scholarly journals Adaptive Autoregressive Model for Reduction of Noise in SPECT

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Reijo Takalo ◽  
Heli Hytti ◽  
Heimo Ihalainen ◽  
Antti Sohlberg
Keyword(s):  
Reconstruction Method ◽  
Back Projection ◽  
Contrast Resolution ◽  
Autoregressive Modelling ◽  
Good Spatial Resolution ◽  
Expectation Maximisation ◽  
Projection Images ◽  
Adaptive Autoregressive Model ◽  
Line Spread ◽  
Spect Images

This paper presents improved autoregressive modelling (AR) to reduce noise in SPECT images. An AR filter was applied to prefilter projection images and postfilter ordered subset expectation maximisation (OSEM) reconstruction images (AR-OSEM-AR method). The performance of this method was compared with filtered back projection (FBP) preceded by Butterworth filtering (BW-FBP method) and the OSEM reconstruction method followed by Butterworth filtering (OSEM-BW method). A mathematical cylinder phantom was used for the study. It consisted of hot and cold objects. The tests were performed using three simulated SPECT datasets. Image quality was assessed by means of the percentage contrast resolution (CR%) and the full width at half maximum (FWHM) of the line spread functions of the cylinders. The BW-FBP method showed the highest CR% values and the AR-OSEM-AR method gave the lowest CR% values for cold stacks. In the analysis of hot stacks, the BW-FBP method had higher CR% values than the OSEM-BW method. The BW-FBP method exhibited the lowest FWHM values for cold stacks and the AR-OSEM-AR method for hot stacks. In conclusion, the AR-OSEM-AR method is a feasible way to remove noise from SPECT images. It has good spatial resolution for hot objects.

Signal Interpretation from Low-dosage Acquisition

2010 ◽  
Vol 49 (05) ◽  
pp. 542-546
Author(s):  
L. Cheng
Keyword(s):  
Low Dose ◽  
Reconstruction Method ◽  
Back Projection ◽  
Filtered Back Projection ◽  
Low Dose Ct ◽  
Computed Tomographic ◽  
Projection Images ◽  
Artifacts Reduction ◽  
Effective Signal ◽  
Low Dosage

Summary Objectives: This paper focuses on how we could analyze and interpret filtered back-projection reconstructed signals from low-dose computed tomographic (CT) imaging systems. There exists a growing imbalance between dosage reduction and effective signal interpretation. At the same time, low-dose applications are undergoing alarming growth. Methods: This paper interprets filtered back-projection images in low-dose CT systems and details the possible properties of the artifacts. The interpretation leads to design of a new multi-image filtered back-projection approach that allows artifacts to be effectively identified across multiple images. We use this approach as a building block to propose a new reconstruction method that enables effective artifacts reduction and efficient implementation. Results: Experiments with both clinical and simulated low-dose images demonstrate the validity and effectiveness of the proposed approach. Conclusions: This study discusses a new FBP-based reconstruction approach based on signal interpretation from low-dosage acquisition. This method uses multiple filtered back-projection images from projection subsets to provide clues for distinguishing underlying clinical structure from artifacts. A framework is derived for effective signal interpretation and artifacts reduction. It requires no hardware change and a minimum amount of extra software support compared with current CT systems. Clinical and simulated low-dose CT scans demonstrated effectiveness of the proposed method.

scholarly journals Novel nonlinear reconstruction method with grey-level quantisation units for electron tomography

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Norio Baba ◽  
Kenji Kaneko ◽  
Misuzu Baba
Keyword(s):  
Cross Section ◽  
Electron Tomography ◽  
Three Dimensional ◽  
Reconstruction Technique ◽  
Reconstruction Method ◽  
Back Projection ◽  
Grey Level ◽  
Discrete Processing ◽  
Structural Image ◽  
Tomography Reconstruction

AbstractWe report a new computed tomography reconstruction method, named quantisation units reconstruction technique (QURT), applicable to electron and other fields of tomography. Conventional electron tomography methods such as filtered back projection, weighted back projection, simultaneous iterative reconstructed technique, etc. suffer from the ‘missing wedge’ problem due to the limited tilt-angle range. QURT demonstrates improvements to solve this problem by recovering a structural image blurred due to the missing wedge and substantially reconstructs the structure even if the number of projection images is small. QURT reconstructs a cross-section image by arranging grey-level quantisation units (QU pieces) in three-dimensional image space via unique discrete processing. Its viability is confirmed by model simulations and experimental results. An important difference from recently developed methods such as discrete algebraic reconstruction technique (DART), total variation regularisation—DART, and compressed sensing is that prior knowledge of the conditions regarding the specimen or the expected cross-section image is not necessary.

scholarly journals Evaluation of a new image reconstruction method for digital breast tomosynthesis: effects on the visibility of breast lesions and breast density

2019 ◽  
Vol 92 (1103) ◽  
pp. 20190345 ◽  
Author(s):  
Julia Krammer ◽  
Sergei Zolotarev ◽  
Inge Hillman ◽  
Konstantinos Karalis ◽  
Dzmitry Stsepankou ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Image Reconstruction ◽  
Image Quality ◽  
Breast Density ◽  
Breast Imaging ◽  
Reconstruction Method ◽  
Back Projection ◽  
Filtered Back Projection ◽  
Breast Tomosynthesis

Objective: To compare image quality and breast density of two reconstruction methods, the widely-used filtered-back projection (FBP) reconstruction and the iterative heuristic Bayesian inference reconstruction (Bayesian inference reconstruction plus the method of total variation applied, HBI). Methods: Thirty-two clinical DBT data sets with malignant and benign findings, n = 27 and 17, respectively, were reconstructed using FBP and HBI. Three experienced radiologists evaluated the images independently using a 5-point visual grading scale and classified breast density according to the American College of Radiology Breast Imaging-Reporting And Data System Atlas, fifth edition. Image quality metrics included lesion conspicuity, clarity of lesion borders and spicules, noise level, artifacts surrounding the lesion, visibility of parenchyma and breast density. Results: For masses, the image quality of HBI reconstructions was superior to that of FBP in terms of conspicuity,clarity of lesion borders and spicules (p < 0.01). HBI and FBP were not significantly different in calcification conspicuity. Overall, HBI reduced noise and supressed artifacts surrounding the lesions better (p < 0.01). The visibility of fibroglandular parenchyma increased using the HBI method (p < 0.01). On average, five cases per radiologist were downgraded from BI-RADS breast density category C/D to A/B. Conclusion: HBI significantly improves lesion visibility compared to FBP. HBI-visibility of breast parenchyma increased, leading to a lower breast density rating. Applying the HBIR algorithm should improve the diagnostic performance of DBT and decrease the need for additional imaging in patients with dense breasts. Advances in knowledge: Iterative heuristic Bayesian inference (HBI) image reconstruction substantially improves the image quality of breast tomosynthesis leading to a better visibility of breast carcinomas and reduction of the perceived breast density compared to the widely-used filtered-back projection (FPB) reconstruction. Applying HBI should improve the accuracy of breast tomosynthesis and reduce the number of unnecessary breast biopsies. It may also reduce the radiation dose for the patients, which is especially important in the screening context.

Improvements to image quality using hybrid and model-based iterative reconstructions: a phantom study

2016 ◽  
Vol 58 (1) ◽  
pp. 53-61 ◽  
Author(s):  
Marie-Louise Aurumskjöld ◽  
Kristina Ydström ◽  
Anders Tingberg ◽  
Marcus Söderberg
Keyword(s):  
Image Quality ◽  
Radiation Dose ◽  
Iterative Reconstruction ◽  
Power Spectra ◽  
Reconstruction Method ◽  
Noise Power ◽  
Contrast Resolution ◽  
Low Contrast ◽  
Model Based ◽  
Reconstruction Methods

Background The number of computed tomography (CT) examinations is increasing and leading to an increase in total patient exposure. It is therefore important to optimize CT scan imaging conditions in order to reduce the radiation dose. The introduction of iterative reconstruction methods has enabled an improvement in image quality and a reduction in radiation dose. Purpose To investigate how image quality depends on reconstruction method and to discuss patient dose reduction resulting from the use of hybrid and model-based iterative reconstruction. Material and Methods An image quality phantom (Catphan® 600) and an anthropomorphic torso phantom were examined on a Philips Brilliance iCT. The image quality was evaluated in terms of CT numbers, noise, noise power spectra (NPS), contrast-to-noise ratio (CNR), low-contrast resolution, and spatial resolution for different scan parameters and dose levels. The images were reconstructed using filtered back projection (FBP) and different settings of hybrid (iDose4) and model-based (IMR) iterative reconstruction methods. Results iDose4 decreased the noise by 15–45% compared with FBP depending on the level of iDose4. The IMR reduced the noise even further, by 60–75% compared to FBP. The results are independent of dose. The NPS showed changes in the noise distribution for different reconstruction methods. The low-contrast resolution and CNR were improved with iDose4, and the improvement was even greater with IMR. Conclusion There is great potential to reduce noise and thereby improve image quality by using hybrid or, in particular, model-based iterative reconstruction methods, or to lower radiation dose and maintain image quality.

Sign in / Sign up

Export Citation Format

Share Document