The development and widespread adoption of iterative reconstruction (IR) algorithms for CT have greatly facilitated the contemporary practice of radiation dose reduction during abdominal CT ...examinations. IR mitigates the increased image noise typically associated with reduced radiation dose levels, thereby maintaining subjective image quality and diagnostic confidence for a variety of clinical tasks. Mounting evidence, however, points to important limitations of this method involving radiologists' ability to perform low-contrast diagnostic tasks, such as the detection of liver metastases or pancreatic masses. Radiologists need to be aware that use of IR can result in a decline of spatial resolution for low-contrast structures and degradation of low-contrast detectability when radiation dose reductions exceed approximately 25%. This article will review the principles of IR algorithm technology, describe the various commercial implementations of IR in CT, and review published studies that have evaluated the ability of IR to preserve diagnostic performance for low-contrast diagnostic tasks. In addition, future developments in CT noise reduction techniques and methods to rigorously evaluate their diagnostic performance will be discussed.
Purpose:
To evaluate the image quality of virtual monochromatic images synthesized from dual-source dual-energy computed tomography (CT) in comparison with conventional polychromatic single-energy CT ...for the same radiation dose.
Methods:
In dual-energy CT, besides the material-specific information, one may also synthesize monochromatic images at different energies, which can be used for routine diagnosis similar to conventional polychromatic single-energy images. In this work, the authors assessed whether virtual monochromatic images generated from dual-source CT scanners had an image quality similar to that of polychromatic single-energy images for the same radiation dose. First, the authors provided a theoretical analysis of the optimal monochromatic energy for either the minimum noise level or the highest iodine contrast to noise ratio (CNR) for a given patient size and dose partitioning between the low- and high-energy scans. Second, the authors performed an experimental study on a dual-source CT scanner to evaluate the noise and iodine CNR in monochromatic images. A thoracic phantom with three sizes of attenuating rings was used to represent four adult sizes. For each phantom size, three dose partitionings between the low-energy (80 kV) and the high-energy (140 kV) scans were used in the dual-energy scan. Monochromatic images at eight energies (40 to 110 keV) were generated for each scan. Phantoms were also scanned at each of the four polychromatic single energy (80, 100, 120, and 140 kV) with the same radiation dose.
Results:
The optimal virtual monochromatic energy depends on several factors: phantom size, partitioning of the radiation dose between low- and high-energy scans, and the image quality metrics to be optimized. With the increase of phantom size, the optimal monochromatic energy increased. With the increased percentage of radiation dose on the low energy scan, the optimal monochromatic energy decreased. When maximizing the iodine CNR in monochromatic images, the optimal energy was lower than that when minimizing noise level. When the total radiation dose was equally distributed between low and high energy in dual-energy scans, for minimum noise, the optimal energies were 68, 71, 74, and 77 keV for small, medium, large, and extra-large (xlarge) phantoms, respectively; for maximum iodine CNR, the optimal energies were 66, 68, 70, 72 keV. With the optimal monochromatic energy, the noise level was similar to and the CNR was better than that in a single-energy scan at 120 kV for the same radiation dose. Compared to an 80 kV scan, however, the iodine CNR in monochromatic images was lower for the small, medium, and large phantoms.
Conclusions:
In dual-source dual-energy CT, optimal virtual monochromatic energy depends on patient size, dose partitioning, and the image quality metric optimized. With the optimal monochromatic energy, the noise level was similar to and the iodine CNR was better than that in 120 kV images for the same radiation dose. Compared to single-energy 80 kV images, the iodine CNR in virtual monochromatic images was lower for small to large phantom sizes.
Peptide–drug conjugates (PDCs) are the next generation of targeted therapeutics drug after antibody–drug conjugates (ADCs), with the core benefits of enhanced cellular permeability and improved drug ...selectivity. Two drugs are now approved for market by US Food and Drug Administration (FDA), and in the last two years, the pharmaceutical companies have been developing PDCs as targeted therapeutic candidates for cancer, coronavirus disease 2019 (COVID-19), metabolic diseases, and so on. The therapeutic benefits of PDCs are significant, but poor stability, low bioactivity, long research and development time, and slow clinical development process as therapeutic agents of PDC, how can we design PDCs more effectively and what is the future direction of PDCs? This review summarises the components and functions of PDCs for therapeutic, from drug target screening and PDC design improvement strategies to clinical applications to improve the permeability, targeting, and stability of the various components of PDCs. This holds great promise for the future of PDCs, such as bicyclic peptide‒toxin coupling or supramolecular nanostructures for peptide-conjugated drugs. The mode of drug delivery is determined according to the PDC design and current clinical trials are summarised. The way is shown for future PDC development.
This review summarizes the function of each component of the peptide–drug conjugate (PDC) and design factors and the desired mechanism of action. Display omitted
To determine the iodine contrast-to-noise ratio (CNR) for abdominal computed tomography (CT) when using energy domain noise reduction and virtual monoenergetic dual-energy (DE) CT images and to ...compare the CNR to that attained with single-energy CT at 80, 100, 120, and 140 kV.
This HIPAA-compliant study was approved by the institutional review board with waiver of informed consent. A syringe filled with diluted iodine contrast material was placed into 30-, 35-, and 45-cm-wide water phantoms and scanned with a dual-source CT scanner in both DE and single-energy modes with matched scanner output. Virtual monoenergetic images were generated, with energies ranging from 40 to 110 keV in 10-keV steps. A previously developed energy domain noise reduction algorithm was applied to reduce image noise by exploiting information redundancies in the energy domain. Image noise and iodine CNR were calculated. To show the potential clinical benefit of this technique, it was retrospectively applied to a clinical DE CT study of the liver in a 59-year-old male patient by using conventional and iterative reconstruction techniques. Image noise and CNR were compared for virtual monoenergetic images with and without energy domain noise reduction at each virtual monoenergetic energy (in kiloelectron volts) and phantom size by using a paired t test. CNR of virtual monoenergetic images was also compared with that of single-energy images acquired with 80, 100, 120, and 140 kV.
Noise reduction of up to 59% (28.7 of 65.7) was achieved for DE virtual monoenergetic images by using an energy domain noise reduction technique. For the commercial virtual monoenergetic images, the maximum iodine CNR was achieved at 70 keV and was 18.6, 16.6, and 10.8 for the 30-, 35-, and 45-cm phantoms. After energy domain noise reduction, maximum iodine CNR was achieved at 40 keV and increased to 30.6, 25.4, and 16.5. These CNRs represented improvement of up to 64% (12.0 of 18.6) with the energy domain noise reduction technique. For single-energy CT at the optimal tube potential, iodine CNR was 29.1 (80 kV), 21.2 (80 kV), and 11.5 (100 kV). For patient images, 39% (24 of 61) noise reduction and 67% (0.74 of 1.10) CNR improvement were observed with the energy domain noise reduction technique when compared with standard filtered back-projection images.
Iodine CNR for adult abdominal CT may be maximized with energy domain noise reduction and virtual monoenergetic DE CT.
Purpose:
To optimize radiation dose efficiency in CT while maintaining image quality, it is important to select the optimal tube potential. The selection of optimal tube potential, however, is highly ...dependent on patient size and diagnostic task. The purpose of this work was to develop a general strategy that allows for automatic tube potential selection for each individual patient and each diagnostic task.
Methods:
The authors propose a general strategy that allows automatic adaptation of the tube potential as a function of patient size and diagnostic task, using a novel index of image quality, “iodine contrast to noise ratio with a noise constraint (iCNR_NC),” to characterize the different image quality requirements by various clinical applications. The relative dose factor (RDF) at each tube potential to achieve a target image quality was then determined as a function of patient size and the noise constraint parameter. A workflow was developed to automatically identify the optimal tube potential that is both dose efficient and practically feasible, incorporating patient size and diagnostic task. An experimental study using a series of semianthropomorphic thoracic phantoms was used to demonstrate how the proposed general strategy can be implemented and how the radiation dose reduction achievable by the tube potential selection depends on phantom sizes and noise constraint parameters.
Results:
The proposed strategy provides a flexible and quantitative way to select the optimal tube potential based on the patient size and diagnostic task. The noise constraint parameter
α
can be adapted for different clinical applications. For example,
α
=
1
for noncontrast routine exams;
α
=
1.1
–
1.25
for contrast-enhanced routine exams; and
α
=
1.5
–
2.0
for CT angiography. For the five thoracic phantoms in the experiment, when
α
=
1
, the optimal tube potentials were 80, 100, 100, 120, 120, respectively. The corresponding RDFs (relative to 120 kV) were 78.0%, 90.9%, 95.2%, 100%, and 100%. When
α
=
1.5
, the optimal tube potentials were 80, 80, 80, 100, 100, respectively, with corresponding RDFs of 34.7%, 44.7%, 54.7%, 60.8%, and 89.5%.
Conclusions:
A general strategy to automatically select the most dose efficient tube potential for CT exams was developed that takes into account patient size and diagnostic task. Dependent on the patient size and the selection of noise constraint parameter for different diagnostic tasks, the dose reduction at each tube potential, quantified explicitly with the RDF, varies significantly.
Purpose:
Efficient optimization of CT protocols demands a quantitative approach to predicting human observer performance on specific tasks at various scan and reconstruction settings. The goal of ...this work was to investigate how well a channelized Hotelling observer (CHO) can predict human observer performance on 2-alternative forced choice (2AFC) lesion-detection tasks at various dose levels and two different reconstruction algorithms: a filtered-backprojection (FBP) and an iterative reconstruction (IR) method.
Methods:
A 35 × 26 cm2 torso-shaped phantom filled with water was used to simulate an average-sized patient. Three rods with different diameters (small: 3 mm; medium: 5 mm; large: 9 mm) were placed in the center region of the phantom to simulate small, medium, and large lesions. The contrast relative to background was −15 HU at 120 kV. The phantom was scanned 100 times using automatic exposure control each at 60, 120, 240, 360, and 480 quality reference mAs on a 128-slice scanner. After removing the three rods, the water phantom was again scanned 100 times to provide signal-absent background images at the exact same locations. By extracting regions of interest around the three rods and on the signal-absent images, the authors generated 21 2AFC studies. Each 2AFC study had 100 trials, with each trial consisting of a signal-present image and a signal-absent image side-by-side in randomized order. In total, 2100 trials were presented to both the model and human observers. Four medical physicists acted as human observers. For the model observer, the authors used a CHO with Gabor channels, which involves six channel passbands, five orientations, and two phases, leading to a total of 60 channels. The performance predicted by the CHO was compared with that obtained by four medical physicists at each 2AFC study.
Results:
The human and model observers were highly correlated at each dose level for each lesion size for both FBP and IR. The Pearson's product-moment correlation coefficients were 0.986 95% confidence interval (CI): 0.958–0.996 for FBP and 0.985 (95% CI: 0.863–0.998) for IR. Bland-Altman plots showed excellent agreement for all dose levels and lesions sizes with a mean absolute difference of 1.0% ± 1.1% for FBP and 2.1% ± 3.3% for IR.
Conclusions:
Human observer performance on a 2AFC lesion detection task in CT with a uniform background can be accurately predicted by a CHO model observer at different radiation dose levels and for both FBP and IR methods.
In principle, dual-energy CT can only accurately decompose a mixture into two materials. To decompose a mixture into three constitute materials using dual-energy CT measurements, a third criteria ...must be provided to solve for three unknowns with only two spectral measurements. One solution is to assume that the sum of the volumes of three constituent materials is equivalent to the volume of the mixture (i.e., volume conservation), but this is not always true. A more generalized solution is to use the principle of mass conservation, which assumes that the sum of the masses of the three constituent materials is equivalent to the mass of the mixture. In this article, a mass-conservation based, three-material decomposition dual-energy CT algorithm is described and experimental validation of the accuracy of the technique presented. The results demonstrate that the proposed method can accurately measure elemental concentrations under low noise imaging conditions. Clinically, this may be applied to measure the mass fraction of any chemical element in a three-material mixture of solutions without the requirement of volume conservation.
Compared to traditional multi-scan single-energy CT (SECT), one potential advantage of single-scan multi-energy CT (MECT) proposed for simultaneous imaging of multiple contrast agents is the ...radiation dose reduction. This phantom study aims to rigorously evaluate whether the radiation dose can truly be reduced in a single-scan MECT protocol (MECT_1s) in biphasic liver imaging with iodine and gadolinium, and small bowel imaging with iodine and bismuth, compared to traditional two-scan SECT protocols (SECT_2s). For MECT_1s, mixed iodine/gadolinium samples were prepared corresponding to late arterial/portal-venous phase for biphasic liver imaging. Mixed iodine/bismuth samples were prepared representing the arterial/enteric enhancement for small bowel imaging. For SECT_2s, separate contrast samples were prepared to mimic separate scans in arterial/venous phase and arterial/enteric enhancement. Samples were placed in a 35 cm wide water phantom and scanned by a research whole-body photon-counting-detector-CT (PCD-CT) system ('chess' mode). MECT images were acquired with optimized kV/threshold settings for each imaging task, and SECT images were acquired at 120 kV. Total CTDIvol was matched for the two protocols. Image-based three-material decomposition was employed in MECT_1s to determine the basis material concentration values, which were converted to CT numbers at 120 kV (i.e. virtual SECT images) to compare with the SECT images directly acquired with SECT_2s. The noise difference between the SECT and the virtual SECT images was compared to evaluate the dose efficiency of MECT_1s. Compared to SECT_2s, MECT_1s was not dose efficient for both imaging tasks. The amount of noise increase is highly task dependent, with noise increased by 203%/278% and 110%/82% in virtual SECT images for iodine/gadolinium and iodine/bismuth quantifications, respectively, corresponding to dose increase by 819%/1328% and 340%/230% in MECT_1s to achieve the same image noise level. MECT with the current PCD-CT technique requires higher radiation dose than SECT to achieve the same image quality.
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