Abdominal multislice CT for obese patients: effect on image quality and radiation dose in a phantom study

RATIONALE AND OBJECTIVES: To evaluate the effect of a modified abdominal multislice computed tomography (CT) protocol for obese patients on image quality and radiation dose. MATERIALS AND METHODS: An adult female anthropomorphic phantom was used to simulate obese patients by adding one or two 4-cm circumferential layers of fat-equivalent material to the abdominal portion. The phantom was scanned with a subcutaneous fat thickness of 0, 4, and 8 cm using the following parameters (detector configuration/beam pitch/table feed per rotation/gantry rotation time/kV/mA): standard protocol A: 16 x 0.625 mm/1.75/17.5 mm/0.5 seconds/140/380, and modified protocol B: 16 x 1.25 mm/1.375/27.5 mm/1.0 seconds/140/380. Radiation doses to six abdominal organs and the skin, image noise values, and contrast-to-noise ratios (CNRs) were analyzed. Statistical analysis included analysis of variance, Wilcoxon rank sum, and Student's t-test (P < .05). RESULTS: Applying the modified protocol B with one or two fat rings, the image noise decreased significantly (P < .05), and simultaneously, the CNR increased significantly compared with protocol A (P < .05). Organ doses significantly increased, up to 54.7%, comparing modified protocol B with one fat ring to the routine protocol A with no fat rings (P < .05). However, no significant change in organ dose was seen for protocol B with two fat rings compared with protocol A without fat rings (range -2.1% to 8.1%) (P > .05). CONCLUSIONS: Using a modified abdominal multislice CT protocol for obese patients with 8 cm or more of subcutaneous fat, image quality can be substantially improved without a significant increase in radiation dose to the abdominal organs.

Thoracoabdominal-aortoiliac multidetector-row CT angiography at 80 and 100 kVp: assessment of image quality and radiation dose

OBJECTIVE: To compare image quality and radiation dose of thoracoabdominal computed tomography (CT) angiography at 80 and 100 kVp and to assess the feasibility of reducing contrast medium volume from 60 to 45 mL at 80 kVp. MATERIALS AND METHODS: This retrospective study had institutional review board approval; informed consent was waived. Seventy-five patients who had undergone thoracoabdominal 64-section multidetector-row CT angiography were divided into 3 groups of 25 patients each. Patients of groups A (tube voltage, 100 kVp) and B (tube voltage, 80 kVp) received 60 mL of contrast medium at 4 mL/s. Patients of group C (tube voltage, 80 kVp) received 45 mL of contrast medium at 3 mL/s. Mean aortoiliac attenuation, image noise, and contrast-to-noise ratio were assessed. The measurement of radiation dose was based on the volume CT dose index. Three independent readers assessed the diagnostic image quality. RESULTS: Mean aortoiliac attenuation for group B (621.1 +/- 90.5 HU) was significantly greater than for groups A and C (485.2 +/- 110.5 HU and 483.1 +/- 119.8 HU; respectively) (P < 0.001). Mean image noise was significantly higher for groups B and C than for group A (P < 0.05). The contrast-to-noise ratio did not significantly differ between the groups (group A, 35.0 +/- 13.8; group B, 31.7 +/- 10.1; group C, 27.3 +/- 11.5; P = 0.08). Mean volume CT dose index in groups B and C (5.2 +/- 0.4 mGy and 4.9 +/- 0.3 mGy, respectively) were reduced by 23.5% and 27.9%, respectively, compared with group A (6.8 +/- 0.8 mGy) (P < 0.001). The average overall diagnostic image quality for the 3 groups was graded as good or better. The score for group A was significantly higher than that for group C (P < 0.01), no difference was seen between group A and B (P = 0.92). CONCLUSIONS: Reduction of tube voltage from 100 to 80 kVp for thoracoabdominal CT angiography significantly reduces radiation dose without compromising image quality. Reduction of contrast medium volume to 45 mL at 80 kVp resulted in lower but still diagnostically acceptable image quality.

Effect of automatic tube current modulation on radiation dose and image quality for low tube voltage multidetector row CT angiography: phantom study

RATIONALE AND OBJECTIVES: To evaluate the effect of automatic tube current modulation on radiation dose and image quality for low tube voltage computed tomography (CT) angiography. MATERIALS AND METHODS: An anthropomorphic phantom was scanned with a 64-section CT scanner using following tube voltages: 140 kVp (Protocol A), 120 kVp (Protocol B), 100 kVp (Protocol C), and 80 kVp (Protocol D). To achieve similar noise, combined z-axis and xy-axes automatic tube current modulation was applied. Effective dose (ED) for the four tube voltages was assessed. Three plastic vials filled with different concentrations of iodinated solution were placed on the phantom's abdomen to obtain attenuation measurements. The signal-to-noise ratio (SNR) was calculated and a figure of merit (FOM) for each iodinated solution was computed as SNR(2)/ED. RESULTS: The ED was kept similar for the four different tube voltages: (A) 5.4 mSv +/- 0.3, (B) 4.1 mSv +/- 0.6, (C) 3.9 mSv +/- 0.5, and (D) 4.2 mSv +/- 0.3 (P > .05). As the tube voltage decreased from 140 to 80 kVp, image noise was maintained (range, 13.8-14.9 HU) (P > .05). SNR increased as the tube voltage decreased, with an overall gain of 119% for the 80-kVp compared to the 140-kVp protocol (P < .05). The FOM results indicated that with a reduction of the tube voltage from 140 to 120, 100, and 80 kVp, at constant SNR, ED was reduced by a factor of 2.1, 3.3, and 5.1, respectively, (P < .001). CONCLUSIONS: As tube voltage decreases, automatic tube current modulation for CT angiography yields either a significant increase in image quality at constant radiation dose or a significant decrease in radiation dose at a constant image quality.

Coronary artery stents: influence of adaptive statistical iterative reconstruction on image quality using 64-HDCT

OBJECTIVE: The assessment of coronary stents with present-generation 64-detector row computed tomography (HDCT) scanners is limited by image noise and blooming artefacts. We evaluated the performance of adaptive statistical iterative reconstruction (ASIR) for noise reduction in coronary stent imaging with HDCT. METHODS AND RESULTS: In 50 stents of 28 patients (mean age 64 ± 10 years) undergoing coronary CT angiography (CCTA) on an HDCT scanner the mean in-stent luminal diameter, stent length, image quality, in-stent contrast attenuation, and image noise were assessed. Studies were reconstructed using filtered back projection (FBP) and ASIR-FBP composites. ASIR resulted in reduced image noise vs. FBP (P < 0.0001). Two readers graded the CCTA stent image quality on a 4-point Likert scale and determined the proportion of interpretable stent segments. The best image quality for all clinical images was obtained with 40 and 60% ASIR with significantly larger luminal area visualization compared with FBP (+42.1 ± 5.4% with 100% ASIR vs. FBP alone; P < 0.0001) while the stent length was decreased (-4.7 ± 0.9%,

image quality compared with FBP reconstruction.

Impact of a 4th generation iterative reconstruction technique on image quality in low-dose computed tomography of the chest in immunocompromised patients

PURPOSE To determine the image quality of an iterative reconstruction (IR) technique in low-dose MDCT (LDCT) of the chest of immunocompromised patients in an intraindividual comparison to filtered back projection (FBP) and to evaluate the dose reduction capability. MATERIALS AND METHODS 30 chest LDCT scans were performed in immunocompromised patients (Brilliance iCT; 20-40 mAs; mean CTDIvol: 1.7 mGy). The raw data were reconstructed using FBP and the IR technique (iDose4™, Philips, Best, The Netherlands) set to seven iteration levels. 30 routine-dose MDCT (RDCT) reconstructed with FBP served as controls (mean exposure: 116 mAs; mean CDTIvol: 7.6 mGy). Three blinded radiologists scored subjective image quality and lesion conspicuity. Quantitative parameters including CT attenuation and objective image noise (OIN) were determined. RESULTS In LDCT high iDose4™ levels lead to a significant decrease in OIN (FBP vs. iDose7: subscapular muscle 139.4 vs. 40.6 HU). The high iDose4™ levels provided significant improvements in image quality and artifact and noise reduction compared to LDCT FBP images. The conspicuity of subtle lesions was limited in LDCT FBP images. It significantly improved with high iDose4™ levels (> iDose4). LDCT with iDose4™ level 6 was determined to be of equivalent image quality as RDCT with FBP. CONCLUSION iDose4™ substantially improves image quality and lesion conspicuity and reduces noise in low-dose chest CT. Compared to RDCT, high iDose4™ levels provide equivalent image quality in LDCT, hence suggesting a potential dose reduction of almost 80%.

Automatic estimation of noise parameters in Fourier-domain optical coherence tomography cross sectional images using statistical information

We present an application and sample independent method for the automatic discrimination of noise and signal in optical coherence tomography Bscans. The proposed algorithm models the observed noise probabilistically and allows for a dynamic determination of image noise parameters and the choice of appropriate image rendering parameters. This overcomes the observer variability and the need for a priori information about the content of sample images, both of which are challenging to estimate systematically with current systems. As such, our approach has the advantage of automatically determining crucial parameters for evaluating rendered image quality in a systematic and task independent way. We tested our algorithm on data from four different biological and nonbiological samples (index finger, lemon slices, sticky tape, and detector cards) acquired with three different experimental spectral domain optical coherence tomography (OCT) measurement systems including a swept source OCT. The results are compared to parameters determined manually by four experienced OCT users. Overall, our algorithm works reliably regardless of which system and sample are used and estimates noise parameters in all cases within the confidence interval of those found by observers.

Biomechanical Role of Bone Anisotropy Estimated on Clinical CT Scans by Image Registration

Image-based modeling is a popular approach to perform patient-specific biomechanical simulations. Accurate modeling is critical for orthopedic application to evaluate implant design and surgical planning. It has been shown that bone strength can be estimated from the bone mineral density (BMD) and trabecular bone architecture. However, these findings cannot be directly and fully transferred to patient-specific modeling since only BMD can be derived from clinical CT. Therefore, the objective of this study was to propose a method to predict the trabecular bone structure using a µCT atlas and an image registration technique. The approach has been evaluated on femurs and patellae under physiological loading. The displacement and ultimate force for femurs loaded in stance position were predicted with an error of 2.5% and 3.7%, respectively, while predictions obtained with an isotropic material resulted in errors of 7.3% and 6.9%. Similar results were obtained for the patella, where the strain predicted using the registration approach resulted in an improved mean squared error compared to the isotropic model. We conclude that the registration of anisotropic information from of a single template bone enables more accurate patient-specific simulations from clinical image datasets than isotropic model.

CT Image Electron Density Quantification in Regions with Metal Implants: Implications for Radiotherapy Treatment Planning

In radiotherapy planning, computed tomography (CT) images are used to quantify the electron density of tissues and provide spatial anatomical information. Treatment planning systems use these data to calculate the expected spatial distribution of absorbed dose in a patient. CT imaging is complicated by the presence of metal implants which cause increased image noise, produce artifacts throughout the image and can exceed the available range of CT number values within the implant, perturbing electron density estimates in the image. Furthermore, current dose calculation algorithms do not accurately model radiation transport at metal-tissue interfaces. Combined, these issues adversely affect the accuracy of dose calculations in the vicinity of metal implants. As the number of patients with orthopedic and dental implants grows, so does the need to deliver safe and effective radiotherapy treatments in the presence of implants. The Medical Physics group at the Cancer Centre of Southeastern Ontario and Queen's University has developed a Cobalt-60 CT system that is relatively insensitive to metal artifacts due to the high energy, nearly monoenergetic Cobalt-60 photon beam. Kilovoltage CT (kVCT) images, including images corrected using a commercial metal artifact reduction tool, were compared to Cobalt-60 CT images throughout the treatment planning process, from initial imaging through to dose calculation. An effective metal artifact reduction algorithm was also implemented for the Cobalt-60 CT system. Electron density maps derived from the same kVCT and Cobalt-60 CT images indicated the impact of image artifacts on estimates of photon attenuation for treatment planning applications. Measurements showed that truncation of CT number data in kVCT images produced significant mischaracterization of the electron density of metals. Dose measurements downstream of metal inserts in a water phantom were compared to dose data calculated using CT images from kVCT and Cobalt-60 systems with and without artifact correction. The superior accuracy of electron density data derived from Cobalt-60 images compared to kVCT images produced calculated dose with far better agreement with measured results. These results indicated that dose calculation errors from metal image artifacts are primarily due to misrepresentation of electron density within metals rather than artifacts surrounding the implants.

Challenges in imaging and predictive modeling of rhizosphere processes

Background Plant-soil interaction is central to human food production and ecosystem function. Thus, it is essential to not only understand, but also to develop predictive mathematical models which can be used to assess how climate and soil management practices will affect these interactions. Scope In this paper we review the current developments in structural and chemical imaging of rhizosphere processes within the context of multiscale mathematical image based modeling. We outline areas that need more research and areas which would benefit from more detailed understanding. Conclusions We conclude that the combination of structural and chemical imaging with modeling is an incredibly powerful tool which is fundamental for understanding how plant roots interact with soil. We emphasize the need for more researchers to be attracted to this area that is so fertile for future discoveries. Finally, model building must go hand in hand with experiments. In particular, there is a real need to integrate rhizosphere structural and chemical imaging with modeling for better understanding of the rhizosphere processes leading to models which explicitly account for pore scale processes.

HIGH-RESOLUTION IMAGING OF THE ATLBS REGIONS: THE RADIO SOURCE COUNTS

The Australia Telescope Low-brightness Survey (ATLBS) regions have been mosaic imaged at a radio frequency of 1.4 GHz with 6 `' angular resolution and 72 mu Jy beam(-1) rms noise. The images (centered at R. A. 00(h)35(m)00(s), decl. -67 degrees 00'00 `' and R. A. 00(h)59(m)17(s), decl. -67.00'00 `', J2000 epoch) cover 8.42 deg(2) sky area and have no artifacts or imaging errors above the image thermal noise. Multi-resolution radio and optical r-band images (made using the 4 m CTIO Blanco telescope) were used to recognize multi-component sources and prepare a source list; the detection threshold was 0.38 mJy in a low-resolution radio image made with beam FWHM of 50 `'. Radio source counts in the flux density range 0.4-8.7 mJy are estimated, with corrections applied for noise bias, effective area correction, and resolution bias. The resolution bias is mitigated using low-resolution radio images, while effects of source confusion are removed by using high-resolution images for identifying blended sources. Below 1 mJy the ATLBS counts are systematically lower than the previous estimates. Showing no evidence for an upturn down to 0.4 mJy, they do not require any changes in the radio source population down to the limit of the survey. The work suggests that automated image analysis for counts may be dependent on the ability of the imaging to reproduce connecting emission with low surface brightness and on the ability of the algorithm to recognize sources, which may require that source finding algorithms effectively work with multi-resolution and multi-wavelength data. The work underscores the importance of using source lists-as opposed to component lists-and correcting for the noise bias in order to precisely estimate counts close to the image noise and determine the upturn at sub-mJy flux density.

一种基于多项式的图像离焦建模方法研究

本文针对显微视觉的图像恢复与3D 重建问题,从显微光学成像的离焦机理和基于点扩散函数的图像模糊化描述出发,通过模糊测度算子分析序列显微图像的离焦分布规律,提出了一种用于构建较为精准离焦模型的方法。该模型采用混合参数多项式结构,与传统高斯模型相比,可以更接近真实离焦过程,这为较为精确的光学显微图像恢复和3D 重构提供了新的技术途径。

图象噪声的模糊聚类平滑方法

模糊C-means算法在聚类分析中已得到了成功的应用,本文提出一种利用模糊C-means算法消除噪声的新方法。一般来说,图象中的噪声点就是其灰度值与其周围象素的灰度值之差超过某个门限值的点。根据这个事实,首先利用模糊C-means算法分类,再利用标准核函数检测出噪声点,然后将噪声点去掉。由于只修改噪声点处的象素灰度值,而对于其它象素的灰度值不予改变,所以本算法能够很好地保护细节和边缘。本方法每次处理3×3个点,而以往的方法只能每次处理一个点,所以本方法能提高运算速度。文中给出了利用本方法对实际图象处理的结果,并与梯度倒数权值法进行了定量的比较。

Fast Learning VIEWNET Architectures for Recognizing 3-D Objects from Multiple 2-D Views

The recognition of 3-D objects from sequences of their 2-D views is modeled by a family of self-organizing neural architectures, called VIEWNET, that use View Information Encoded With NETworks. VIEWNET incorporates a preprocessor that generates a compressed but 2-D invariant representation of an image, a supervised incremental learning system that classifies the preprocessed representations into 2-D view categories whose outputs arc combined into 3-D invariant object categories, and a working memory that makes a 3-D object prediction by accumulating evidence from 3-D object category nodes as multiple 2-D views are experienced. The simplest VIEWNET achieves high recognition scores without the need to explicitly code the temporal order of 2-D views in working memory. Working memories are also discussed that save memory resources by implicitly coding temporal order in terms of the relative activity of 2-D view category nodes, rather than as explicit 2-D view transitions. Variants of the VIEWNET architecture may also be used for scene understanding by using a preprocessor and classifier that can determine both What objects are in a scene and Where they are located. The present VIEWNET preprocessor includes the CORT-X 2 filter, which discounts the illuminant, regularizes and completes figural boundaries, and suppresses image noise. This boundary segmentation is rendered invariant under 2-D translation, rotation, and dilation by use of a log-polar transform. The invariant spectra undergo Gaussian coarse coding to further reduce noise and 3-D foreshortening effects, and to increase generalization. These compressed codes are input into the classifier, a supervised learning system based on the fuzzy ARTMAP algorithm. Fuzzy ARTMAP learns 2-D view categories that are invariant under 2-D image translation, rotation, and dilation as well as 3-D image transformations that do not cause a predictive error. Evidence from sequence of 2-D view categories converges at 3-D object nodes that generate a response invariant under changes of 2-D view. These 3-D object nodes input to a working memory that accumulates evidence over time to improve object recognition. ln the simplest working memory, each occurrence (nonoccurrence) of a 2-D view category increases (decreases) the corresponding node's activity in working memory. The maximally active node is used to predict the 3-D object. Recognition is studied with noisy and clean image using slow and fast learning. Slow learning at the fuzzy ARTMAP map field is adapted to learn the conditional probability of the 3-D object given the selected 2-D view category. VIEWNET is demonstrated on an MIT Lincoln Laboratory database of l28x128 2-D views of aircraft with and without additive noise. A recognition rate of up to 90% is achieved with one 2-D view and of up to 98.5% correct with three 2-D views. The properties of 2-D view and 3-D object category nodes are compared with those of cells in monkey inferotemporal cortex.

Estudo de otimização de imagem e dose em mamografia digital

O objetivo deste trabalho foi efetuar um estudo de otimização dos parâmetros de exposição do mamógrafo digital da marca GE, modelo Senographe DS instalado no Instituto Português de Oncologia de Coimbra (IPOC) baseado numa análise contraste detalhe e avaliar o impacto do resultado obtido na melhoria da capacidade de detecção clínica das estruturas da mama. O mamógrafo em estudo dispõe de um sistema de controle automático da exposição designado por sistema AOP (Automatic Optimization of Parameters) que foi otimizado pelo fabricante a partir da razão contraste ruído. O sistema AOP é usado na prática clínica na quase totalidade dos exames de mamografia realizados no IPOC. Tendo em conta o tipo de estrutura que se pretende visualizar na mamografia, nomeadamente estruturas de baixo contraste como as massas e estruturas de dimensão submilimétrica como as microcalcificações, a análise contraste detalhe poderia constituir uma abordagem mais adequada para o estudo de otimização dos parâmetros de exposição uma vez que permitiria uma avaliação conjunta do contraste, da resolução espacial e do ruído da imagem. Numa primeira fase do trabalho foi efetuada a caracterização da prática clínica realizada no mamógrafo em estudo em termos de espessura de mama comprimida “típica”, dos parâmetros técnicos de exposição e das opções de processamento das imagens aplicadas pelo sistema AOP (combinação alvo/filtro, tensão aplicada na ampola - kVp e produto corrente-tempo da ampola - mAs). Numa segunda fase foi realizado um estudo de otimização da qualidade da imagem versus dose na perspectiva dos parâmetros físicos. Para tal foi efetuada uma análise contrastedetalhe no objeto simulador de mama CDMAM e usada uma figura de mérito definida a partir do IQFinv (inverted image quality figure) e da dose glandular média. Os resultados apontaram para uma diferença entre o ponto ótimo resultante do estudo de otimização e o ponto associado aos parâmetros de exposição escolhidos pelo sistema AOP, designadamente no caso da mama pequena. Sendo a qualidade da imagem na perspectiva clínica um conceito mais complexo cujo resultado da apreciação de uma imagem de boa qualidade deve ter em conta as diferenças entre observadores, foi efetuado na última parte deste trabalho um estudo do impacto clínico da proposta de otimização da qualidade de imagem. A partir das imagens realizadas com o objeto simulador antropomórfico TOR MAM simulando uma mama pequena, seis médicos(as) radiologistas com mais de 5 anos de experiência em mamografia avaliaram as imagens “otimizadas” obtidas utilizando-se os parâmetros técnicos de exposição resultantes do estudo de otimização e a imagem resultante da escolha do sistema AOP. A análise estatística das avaliações feitas pelos médicos indica que a imagem “otimizada” da mama pequena apresenta uma melhor visualização das microcalcificações sem perda da qualidade da imagem na deteção de fibras e de massas em comparação com a imagem “standard”. Este trabalho permitiu introduzir uma nova definição da figura de mérito para o estudo de otimização da qualidade da imagem versus dose em mamografia. Permitiu também estabelecer uma metodologia consistente que pode facilmente ser aplicada a qualquer outro mamógrafo, contribuindo para a área da otimização em mamografia digital que é uma das áreas mais relevantes no que toca à proteção radiológica do paciente.

Finding Texture Boundaries in Images

Texture provides one cue for identifying the physical cause of an intensity edge, such as occlusion, shadow, surface orientation or reflectance change. Marr, Julesz, and others have proposed that texture is represented by small lines or blobs, called 'textons' by Julesz [1981a], together with their attributes, such as orientation, elongation, and intensity. Psychophysical studies suggest that texture boundaries are perceived where distributions of attributes over neighborhoods of textons differ significantly. However, these studies, which deal with synthetic images, neglect to consider two important questions: How can these textons be extracted from images of natural scenes? And how, exactly, are texture boundaries then found? This thesis proposes answers to these questions by presenting an algorithm for computing blobs from natural images and a statistic for measuring the difference between two sample distributions of blob attributes. As part of the blob detection algorithm, methods for estimating image noise are presented, which are applicable to edge detection as well.