Optimization of x-ray spectra in digital mammography through Monte Carlo simulations

In this work, a Monte Carlo code was used to investigate the performance of different x-ray spectra in digital mammography, through a figure of merit (FOM), defined as FOM = CNR2/(D) over bar (g), with CNR being the contrast-to-noise ratio in image and (D) over bar (g) being the average glandular dose. The FOM was studied for breasts with different thicknesses t (2 cm <= t <= 8 cm) and glandular contents (25%, 50% and 75% glandularity). The anode/filter combinations evaluated were those traditionally employed in mammography (Mo/Mo, Mo/Rh, Rh/Rh), and a W anode combined with Al or K-edge filters (Zr, Mo, Rh, Pd, Ag, Cd, Sn), for tube potentials between 22 and 34 kVp. Results show that the W anode combined with K-edge filters provides higher values of FOM for all breast thicknesses investigated. Nevertheless, the most suitable filter and tube potential depend on the breast thickness, and for t >= 6 cm, they also depend on breast glandularity. Particularly for thick and dense breasts, a W anode combined with K-edge filters can greatly improve the digital technique, with the values of FOM up to 200% greater than that obtained with the anode/filter combinations and tube potentials traditionally employed in mammography. For breasts with t < 4 cm, a general good performance was obtained with the W anode combined with 60 mu m of the Mo filter at 24-25 kVp, while 60 mu m of the Pd filter provided a general good performance at 24-26 kVp for t = 4 cm, and at 28-30 and 29-31 kVp for t = 6 and 8 cm, respectively.

A nonlinear elasticity phantom containing spherical inclusions

The strain image contrast of some in vivo breast lesions changes with increasing applied load. This change is attributed to differences in the nonlinear elastic properties of the constituent tissues suggesting some potential to help classify breast diseases by their nonlinear elastic properties. A phantom with inclusions and long-term stability is desired to serve as a test bed for nonlinear elasticity imaging method development, testing, etc. This study reports a phantom designed to investigate nonlinear elastic properties with ultrasound elastographic techniques. The phantom contains four spherical inclusions and was manufactured from a mixture of gelatin, agar and oil. The phantom background and each of the inclusions have distinct Young's modulus and nonlinear mechanical behavior. This phantom was subjected to large deformations (up to 20%) while scanning with ultrasound, and changes in strain image contrast and contrast-to-noise ratio between inclusion and background, as a function of applied deformation, were investigated. The changes in contrast over a large deformation range predicted by the finite element analysis (FEA) were consistent with those experimentally observed. Therefore, the paper reports a procedure for making phantoms with predictable nonlinear behavior, based on independent measurements of the constituent materials, and shows that the resulting strain images (e. g., strain contrast) agree with that predicted with nonlinear FEA.

Delayed gadolinium-enhanced magnetic resonance imaging of hip joint cartilage: pearls and pitfalls

With the increasing advances in hip joint preservation surgery, accurate diagnosis and assessment of femoral head and acetabular cartilage status is becoming increasingly important. Magnetic resonance imaging (MRI) of the hip does present technical difficulties. The fairly thin cartilage lining necessitates high image resolution and high contrast-to-noise ratio (CNR). With MR arthrography (MRA) using intraarticular injected gadolinium, labral tears and cartilage clefts may be better identified through the contrast medium filling into the clefts. However, the ability of MRA to detect varying grades of cartilage damage is fairly limited and early histological and biochemical changes in the beginning of osteoarthritis (OA) cannot be accurately delineated. Traditional MRI thus lacks the ability to analyze the biological status of cartilage degeneration. The technique of delayed gadolinium-enhanced MRI of cartilage (dGEMRIC) is sensitive to the charge density of cartilage contributed by glycosaminoglycans (GAGs), which are lost early in the process of OA. Therefore, the dGEMRIC technique has a potential to detect early cartilage damage that is obviously critical for decision-making regarding time and extent of intervention for joint-preservation. In the last decade, cartilage imaging with dGEMRIC has been established as an accurate and reliable tool for assessment of cartilage status in the knee and hip joint.This review outlines the current status of dGEMRIC for assessment of hip joint cartilage. Practical modifications of the standard technique including three-dimensional (3D) dGEMRIC and dGEMRIC after intra-articular gadolinium instead of iv-dGEMRIC will also be addressed.

Magnetic resonance (MR) cholangiography: quantitative and qualitative comparison of 3.0 Tesla with 1.5 Tesla

OBJECTIVES: To determine quantitative and qualitative image quality in patients undergoing magnetic resonance (MR) cholangiography at 3.0 Tesla (T) compared with 1.5 T. MATERIALS AND METHODS: Fifty patients (30 women; mean age, 51 years) underwent MR cholangiography at 1.5 T; another 50 patients (25 women; mean age 51 years) were scanned at 3.0 T. MR sequence protocol consisted of breath-hold single-slice rapid acquisition with relaxation enhancement (RARE) and a respiratory-triggered 3D turbo spin echo (3D TSE) sequence. Maximum intensity projections were generated from the 3D TSE datasets. Contrast-to-noise ratio (CNR) measurements between the common bile duct (CBD), left and right intrahepatic duct (LHD, RHD), and periductal tissue were performed. Three radiologists assessed qualitatively the visibility of the CBD, LHD, and RHD and the overall diagnostic quality. RESULTS: Mean gain in CNR at 3.0 T versus 1.5 T in all 3 locations ranged for the RARE sequence from 7.7% to 38.1% and for the 3D TSE from 0.5% to 26.1% (P > 0.05 for all differences). Qualitative analysis did not reveal any significant difference between the 2 field strengths (P > 0.05). CONCLUSIONS: MR cholangiography at 3.0 T shows a trend toward higher CNR without improving image quality significantly.

Hypervascular liver tumors: low tube voltage, high tube current multi-detector row CT for enhanced detection--phantom study

PURPOSE: To prospectively evaluate, for the depiction of simulated hypervascular liver lesions in a phantom, the effect of a low tube voltage, high tube current computed tomographic (CT) technique on image noise, contrast-to-noise ratio (CNR), lesion conspicuity, and radiation dose. MATERIALS AND METHODS: A custom liver phantom containing 16 cylindric cavities (four cavities each of 3, 5, 8, and 15 mm in diameter) filled with various iodinated solutions to simulate hypervascular liver lesions was scanned with a 64-section multi-detector row CT scanner at 140, 120, 100, and 80 kVp, with corresponding tube current-time product settings at 225, 275, 420, and 675 mAs, respectively. The CNRs for six simulated lesions filled with different iodinated solutions were calculated. A figure of merit (FOM) for each lesion was computed as the ratio of CNR2 to effective dose (ED). Three radiologists independently graded the conspicuity of 16 simulated lesions. An anthropomorphic phantom was scanned to evaluate the ED. Statistical analysis included one-way analysis of variance. RESULTS: Image noise increased by 45% with the 80-kVp protocol compared with the 140-kVp protocol (P < .001). However, the lowest ED and the highest CNR were achieved with the 80-kVp protocol. The FOM results indicated that at a constant ED, a reduction of tube voltage from 140 to 120, 100, and 80 kVp increased the CNR by factors of at least 1.6, 2.4, and 3.6, respectively (P < .001). At a constant CNR, corresponding reductions in ED were by a factor of 2.5, 5.5, and 12.7, respectively (P < .001). The highest lesion conspicuity was achieved with the 80-kVp protocol. CONCLUSION: The CNR of simulated hypervascular liver lesions can be substantially increased and the radiation dose reduced by using an 80-kVp, high tube current CT technique.

Effect of X-ray tube parameters, iodine concentration, and patient size on image quality in pulmonary computed tomography angiography: a chest-phantom-study

OBJECTIVES: The aim of this phantom study was to evaluate the contrast-to-noise ratio (CNR) in pulmonary computed tomography (CT)-angiography for 300 and 400 mg iodine/mL contrast media using variable x-ray tube parameters and patient sizes. We also analyzed the possible strategies of dose reduction in patients with different sizes. MATERIALS AND METHODS: The segmental pulmonary arteries were simulated by plastic tubes filled with 1:30 diluted solutions of 300 and 400 mg iodine/mL contrast media in a chest phantom mimicking thick, intermediate, and thin patients. Volume scanning was done with a CT scanner at 80, 100, 120, and 140 kVp. Tube current-time products (mAs) varied between 50 and 120% of the optimal value given by the built-in automatic dose optimization protocol. Attenuation values and CNR for both contrast media were evaluated and compared with the volume CT dose index (CTDI(vol)). Figure of merit, calculated as CNR/CTDIvol, was used to quantify image quality improvement per exposure risk to the patient. RESULTS: Attenuation of iodinated contrast media increased both with decreasing tube voltage and patient size. A CTDIvol reduction by 44% was achieved in the thin phantom with the use of 80 instead of 140 kVp without deterioration of CNR. Figure of merit correlated with kVp in the thin phantom (r = -0.897 to -0.999; P < 0.05) but not in the intermediate and thick phantoms (P = 0.09-0.71), reflecting a decreasing benefit of tube voltage reduction on image quality as the thickness of the phantom increased. Compared with the 300 mg iodine/mL concentration, the same CNR for 400 mg iodine/mL contrast medium was achieved at a lower CTDIvol by 18 to 40%, depending on phantom size and applied tube voltage. CONCLUSIONS: Low kVp protocols for pulmonary embolism are potentially advantageous especially in thin and, to a lesser extent, in intermediate patients. Thin patients profit from low voltage protocols preserving a good CNR at a lower exposure. The use of 80 kVp in obese patients may be problematic because of the limitation of the tube current available, reduced CNR, and high skin dose. The high CNR of the 400 mg iodine/mL contrast medium together with lower tube energy and/or current can be used for exposure reduction.

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.

Cancer and inflammation: differentiation by USPIO-enhanced MR imaging.

PURPOSE To assess ultrasmall superparamagnetic iron oxide particles (USPIO) -enhanced MR imaging for the differentiation of malignant from benign, inflammatory lesions. MATERIALS AND METHODS In this study, approved by the local animal care committee, VX2 carcinoma and intramuscular abscesses were implanted into the hind thighs of New Zealand White rabbits. MR imaging was performed pre contrast and serially for 24 h after the injection of USPIO. MR findings were compared with histopathologic results based on Prussian blue stains for the presence of iron. RESULTS Twenty-four hours after the Ferumoxtran-injection, no changes were observed in VX2 carcinomas, whereas a mean reduction of the contrast-to-noise ratio (CNR) of approximately 90% was noticed in abscesses as well as in necrotic tumors. On histopathologic examination, abscess and necrotic parts of the tumor were found to include iron-containing monocytes demonstrating that the reduction in CNR was caused by USPIO-tagged monocytes. CONCLUSION Our results prove the ability of USPIO-enhanced MRI to differentiate benign, inflammatory from malignant lesions.

Diagnostic confidence and image quality of CT pulmonary angiography at 100 kVp in overweight and obese patients.

AIM To compare image quality and diagnostic confidence of 100 kVp CT pulmonary angiography (CTPA) in patients with body weights (BWs) below and above 100kg. MATERIALS AND METHODS The present retrospective study comprised 216 patients (BWs of 75-99kg, 114 patients; 100-125kg, 88 patients; >125kg, 14 patients), who received 100 kVp CTPA to exclude pulmonary embolism. The attenuation was measured and the contrast-to-noise ratio (CNR) was calculated in the pulmonary trunk. Size-specific dose estimates (SSDEs) were evaluated. Three blinded radiologists rated subjective image quality and diagnostic confidence. Results between the BW groups and between three body mass index (BMI) groups (BMI <25kg/m(2), BMI = 25-29.9kg/m(2), and BMI ≥30kg/m(2), i.e., normal weight, overweight, and obese patients) were compared using the Kruskal-Wallis test. RESULTS Vessel attenuation was higher and SDDE was lower in the 75-99kg group than at higher BWs (p-values between <0.001 and 0.03), with no difference between the 100-125 and >125kg groups (p = 0.892 and 1). Subjective image quality and diagnostic confidence were not different among the BW groups (p = 0.225 and 1). CNR was lower (p < 0.006) in obese patients than in normal weight or overweight subjects. Diagnostic confidence was not different in the BMI groups (p = 0.105). CONCLUSION CTPA at 100 kVp tube voltage can be used in patients weighing up to 125kg with no significant deterioration of subjective image quality and confidence. The applicability of 100 kVp in the 125-150kg BW range needs further testing in larger collectives.

Prospective randomised comparison of diagnostic confidence and image quality with normal-dose and low-dose CT pulmonary angiography at various body weights.

OBJECTIVES To find a threshold body weight (BW) below 100 kg above which computed tomography pulmonary angiography (CTPA) using reduced radiation and a reduced contrast material (CM) dose provides significantly impaired quality and diagnostic confidence compared with standard-dose CTPA. METHODS In this prospectively randomised study of 501 patients with suspected pulmonary embolism and BW <100 kg, 246 were allocated into the low-dose group (80 kVp, 75 ml CM) and 255 into the normal-dose group (100 kVp, 100 ml CM). Contrast-to-noise ratio (CNR) in the pulmonary trunk was calculated. Two blinded chest radiologists independently evaluated subjective image quality and diagnostic confidence. Data were compared between the normal-dose and low-dose groups in five BW subgroups. RESULTS Vessel attenuation did not differ between the normal-dose and low-dose groups within each BW subgroup (P = 1.0). The CNR was higher with the normal-dose compared with the low-dose protocol (P < 0.006) in all BW subgroups except for the 90-99 kg subgroup (P = 0.812). Subjective image quality and diagnostic confidence did not differ between CT protocols in all subgroups (P between 0.960 and 1.0). CONCLUSIONS Subjective image quality and diagnostic confidence with 80 kVp CTPA is not different from normal-dose protocol in any BW group up to 100 kg. KEY POINTS • 80 kVp CTPA is safe in patients weighing <100 kg • Reduced radiation and iodine dose still provide high vessel attenuation • Image quality and diagnostic confidence with low-dose CTPA is good • Diagnostic confidence does not deteriorate in obese patients weighing <100 kg.

Comparison of Routine Brain Imaging at 3 T and 7 T.

OBJECTIVE The aim of this study was to compare quantitative and semiquantitative parameters (signal-to-noise ratio [SNR], contrast-to-noise ratio [CNR], image quality, diagnostic confidence) from a standard brain magnetic resonance imaging examination encompassing common neurological disorders such as demyelinating disease, gliomas, cerebrovascular disease, and epilepsy, with comparable sequence protocols and acquisition times at 3 T and at 7 T. MATERIALS AND METHODS Ten healthy volunteers and 4 subgroups of 40 patients in total underwent comparable magnetic resonance protocols with standard diffusion-weighted imaging, 2D and 3D turbo spin echo, 2D and 3D gradient echo and susceptibility-weighted imaging of the brain (10 sequences) at 3 T and 7 T. The subgroups comprised patients with either lesional (n = 5) or nonlesional (n = 4) epilepsy, intracerebral tumors (n = 11), demyelinating disease (n = 11) (relapsing-remitting multiple sclerosis [MS, n = 9], secondary progressive MS [n = 1], demyelinating disease not further specified [n = 1]), or chronic cerebrovascular disorders [n = 9]). For quantitative analysis, SNR and CNR were determined. For a semiquantitative assessment of the diagnostic confidence, a 10-point scale diagnostic confidence score (DCS) was applied. Two experienced radiologists with additional qualification in neuroradiology independently assessed, blinded to the field strength, 3 pathology-specific imaging criteria in each of the 4 disease groups and rated their diagnostic confidence. The overall image quality was semiquantitatively assessed using a 4-point scale taking into account whether diagnostic decision making was hampered by artifacts or not. RESULTS Without correction for spatial resolution, SNR was higher at 3 T except in the T2 SPACE 3D, DWI single shot, and DIR SPACE 3D sequences. The SNR corrected by the ratio of 3 T/7 T voxel sizes was higher at 7 T than at 3 T in 10 of 11 sequences (all except for T1 MP2RAGE 3D).In CNR, there was a wide variation between sequences and patient cohorts, but average CNR values were broadly similar at 3 T and 7 T.DCS values for all 4 pathologic entities were higher at 7 T than at 3 T. The DCS was significantly higher at 7 T for diagnosis and exclusion of cortical lesions in vascular disease. A tendency to higher DCS at 7 T for cortical lesions in MS was observed, and for the depiction of a central vein and iron deposits within MS lesions. Despite motion artifacts, DCS values were higher at 7 T for the diagnosis and exclusion of hippocampal sclerosis in mesial temporal lobe epilepsy (improved detection of the hippocampal subunits). Interrater agreement was 69.7% at 3 T and 93.3% at 7 T. There was no significant difference in the overall image quality score between 3 T and 7 T taking into account whether diagnostic decision making was hampered by artifacts or not. CONCLUSIONS Ultra-high-field magnetic resonance imaging at 7 T compared with 3 T yielded an improved diagnostic confidence in the most frequently encountered neurologic disorders. Higher spatial resolution and contrast were identified as the main contributory factors.

Fast magnetic resonance temperature imaging for focused ultrasound thermal therapy

The current standard for temperature sensitive imaging using magnetic resonance (MR) is 2-D, spoiled, fast gradient-echo (fGRE) phase-difference imaging exploiting temperature dependent changes in the proton resonance frequency (PRF). The echo-time (TE) for optimal sensitivity is larger than the typical repetition time (TR) of an fGRE sequence. Since TE must be less than TR in the fGRE sequence, this limits the technique's achievable sensitivity, spatial, and temporal resolution. This adversely affects both accuracy and volume coverage of the measurements. Accurate measurement of the rapid temperature changes associated with pulsed thermal therapies, such as high-intensity focused ultrasound (FUS), at optimal temperature sensitivity requires faster acquisition times than those currently available. ^ Use of fast MR acquisition strategies, such as interleaved echo-planar and spiral imaging, can provide the necessary increase in temporal performance and sensitivity while maintaining adequate signal-to-noise and in-plane spatial resolution. This research explored the adaptation and optimization of several fast MR acquisition methods for thermal monitoring of pulsed FUS thermal therapy. Temperature sensitivity, phase-difference noise and phase-difference to phase-difference-to noise ratio for the different pulse sequences were evaluated under varying imaging parameters in an agar gel phantom to establish optimal sequence parameters for temperature monitoring. The temperature sensitivity coefficient of the gel phantom was measured, allowing quantitative temperature extrapolations. ^ Optimized fast sequences were compared based on the ability to accurately monitor temperature changes at the focus of a high-intensity focused ultrasound unit, volume coverage, and contrast-to-noise ratio in the temperature maps. Operating parameters, which minimize complex phase-difference measurement errors introduced by use of the fast-imaging methods, were established. ^

Advanced Techniques for Image Quality Assessment of Modern X-ray Computed Tomography Systems

X-ray computed tomography (CT) imaging constitutes one of the most widely used diagnostic tools in radiology today with nearly 85 million CT examinations performed in the U.S in 2011. CT imparts a relatively high amount of radiation dose to the patient compared to other x-ray imaging modalities and as a result of this fact, coupled with its popularity, CT is currently the single largest source of medical radiation exposure to the U.S. population. For this reason, there is a critical need to optimize CT examinations such that the dose is minimized while the quality of the CT images is not degraded. This optimization can be difficult to achieve due to the relationship between dose and image quality. All things being held equal, reducing the dose degrades image quality and can impact the diagnostic value of the CT examination.

A recent push from the medical and scientific community towards using lower doses has spawned new dose reduction technologies such as automatic exposure control (i.e., tube current modulation) and iterative reconstruction algorithms. In theory, these technologies could allow for scanning at reduced doses while maintaining the image quality of the exam at an acceptable level. Therefore, there is a scientific need to establish the dose reduction potential of these new technologies in an objective and rigorous manner. Establishing these dose reduction potentials requires precise and clinically relevant metrics of CT image quality, as well as practical and efficient methodologies to measure such metrics on real CT systems. The currently established methodologies for assessing CT image quality are not appropriate to assess modern CT scanners that have implemented those aforementioned dose reduction technologies.

Thus the purpose of this doctoral project was to develop, assess, and implement new phantoms, image quality metrics, analysis techniques, and modeling tools that are appropriate for image quality assessment of modern clinical CT systems. The project developed image quality assessment methods in the context of three distinct paradigms, (a) uniform phantoms, (b) textured phantoms, and (c) clinical images.

The work in this dissertation used the “task-based” definition of image quality. That is, image quality was broadly defined as the effectiveness by which an image can be used for its intended task. Under this definition, any assessment of image quality requires three components: (1) A well defined imaging task (e.g., detection of subtle lesions), (2) an “observer” to perform the task (e.g., a radiologists or a detection algorithm), and (3) a way to measure the observer’s performance in completing the task at hand (e.g., detection sensitivity/specificity).

First, this task-based image quality paradigm was implemented using a novel multi-sized phantom platform (with uniform background) developed specifically to assess modern CT systems (Mercury Phantom, v3.0, Duke University). A comprehensive evaluation was performed on a state-of-the-art CT system (SOMATOM Definition Force, Siemens Healthcare) in terms of noise, resolution, and detectability as a function of patient size, dose, tube energy (i.e., kVp), automatic exposure control, and reconstruction algorithm (i.e., Filtered Back-Projection– FPB vs Advanced Modeled Iterative Reconstruction– ADMIRE). A mathematical observer model (i.e., computer detection algorithm) was implemented and used as the basis of image quality comparisons. It was found that image quality increased with increasing dose and decreasing phantom size. The CT system exhibited nonlinear noise and resolution properties, especially at very low-doses, large phantom sizes, and for low-contrast objects. Objective image quality metrics generally increased with increasing dose and ADMIRE strength, and with decreasing phantom size. The ADMIRE algorithm could offer comparable image quality at reduced doses or improved image quality at the same dose (increase in detectability index by up to 163% depending on iterative strength). The use of automatic exposure control resulted in more consistent image quality with changing phantom size.

Based on those results, the dose reduction potential of ADMIRE was further assessed specifically for the task of detecting small (<=6 mm) low-contrast (<=20 HU) lesions. A new low-contrast detectability phantom (with uniform background) was designed and fabricated using a multi-material 3D printer. The phantom was imaged at multiple dose levels and images were reconstructed with FBP and ADMIRE. Human perception experiments were performed to measure the detection accuracy from FBP and ADMIRE images. It was found that ADMIRE had equivalent performance to FBP at 56% less dose.

Using the same image data as the previous study, a number of different mathematical observer models were implemented to assess which models would result in image quality metrics that best correlated with human detection performance. The models included naïve simple metrics of image quality such as contrast-to-noise ratio (CNR) and more sophisticated observer models such as the non-prewhitening matched filter observer model family and the channelized Hotelling observer model family. It was found that non-prewhitening matched filter observers and the channelized Hotelling observers both correlated strongly with human performance. Conversely, CNR was found to not correlate strongly with human performance, especially when comparing different reconstruction algorithms.

The uniform background phantoms used in the previous studies provided a good first-order approximation of image quality. However, due to their simplicity and due to the complexity of iterative reconstruction algorithms, it is possible that such phantoms are not fully adequate to assess the clinical impact of iterative algorithms because patient images obviously do not have smooth uniform backgrounds. To test this hypothesis, two textured phantoms (classified as gross texture and fine texture) and a uniform phantom of similar size were built and imaged on a SOMATOM Flash scanner (Siemens Healthcare). Images were reconstructed using FBP and a Sinogram Affirmed Iterative Reconstruction (SAFIRE). Using an image subtraction technique, quantum noise was measured in all images of each phantom. It was found that in FBP, the noise was independent of the background (textured vs uniform). However, for SAFIRE, noise increased by up to 44% in the textured phantoms compared to the uniform phantom. As a result, the noise reduction from SAFIRE was found to be up to 66% in the uniform phantom but as low as 29% in the textured phantoms. Based on this result, it clear that further investigation was needed into to understand the impact that background texture has on image quality when iterative reconstruction algorithms are used.

To further investigate this phenomenon with more realistic textures, two anthropomorphic textured phantoms were designed to mimic lung vasculature and fatty soft tissue texture. The phantoms (along with a corresponding uniform phantom) were fabricated with a multi-material 3D printer and imaged on the SOMATOM Flash scanner. Scans were repeated a total of 50 times in order to get ensemble statistics of the noise. A novel method of estimating the noise power spectrum (NPS) from irregularly shaped ROIs was developed. It was found that SAFIRE images had highly locally non-stationary noise patterns with pixels near edges having higher noise than pixels in more uniform regions. Compared to FBP, SAFIRE images had 60% less noise on average in uniform regions for edge pixels, noise was between 20% higher and 40% lower. The noise texture (i.e., NPS) was also highly dependent on the background texture for SAFIRE. Therefore, it was concluded that quantum noise properties in the uniform phantoms are not representative of those in patients for iterative reconstruction algorithms and texture should be considered when assessing image quality of iterative algorithms.

The move beyond just assessing noise properties in textured phantoms towards assessing detectability, a series of new phantoms were designed specifically to measure low-contrast detectability in the presence of background texture. The textures used were optimized to match the texture in the liver regions actual patient CT images using a genetic algorithm. The so called “Clustured Lumpy Background” texture synthesis framework was used to generate the modeled texture. Three textured phantoms and a corresponding uniform phantom were fabricated with a multi-material 3D printer and imaged on the SOMATOM Flash scanner. Images were reconstructed with FBP and SAFIRE and analyzed using a multi-slice channelized Hotelling observer to measure detectability and the dose reduction potential of SAFIRE based on the uniform and textured phantoms. It was found that at the same dose, the improvement in detectability from SAFIRE (compared to FBP) was higher when measured in a uniform phantom compared to textured phantoms.

The final trajectory of this project aimed at developing methods to mathematically model lesions, as a means to help assess image quality directly from patient images. The mathematical modeling framework is first presented. The models describe a lesion’s morphology in terms of size, shape, contrast, and edge profile as an analytical equation. The models can be voxelized and inserted into patient images to create so-called “hybrid” images. These hybrid images can then be used to assess detectability or estimability with the advantage that the ground truth of the lesion morphology and location is known exactly. Based on this framework, a series of liver lesions, lung nodules, and kidney stones were modeled based on images of real lesions. The lesion models were virtually inserted into patient images to create a database of hybrid images to go along with the original database of real lesion images. ROI images from each database were assessed by radiologists in a blinded fashion to determine the realism of the hybrid images. It was found that the radiologists could not readily distinguish between real and virtual lesion images (area under the ROC curve was 0.55). This study provided evidence that the proposed mathematical lesion modeling framework could produce reasonably realistic lesion images.

Based on that result, two studies were conducted which demonstrated the utility of the lesion models. The first study used the modeling framework as a measurement tool to determine how dose and reconstruction algorithm affected the quantitative analysis of liver lesions, lung nodules, and renal stones in terms of their size, shape, attenuation, edge profile, and texture features. The same database of real lesion images used in the previous study was used for this study. That database contained images of the same patient at 2 dose levels (50% and 100%) along with 3 reconstruction algorithms from a GE 750HD CT system (GE Healthcare). The algorithms in question were FBP, Adaptive Statistical Iterative Reconstruction (ASiR), and Model-Based Iterative Reconstruction (MBIR). A total of 23 quantitative features were extracted from the lesions under each condition. It was found that both dose and reconstruction algorithm had a statistically significant effect on the feature measurements. In particular, radiation dose affected five, three, and four of the 23 features (related to lesion size, conspicuity, and pixel-value distribution) for liver lesions, lung nodules, and renal stones, respectively. MBIR significantly affected 9, 11, and 15 of the 23 features (including size, attenuation, and texture features) for liver lesions, lung nodules, and renal stones, respectively. Lesion texture was not significantly affected by radiation dose.

The second study demonstrating the utility of the lesion modeling framework focused on assessing detectability of very low-contrast liver lesions in abdominal imaging. Specifically, detectability was assessed as a function of dose and reconstruction algorithm. As part of a parallel clinical trial, images from 21 patients were collected at 6 dose levels per patient on a SOMATOM Flash scanner. Subtle liver lesion models (contrast = -15 HU) were inserted into the raw projection data from the patient scans. The projections were then reconstructed with FBP and SAFIRE (strength 5). Also, lesion-less images were reconstructed. Noise, contrast, CNR, and detectability index of an observer model (non-prewhitening matched filter) were assessed. It was found that SAFIRE reduced noise by 52%, reduced contrast by 12%, increased CNR by 87%. and increased detectability index by 65% compared to FBP. Further, a 2AFC human perception experiment was performed to assess the dose reduction potential of SAFIRE, which was found to be 22% compared to the standard of care dose.

In conclusion, this dissertation provides to the scientific community a series of new methodologies, phantoms, analysis techniques, and modeling tools that can be used to rigorously assess image quality from modern CT systems. Specifically, methods to properly evaluate iterative reconstruction have been developed and are expected to aid in the safe clinical implementation of dose reduction technologies.

Dosimetric Evaluation of Metal Artefact Reduction using Metal Artefact Reduction (MAR) Algorithm and Dual-energy Computed Tomography (CT) Method

Purpose: Computed Tomography (CT) is one of the standard diagnostic imaging modalities for the evaluation of a patient’s medical condition. In comparison to other imaging modalities such as Magnetic Resonance Imaging (MRI), CT is a fast acquisition imaging device with higher spatial resolution and higher contrast-to-noise ratio (CNR) for bony structures. CT images are presented through a gray scale of independent values in Hounsfield units (HU). High HU-valued materials represent higher density. High density materials, such as metal, tend to erroneously increase the HU values around it due to reconstruction software limitations. This problem of increased HU values due to metal presence is referred to as metal artefacts. Hip prostheses, dental fillings, aneurysm clips, and spinal clips are a few examples of metal objects that are of clinical relevance. These implants create artefacts such as beam hardening and photon starvation that distort CT images and degrade image quality. This is of great significance because the distortions may cause improper evaluation of images and inaccurate dose calculation in the treatment planning system. Different algorithms are being developed to reduce these artefacts for better image quality for both diagnostic and therapeutic purposes. However, very limited information is available about the effect of artefact correction on dose calculation accuracy. This research study evaluates the dosimetric effect of metal artefact reduction algorithms on severe artefacts on CT images. This study uses Gemstone Spectral Imaging (GSI)-based MAR algorithm, projection-based Metal Artefact Reduction (MAR) algorithm, and the Dual-Energy method.

Materials and Methods: The Gemstone Spectral Imaging (GSI)-based and SMART Metal Artefact Reduction (MAR) algorithms are metal artefact reduction protocols embedded in two different CT scanner models by General Electric (GE), and the Dual-Energy Imaging Method was developed at Duke University. All three approaches were applied in this research for dosimetric evaluation on CT images with severe metal artefacts. The first part of the research used a water phantom with four iodine syringes. Two sets of plans, multi-arc plans and single-arc plans, using the Volumetric Modulated Arc therapy (VMAT) technique were designed to avoid or minimize influences from high-density objects. The second part of the research used projection-based MAR Algorithm and the Dual-Energy Method. Calculated Doses (Mean, Minimum, and Maximum Doses) to the planning treatment volume (PTV) were compared and homogeneity index (HI) calculated.

Results: (1) Without the GSI-based MAR application, a percent error between mean dose and the absolute dose ranging from 3.4-5.7% per fraction was observed. In contrast, the error was decreased to a range of 0.09-2.3% per fraction with the GSI-based MAR algorithm. There was a percent difference ranging from 1.7-4.2% per fraction between with and without using the GSI-based MAR algorithm. (2) A range of 0.1-3.2% difference was observed for the maximum dose values, 1.5-10.4% for minimum dose difference, and 1.4-1.7% difference on the mean doses. Homogeneity indexes (HI) ranging from 0.068-0.065 for dual-energy method and 0.063-0.141 with projection-based MAR algorithm were also calculated.

Conclusion: (1) Percent error without using the GSI-based MAR algorithm may deviate as high as 5.7%. This error invalidates the goal of Radiation Therapy to provide a more precise treatment. Thus, GSI-based MAR algorithm was desirable due to its better dose calculation accuracy. (2) Based on direct numerical observation, there was no apparent deviation between the mean doses of different techniques but deviation was evident on the maximum and minimum doses. The HI for the dual-energy method almost achieved the desirable null values. In conclusion, the Dual-Energy method gave better dose calculation accuracy to the planning treatment volume (PTV) for images with metal artefacts than with or without GE MAR Algorithm.

Contrast transfer function correction applied to cryo-electron tomography and sub-tomogram averaging

Cryo-electron tomography together with averaging of sub-tomograms containing identical particles can reveal the structure of proteins or protein complexes in their native environment. The resolution of this technique is limited by the contrast transfer function (CTF) of the microscope. The CTF is not routinely corrected in cryo-electron tomography because of difficulties including CTF detection, due to the low signal to noise ratio, and CTF correction, since images are characterised by a spatially variant CTF. Here we simulate the effects of the CTF on the resolution of the final reconstruction, before and after CTF correction, and consider the effect of errors and approximations in defocus determination. We show that errors in defocus determination are well tolerated when correcting a series of tomograms collected at a range of defocus values. We apply methods for determining the CTF parameters in low signal to noise images of tilted specimens, for monitoring defocus changes using observed magnification changes, and for correcting the CTF prior to reconstruction. Using bacteriophage PRDI as a test sample, we demonstrate that this approach gives an improvement in the structure obtained by sub-tomogram averaging from cryo-electron tomograms.