Citations

Abstract Emphysema’s significant morbidity and mortality underscore the need for reliable outcome metrics in clinical trials. However, commonly accepted chronic obstructive pulmonary disease outcome measures do not adequately capture emphysema severity or progression. Computed tomography (CT) metrics have been validated as accurate indicators of pathological emphysema and predictors of chronic obstructive pulmonary disease progression, exacerbations, and mortality. This position paper reviews the evidence supporting CT densitometry as a biomarker for emphysema, establishes implementation standards, and highlights areas for future research. A systematic literature review addressed three key questions: whether CT densitometry can be used as a diagnostic biomarker of emphysema, whether CT densitometry can be used as a prognostic biomarker, and whether longitudinal change in densitometry can be used as a disease progression monitoring biomarker. Emphysema metrics, such as the percentage of low attenuation areas below −950 Hounsfield units, are validated, highly reproducible diagnostic and prognostic biomarkers. Volume-adjusted lung density is recommended for disease monitoring. Both metrics demonstrate a scan–rescan intraclass correlation coefficient of 0.99 with proper technique. The paper also discusses relevant CT physics, techniques, and sources of variation, including technical factors, physiological changes, and software analysis. Key recommendations for clinical trials include using standardized CT techniques, proper subject selection, and longitudinal evaluation with volume-adjusted lung density.

Abstract Background Accurate low‐dose CT simulation is required to efficiently assess reconstruction and dose reduction techniques. Projection domain noise insertion requires proprietary information from manufacturers. Analytic image domain noise insertion methods are successful for linear reconstruction algorithms, however extending them to non‐linear algorithms remains challenging. Emerging, deep‐learning‐based image domain noise insertion methods have potential, but few approaches have explicitly incorporated physics information and a texture‐synthesis model to guide the generation of locally and globally correlated noise texture. Purpose We proposed a p hysics‐informed model‐based gener a tive neura l network for simulating scann e r‐ and algori t hm‐specific low‐dose C T e xams (PALETTE). It is expected to provide an alternative to projection domain noise insertion methods in the absence of manufacturers’ proprietary information and tools. Methods PALETTE integrated a physics‐based noise prior generation process, a Noise2Noisier sub‐network, and a noise texture synthesis sub‐network. The Noise2Noisier sub‐network provided a bias prior, which, combined with the noise prior, served as the inputs to noise texture synthesis sub‐network. Explicit regularizations in spatial and frequency domains were developed to account for noise spatial correlation and frequency characteristics. For proof‐of‐concept, PALETTE was trained and validated for a commercial iterative reconstruction algorithm (SAFIRE, Siemens Healthineers), using the paired routine and 25% dose images from CT phantoms (lateral size 30–40 cm; three training and four testing phantoms) and open‐access patient cases (10 training and 20 testing cases). In phantom validation, noise power spectra (NPS) were compared in water background and tissue‐mimicking inserts, using peak frequency and mean‐absolute‐error (MAE). In patient case evaluation, visual inspection and quantitative assessment were conducted on axial, coronal, and sagittal planes. Local and global noise texture were visually inspected in low‐dose CT images and the difference images between routine and low dose. Noise levels in liver and fat were measured. Local and global 2D Fourier magnitude spectra of the difference images and the corresponding radial mean profiles were used to assess similarity in noise frequency components within tissues and entire field‐of‐view, using spectral correlation mapper (SCM) and spectral angle mapper (SAM). Several baseline neural network models (e.g., GAN) were included in the evaluation. Statistical significance was tested using a t ‐test for related samples. Results PALETTE‐derived NPS showed accurate noise peak frequency (PALETTE/reference: water 1.40/1.40 lp/cm; inserts 1.7/1.7lp/cm) and small MAE (≤0.65 HU 2 cm 2 ). PALETTE created anatomy‐dependent noise texture, showing realistic local and global granularity and streaks. No statistically significant difference was observed in noise levels ( p > 0.05). Noise range was comparable across 3D image volume (PALETTE / reference):liver – [18.0, 53.4] / [19.3, 50.0] HU; fat – [11.7, 42.4] / [12.1, 41.3] HU. Percent absolute difference of local noise was small (mean ± standard deviation): liver 4.1%±3.1%, fat 4.6%±3.1%. Noise frequency distribution was close to the reference (mean per case): SCM ≥ 0.92, SAM ≤ 0.22. Additionally, PALETTE outperformed all baseline models in visual inspection and quantitative comparison. Conclusion PALETTE can provide high‐quality image domain noise insertion for simulating accurate low‐dose CT images created with a commercial non‐linear reconstruction algorithm.

Abstract Background Multi‐energy CT (MECT) enables quantification of material concentrations by measuring linear attenuation coefficient line integrals with multiple x‐ray spectra. Photon counting detector (PCD)‐CT utilizes a detector‐based approach for MECT that can suffer from substantial spectral overlap, resulting in amplified material quantification noise. Dual‐source dual‐kV approaches for MECT are currently utilized in some energy‐integrating detector (EID)‐CT systems and can potentially be utilized with PCD‐CT for improved spectral separation. Purpose To evaluate the iodine quantification performance of a novel dual‐source (DS)‐PCD‐CT scan mode and compare to single‐source (SS)‐PCD‐CT and DS‐EID‐CT. Materials and Methods A 30 cm × 40 cm solid water phantom with four iodine inserts (2, 5, 10, and 15 mg/mL) was scanned with the three spectral modalities: SS‐PCD‐CT with two energy thresholds, DS‐PCD‐CT (90/Sn150 kV), and DS‐EID‐CT (90/Sn150 kV). For each modality, full‐dose (12 mGy) and half‐dose scans were acquired, and images were reconstructed with filtered back‐projection using a quantitative (Qr40) kernel. When scanning in a DS configuration, the total radiation dose budget is split between two detectors, increasing the strength of a signal‐dependent filter compared to a SS acquisition. To account for this effect, the modulation transfer function (MTF) for each modality was measured from a 0.05 mm tungsten wire. A linear spatial filter was applied to the SS images to match their MTF to that of the DS images. The resulting high‐ and low‐energy images were input into an image‐domain least squares material decomposition algorithm with iodine and water as the two basis materials. Iodine quantification accuracy and noise measured from the iodine basis images were used as figures of merit, and t‐tests used to compare between modalities. Results The 10% MTF cutoffs were 0.56, 0.57, 0.60, and 0.57 lp/mm for DS‐EID‐CT, DS‐PCD‐CT, SS‐PCD‐CT, and SS‐PCD‐CT after MTF‐matching, respectively, with the SS‐PCD‐CT MTF cutoff dropping to 0.58 lp/mm at half‐dose. Without accounting for the signal‐dependent filter by matching the MTFs, the noise in iodine material basis images from SS‐PCD‐CT was 10% higher than that of DS‐EID‐CT. After matching the MTFs, the noise in the SS‐PCD‐CT iodine image was 9%–22% lower than that of the DS‐EID‐CT. The lowest iodine image noise was from the DS‐PCD‐CT, which was 39%–41% lower than the DS‐EID‐CT. The DS‐PCD‐CT noise magnitude was significantly different from the other modalities. Mean iodine quantification accuracy across all measured concentrations was within 5% for all modalities at full dose, but was only below 5% for the DS‐PCD‐CT at half‐dose. Conclusions SS‐PCD‐CT with two energy thresholds outperformed DS‐EID‐CT in terms of image noise in iodine basis images when spatial resolution was matched. DS‐PCD‐CT gave the lowest noise due to the combination of PCD technology and improved spectral separation from the different x‐ray spectra.

AbstractBackgroundPhoton counting X‐ray detectors (PCDs) provide better spatial resolution than energy integrating X‐ray detectors, but even higher resolution is desired in some applications. Certain charge sharing compensation techniques such as coincidence counting preferentially detect photons that arrive at the boundary between pixels, and this could be used for subpixel localization of incident photons and enhanced spatial resolution.PurposeTo estimate improvements to spatial resolution and detective quantum efficiency that are possible when using coincidence counting for high‐resolution, non‐spectral imaging.MethodsThe modulation transfer function (MTF), noise power spectrum (NPS), and detective quantum efficiency (DQE) were estimated using numerical simulations of a two‐dimensional parallel‐beam CT system. Coincidence counters were modeled either geometrically or with Monte Carlo simulations. The geometric model consisted of narrow coincidence counters that interlaced with wider ordinary pixels, and showed that using standard filtered backprojection decreases low‐frequency DQE, but that a frequency weighting technique could be used to restore DQE. The Monte Carlo simulations were used to estimate the possible improvements that could be expected from real systems. The pixel pitch was 0.25 mm and the source apertures considered were 0, 0.125, and 0.5 mm. A numerical stent phantom was also used to illustrate possible improvements.ResultsAssuming a 0.125 mm source aperture and the Monte Carlo model, the limiting MTF (at 10%) increased from 20 to 40 lp/cm using coincidence counters. This can be explained by the increased sampling (and higher Nyquist limit) possible from coincidence counters. In the geometric model, coincidence counters are compared to conventional double sampling techniques such as in‐plane flying focal spot, and the MTF at 38 lp/cm increased from 5% to 53%. Without the frequency weighting technique, low‐frequency DQE was reduced by about 20%, but these losses are recovered with frequency weighting. Improvements are much more modest with the 0.25 mm source aperture because the system becomes source‐limited.ConclusionsCoincidence counting could be used to increase spatial resolution in PCDs. The increases in system resolution could be large if a high‐resolution X‐ray source were available.

Background SynthesiZed Improved Resolution and Concurrent nOise reductioN (ZIRCON) is a multi-kernel synthesis method that creates a single series of thin-slice computed tomography (CT) images displaying low noise and high spatial resolution, increasing reader efficiency and minimizing partial volume averaging. Purpose To compare the diagnostic performance of a single set of ZIRCON images to two routine clinical image series using conventional CT head and bone reconstruction kernels for diagnosing intracranial findings and fractures in patients with trauma or suspected acute neurologic deficit. Material and Methods In total, 50 patients underwent clinically indicated head CT in the ER (15 normal, 35 abnormal cases). A non-reader neuroradiologist established the reference standard. Three neuroradiologists reviewed two routine clinical series (head and bone kernels) and a single ZIRCON series, detecting intracranial findings or fractures and rating confidence (0–100). Sensitivity, specificity, and jackknife free-response receiver operating characteristic (JAFROC) figure of merit (FOM) were compared (limit of non-inferiority: −0.10). Results ZIRCON and conventional images demonstrated comparable performance for fractures (sensitivity: 51.5% vs. 54.5%; specificity: 40.2% vs. 34.2%) and intracranial findings (sensitivity: 88.2% vs. 91.4%; specificity: 77.2% vs. 73.7%).The estimated difference of JAFROC FOM demonstrated ZIRCON non-inferiority for acute pathologies overall (0.003 [95% CI=−0.051–0.057]) and fractures (0.048 [95% CI=−0.050–0.145]) but not for intracranial findings alone (−0.024 [95% CI=−0.100–0.052]). Conclusion Thin-slice, low noise, and high spatial resolution images can be created to display intracranial findings and fractures replacing multiple images series in head CT with similar performance. Future studies in more patients and further algorithmic development are warranted.

AbstractBackgroundThe first commercially available photon‐counting‐detector CT (PCD‐CT) has been introduced for clinical use. However, its spectral performance on single‐ and dual‐contrast imaging tasks has not been comprehensively assessed.PurposeTo evaluate the spectral imaging performance of a clinical PCD‐CT system for single‐contrast material [iodine (I) or gadolinium (Gd)] and dual‐contrast materials (I and Gd) in comparison with a dual‐source dual‐energy CT (DS‐DECT).MethodsIodine (5, 10, and 15 mg/mL) and gadolinium (3.3, 6.6, and 9.9 mg/mL) samples, and their mixtures (I/Gd: 5/3.3 and 10/6.6 mg/mL) were prepared and placed in two torso‐shaped water phantoms (lateral dimensions: 30 and 40 cm). These phantoms were scanned on a PCD‐CT (NAEOTOM Alpha, Siemens) at 90, 120, and 140 kV. The same phantoms were scanned on a DS‐DECT (SOMATOM Force, Siemens) with 70/Sn150, 80/Sn150, 90/Sn150, and 100/Sn150 kV. The radiation dose levels were matched [volume CT dose index (CTDIvol): 10 mGy for the 30 cm phantom and 20 mGy for the 40 cm phantom] across all tube voltage settings and between scanners. Two‐material decomposition (I/water or Gd/water) was performed on iodine or gadolinium samples, and three‐material decomposition (I/Gd/water) on both individual samples and mixtures. On each decomposed image, mean mass concentration (± standard deviation) was measured in circular region‐of‐interests placed on the contrast samples. Root‐mean‐square‐error (RMSE) values of iodine and gadolinium concentrations were reported based on the measurements across all contrast samples and repeated on 10 consecutive slices.ResultsFor all material decomposition tasks on the DS‐DECT, the kV pairs with greater spectral separation (70/Sn150 kV and 80/Sn150 kV) yielded lower RMSE values than other DS‐DECT and PCD‐CT alternatives. Specifically, for the optimal 70/Sn150 kV, RMSE values were 1.2 ± 0.1 mg/mL (I) for I/water material decomposition, 1.0 ± 0.1 mg/mL (Gd) for Gd/water material decomposition, and 4.5 ± 0.2 mg/mL (I) and 3.7 ± 0.2 mg/mL (Gd), respectively, for I/Gd/water material decomposition. On the PCD‐CT, the optimal tube voltages were 120 or 140 kV for I/water decomposition with RMSE values of 2.0 ± 0.1 mg/mL (I). For Gd/water decomposition on PCD‐CT, the optimal tube voltage was 140 kV with gadolinium RMSE values of 1.5 ± 0.1 mg/mL (Gd), with the 90 kV setting on PCD‐CT generating higher RMSE values for gadolinium concentration compared to all DS‐DECT and PCD‐CT alternatives. For three material decomposition, both imaging modalities demonstrated substantially higher RMSE values for iodine and gadolinium, with 90 kV being the optimal tube potential for Gd/I quantitation on PCD‐CT [5.4 ± 0.3 mg/mL (I) and 3.9 ± 0.2 mg/mL (Gd)], and DS‐DECT at 100/Sn150 kV having larger RMSE values for both materials compared to the alternatives for either modality.ConclusionOptimal tube voltage for material decomposition on the clinical PCD‐CT is task‐dependent but inferior to DS‐DECT using 70/Sn150 kV or 80/Sn150 kV in two‐material decomposition for single‐contrast imaging (iodine/water or gadolinium/water). Three material decomposition (iodine/gadolinium/water) in dual‐contrast imaging yields substantially higher RMSE for both imaging platforms.

AbstractPurposeA dual‐source CT system can be operated in a high‐pitch helical mode to provide a temporal resolution of 66 ms, which reduces motion artifacts in CT pulmonary angiography (CTPA). It can also be operated in a multi‐energy (ME) mode to provide iodine maps, beneficial in the evaluation of pulmonary embolism (PE). No energy‐integrating detector (EID) CT can perform simultaneous ME and high‐pitch acquisition. This phantom study aimed to evaluate the ability of a photon‐counting‐detector (PCD) CT to perform simultaneous high‐pitch and ME imaging for CTPA.MethodsA motion phantom was used to mimic the respiratory motion. Two tubes filled with iodine with intravascular thrombus mimicked by injecting glue within the tubes were placed along with 5, 10, and 15 mg/mL iodine samples, on a motion phantom at 20 and 30 revolutions per minute. Separate high‐pitch and ME EID‐CT scans and a single high‐pitch ME PCD scan were acquired and virtual monoenergetic images and iodine maps reconstructed. Percent thrombus occlusion was measured and compared between static and moving images.ResultsWhen there was motion, EID‐CT ME suffered from significant motion artifacts. The measured iodine concentrations with PCD‐CT in high‐pitch ME were more stable when there was a motion, with a lower standard deviation than EID‐CT in ME mode. The estimated percent thrombus occlusion dropped significantly with applied motion on EID‐CT, while PCD‐CT high‐pitch ME mode showed good agreement between measurements on static or moving images.ConclusionPCD‐CT with combined ME and high‐pitch mode facilitates simultaneous accurate iodine quantification and assessment of intravascular occlusion.

AbstractBackgroundInserting noise into existing patient projection data to simulate lower‐radiation‐dose exams has been frequently used in traditional energy‐integrating‐detector (EID)‐CT to optimize radiation dose in clinical protocols and to generate paired images for training deep‐learning‐based reconstruction and noise reduction methods. Recent introduction of photon counting detector CT (PCD‐CT) also requires such a method to accomplish these tasks. However, clinical PCD‐CT scanners often restrict the users access to the raw count data, exporting only the preprocessed, log‐normalized sinogram. Therefore, it remains a challenge to employ projection domain noise insertion algorithms on PCD‐CT.PurposeTo develop and validate a projection domain noise insertion algorithm for PCD‐CT that does not require access to the raw count data.Materials and MethodsA projection‐domain noise model developed originally for EID‐CT was adapted for PCD‐CT. This model requires, as input, a map of the incident number of photons at each detector pixel when no object is in the beam. To obtain the map of incident number of photons, air scans were acquired on a PCD‐CT scanner, then the noise equivalent photon number (NEPN) was calculated from the variance in the log normalized projection data of each scan. Additional air scans were acquired at various mA settings to investigate the impact of pulse pileup on the linearity of NEPN measurement. To validate the noise insertion algorithm, Noise Power Spectra (NPS) were generated from a 30 cm water tank scan and used to compare the noise texture and noise level of measured and simulated half dose and quarter dose images. An anthropomorphic thorax phantom was scanned with automatic exposure control, and noise levels at different slice locations were compared between simulated and measured half dose and quarter dose images. Spectral correlation between energy thresholds T1 and T2, and energy bins, B1 and B2, was compared between simulated and measured data across a wide range of tube current. Additionally, noise insertion was performed on a clinical patient case for qualitative assessment.ResultsThe NPS generated from simulated low dose water tank images showed similar shape and amplitude to that generated from the measured low dose images, differing by a maximum of 5.0% for half dose (HD) T1 images, 6.3% for HD T2 images, 4.1% for quarter dose (QD) T1 images, and 6.1% for QD T2 images. Noise versus slice measurements of the lung phantom showed comparable results between measured and simulated low dose images, with root mean square percent errors of 5.9%, 5.4%, 5.0%, and 4.6% for QD T1, HD T1, QD T2, and HD T2, respectively. NEPN measurements in air were linear up until 112 mA, after which pulse pileup effects significantly distort the air scan NEPN profile. Spectral correlation between T1 and T2 in simulation agreed well with that in the measured data in typical dose ranges.ConclusionsA projection‐domain noise insertion algorithm was developed and validated for PCD‐CT to synthesize low‐dose images from existing scans. It can be used for optimizing scanning protocols and generating paired images for training deep‐learning‐based methods.