for images with b value of 600 sec/mm(2) and maximum lesion size most significantly discriminated HNG from non-HNG (AUC = 0.81). The preliminary findings suggest that DCE and DW MR imaging features may aid in identifying patients with high-risk DCIS. Further study may yield a model combining MR characteristics with histopathologic data to facilitate lesion-specific targeted therapies. © RSNA, 2012.

BackgroundBreast cancer screening and diagnosis remain to be improved. This study aimed to explore the diagnostic value of contrast-enhanced spectral mammography (CESM) for breast lesions through qualitative and quantitative analyses, and to evaluate the consistency of enhancement curves between CESM and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).MethodsThis retrospective study included patients in whom CESM was performed because of suspected (clinical or ultrasound) breast lesions between January 2023 and February 2024 at the Shanxi Bethune Hospital. CESM images were analyzed to obtain mass shape, margin, enhancement, and enlarged lymph nodes. The lesion grey value (LGV) of the region of interest, contrast-to-noise ratio (CNR), and contrast ratio were calculated. The diagnostic value of the qualitative and quantitative metrics for benign and malignant breast lesions was evaluated using receiver operating characteristic (ROC) curves. DCE-MRI images were post-processed, and the enhancement curves were evaluated. A κ consistency analysis was performed to evaluate the enhancement curves between the CESM and DCE-MRI.ResultsThis study included 202 female patients: 51 with benign lesions (46.55±11.84 years) and 151 with malignant lesions (51.32±11.21 years). Qualitative analysis showed differences between benign and malignant breast lesions in shape, margin, enhancement, and enlarged lymph nodes in CESM (all P<0.05). The LGV, CNR, and contrast ratio of malignant lesions were higher than in benign lesions (all P<0.001). Regarding the enhancement curves between CESM and DCE-MRI, the concordance rate was 85.42%. Specifically, the concordance rates for enhancement types I, II, and III were 76.19%, 81.67%, and 92.06%, respectively (κ=0.764). The areas under the curves for the qualitative, quantitative, and combined analyses of benign and malignant breast lesions were 0.857, 0.895, and 0.957, respectively.ConclusionsThere were significant differences in the qualitative and quantitative metrics of CESM between benign and malignant lesions, and the combined CESM qualitative and quantitative analysis achieved a high diagnostic value. Moreover, the enhancement curve of CESM demonstrated concordance with those of DCE-MRI. These suggest that CESM may have a promising application in the early diagnosis of breast cancer.

Objective To evaluate the image quality of displaying the pulmonary artery and pulmonary emboli at different tube voltages and find the optimal tube voltage.Methods Sixty-six patients underwent contrast-enhanced dual energy CT pulmonary angiography (CTPA) from August 2007 to February 2010.Data obtained with 80 kVp,weighted average 120 kVp,and 140 kVp were divided into group Ⅰ,Ⅱ,and Ⅲ respectively.CT value of pulmonary artery ( grade 1—4),emboli,and muscles were measured.Corresponding signal to noise ratio (SNR) and contrast to noise ratio (CNR) were calculated.One-way ANOVA analysis and Friedman test were used to analyze statistical significance among these values.Image quality of CTPA from these three groups was assessed.The inter-reader agreement of image quality was performed by kappa test.Results CT value of branch 1 to 4 of ptlmonary artery was (446 ± 140) HU,(433±130) HU,(411 ±138) HU,and (392±127) HU in group Ⅰ,(303±91) HU,(290±85) HU,(276±86) HU,and (263 ±85) HU in group Ⅱ,and (244 ±70) HU,(230 ±63) HU,(216 ±72) HU,and (205 ±68) HU in group Ⅲ.There was significant difference for CT values (P ＜0.01) among three groups.CT value of pulmonary artery in 80 kVp images was higher than 140 kyp and 120 Kvp.On quantitative analysis of 37 patients,CNR value of pulmonary clots was 8.3 ± 3.3,8.0 ± 2.9,and 5.6 ± 2.2 in group Ⅰ,Ⅱ,and ⅢⅢ,respectively.CNR values of pulmonary clots in 80 kVp images and 120 kVp images were higher than that in 140 kVp (P ＜0.01 ).There was no statistical difference for image quality of CTPA among the three groups ( P ＞ 0.05 ).Agreement of image quality of CTPA derived from 80 kVp (kappa value =0.789,P ＜0.01 ) was superior to that of 120 kVp (kappa value =0.652,P ＜0.01 ) and 140 kVp ( kappa value = 0.509,P ＜ 0.01 ).Conclusions CT value of pulmonary artery in 80 kVp images was higher than that of weighted average 120 kVp and 140 kVp,and the image quality and CNR was comparable with that of weighted average 120 kVp images.This tube voltage ( 80 kVp) is optimal for CT pulmonary angiography in the evaluation of patients with suspected pulmonary embolism. Key words: Pulmonary embolism; Tomography; X-ray computed; Radiation dosage; Angiography

Purpose: To test the association between contrast-to-noise ratio (CNR) measurements made on digital mammograms (DM), human reader performance in a lesion detection task using the same images, and image quality (IQ) as predicted by phantom measurements. Methods: DM from 162 women were evaluated for their CNR using a novel metric for application on clinical images. The original unprocessed images were tested (100% dose), as well as the same images after processing to simulate a 50% and 25% relative dose level. IQ measurements from a CDMAM phantom images, as well as human reader calcification cluster detectability ratings on the clinical image set for the three treatments were used to provide ground truth for human lesion detection performance. Analysis was performed to test for association between DM image CNR at the three dose levels, the CDMAM measurements, and reader performance as quantified by a reader-averaged jack-knifed free response operating characteristic (JAFROC) figure of merit (FoM). Results: The clinical image CNR was strongly correlated with the JAFROC FoM and CDMAM threshold gold thicknesses (r=0.98, and r=0.99 @ 0.25 mm, r=0.94 @ 0.1 mm discs, respectively). On a per-image basis, strong associations between CNR and measures of beam quality and exposure were also found that indicate sensitivity to imaging technique factors while remaining independent of signal variations due to breast parenchyma. Conclusions: Using a clinical image CNR it is possible to objectively predict IQ in mammographic images. As such, this metric could provide a means to perform a practical continuous DM system performance monitoring.

PurposeTo determine the iodine contrast-to-noise ratio (CNR) for abdominal computed tomography (CT) when using energy domain noise reduction and virtual monoenergetic dual-energy (DE) CT images and to compare the CNR to that attained with single-energy CT at 80, 100, 120, and 140 kV.Materials and MethodsThis HIPAA-compliant study was approved by the institutional review board with waiver of informed consent. A syringe filled with diluted iodine contrast material was placed into 30-, 35-, and 45-cm-wide water phantoms and scanned with a dual-source CT scanner in both DE and single-energy modes with matched scanner output. Virtual monoenergetic images were generated, with energies ranging from 40 to 110 keV in 10-keV steps. A previously developed energy domain noise reduction algorithm was applied to reduce image noise by exploiting information redundancies in the energy domain. Image noise and iodine CNR were calculated. To show the potential clinical benefit of this technique, it was retrospectively applied to a clinical DE CT study of the liver in a 59-year-old male patient by using conventional and iterative reconstruction techniques. Image noise and CNR were compared for virtual monoenergetic images with and without energy domain noise reduction at each virtual monoenergetic energy (in kiloelectron volts) and phantom size by using a paired t test. CNR of virtual monoenergetic images was also compared with that of single-energy images acquired with 80, 100, 120, and 140 kV.ResultsNoise reduction of up to 59% (28.7/65.7) was achieved for DE virtual monoenergetic images by using an energy domain noise reduction technique. For the commercial virtual monoenergetic images, the maximum iodine CNR was achieved at 70 keV and was 18.6, 16.6, and 10.8 for the 30-, 35-, and 45-cm phantoms. After energy domain noise reduction, maximum iodine CNR was achieved at 40 keV and increased to 30.6, 25.4, and 16.5. These CNRs represented improvement of up to 64% (12.0/18.6) with the energy domain noise reduction technique. For single-energy CT at the optimal tube potential, iodine CNR was 29.1 (80 kV), 21.2 (80 kV), and 11.5 (100 kV). For patient images, 39% (24/61) noise reduction and 67% (0.74/1.10) CNR improvement were observed with the energy domain noise reduction technique when compared with standard filtered back-projection images.ConclusionIodine CNR for adult abdominal CT may be maximized with energy domain noise reduction and virtual monoenergetic DE CT.

A system for cone-beam computed tomography (CBCT) based on a flat-panel imager (FPI) is used to examine the magnitude and effects of x-ray scatter in FPI-CBCT volume reconstructions. The system is being developed for application in image-guided therapies and has previously demonstrated spatial resolution and soft-tissue visibility comparable or superior to a conventional CT scanner under conditions of low x-ray scatter. For larger objects consistent with imaging of human anatomy (e.g., the pelvis) and for increased cone angle (i.e., larger volumetric reconstructions), however, the effects of x-ray scatter become significant. The magnitude of x-ray scatter with which the FPI-CBCT system must contend is quantified in terms of the scatter-to-primary energy fluence ratio (SPR) and scatter intensity profiles in the detector plane, each measured as a function of object size and cone angle. For large objects and cone angles (e.g., a pelvis imaged with a cone angle of 6 degrees), SPR in excess of 100% is observed. Associated with such levels of x-ray scatter are cup and streak artifacts as well as reduced accuracy in reconstruction values, quantified herein across a range of SPR consistent with the clinical setting. The effect of x-ray scatter on the contrast, noise, and contrast-to-noise ratio (CNR) in FPI-CBCT reconstructions was measured as a function of SPR and compared to predictions of a simple analytical model. The results quantify the degree to which elevated SPR degrades the CNR. For example, FPI-CBCT images of a breast-equivalent insert in water were degraded in CNR by nearly a factor of 2 for SPR ranging from approximately 2% to 120%. The analytical model for CNR provides a quantitative understanding of the relationship between CNR, dose, and spatial resolution and allows knowledgeable selection of the acquisition and reconstruction parameters that, for a given SPR, are required to restore the CNR to values achieved under conditions of low x-ray scatter. For example, for SPR = 100%, the CNR in FPI-CBCT images can be fully restored by: (1) increasing the dose by a factor of 4 (at full spatial resolution); (2) increasing dose and slice thickness by a factor of 2; or (3) increasing slice thickness by a factor of 4 (with no increase in dose). Other reconstruction parameters, such as transaxial resolution length and reconstruction filter, can be similarly adjusted to achieve CNR equal to that obtained in the scatter-free case.

The purpose of this study was to evaluate the detection of a minor leak of a subarachnoid hemorrhage (SAH) using material-density images (MDI) in a computer simulation. Iron (Fe) in the structure of hemoglobin was used as a focus for the imaging process, and the MDI were reconstructed using both iron and water as basis materials. The effect of the selection of the two X-ray energy spectra used for data acquisition on the MDI was also investigated. Numerical simulation was performed using both energy spectra and substance attenuation data. An acute SAH was simulated by mixing cerebrospinal fluid (CSF) and blood (hematocrit [Hct], 45%) ranging from 0% to 100%. The single energy computed tomography (SECT) images and the MDI using iron and water as basis materials (Iron density image: IDI and water density image: WDI) were obtained. The computed tomography (CT) values of the SECT images and the values associated with the IDI and WDI were measured for each different blood concentration. Furthermore, the SAH / CSF ratio and contrast to noise ratio (CNR) of the SECT image and the WDI were calculated. In order to investigated the influence on the combination of two X-ray energy spectra used for data acquisition, three combinations of the energy spectra were set and used: 80-120 kVp, 80-150 kVp, 80-150 kVp (0.5 mm tin filter addition: + Sn). The CT number of the SECT increased linearly with increasing blood concentration in the CSF. The iron density of the IDI demonstrated a decreasing tendency. However, the water density of the WDI linearly increased. It was shown that it was difficult to detect a minor leak from a SAH using IDI, but it was detectable using information obtained from the WDI as an indication of the increase in erythrocytes. In the scaled WDI, the slope of the SAH / CSF ratio was larger than SECT. However, the CNR of the SECT image showed a larger value than the scaled WDI. It was shown that the scaled WDI may allow a greater chance of detection of the minor leak of a SAH than the SECT. However, it was also noted that the CNR decreased due to image noise. It was also observed that the greater the separation imposed between the two X-ray energy spectra used for data acquisition, the smaller the standard deviation in the scaled WDI.

This paper presents K-edge filtering and energy weighting methods which enhance the contrast with less radiation does. Usually, energy weighting methods are used with photon-counting detector based CT for each energy bin data obtained to enhance the quality of image. However, we used these methods combine with K-edge filtering in energy-integrating detector. Using K-edge filtering, different energy bin data for energy weighting methods were obtained, and then energy weighting factors were calculated to enhance the contrast of image. We report an evaluation of the contrast-to-noise ratio (CNR) of reconstructed image with and without these two methods. This evaluation was proceeded with two phantoms; one is the phantom created personally, and the other is Sendentexct IQ dental CBCT (SENDENTEXCT, EU). As for the phantom created personally, the CNR of images reconstructed with these methods were increased than CNR of standard images. It was seen that 31% to 81% in each energy weighting method for optimizing each material (cortical bone, inner bone, soft tissue, iodine (18.5 g/l), iodine (37 g/l)). In conclusion, we can enhance the contrast of CT images with less radiation dose using K-edge filtering and energy weighting method.

We present a new spatial compounding method to improve the contrast of ultrasonic images for non-delayed sequential beamforming (NDSB). Sequential beamforming adopts more than one beamformer to reconstruct B-mode images which has the advantage of simple front-end electronics and fast data transfer rate. Via field pattern analysis, we propose a compounding method where two more sub-images can be reconstructed along with the NDSB sub-image. These sub-images can be seen as being produced with different transmit origins; thus, their summation enhances image contrast. Image quality was analyzed in terms of spatial resolution, contrast ratio (CR), and contrast-to-noise ratio (CNR). The proposed compounding method improves the lateral resolution up to 41%. In vitro results confirm a 13.0-dB CR and 4.0-dB CNR improvement. In vivo results reveal 10.9-dB and 6.0-dB improvement in CR and CNR for cross-section jugular vein and 8.0-dB and 4.5-dB improvement in CR and CNR for the longitudinal-section carotid artery.

To evaluate the image quality and radiation dose of low-dose (LD) computed tomography (LD-CT) combining with iterative model reconstruction (IMR) algorithm for response assessment in patients after treatment of malignant tumor compared with routine-dose CT (RD-CT). Forty-seven patients [mean age 57.8±10.9 years, 30 males, body mass index (BMI) 22.09±2.35 kg/m2] after treatment of malignant tumor underwent contrast-enhanced chest and abdomen CT twice for response assessment with an interval of 6 months according to clinical routine. The first CT scans were performed with RD protocol at 120 kVp and images were reconstructed with filtered back projection (FBP) algorithm; while the second scans were performed with LD protocol at 100 kVp and images were reconstructed with FBP and IMR algorithm respectively. All scans were performed using an automatic tube current modulation technique with 150 mAs as reference. Objective image quality including CT attenuation, image noise, and contrast to noise ratio (CNR), and subjective image quality including artifacts, noise, visualization of small structures and confidence of targeted lesions, as well as lesion detection were assessed and compared. Effective radiation dose of LD-CT scans was reduced 54.8% compared to RD-CT scans (26.89±3.35 vs. 12.14±2.09 mSv). Higher CT attenuation was found in both LD-IMR and LD-FBP images compared to RD-FBP images. Better subjective image quality and CNR as well as lower objective noise were found in LD-IMR images (all, P<0.05). Two small lesions with the diameter less than 1 cm were missed in LD-FBP images, which were able to be observed in LD-IMR images. IMR is able to help more than half of reduction of radiation dose without compromising the quality of diagnostic information in patients after treatment of malignant tumors to chest and abdomen CT for response assessment.

To qualitatively and quantitatively evaluate the feasibility of CT virtual unenhanced (VUE) spectral imaging in renal cell carcinoma. Thirty-six patients with histopathologically proven renal cell carcinoma underwent gemstone spectral imaging (GSI) including ture unenhanced (TUE), arterial, cortex and medullary phase acquisitions. The triphasic GSI dataset was sent to workstation and 3 sets of VUE images (including VUE a, VUE c, VUE m) were obtained by subtract iodine from iodine-water images in the triphasic enhanced GSI images, respectively. The quality of VUE s and TUE images was evaluated on a five-point scale.Interobserver agreement with regard to image quality was assessed using Cohen's Kappa, and four groups of image quality was compared with ANOVA analysis. The contrast-to-noise ratio (CNR) of mass-to-kidney in the TUE and VUE s images was calculated, and difference between these four datasets was compared with ANOVA analysis. Using appearance of masses on triphasic images as a standard, the masses detection ratio of four groups was calculated and compared with chi-square test. Interobserver agreement with regard to image quality was excellent (κ>0.600). There was no significant difference among the image quality of TUE and triphasic images of VUEs (P>0.05). The CNR of TUE (0.7 ± 0.6) was significant worse than that of triphasic images (1.7 ± 1.0, 1.9 ± 1.2, 2.5 ± 1.4, respectively) (P<0.05). There was no significant difference among the CNR of triphasic images (P>0.05). VUE images obtain from triphasic phases may be a surrogate for conventional unenhanced scan in renal cell carcinoma diagnosis.

This retrospective study evaluated the utility of contrast-enhanced T1-weighted 3D fast spin-echo-based sampling perfection with application-optimized contrasts by using different flip angle evolutions (SPACE) sequences for brain metastasis detection on 3T MRI compared with a gradient-recalled echo-based 3D FLASH sequence. We identified all patients at a single institution who underwent SPACE and 3D FLASH sequences as part of a practice quality-improvement project. Their medical records were retrospectively reviewed. Five certified neuroradiologists reviewed the images, with at least 2 weeks' separation between scoring sequences for the same patient. We evaluated the following parameters: number of metastatic lesions, number of indeterminate lesions, lesion margin, contrast-to-noise ratio (CNR), extent of image artifacts, and overall image quality. The CNR was also quantified for solidly enhancing lesions of >1 cm. We identified 220 patients who underwent SPACE and 3D FLASH sequences (the order of the sequences was equally distributed). Of these, 79 had brain metastases on imaging, and 7 were excluded; thus, 72 patients were included in the study. Twenty patients were scored by 2 radiologists. Of the 92 evaluations, SPACE detected more lesions than 3D FLASH in 35, while 3D FLASH detected more lesions in 10. More indeterminate lesions were seen on 3D FLASH (n = 27) than on SPACE (n = 9). For the lesion margin, CNR, and overall image quality on a Likert scale, SPACE performed significantly better than 3D FLASH, with fewer image artifacts (P < .00001). Higher quantitative CNRs were found on SPACE than on 3D FLASH images, though this result was not statistically significant (median = 22.9 versus 15.5, respectively, P = .134). There was a high interreader lesion detection concordance with the Krippendorf α ordinals at 0.962 for SPACE, 0.870 for 3D FLASH, and 0.918 for the 2 sequences combined. Compared with 3D FLASH, the SPACE sequence detected more metastatic lesions and was rated higher for image quality, lesion margin, and CNR, with fewer artifacts. Importantly, the SPACE sequence resulted in increased reader confidence, with fewer indeterminate lesions detected.

Computed diffusion-weighted magnetic resonance imaging (cDWI) refers to the synthesizing of arbitrary b value diffusion-weighted images (DWI) from a set of measured b value images by voxelwise fitting. The objectives of this study were to quantitatively analyze the noise and the contrast-to-noise ratio (CNR) in cDWI as a function of b value by numerical simulations and by measurements in patients with prostate cancer and to compare cDWI to directly measured DWI at a b value of 1400 s/mm2. Numerical simulations were performed to assess image noise and CNR in both cDWI and regular DWI. Forty-two patients with prostate cancer (age, 51-73 years; prostate specific antigen level, 0.5-30 ng/mL; and biopsy Gleason score, 6-9) received 2 DWI examinations at 3.0 T (one with b values of 100, 500, and 1400 s/mm2 and another with b values of 0, 100, 400, and 800 s/mm2) to create cDWI images at arbitrary b values, both with and without incorporating a b value of 0 s/mm2 in their calculation. Regions of interest were drawn to compare the scan time adjusted CNR (CNR eff) between cDWI and directly measured DWI at b = 1400 s/mm2 on tumor-suspicious lesions and normal-appearing regions. In the numerical simulations, noise depended strongly on the b value, the diffusion coefficient, and the signal intensity at a b value of 0 s/mm2 in cDWI but not in regular DWI. The CNR between simulated tumor and normal regions showed a continuous increase with increasing b value. Both these findings were also observed in tumor-suspicious and normal-appearing regions in in vivo data. In vivo prostate DWI at a b value of 1400 s/mm2 showed a similar CNR eff between the tumor-suspicious regions and the normal-appearing tissue in cDWI as in the directly measured DWI (P = 0.395). The CNR eff between tumor-suspicious and normal-appearing prostate tissue in DWI images at a b value of 1400 s/mm2 is comparable in cDWI and directly measured DWI. Computed DWI at even higher b values, calculated from measured images with b values between 0 and 800 s/mm2, yields higher CNR eff than measured DWI, which may be of clinical aid in the management of prostate cancer.

Previously, we have demonstrated the ability of Bayesian image estimation (BIE) to reduce scatter and improve image contrast to noise ratio (CNR) in chest radiography without degradation of resolution. Here, we compare the effectiveness of BIE to a standard 12:1 grid. Images of a geometric phantom with two inches of added polystyrene were obtained both with and without a 12:1, 150 lp/mm grid. Images were acquired with standard protocols: 120 kVp, 72 inch source to image distance, and PA positioning. Images were acquired on a calibrated photostimulable phosphor system. An image exposure was used corresponding to the same patient dose as when acquiring film/screen chest images using a phototimer. The image acquired without the grid was processed by BIE for 6 iterations. Contrast, noise, and CNR were calculated and compared for the image acquired with the grid and the BIE processed image in different regions. BIE processing improved image CNR by 200 to 350% over that provided by the anti-scatter grid for the different regions. BIE provides higher CNR than that of a 12:1 grid. Because of this increase in CNR, Bayesian processed images will show an increase in detectability of low contrast objects, such as subtle lung nodules.