Low-contrast Structures Research Articles

Features of the amplification of the electromagnetic field and the density of states of photonic crystal structures in plants

Calculations were performed using the transfer matrix of one-dimensional photonic crystals with low contrast and asymmetric structures of different periods. Such structures have been found in many plants. When joining two successively connected sublattices with different periods, an increase is observed in the amplitude of the electromagnetic field and the density of photon states inside the structure, and their change depends on the asymmetry of the thickness of the sublattices

Radioheliographs and Radio Reflectors in Solar Plasma Studies

The paper covers characteristics of big interferometers and reflecting radio telescopes intended for the studies of the solar atmosphere radiation. Solar radio interferometers (radioheliographs) have proven to be more cost-effective than single dish radio telescopes, since using the aperture synthesis allows for fast imaging of the Sun and its active regions with higher spatial resolution, thus, studying solar eruptive processes more efficiently. From the other hand, the studies of many fundamental aspects of the solar atmosphere physics requiring studies of low-contrast structures before and after flares gained no proper development. The efficiency of radio reflectors was enhanced in last decades mainly due to the development of matrix methods of reception, focal arrays, and also due to spectroscopic analysis in the wide wavelength range with high flux density sensitivity. Meanwhile, the diversity of issues related to the physics of the solar atmosphere and active processes requires an optimal combination of capabilities of various microwave interferometric and reflecting systems for joint studies of the Sun. In view of the started implementation of solar observation programs with a new instrument, ALMA, and obtaining newly observed data in the millimeter and submillimeter ranges, the areas of development of solar studies with the RATAN-600 radio telescope in the wide wavelength range are discussed.

Evaluation of Image Registration Accuracy for Tumor and Organs at Risk in the Thorax for Compliance With TG 132 Recommendations

PurposeTo evaluate accuracy for 2 deformable image registration methods (in-house B-spline and MIM freeform) using image pairs exhibiting changes in patient orientation and lung volume and to assess the appropriateness of registration accuracy tolerances proposed by the American Association of Physicists in Medicine Task Group 132 under such challenging conditions via assessment by expert observers. Methods and MaterialsFour-dimensional computed tomography scans for 12 patients with lung cancer were acquired with patients in prone and supine positions. Tumor and organs at risk were delineated by a physician on all data sets: supine inhale (SI), supine exhale, prone inhale, and prone exhale. The SI image was registered to the other images using both registration methods. All SI contours were propagated using the resulting transformations and compared with physician delineations using Dice similarity coefficient, mean distance to agreement, and Hausdorff distance. Additionally, propagated contours were anonymized along with ground-truth contours and rated for quality by physician-observers. ResultsAveraged across all patients, the accuracy metrics investigated remained within tolerances recommended by Task Group 132 (Dice similarity coefficient >0.8, mean distance to agreement <3 mm). MIM performed better with both complex (vertebrae) and low-contrast (esophagus) structures, whereas the in-house method performed better with lungs (whole and individual lobes). Accuracy metrics worsened but remained within tolerances when propagating from supine to prone; however, the Jacobian determinant contained regions with negative values, indicating localized nonphysiologic deformations. For MIM and in-house registrations, 50% and 43.8%, respectively, of propagated contours were rated acceptable as is and 8.2% and 11.0% as clinically unacceptable. ConclusionsThe deformable image registration methods performed reliably and met recommended tolerances despite anatomically challenging cases exceeding typical interfraction variability. However, additional quality assurance measures are necessary for complex applications (eg, dose propagation). Human review rather than unsupervised implementation should always be part of the clinical registration workflow.

Progressive Dual-Domain Filter for Enhancing and Denoising Optical Remote-Sensing Images

Enhancement and denoising have always been a pair of conflicting problems in image processing of computer vision. Inspired by an earlier dual-domain filter (DDF), this letter proposes a progressive DDF to simultaneously enhance and denoise low-quality optical remote-sensing images. The main procedure of the proposed enhancement filter has two parts. First, a bilateral filter is exploited as a guide filter to obtain high-contrast images, which are enhanced by a histogram modification method. Then, low-contrast useful structures are restored by a short-time Fourier transform and are enhanced using an adaptive correction parameter. Both the quantitative and qualitative results of experiments on synthetic and real-world low-quality remote-sensing images demonstrate that the proposed method performs well on contrast enhancement, structure preservation, and noise reduction. Moreover, its satisfactory computation time resulting from its simple implementation makes it suitable for extensive application.

Advanced Modeled Iterative Reconstruction (ADMIRE) Facilitates Radiation Dose Reduction in Abdominal CT

This study aimed to determine the potential degree of radiation dose reduction achievable using Advanced Modeled Iterative Reconstruction (ADMIRE) in abdominal computed tomography (CT) while maintaining image quality. Moreover, this study compared differences in image noise reduction of this iterative algorithm with radiation dose reduction. Eleven consecutive patients scheduled for abdominal CT were scanned according to our institute's standard protocol (100 kV, 289 reference mAs). Using a proprietary reconstruction software, CT images of these patients were reconstructed as either full-dose weighted filtered back projections or with simulated radiation dose reductions down to 10% of the full-dose level and ADMIRE at either strength 3 or strength 5. Images were marked with arrows pointing on anatomic structures of the abdomen, differing in their contrast to the surrounding tissue. Structures were grouped into high-, medium-, and low-contrast subgroups. In addition, the intrinsic noise of these structures was measured. That followed, image pairs were presented to observers, with five readers assessing image quality using two-alternative-forced-choice comparisons. In total, 3000 comparisons were performed that way. Both ADMIRE 3 and 5 decreased noise of the anatomic structures significantly compared to the filtered back projection, with an additional significant difference between ADMIRE 3 and 5. Radiation dose reduction potential for ADMIRE ranged from 29.0% to 53.5%, with no significant differences between ADMIRE 3 and 5 within the contrast subgroups.The potential levels of radiation dose reduction for ADMIRE 3 differed significantly between high-, medium-, and low-contrast structures, whereas for ADMIRE 5, there was only a significant difference between the high- and the medium-contrast subgroups. Although ADMIRE 5 permits significantly higher noise reduction potential than ADMIRE 3, it does not facilitate higher levels of radiation dose reduction. ADMIRE nonetheless holds remarkable potential for radiation dose reduction, which features a certain dependency on the contrast of the structure of interest. Applying ADMIRE with a strength of 3 in abdominal CT may permit radiation dose reduction of about 30%.

Dependence of low contrast detail on exposure dose and tube voltage in digital flat-panel detector radiography—a pre-clinical phantom study

Purpose. Pre-clinical determination of reasonable simplified examination protocols before clinical application to assess the performance of a direct digital radiography (DDR) system. Material and methods. The experimental setup consists of a DDR system with fixed flat-panel detector (CsI). A phantom with low-contrast structures (LCS) was examined in two settings in order to simulate standard radiographs: (1) small peripheral joints like the wrist (no patient phantom, no anti-scatter grid) and (2) big proximal joints like the hip (20 cm water tank patient simulation and with an anti-scatter grid). Setup (1) was manually exposed at 55 kV/2 mAs, 60 k V/1.6 mAs and 65 k V/1.25 mAs. Setup (2) was automatically exposed at 1.6, 2.5, 4 and 6 μGy per kV-setting (80 ± 5 kV). Contrast-detail curves (CDC) were generated by analyzer software. Results. Exposures of setup (1) showed similar CDC (p = 0.99) with a decreasing curve with larger diameter of LCS. In all exposures of setup (2) CDC were similar for all kV-settings (p > 0.93) with a decreasing curve with larger LCS diameter. CDC insignificantly decrease with increasing exposure dose (p > 0.38). Conclusion. Low-contrast detectability was independent of kV and marginally dependent on the dose, both within a certain range. This could be used to further reduce radiation dose and simplify examination protocols to be tested in a further clinical study.

21. Comparison of abdominal CT protocols: A multi-center study on image quality and radiation dose levels

Introduction (1) To highlight the spread of patient exposure and image quality performances for various abdominal protocols. (2) To ensure that radiation dose reductions in abdominal CT protocols do not impair the detection of low-contrast structures. Methods An anthropomorphic abdominal phantom (QRM, Moehrendorf, Germany) with two optional rings (2.5 cm and 5 cm), representing the attenuation of an adult abdomen of 50 kg (small phantom), 75 kg (medium phantom) and 100 kg (large phantom), was scanned on 70 CT machines in the Western part of Switzerland. The phantom contains in the axial plane four spheres of 5, 6 and 8 mm in diameter with a contrast of 20 HU relative to the background at 120 kV. For statistical reasons, each phantom size was systematically scanned 10 times using local clinical settings of the portal phase for the detection of focal liver lesions (FLL); 40 regions of interest (ROIs) including the target and 150 ROIs with background noise only were extracted. Low contrast detectability (LCD) was objectively assessed using a Channelized Hotelling mathematical model Observer (CHO) with ten dense differences of Gaussian channels for the calculation of a receiver operating characteristic (ROC) curve. For each lesion size, the area under the ROC curve (AUC) was used as a figure of merit (FOM). A new image quality metric, called the weighted AUC (AUC), was proposed to combine the AUC results of all 3 different lesion sizes in a single metric. The displayed CTDIvol was used as a radiation dose metric. Results The median dose used for acquisitions is equal to 5.8 mGy, 10.5 mGy and 16.3 mGy, respectively for the small, medium and large phantoms. For the small phantom, the median image quality is equal to 0.977 with an interquartile range (IQR) equal to 0.027. For the medium phantom the dispersion increased and the median image quality decreased (AUCw = 0.926 and IQR = 0.05). For the large phantom, despite a large range of CTDIvol, the median image quality also decreased (AUCw = 0.89 and IQR = 0.068) and six centers appeared as outliers with relatively limited AUCw scores (AUCw inferior to 0.75), due to very low CTDIvol values. Conclusions The use of a CHO model showed that the majority of institutions performed reasonably well when searching FLL. However, for some outliers, the CTDIvol values were so small that image quality outcomes were clearly not adequate for the intended task. Moreover, the spread of image quality levels was associated with a large CTDIvol distribution, showing that the concept of diagnostic reference levels should be revisited to take into account the image quality aspect. This would ensure comparable diagnostic performance image information content whatever the type of CT unit.

A reproducible semi-automatic method to quantify the muscle-lipid distribution in clinical 3D CT images of the thigh.

Many studies use threshold-based techniques to assess in vivo the muscle, bone and adipose tissue distribution of the legs using computed tomography (CT) imaging. More advanced techniques divide the legs into subcutaneous adipose tissue (SAT), anatomical muscle (muscle tissue and adipocytes within the muscle border) and intra- and perimuscular adipose tissue. In addition, a so-called muscle density directly derived from the CT-values is often measured. We introduce a new integrated approach to quantify the muscle-lipid system (MLS) using quantitative CT in patients with sarcopenia or osteoporosis. The analysis targets the thigh as many CT studies of the hip do not include entire legs The framework consists of an anatomic coordinate system, allowing delineation of reproducible volumes of interest, a robust semi-automatic 3D segmentation of the fascia and a comprehensive method to quantify of the muscle and lipid distribution within the fascia. CT density-dependent features are calibrated using subject-specific internal CT values of the SAT and external CT values of an in scan calibration phantom. Robustness of the framework with respect to operator interaction, image noise and calibration was evaluated. Specifically, the impact of inter- and intra-operator reanalysis precision and addition of Gaussian noise to simulate lower radiation exposure on muscle and AT volumes, muscle density and 3D texture features quantifying MLS within the fascia, were analyzed. Existing data of 25 subjects (age: 75.6 ± 8.7) with porous and low-contrast muscle structures were included in the analysis. Intra- and inter-operator reanalysis precision errors were below 1% and mostly comparable to 1% of cohort variation of the corresponding features. Doubling the noise changed most 3D texture features by up to 15% of the cohort variation but did not affect density and volume measurements. The application of the novel technique is easy with acceptable processing time. It can thus be employed for a comprehensive quantification of the muscle-lipid system enabling radiomics approaches to musculoskeletal disorders.

Iterative Reconstructions in Reduced-Dose CT: Which Type Ensures Diagnostic Image Quality in Young Oncology Patients?

To compare adaptive statistical iterative reconstruction (ASIR) and model-based iterative reconstruction (MBIR) algorithms for reduced-dose computed tomography (CT). Forty-four young oncology patients (mean age 30 ± 9 years) were included. After routine thoraco-abdominal CT (dose 100%, average CTDIvol 9.1 ± 2.4 mGy, range 4.4-16.9 mGy), follow-up CT was acquired at 50% (average CTDIvol 4.5 ± 1.2 mGy, range 2.2-8.4 mGy) in 29 patients additionally at 20% dose (average CTDIvol 1.9 ± 0.5 mGy, range 0.9-3.4 mGy). Each reduced-dose CT was reconstructed using both ASIR and MBIR. Four radiologists (two juniors and two seniors) blinded to dose and technique read each set of CT images regarding objective and subjective image qualities (high- or low-contrast structures), subjective noise or pixilated appearance, diagnostic confidence, and lesion detection. At all dose levels, objective image noise was significantly lower with MBIR than with ASIR (P < 0.001). The subjective image quality for low-contrast structures was significantly higher with MBIR than with ASIR (P < 0.001). Reduced-dose abdominal CT images of patients with higher body mass index (BMI) were read with significantly higher diagnostic confidence than images of slimmer patients (P < 0.001) and had higher subjective image quality, regardless of technique. Although MBIR images appeared significantly more pixilated than ASIR images, they were read with higher diagnostic confidence, especially by juniors (P < 0.001). Reduced-dose CT during the follow-up of young oncology patients should be reconstructed with MBIR to ensure diagnostic quality. Elevated body mass index does not hamper the quality of reduced-dose CT.

Structured Learning for 3-D Perivascular Space Segmentation Using Vascular Features.

The goal of this paper is to automatically segment perivascular spaces (PVSs) in brain from high-resolution 7T magnetic resonance (MR) images. We propose a structured-learning-based segmentation framework to extract the PVSs from high-resolution 7T MR images. Specifically, we integrate three types of vascular filter responses into a structured random forest for classifying voxels into two categories, i.e., PVS and background. In addition, we propose a novel entropy-based sampling strategy to extract informative samples in the background for training an explicit classification model. Since the vascular filters can extract various vascular features, even thin and low-contrast structures can be effectively extracted from noisy backgrounds. Moreover, continuous and smooth segmentation results can be obtained by utilizing patch-based structured labels. The performance of our proposed method is evaluated on 19 subjects with 7T MR images, with the Dice similarity coefficient reaching 66%. The joint use of entropy-based sampling strategy, vascular features, and structured learning can improve the segmentation accuracy. Instead of manual annotation, our method provides an automatic way for PVS segmentation. Moreover, our method can be potentially used for other vascular structure segmentation because of its data-driven property.

Table-top phase-contrast imaging employing photon-counting detectors towards mammographic applications

In mammography the difficult task to detect microcalcifications (≈ 100 μm) and low contrast structures in the breast has been a topic of interest from its beginnings. The possibility to improve the image quality requires the effort to employ novel X-ray imaging techniques, such as phase-contrast, and high resolution detectors. Phase-contrast techniques are promising tools for medical diagnosis because they provide additional and complementary information to traditional absorption-based X-ray imaging methods. In this work a Hamamatsu microfocus X-ray source with tungsten anode and a photon counting detector (Timepix operated in Medipix mode) was used. A significant improvement in the detection of phase-effects using Medipix detector was observed in comparison to an standard flat-panel detector. An optimization of geometrical parameters reveals the dependency on the X-ray propagation path and the small angle deviation. The quantification of these effects was achieved taking into account the image noise, contrast, spatial resolution of the phase-enhancement, absorbed dose, and energy dependence.

Investigation of noise sources for digital radiography systems.

The performance of digital radiography systems can be evaluated in terms of spatial resolution and noise. Noise plays an important role in the achievable image quality for detecting small and low-contrast structures in digital images created by these systems. Our aim in this study was to investigate the noise sources both in the spatial and frequency domain for three digital radiography systems, one digital fluoroscopy system, and one digital mammography system, and to obtain information about the effective operating dose range of these detectors. Noise evaluation in the spatial domain was done with the relative standard deviation-detector air kerma relationship evaluation method. The characterization of the noise in the spatial domain gives information about the types of noise, but does not give information about the noise power distribution in frequency space. Therefore, noise evaluation in the frequency domain was carried out by noise power spectrum measurement. The observed dominant noise component at lower detector doses was electronic noise for the digital mammography system, whereas structured noise was observed to make up nearly half of the total noise at higher detector doses for one of the digital radiography systems. The structured noise component was increased by use of a grid in these systems, independent of the grid ratio and grid frequency, but this increase was lower for higher grid frequencies. Furthermore, the structured noise coefficient was decreased with gain and offset calibrations. The five systems which we evaluated behaved as a quantum noise limited for clinically used detector doses.

Erratum: Evaluation of the tool "Reg Refine" for user-guided deformable image registration.

"Reg Refine" is a tool available in the MIM Maestro v6.4.5 platform (www.mim-software.com) that allows the user to actively participate in the deformable image registration process. The purpose of this work was to evaluate the efficacy of this tool and investigate strategies for how to apply it effectively. This was done by performing DIR on two publicly available ground-truth models, the Pixel-based Breathing Thorax Model (POPI) for lung, and the Deformable Image Registration Evaluation Project (DIREP) for head and neck. Image noise matched in both magnitude and texture to clinical CBCT scans was also added to each model to simulate the use case of CBCT-CT alignment. For lung, the results showed Reg Refine effective at improving registration accuracy when controlled by an expert user within the context of large lung deformation. CBCT noise was also shown to have no effect on DIR performance while using the MIM algorithm for this site. For head and neck, the results showed CBCT noise to have a large effect on the accuracy of registration, specifically for low-contrast structures such as the brain-stem and parotid glands. In these cases, the Reg Refine tool was able to improve the registration accuracy when controlled by an expert user. Several strategies for how to achieve these results have been outlined to assist other users and provide feedback for developers of similar tools.

Segmentation of Perivascular Spaces Using Vascular Features and Structured Random Forest from 7T MR Image.

Quantitative analysis of perivascular spaces (PVSs) is important to reveal the correlations between cerebrovascular lesions and neurodegenerative diseases. In this study, we propose a learning-based segmentation framework to extract the PVSs from high-resolution 7T MR images. Specifically, we integrate three types of vascular filter responses into a structured random forest for classifying voxels into PVS and background. In addition, we also propose a novel entropy-based sampling strategy to extract informative samples in the background for training the classification model. Since various vascular features can be extracted by the three vascular filters, even thin and low-contrast structures can be effectively extracted from the noisy background. Moreover, continuous and smooth segmentation results can be obtained by utilizing the patch-based structured labels. The segmentation performance is evaluated on 19 subjects with 7T MR images, and the experimental results demonstrate that the joint use of entropy-based sampling strategy, vascular features and structured learning improves the segmentation accuracy, with the Dice similarity coefficient reaching 66 %.

Contrast Invariant Interest Point Detection by Zero-Norm LoG Filter.

The Laplacian of Gaussian (LoG) filter is widely used in interest point detection. However, low-contrast image structures, though stable and significant, are often submerged by the high-contrast ones in the response image of the LoG filter, and hence are difficult to be detected. To solve this problem, we derive a generalized LoG filter, and propose a zero-norm LoG filter. The response of the zero-norm LoG filter is proportional to the weighted number of bright/dark pixels in a local region, which makes this filter be invariant to the image contrast. Based on the zero-norm LoG filter, we develop an interest point detector to extract local structures from images. Compared with the contrast dependent detectors, such as the popular scale invariant feature transform detector, the proposed detector is robust to illumination changes and abrupt variations of images. Experiments on benchmark databases demonstrate the superior performance of the proposed zero-norm LoG detector in terms of the repeatability and matching score of the detected points as well as the image recognition rate under different conditions.

Sparse-view computed tomography image reconstruction via a combination of L(1) and SL(0) regularization.

Low-dose computed tomography reconstruction is an important issue in the medical imaging domain. Sparse-view has been widely studied as a potential strategy. Compressed sensing (CS) method has shown great potential to reconstruct high-quality CT images from sparse-view projection data. Nonetheless, low-contrast structures tend to be blurred by the total variation (TV, L1-norm of the gradient image) regularization. Moreover, TV will produce blocky effects on smooth and edge regions. To overcome this limitation, this study has proposed an iterative image reconstruction algorithm by combining L1 regularization and smoothed L0 (SL0) regularization. SL0 is a smooth approximation of L0 norm and can solve the problem of L0 norm being sensitive to noise. To evaluate the proposed method, both qualitative and quantitative studies were conducted on a digital Shepp-Logan phantom and a real head phantom. Experimental comparative results have indicated that the proposed L1/SL0-POCS algorithm can effectively suppress noise and artifacts, as well as preserve more structural information compared to other existing methods.

Denoised and texture enhanced MVCT to improve soft tissue conspicuity

MVCT images have been used in TomoTherapy treatment to align patients based on bony anatomies but its usefulness for soft tissue registration, delineation, and adaptive radiation therapy is limited due to insignificant photoelectric interaction components and the presence of noise resulting from low detector quantum efficiency of megavoltage x-rays. Algebraic reconstruction with sparsity regularizers as well as local denoising methods has not significantly improved the soft tissue conspicuity. The authors aim to utilize a nonlocal means denoising method and texture enhancement to recover the soft tissue information in MVCT (DeTECT). A block matching 3D (BM3D) algorithm was adapted to reduce the noise while keeping the texture information of the MVCT images. Following imaging denoising, a saliency map was created to further enhance visual conspicuity of low contrast structures. In this study, BM3D and saliency maps were applied to MVCT images of a CT imaging quality phantom, a head and neck, and four prostate patients. Following these steps, the contrast-to-noise ratios (CNRs) were quantified. By applying BM3D denoising and saliency map, postprocessed MVCT images show remarkable improvements in imaging contrast without compromising resolution. For the head and neck patient, the difficult-to-see lymph nodes and vein in the carotid space in the original MVCT image became conspicuous in DeTECT. For the prostate patients, the ambiguous boundary between the bladder and the prostate in the original MVCT was clarified. The CNRs of phantom low contrast inserts were improved from 1.48 and 3.8 to 13.67 and 16.17, respectively. The CNRs of two regions-of-interest were improved from 1.5 and 3.17 to 3.14 and 15.76, respectively, for the head and neck patient. DeTECT also increased the CNR of prostate from 0.13 to 1.46 for the four prostate patients. The results are substantially better than a local denoising method using anisotropic diffusion. The authors showed that it is feasible to extract more soft tissue contrast information from the noisy MVCT images using a nonlocal means 3D block matching method in combination with saliency maps, revealing information that was originally unperceivable to human observers.

Effect of dose reduction on image registration and image quality for cone-beam CT in radiotherapy.

The additional radiation exposure applied to patients undergoing cone-beam computed tomography (CBCT) for image registration in radiation therapy is of great concern. Since a decrease in CBCT dose is linked to a degradation of image quality, the consequences of dose reduction on the registration process have to be investigated. This paper examines image quality and registration of low-contrast structures on an Elekta XVI for the two treatment areas prostate and chest while gradually decreasing the mAs per frame and the number of projections per CBCT to achieve dose reduction. Ideal results for image quality were obtained for 1.6 mAs/frame and 377 projections in prostate scans and 0.63 mAs/frame and 440 projections in chest images. Lower as well as higher total mAs lead to a decrease in image quality. In spite of poor image quality, registration can be successfully performed even for lowest possible settings. The results for registration allow an extensive dose reduction in both treatment areas. Very low mAs, however, do not qualify for clinical use because subjective judgment of the registration process is impossible. Compared to default presets the use of settings for acceptable image quality already permit a decrease in exposure of about 40 % (29.0 to 16.7 mGy) in prostate scans and 60 % (18.3 to 7.7 mGy) in chest scans.

Image reconstruction from few views by ℓ0-norm optimization

In the medical computer tomography (CT) field, total variation (TV), which is the ℓ1-norm of the discrete gradient transform (DGT), is widely used as regularization based on the compressive sensing (CS) theory. To overcome the TV model's disadvantageous tendency of uniformly penalizing the image gradient and over smoothing the low-contrast structures, an iterative algorithm based on the ℓ0-norm optimization of the DGT is proposed. In order to rise to the challenges introduced by the ℓ0-norm DGT, the algorithm uses a pseudo-inverse transform of DGT and adapts an iterative hard thresholding (IHT) algorithm, whose convergence and effective efficiency have been theoretically proven. The simulation demonstrates our conclusions and indicates that the algorithm proposed in this paper can obviously improve the reconstruction quality.

Comparison of digitally reconstructed radiographs generated from axial and helical CT scanning modes: a phantom study

Digitally reconstructed radiographs (DRRs) play a vital role for verifying patient position for many radiotherapy treatments. As DRRs are generated from CT scans, image quality may be affected by the scanning mode (axial or helical). The aim of this study was to investigate the high and low contrast resolution and the spatial linearity in DRRs as a function of CT scanning mode and to highlight the significance of this variation, if any. A commercial CT phantom (Fluke Biomedical Model 76-417) was scanned with a Siemens Somatom Sensation 4 CT scanner using six variations of field of view, scanning mode and helical pitch. The image quality of the DRR's produced from the scans was evaluated in terms of high contrast resolution using the modulation transfer function, low contrast resolution using Image Quality Score method and spatial linearity. The results indicated that the high contrast resolution for the axial mode was comparable to that for the helical modes. The low contrast resolution with axial scanning was comparable to scans performed with a helical pitch of 1 but deteriorated at pitches greater than 1. The field of view was not found to impact the low contrast resolution. When changing scanning parameters the impact on DRR quality should be considered. For DRRs, particularly where visualisation of low contrast structures is desired, a helical scan with a pitch of 1 is recommended.