Modifying constrained least‐squares restoration for application to single photon emission computed tomography projection images

Penney, Bill C.; King, Michael A.; Schwinger, Ronald B.; Baker, Stephen P.; Doherty, Paul W.

doi:10.1118/1.596227

Cited by 16 publications

(15 citation statements)

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“…Image manipulation tech- niques have been performed to overcome the limitations of displays or to correct artifacts due to the image acquisition process. [54][55][56][57][58]39,[59][60][61][62][63][64][65][66] Moseley and Munro, 39 for example, developed a method to display portal images that permits the user to optimize display contrast without saturating parts of the image. The difference between the average signal in small regions and the global average was subtracted from the original image to reduce changes in average signal that occur over large spatial dimensions without obscuring changes in signal that occur over small spatial dimensions.…”

Section: Image Restorationmentioning

confidence: 99%

“…This method was later extended to SPECT images. 58 A wavelet-based neural network filter was developed by Qian and Clarke 59 to reduce noise in gamma-camera imaging of beta-emitting isotopes required for the management of antibody therapy. Other areas where noise reduction is particularly important include dual-energy imaging 68,69 and low-dose computed tomography ͑CT͒; 70 noise reduction techniques developed in these areas are often modality specific and require access to the raw data.…”

Section: Noise Reductionmentioning

confidence: 99%

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Anniversary Paper: Image processing and manipulation through the pages ofMedical Physics

Armato

Ginneken

2008

Medical Physics

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The language of radiology has gradually evolved from "the film" (the foundation of radiology since Wilhelm Roentgen's 1895 discovery of x-rays) to "the image," an electronic manifestation of a radiologic examination that exists within the bits and bytes of a computer. Rather than simply storing and displaying radiologic images in a static manner, the computational power of the computer may be used to enhance a radiologist's ability to visually extract information from the image through image processing and image manipulation algorithms. Image processing tools provide a broad spectrum of opportunities for image enhancement. Gray-level manipulations such as histogram equalization, spatial alterations such as geometric distortion correction, preprocessing operations such as edge enhancement, and enhanced radiography techniques such as temporal subtraction provide powerful methods to improve the diagnostic quality of an image or to enhance structures of interest within an image. Furthermore, these image processing algorithms provide the building blocks of more advanced computer vision methods. The prominent role of medical physicists and the AAPM in the advancement of medical image processing methods, and in the establishment of the "image" as the fundamental entity in radiology and radiation oncology, has been captured in 35 volumes of Medical Physics.

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Section: Image Restorationmentioning

confidence: 99%

Section: Noise Reductionmentioning

confidence: 99%

Anniversary Paper: Image processing and manipulation through the pages ofMedical Physics

Armato

Ginneken

2008

Medical Physics

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“…4͒, indicate that the restoration approach can improve F-18 collimated imaging. The restoration approach has been successfully applied in nuclear medicine imaging, [12][13][14][15][16] to compensate for the effects of system blurring, scatter radiation, noise, and septal penetration. Septal penetration is the major cause of the PSF degradation in 511 keV collimated imaging and in clinical situations may account for 30%-50% of photons detected.…”

Section: Creation Of the Restoration Filtermentioning

confidence: 99%

“…The purpose of this paper is to investigate the correction methods which can improve F-18 FDG images and make them more similar to the Tc-99m MIBI images. The selfscatter correction, using a third 410 keV window, 6 is compared with F-18 restored, [12][13][14][15][16] and unprocessed images.…”

Section: Introductionmentioning

confidence: 99%

Improvement of the fluorine‐18 fluorodeoxyglucose images in simultaneous F‐18 FDG/Tc‐99m collimated spect imaging

Knešaurek

Macháč

1999

Medical Physics

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Collimated F-18 FDG SPECT imaging has been shown to be an acceptable alternative to F-18 FDG PET imaging for evaluating injured but viable myocardium. Ultra-energy (UHE) imaging is usually performed in simultaneous F-18 FDG/Tc-99m MIBI studies. The main limitations of this technique are degradation of the Tc-99m MIBI images due to F-18 downscatter to the Tc-99m window, and loss of resolution in Tc-99m images caused by using a UHE rather than a low-energy collimator. The quality of F-18 images has not been addressed. In our clinical and phantom studies we have found that F-18 images are inferior to simultaneously acquired Tc-99m MIBI images. This paper compares two correction methods for F-18 FDG images in a realistic cardiac phantom study. One approach is subtractive scatter correction, which employs a third 410 keV energy window image to estimate scatter. The other approach is based on restoration. The phantom acquisition was performed with 7.2 MBq of F-18 and 22.2 MBq of Tc-99m injected into the left ventricular (LV) wall. Three inserts, 3 cm, 2 cm, and 1 cm in diameter, were placed in the LV wall to simulate infarcts. Circumferential profiles were drawn from three successive short-axis slices and compared with true phantom data. The differences were calculated as root-mean-square error (rmse). Scatter correction improved rmse only 4.5 +/- 0.3%, while restoration improved rmse 16.1 +/- 0.4%, when compared with raw data. The same differences, measured as rmse, were 9.5 +/- 0.5, 6.8 +/- 0.4, and 5.1 +/- 0.5 for raw, scatter corrected, and restored F-18 data, respectively, when compared with Tc-99m window 140 keV data. The amount of noise, measured as root-mean-square % (rms%) was 5.3 +/- 0.5% for the Tc-99m image, 4.9 +/- 0.7% for the F-18 restored image, 6.2 +/- 0.6% for the raw F-18 image, and 6.5 +/- 0.9% for the scatter corrected F-18 image. The contrast measured for 2 cm and 3 cm inserts was 0.17 +/- 0.07 and 0.26 +/- 0.06 for F-18 raw data, 0.19 +/- 0.08 and 0.29 +/- 0.06 for the scatter corrected F-18 image, and 0.28 +/- 0.06 and 0.43 +/- 0.07 for the restored F-18 image. The contrast was 0.20 +/- 0.07 and 0.46 +/- 0.05 for the Tc-99m 140 keV window image. The restoration approach provided F-18 images of better contrast and detectibility than uncorrected or scatter corrected F-18 images. Restored F-18 images match better with the simultaneously acquired Tc-99m images.

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“…͑1͒ The first class tries to correct or modify the emission data prior to reconstruction, using stationary, [6][7][8][9] or nonstationary, [10][11][12] filtering of the projections. Alternatively, the frequency-distance relationship may be used to take into account the depth-dependent loss of resolution.…”

Section: Introductionmentioning

confidence: 99%

Iterative three‐dimensional expectation maximization restoration of single photon emission computed tomography images: Application in striatal imaging

et al. 2005

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Single photon emission computed tomography imaging suffers from poor spatial resolution and high statistical noise. Consequently, the contrast of small structures is reduced, the visual detection of defects is limited and precise quantification is difficult. To improve the contrast, it is possible to include the spatially variant point spread function of the detection system into the iterative reconstruction algorithm. This kind of method is well known to be effective, but time consuming. We have developed a faster method to account for the spatial resolution loss in three dimensions, based on a postreconstruction restoration method. The method uses two steps. First, a noncorrected iterative ordered subsets expectation maximization (OSEM) reconstruction is performed and, in the second step, a three-dimensional (3D) iterative maximum likelihood expectation maximization (ML-EM) a posteriori spatial restoration of the reconstructed volume is done. In this paper, we compare to the standard OSEM-3D method, in three studies (two in simulation and one from experimental data). In the two first studies, contrast, noise, and visual detection of defects are studied. In the third study, a quantitative analysis is performed from data obtained with an anthropomorphic striatal phantom filled with 123-I. From the simulations, we demonstrate that contrast as a function of noise and lesion detectability are very similar for both OSEM-3D and OSEM-R methods. In the experimental study, we obtained very similar values of activity-quantification ratios for different regions in the brain. The advantage of OSEM-R compared to OSEM-3D is a substantial gain of processing time. This gain depends on several factors. In a typical situation, for a 128 x 128 acquisition of 120 projections, OSEM-R is 13 or 25 times faster than OSEM-3D, depending on the calculation method used in the iterative restoration. In this paper, the OSEM-R method is tested with the approximation of depth independent resolution. For the striatum this approximation is appropriate, but for other clinical situations we will need to include a spatially varying response. Such a response is already included in OSEM-3D.

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Modifying constrained least‐squares restoration for application to single photon emission computed tomography projection images

Cited by 16 publications

References 0 publications

Anniversary Paper: Image processing and manipulation through the pages ofMedical Physics

Anniversary Paper: Image processing and manipulation through the pages ofMedical Physics

Improvement of the fluorine‐18 fluorodeoxyglucose images in simultaneous F‐18 FDG/Tc‐99m collimated spect imaging

Iterative three‐dimensional expectation maximization restoration of single photon emission computed tomography images: Application in striatal imaging

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