Selecting the tube current when using iterative reconstruction is challenging due to the varying relationship between contrast, noise, spatial resolution, and dose across different algorithms. This study proposes a task-based automated exposure control (AEC) method using a generalized detectability index (d'gen). The proposed method leverages existing AEC methods that are based on a prescribed noise level. The generalized d'gen metric is calculated using look-up tables of task-based modulation transfer function and noise power spectrum. Look-up tables were generated by scanning a 20-cmdiameter American College of Radiology (ACR) phantom and reconstructing with a reference reconstruction algorithm and four levels of an in-house iterative reconstruction algorithm (IR1-4). This study tested the validity of the assumption that the look-up tables can be approximated as being independent of dose level. Preliminary feasibility of the proposed d'gen-AEC method to provide a desired image quality level for different iterative reconstruction algorithms was evaluated for the ACR phantom. The image quality ((d'gen) resulting from the proposed d'gen-AEC method was 3.8 (IR1), 3.9 (IR2), 3.9 (IR3), 3.8 (IR4) compared to the desired d'gen of 3.9 for the reference image. For comparison, images acquired to match the noise standard deviation of the reference image demonstrated reduced image quality (d'gen). of 3.3 for IR1, 3.0 for IR2, 2.5 for IR3, and 1.8 for IR4). For all four iterative reconstruction methods, the d'gen-AEC method resulted in consistent image quality in terms of detectability index at lower dose than the reference scan. The results provide preliminary evidence that the proposed d'gen-AEC can provide consistent image quality across different iterative reconstruction approaches.
This study investigated a fractal dimension algorithm for noise texture quantification in CT images. Quantifying noise in CT images is important for assessing image quality. Noise is typically quantified by calculating noise standard deviation and noise power spectrum (NPS). Different reconstruction kernels and iterative reconstruction approaches affect both the noise magnitude and noise texture. The shape of the NPS can be used as a noise texture descriptor. However, the NPS requires numerous images for calculation and is a vector quantity. This study proposes the metric of fractal dimension to quantify noise texture, because fractal dimension is a single scalar metric calculated from a small number of images. Fractal dimension measures the complexity of a pattern. In this study, the ACR CT phantom was scanned and images were reconstructed using filtered back-projection with three reconstruction kernels: bone, soft and standard. Regions of interest were extracted from the uniform section of the phantom for NPS and fractal dimension calculation. The results demonstrated a mean fractal dimension of 1.86 for soft kernel, 1.92 for standard kernel, and 2.16 for bone kernel. Increasing fractal dimension corresponded to shift in the NPS towards higher spatial frequencies and grainier noise appearance. Stable fractal dimension was calculated from two ROI’s compared to more than 250 ROI’s used for NPS calculation. The scalar fractal dimension metric may be a useful noise texture descriptor for evaluating or optimizing reconstruction algorithms.
This study experimentally evaluated the effect of window width (WW) and window level (WL) on the task based detectability index (d’). Window-level transformation is frequently performed on CT images to improve visualization in clinical arena. Numerous model observers and metrics have been used to assess CT image quality. However, objective assessment is typically performed on the reconstructed CT image without considering the WW and WL settings used by the reader. In this study, the ACR CT phantom was scanned at 120 kV and 90 mAs and images were reconstructed using filtered backprojection. The bone and acrylic contrast objects from module one of the ACR phantom were selected for calculating the effect of the WW/WL on the detectability index (d’). The d’ for each object at 90 mAs was calculated for a range of WW and WL values. The results demonstrated that the d’ values were affected by the WW and WL settings. For example, WL setting of 20 HU and window width of 150 HU resulted in a 35% decrease in d’ compared to that of the untransformed image. A WL of 20 and WW of 1400 resulted in a 33% decrease in the d’ value for the high contrast object. The investigated WW and WL settings did not improve d’ for any of the investigated objects when reconstructed with the standard kernel. The results suggest that d’ is affected by the WW/WL settings. However, because d’ does not model the contrast adaptation of the human visual system, it may not represent the change in perceived image quality with window-level transformation.
This study quantitatively evaluated the performance of the exponential transformation of the free-response operating characteristic curve (EFROC) metric, with the Channelized Hotelling Observer (CHO) as a reference. The CHO has been used for image quality assessment of reconstruction algorithms and imaging systems and often it is applied to study the signal-location-known cases. The CHO also requires a large set of images to estimate the covariance matrix. In terms of clinical applications, this assumption and requirement may be unrealistic. The newly developed location-unknown EFROC detectability metric is estimated from the confidence scores reported by a model observer. Unlike the CHO, EFROC does not require a channelization step and is a non-parametric detectability metric. There are few quantitative studies available on application of the EFROC metric, most of which are based on simulation data. This study investigated the EFROC metric using experimental CT data. A phantom with four low contrast objects: 3mm (14 HU), 5mm (7HU), 7mm (5 HU) and 10 mm (3 HU) was scanned at dose levels ranging from 25 mAs to 270 mAs and reconstructed using filtered backprojection. The area under the curve values for CHO (AUC) and EFROC (AFE) were plotted with respect to different dose levels. The number of images required to estimate the non-parametric AFE metric was calculated for varying tasks and found to be less than the number of images required for parametric CHO estimation. The AFE metric was found to be more sensitive to changes in dose than the CHO metric. This increased sensitivity and the assumption of unknown signal location may be useful for investigating and optimizing CT imaging methods. Future work is required to validate the AFE metric against human observers.
Because x-ray based image-guided vascular interventions are minimally invasive they are currently the most preferred method of treating disorders such as stroke, arterial stenosis, and aneurysms; however, the x-ray exposure to the patient during long image-guided interventional procedures could cause harmful effects such as cancer in the long run and even tissue damage in the short term. ROI fluoroscopy reduces patient dose by differentially attenuating the incident x-rays outside the region-of-interest. To reduce the noise in the dose-reduced regions previously recursive temporal filtering was successfully demonstrated for neurovascular interventions. However, in cardiac interventions, anatomical motion is significant and excessive recursive filtering could cause blur. In this work the effects of three noise-reduction schemes, including recursive temporal filtering, spatial mean filtering, and a combination of spatial and recursive temporal filtering, were investigated in a simulated ROI dose-reduced cardiac intervention.
First a model to simulate the aortic arch and its movement was built. A coronary stent was used to simulate a bioprosthetic valve used in TAVR procedures and was deployed under dose-reduced ROI fluoroscopy during the simulated heart motion. The images were then retrospectively processed for noise reduction in the periphery, using recursive temporal filtering, spatial filtering and a combination of both.
Quantitative metrics for all three noise reduction schemes are calculated and are presented as results. From these it can be concluded that with significant anatomical motion, a combination of spatial and recursive temporal filtering scheme is best suited for reducing the excess quantum noise in the periphery. This new noise-reduction technique in combination with ROI fluoroscopy has the potential for substantial patient-dose savings in cardiac interventions.
High-resolution 3D bone-tissue structure measurements may provide information critical to the understanding of the bone regeneration processes and to the bone strength assessment. Tissue engineering studies rely on such nondestructive measurements to monitor bone graft regeneration area. In this study, we measured bone yield, fractal dimension and trabecular thickness through micro-CT slices for different grafts and controls. Eight canines underwent surgery to remove a bone volume (defect) in the canine’s jaw at a total of 44 different locations. We kept 11 defects empty for control and filled the remaining ones with three regenerative materials; NanoGen (NG), a FDA-approved material (n=11), a novel NanoCalcium Sulfate (NCS) material (n=11) and NCS alginate (NCS+alg) material (n=11). After a minimum of four and eight weeks, the canines were sacrificed and the jaw samples were extracted. We used a custombuilt micro-CT system to acquire the data volume and developed software to measure the bone yield, fractal dimension and trabecular thickness. The software used a segmentation algorithm based on histograms derived from volumes of interest indicated by the operator. Using bone yield and fractal dimension as indices we are able to differentiate between the control and regenerative material (p<0.005). Regenerative material NCS showed an average 63.15% bone yield improvement over the control sample, NCS+alg showed 55.55% and NanoGen showed 37.5%. The bone regeneration process and quality of bone were dependent upon the position of defect and time period of healing. This study presents one of the first quantitative comparisons using non-destructive Micro-CT analysis for bone regenerative material in a large animal with a critical defect model. Our results indicate that Micro-CT measurement could be used to monitor invivo bone regeneration studies for greater regenerative process understanding.
Perfusion imaging is the most applied modality for the assessment of acute stroke. Parameters such as Cerebral Blood Flow (CBF), Cerebral Blood volume (CBV) and Mean Transit Time (MTT) are used to distinguish the tissue infarct core and ischemic penumbra. Due to lack of standardization these parameters vary significantly between vendors and software even when provided with the same data set. There is a critical need to standardize the systems and make them more reliable. We have designed a uniform phantom to test and verify the perfusion systems. We implemented a flow loop with different flow rates (250, 300, 350 ml/min) and injected the same amount of contrast. The images of the phantom were acquired using a Digital Angiographic system. Since this phantom is uniform, projection images obtained using DSA is sufficient for initial validation. To validate the phantom we measured the contrast concentration at three regions of interest (arterial input, venous output, perfused area) and derived time density curves (TDC). We then calculated the maximum slope, area under the TDCs and flow. The maximum slope calculations were linearly increasing with increase in flow rate, the area under the curve decreases with increase in flow rate. There was 25% error between the calculated flow and measured flow. The derived TDCs were clinically relevant and the calculated flow, maximum slope and areas under the curve were sensitive to the measured flow. We have created a systematic way to calibrate existing perfusion systems and assess their reliability.
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