Lower Contrast-to-noise Ratio Research Articles

Regional nigral neuromelanin degeneration in asymptomatic leucine-rich repeat kinase 2 gene carrier using MRI

Asymptomatic Leucine-Rich Repeat Kinase 2 Gene (LRRK2) carriers are at risk for developing Parkinson's disease (PD). We studied presymptomatic substantia nigra pars compacta (SNc) regional neurodegeneration in asymptomatic LRRK2 carriers compared to idiopathic PD patients using neuromelanin-sensitive MRI technique (NM-MRI). Fifteen asymptomatic LRRK2 carriers, 22 idiopathic PD patients, and 30 healthy controls (HCs) were scanned using NM-MRI. We computed volume and contrast-to-noise ratio (CNR) derived from the whole SNc and the sensorimotor, associative, and limbic SNc regions. An analysis of covariance was performed to explore the differences of whole and regional NM-MRI values among the groups while controlling the effect of age and sex. In whole SNc, LRRK2 had significantly lower CNR than HCs but non-significantly higher volume and CNR than PD patients, and PD patients significantly lower volume and CNR compared to HCs. Inside SNc regions, there were significant group effects for CNR in all regions and for volumes in the associative region, with a trend in the sensorimotor region but no significant changes in the limbic region. PD had reduced volume and CNR in all regions compared to HCs. Asymptomatic LRRK2 carriers showed globally decreased SNc volume and CNR suggesting early nigral neurodegeneration in these subjects at risk of developing PD.

Diagnostic value of qualitative and quantitative enhancement parameters on contrast-enhanced mammography.

To determine whether qualitative and quantitative enhancement parameters obtained from contrast-enhanced mammography (CEM) can be used in predicting malignancy. After review board approval, consecutive 136 suspicious lesions with definite diagnosis were retrospectively analyzed on CEM. Acquisition was routinely started with craniocaudal view and ended with mediolateral oblique view of the affected breast. Lesion conspicuity (low, moderate, high), internal enhancement pattern (homogeneous, heterogeneous, rim), contrast-to-noise ratio (CNR), percentage of signal difference (PSD) and relative enhancement from early to late view were analyzed. PSD and relative enhancements were used to determine patterns of descending, steady or ascending enhancements. Receiver operating characteristic analysis, Cohen's kappa statistics and Spearman correlation tests were used. There were 29 benign and 107 malignant lesions. 64% of the malignant lesions exhibited high conspicuity compared to 14% of the benign lesions (P < 0.001). CNR values were higher in malignant lesions compared to benign ones (P ≤ 0.004). CNR from early view yielded 82% sensitivity, 72% specificity and PSD yielded 79% sensitivity, 65% specificity. Descending pattern and rim enhancement observed in 44% and 21% of breast cancers, respectively, and both provided 96% positive predictive value for malignancy. Diagnostic accuracy of quantitative parameters was higher than that of qualitative parameters. High CNR, rim enhancement, and descending pattern were features commonly seen in malignant lesions, while low CNR, homogeneous enhancement, and ascending pattern were commonly seen in benign lesions.

Dual-energy computed tomography imaging with megavoltage and kilovoltage X-ray spectra.

Single-energy computed tomography (CT) often suffers from poor contrast yet remains critical for effective radiotherapy treatment. Modern therapy systems are often equipped with both megavoltage (MV) and kilovoltage (kV) X-ray sources and thus already possess hardware for dual-energy (DE) CT. There is unexplored potential for enhanced image contrast using MV-kV DE-CT in radiotherapy contexts. A single-line integral toy model was designed for computing basis material signal-to-noise ratio (SNR) using estimation theory. Five dose-matched spectra (3kV, 2 MV) and three variables were considered: spectral combination, spectral dose allocation, and object material composition. The single-line model was extended to a simulated CT acquisition of an anthropomorphic phantom with and without a metal implant. Basis material sinograms were computed and synthesized into virtual monoenergetic images (VMIs). MV-kV and kV-kV VMIs were compared with single-energy images. The 80kV-140kV pair typically yielded the best SNRs, but for bone thicknesses , the detunedMV-80kV pair surpassed it. Peak MV-kV SNR was achieved with dose allocated to the MV spectrum. In CT simulations of the pelvis with a steel implant, MV-kV VMIs yielded a higher contrast-to-noise ratio (CNR) than single-energy CT and kV-kV DE-CT. Without steel, the MV-kV VMIs produced higher contrast but lower CNR than single-energy CT. This work analyzes MV-kV DE-CT imaging and assesses its potential advantages. The technique may be used for metal artifact correction and generation of VMIs with higher native contrast than single-energy CT. Improved denoising is generally necessary for greater CNR without metal.

The Small Pixel Effect in Ultra-High-Resolution Photon-Counting CT of the Lumbar Spine

Objectives Image acquisition in ultra-high-resolution (UHR) scan mode does not impose a dose penalty in photon-counting CT (PCCT). This study aims to investigate the dose saving potential of using UHR instead of standard-resolution PCCT for lumbar spine imaging. Materials and Methods Eight cadaveric specimens were examined with 7 dose levels (5–35 mGy) each in UHR (120 × 0.2 mm) and standard-resolution acquisition mode (144 × 0.4 mm) on a first-generation PCCT scanner. The UHR images were reconstructed with 3 dedicated bone kernels (Br68 [spatial frequency at 10% of the modulation transfer function 14.5 line pairs/cm], Br76 [21.0], and Br84 [27.9]), standard-resolution images with Br68 and Br76. Using automatic segmentation, contrast-to-noise ratios (CNRs) were established for lumbar vertebrae and psoas muscle tissue. In addition, image quality was assessed subjectively by 19 independent readers (15 radiologists, 4 surgeons) using a browser-based forced choice comparison tool totaling 16,974 performed pairwise tests. Pearson's correlation coefficient (r) was used to analyze the relationship between CNR and subjective image quality rankings, and Kendall W was calculated to assess interrater agreement. Results Irrespective of radiation exposure level, CNR was higher in UHR datasets than in standard-resolution images postprocessed with the same reconstruction parameters. The use of sharper convolution kernels entailed lower CNR but higher subjective image quality depending on radiation dose. Subjective assessment revealed high interrater agreement (W = 0.86; P &lt; 0.001) with UHR images being preferred by readers in the majority of comparisons on each dose level. Substantial correlation was ascertained between CNR and the subjective image quality ranking (all r's ≥ 0.95; P &lt; 0.001) Conclusions In PCCT of the lumbar spine, UHR mode's smaller pixel size facilitates a considerable CNR increase over standard-resolution imaging, which can either be used for dose reduction or higher spatial resolution depending on the selected convolution kernel.

Comparison of image quality, contrast administration, and radiation doses in pediatric abdominal dual-layer detector dual-energy CT using propensity score matching analysis

PurposeTo compare the image quality, contrast administration, and radiation dose between single-energy CT (SECT) and dual-energy CT (DECT) in pediatric patients. MethodsFrom March to December 2021, children who underwent abdominal SECT or DECT were retrospectively included in this study. The DECT group received 10–30 % less contrast than the routine dose. CT images were obtained at hepatic venous phase using a routine reconstruction method (iDose4). DECT scans were additionally reconstructed with a virtual monoenergetic image (VMI) at 40 and 65 keV. Quantitative image evaluations compared the contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) of the liver, portal vein, and pancreas. Qualitative analysis assessed degree of contrast enhancement, lesion or organ conspicuity, image noise, artificiality, and overall image quality. ResultsAmong 318 patients, 112 (median age, 16 years; 56 in each group) were included after propensity score matching. Compared with the SECT group, DECT group with iDose4 demonstrated lower CNRs and SNRs, while VMI at 40 or 65 keV showed no significant difference. In qualitative analysis, iDose4 produced higher scores on artificiality, and VMI at 40 keV demonstrated superior contrast enhancement and lesion conspicuity in the DECT group. Overall image quality was higher with VMI 65 keV among the DECT patients, and there was no significant difference compared to SECT. The volume CT dose index (CTDIvol) did not differ significantly between the two groups (median, 2.8 mGy vs. 2.9 mGy; p = 0.802). The injected contrast volume was reduced by 10 % in the DECT group. ConclusionPediatric abdominal DECT with reduced contrast administration showed no significant differences in image quality and radiation dose compared to SECT.

Optimal conspicuity of pancreatic ductal adenocarcinoma in virtual monochromatic imaging reconstructions on a photon-counting detector CT: comparison to conventional MDCT

PurposeTo analyze the conspicuity of pancreatic ductal adenocarcinoma (PDAC) in virtual monoenergetic images (VMI) on a novel photon-counting detector CT (PCD-CT) in comparison to energy-integrating CT (EID-CT).MethodsInclusion criteria comprised initial diagnosis of PDAC (reference standard: histopathological analysis) and standardized contrast-enhanced CT imaging either on an EID-CT or a PCD-CT. Patients were excluded due to different histopathological diagnosis or missing tumor delineation on CT. On the PCD-CT, 40–190 keV VMI reconstructions were generated. Image noise, tumor-to-pancreas ratio (TPR) and contrast-to-noise ratio (CNR) were analyzed by ROI-based measurements in arterial and portal venous contrast phase. Two board-certified radiologist evaluated image quality and tumor delineation at both, EID-CT and PCD-CT (40 and 70 keV).ResultsThirty-eight patients (mean age 70.4 years ± 10.3 [range 45–91], 27 males; PCD-CT: n=19, EID-CT: n=19) were retrospectively included. On the PCD-CT, tumor conspicuity (reflected by low TPR and high CNR) was significantly improved at low-energy VMI series (≤ 70 keV compared to > 70 keV), both in arterial and in portal venous contrast phase (P < 0.001), reaching the maximum at 40 keV. Comparison between PCD-CT and EID-CT showed significantly higher CNR on the PCD-CT in portal venous contrast phase at < 70 keV (P < 0.016). On the PCD-CT, tumor conspicuity was improved in portal venous contrast phase compared to arterial contrast phase especially at the lower end of the VMI spectrum (≤ 70 keV). Qualitative analysis revealed that tumor delineation is improved in 40 keV reconstructions compared to 70 keV reconstructions on a PCD-CT.ConclusionPCD-CT VMI reconstructions (≤ 70 keV) showed significantly improved conspicuity of PDAC in quantitative and qualitative analysis in both, arterial and portal venous contrast phase, compared to EID-CT, which may be important for early detection of tumor tissue in clinical routine. Tumor delineation was superior in portal venous contrast phase compared to arterial contrast phase.

Clinical feasibility of CS-VIBE accelerates MRI techniques in diagnosing intracranial metastasis

Our objective was to evaluate and compare the diagnostic performance of post-contrast 3D compressed-sensing volume-interpolated breath-hold examination (CS-VIBE) and 3D T1 magnetization-prepared rapid-acquisition gradient-echo (MPRAGE) in detecting intracranial metastasis. Additionally, we analyzed and compared the image quality between the two. We enrolled 164 cancer patients who underwent contrast-enhanced brain MRI. Two neuroradiologists independently reviewed all the images. The signal-to-noise ratio (SNR), contrast-to noise ratio (CNR) were compared between two sequences. For patients with intracranial metastasis, we measured enhancement degree and CNRlesion/parenchyma of the lesion. The overall image quality, motion artifact, gray-white matter discrimination and enhancing lesion conspicuity were analyzed. Both MPRAGE and CS-VIBE showed similar performance in diagnosing intracranial metastasis. Overall image quality of CS-VIBE was better with less motion artifact; however conventional MPRAGE was superior in enhancing lesion conspicuity. Overall, the SNR and CNR of conventional MPRAGE were higher than those of CS-VIBE. For 30 enhancing intracranial metastatic lesions, MPRAGE showed a lower CNR (p = 0.02) and contrast ratio (p = 0.03). MPRAGE and CS-VIBE were preferred in 11.6 and 13.4% of cases, respectively. In comparison with conventional MPRAGE, CS-VIBE achieved comparable image quality and visualization, with the scan time being half of that of MPRAGE.

The effect of different spatial resolutions and enhancement filters on radiographic detection of simulated furcation defects with intraoral digital radiography

The objective of this study was to evaluate the effects of spatial resolution and the application of enhancement filters in the diagnosis of simulated furcation defects and image quality with intraoral radiographs. Periapical images were acquired with photostimulable phosphor plates of molars in dry skulls (n=8) and mandibles (n=10) with 4 stages of furcation defect simulation. The plates were scanned in fast scan and high resolution modes to produce different spatial resolutions. Four image filters were applied. Six observers scored the detection of furcation defects. Mean values of area under the curve in receiver operating characteristic evaluation, accuracy, sensitivity, and specificity were calculated. Mean gray value (brightness), noise, and contrast-to-noise ratio (CNR) were calculated for enamel, dentin, and alveolar bone to evaluate image quality. Analysis of variance compared the values between the different spatial resolutions and filters. The significance of difference was established at P < .05. There were no significant differences in overall diagnostic values comparing image spatial resolutions and filters. Diagnostic outcomes were significantly better for the largest defects than the smallest lesions but were generally poor in detecting lesions. All structures showed greater brightness in high resolution. . Noise was greater in all structures with all enhancement filters except inversion. Dentin and alveolar bone exhibited more noise and lower CNR in high resolution. Varying spatial resolution and applying enhancement filters did not significantly affect the diagnosis of furcation defects.

Deep Learning Neural Network Performance on NDT Digital X-ray Radiography Images: Analyzing the Impact of Image Quality Parameters-An Experimental Study.

In response to the growing inspection demand exerted by process automation in component manufacturing, non-destructive testing (NDT) continues to explore automated approaches that utilize deep-learning algorithms for defect identification, including within digital X-ray radiography images. This necessitates a thorough understanding of the implication of image quality parameters on the performance of these deep-learning models. This study investigated the influence of two image-quality parameters, namely signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), on the performance of a U-net deep-learning semantic segmentation model. Input images were acquired with varying combinations of exposure factors, such as kilovoltage, milli-ampere, and exposure time, which altered the resultant radiographic image quality. The data were sorted into five different datasets according to their measured SNR and CNR values. The deep-learning model was trained five distinct times, utilizing a unique dataset for each training session. Training the model with high CNR values yielded an intersection-over-union (IoU) metric of 0.9594 on test data of the same category but dropped to 0.5875 when tested on lower CNR test data. The result of this study emphasizes the importance of achieving a balance in training dataset according to the investigated quality parameters in order to enhance the performance of deep-learning segmentation models for NDT digital X-ray radiography applications.

Low dose of contrast agent and low radiation liver computed tomography with deep-learning-based contrast boosting model in participants at high-risk for hepatocellular carcinoma: prospective, randomized, double-blind study.

To investigate the image quality and lesion conspicuity of a deep-learning-based contrast-boosting (DL-CB) algorithm on double-low-dose (DLD) CT of simultaneous reduction of radiation and contrast doses in participants at high-risk for hepatocellular carcinoma (HCC). Participants were recruited and underwent four-phase dynamic CT (NCT04722120). They were randomly assigned to either standard-dose (SD) or DLD protocol. All CT images were initially reconstructed using iterative reconstruction, and the images of the DLD protocol were further processed using the DL-CB algorithm (DLD-DL). The primary endpoint was the contrast-to-noise ratio (CNR), the secondary endpoint was qualitative image quality (noise, hepatic lesion, and vessel conspicuity), and the tertiary endpoint was lesion detection rate. The t-test or repeated measures analysis of variance was used for analysis. Sixty-eight participants with 57 focal liver lesions were enrolled (20 with HCC and 37 with benign findings). The DLD protocol had a 19.8% lower radiation dose (DLP, 855.1 ± 254.8mGy·cm vs. 713.3 ± 94.6mGy·cm, p = .003) and 27% lower contrast dose (106.9 ± 15.0mL vs. 77.9 ± 9.4mL, p < .001) than the SD protocol. The comparative analysis demonstrated that CNR (p < .001) and portal vein conspicuity (p = .002) were significantly higher in the DLD-DL than in the SD protocol. There was no significant difference in lesion detection rate for all lesions (82.7% vs. 73.3%, p = .140) and HCCs (75.7% vs. 70.4%, p = .644) between the SD protocol and DLD-DL. DL-CB on double-low-dose CT provided improved CNR of the aorta and portal vein without significant impairment of the detection rate of HCC compared to the standard-dose acquisition, even in participants at high risk for HCC. • Deep-learning-based contrast-boosting algorithm on double-low-dose CT provided an improved contrast-to-noise ratio compared to standard-dose CT. • The detection rate of focal liver lesions was not significantly differed between standard-dose CT and a deep-learning-based contrast-boosting algorithm on double-low-dose CT. • Double-low-dose CT without a deep-learning algorithm presented lower CNR and worse image quality.

Does the size of an object containing dental implant affect the expression of artifacts in cone beam computed tomography imaging?

BackgroundArtifacts fault image quality but handling several factors can affect it. This study was conducted to investigate the effect of object size on artifacts in cone-beam computed tomography systems.MethodsFive phantoms, each containing a titanium implant in a sheep bone block, were fabricated of various sizes ranging from XS to XL: The M phantom was the same size as the device’s field of view (FOV). The L and XL phantoms were 20 and 40% larger than the FOV while the S and XS phantoms were 20 and 40% smaller than FOV, respectively. Ballistic gelatin was used to fill the phantoms. Phantoms were scanned by NewTom VGI and HDXWill Q-FACE. The mean and standard deviation (SD) of gray values in each 120 ROI was obtained by OnDemand software. The contrast to noise ratio (CNR) was also calculated.ResultsThe gray value in S and M phantoms were more homogenous. The lowest SD value (10.20) was found in S phantom. The highest value for SD (125.16) was observed in XL phantom. The lowest (4.47) and highest (9.92) CNR were obtained in XL and S phantoms, respectively. HDXWill Q-FACE recorded a higher SD and a lower CNR than NewTom VGI (P < 0.05).ConclusionObject dimensions of the FOV size or up to 20% smaller provided better image quality. Since the dimensions of soft tissue in most patients are larger than the selective FOV, it is recommended that in CBCT artifacts studies, an object with dimensions closer to the patient’s dimensions be used to better relate the results with the clinical condition, because the sample dimensions affect the amount of artifacts.

Image reconstruction using small-voxel size improves small lesion detection for positron emission tomography.

BackgroundPET/CT imaging is widely used in oncology and provides both metabolic and anatomic information. Because of the relatively poor spatial resolution of PET, the detection of small lesions is limited. The low spatial resolution introduces the partial-volume effect (PVE) which negatively affects images both qualitatively and quantitatively. The aim of the study was to investigate the effect of small-voxel (2 mm in-line pixel size) vs. standard-voxel (4 mm in-line pixel size) reconstruction on lesion detection and image quality in a range of activity ratios.Materials and methodsThe National Electrical Manufacturers Association (NEMA) body phantom and the Micro Hollow-Sphere phantom spheres were filled with a solution of [18F]fluorodeoxyglucose ([18F]FDG) in sphere-to-background ratios of 2:1, 3:1, 4:1 and 8:1. In all images reconstructed with 2 mm and 4 mm in-line pixel size the visual lesion delineation, contrast recovery coefficient (CRC) and contrast-to-noise ratio (CNR) were evaluated.ResultsFor smaller (≤ 13 mm) phantom spheres, significantly higher CRC and CNR using small-voxel reconstructions were found, also improving visual lesion delineation. CRC did not differ significantly for larger (≥ 17 mm) spheres using 2 mm and 4 mm in-line pixel size, but CNR was significantly lower; however, lower CNR did not affect visual lesion delineation.ConclusionsSmall-voxel reconstruction consistently improves precise small lesion delineation, lesion contrast and image quality.

CONTRAST-TO-NOISE RATIO IS A USEFUL PREDICTOR OF EARLY DISPLACEMENT OF LARGE SUBMACULAR HEMORRHAGE BY INTRAVITREAL SF6 GAS INJECTION.

To investigate predictors of early displacement of submacular hemorrhage (SMH) by simple intravitreal SF6 gas injection. This retrospective study included 16 eyes of 16 consecutive patients (age: 74.5 ± 7.7 years; 15 men) with large SMH treated with simple intravitreal SF6 gas before inception of subretinal tissue plasminogen activator injection at our institution. The SMH displacement was graded at 1-week posttreatment as 0, 1, or 2. Central retinal thickness, central choroidal thickness, SMH height, SMH area, disease duration, use of anticoagulant or antiplatelet drugs, and contrast-to-noise ratio (CNR) of SMH on optical coherence tomography images were recorded. Correlations between displacement grading and baseline parameter were analyzed. Univariable correlation analysis revealed association of the 1-week displacement grading with the CNR (P = 0.004; r = -0.68) and SMH height (P = 0.03; r = -0.55). The CNR was most strongly associated with 1-week displacement on multivariable correlation analysis (P = 0.01; β = -0.60). Findings of the present study showed that the CNR of SMH was a useful predictor of early displacement of large SMH after simple intravitreal SF6 gas injection. When vitrectomy with subretinal injection of tissue plasminogen activator is difficult in patients with large SMH, with low CNR on optical coherence tomography, simple intravitreal SF6 gas injection may be a treatment option.

Image quality of dual-energy cone-beam CT with total nuclear variation regularization

Despite the improvements in image quality of cone beam computed tomography (CBCT) scans, application remains limited to patient positioning. In this study, we propose to improve image quality by dual energy (DE) imaging and iterative reconstruction using least squares fitting with total variation (TV) regularization. The generalization of TV called total nuclear variation (TNV) was used to generate DE images. We acquired single energy (SE) and DE scans of an image quality phantom (IQP) and of an anthropomorphic human male phantom (HMP). The DE scans were dual arc acquisitions of 70 kV and 130 kV with a variable dose partitioning between low energy (LE) and high energy (HE) arcs. To investigate potential benefits from a larger spectral separation between LE and HE, DE scans with an additional 2 mm copper beam filtration in the HE arc were acquired for the IQP. The DE TNV scans were compared to SE scans reconstructed with FDK and iterative TV with varying parameters. The contrast-to-noise ratio (CNR), spatial frequency, and structural similarity (SSIM) were used as image quality metrics. Results showed largely improved image quality for DE TNV over FDK for both phantoms. DE TNV with the highest dose allocation in the LE arm yielded the highest CNR. Compared to SE TV, these DE TNV results had a slightly lower CNR with similar spatial resolution for the IQP. A decrease in the dose allocated to the LE arm improved the spatial resolution with a trade-off against CNR. For the HMP, DE TNV displayed a lower CNR and/or lower spatial resolution depending on the reconstruction parameters. Regarding the SSIM, DE TNV was superior to FDK and SE TV for both phantoms. The additional beam filtration for the IQP led to improved image quality in all metrics, surpassing the SE TV results in CNR and spatial resolution.

Quantitative analysis of repaired rabbit supraspinatus tendons (± channeling) using magnetic resonance imaging at 7 Tesla.

The quantitative assessment of supraspinatus tendons by conventional magnetic resonance is limited by low contrast-to-noise ratio (CNR). Magnetic resonance imaging (MRI) scanners operating at 7 Tesla offer high signal-to noise ratio (SNR), low CNR and high spatial resolution that are well-suited for rapidly relaxing tissues like tendons. Few studies have applied T2 and T2* mapping to musculoskeletal imaging and to the rotator cuff tendons. Our objective was to analyze the T2 and T2* relaxation times from surgically repaired supraspinatus tendons and the effect of bone channeling. One supraspinatus tendon of 112 adult female New Zealand white rabbits was surgically detached and repaired one week later. Rabbits were randomly assigned to channeling (n=64) or control (n=48) groups and harvested at 0, 1, 2, and 4 weeks. A 7T magnet was used for signal acquisition. For T2 mapping, a sagittal multi slice 2D multi-echo spin-echo (MESE) CPMG sequence with fat saturation was applied and T2* mapping was performed using a 3D UTE sequence. Magnetic resonance images from supraspinatus tendons were analyzed by two raters. Three regions of interest were manually drawn on the first T2-weighted dataset. For T2 and T2*, different ROI masks were generated to obtain relaxation times. T2-weighted maps but not T2*-weighted maps generated reliable signals for relaxation time measurement. Torn supraspinatus tendons had lower T2 than controls at the time of repair (20.0±3.4 vs. 25.6±3.9 ms; P<0.05). T2 increased at 1, 2 and 4 postoperative weeks: 22.7±3.1, 23.3±3.9 and 24.0±5.1 ms, respectively, and values were significantly different from contralateral supraspinatus tendons (24.8±3.1; 26.8±4.3 and 26.5±3.6 ms; all P<0.05). Bone channeling did not affect T2 (P>0.05). Supraspinatus tendons detached for 1 week had shorter T2 relaxation time compared to contralateral as measured with 7T MRI.

Effective dose and image quality for intraoperative imaging with a cone-beam CT and a mobile multi-slice CT in spinal surgery: A phantom study.

To compare the effective dose (ED) and image quality (IQ) of O-arm cone-beam CT (Medtronic, Minneapolis, MN, USA) and Airo multi-slice CT (Brainlab AG, Munich, Germany) for intraoperative-CT (i-CT) in spinal surgery. The manufacturer-defined protocols available in the O-arm and Airo systems for three-dimensional lumbar spine imaging were compared. Organ dose was measured both with thermo-luminescent dosimeters and GafChromic films in the Alderson RadiationTherapy anthropomorphic phantom. A subjective analysis was performed by neurosurgeons to compare the clinical IQ of the anthropomorphic phantom images acquired with the different i-CT systems and imaging protocols. Image uniformity, noise, contrast-to-noise-ratio (CNR), and spatial resolution were additionally assessed with the Catphan 504 phantom. O-arm i-CT caused 56% larger ED than Airo due to the high definition (HD) imaging protocol. The noise was larger for O-arm images leading to a lower CNR than that measured for Airo. Moreover, scattering and beam hardening effects were observed in the O-arm images. Better spatial resolution was measured for the O-arm system (9 lp/cm) than for Airo (4 lp/cm). For all the investigated protocols, O-arm was found to be better for identifying anatomical features important for accurate pedicle screw positioning. According to phantom measurements, the HD protocol of O-arm offered better clinical IQ than Airo but larger ED. The larger noise of O-arm images did not compromise the clinical IQ while the superior spatial resolution of this system allowed a better visibility of anatomical features important for pedicle screw positioning in the lumbar region.

Noise dependency in vascular parameters from combined gradient-echo and spin-echo DSC MRI

Dynamic susceptibility contrast (DSC) imaging is a widely used technique for assessment of cerebral blood volume (CBV). With combined gradient-echo and spin-echo DSC techniques, measures of the underlying vessel size and vessel architecture can be obtained from the vessel size index (VSI) and vortex area, respectively. However, how noise, and specifically the contrast-to-noise ratio (CNR), affect the estimations of these parameters has largely been overlooked. In order to address this issue, we have performed simulations to generate DSC signals with varying levels of CNR, defined by the peak of relaxation rate curve divided by the standard deviation of the baseline. Moreover, DSC data from 59 brain cancer patients were acquired at two different 3 T-scanners (N = 29 and N = 30, respectively), where CNR and relative parameter maps were obtained. Our simulations showed that the measured parameters were affected by CNR in different ways, where low CNR led to overestimations of CBV and underestimations of VSI and vortex area. In addition, a higher noise-sensitivity was found in vortex area than in CBV and VSI. Results from clinical data were consistent with simulations, and indicated that CNR < 4 gives highly unreliable measurements. Moreover, we have shown that the distribution of values in the tumour regions could change considerably when voxels with CNR below a given cut off are excluded when generating the relative parameter maps. The widespread use of CBV and attractive potential of VSI and vortex area, makes the noise-sensitivity of these parameters found in our study relevant for further use and development of the DSC imaging technique. Our results suggest that the CNR has considerable impact on the measured parameters, with the potential to affect the clinical interpretation of DSC-MRI, and should therefore be taken into account in the clinical decision-making process.

Effect of miscentering and low-dose protocols on contrast resolution in computed tomography head examination

BackgroundUnoptimized protocols, including a miscentered position, might affect the outcome of diagnostic in CT examinations. In this study, we investigate the effects of miscentering position during CT head examination on the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). MethodWe simulate the CT head examination using a water phantom with a standard protocol (120 kVp/180 mAs) and a low dose protocol (100 kVp/142 mAs). The table height was adjusted to simulate miscentering by 5 cm from the isocenter, where the height was miscentered superiorly (MCS) at 109, 114, 119, and 124 cm, and miscentered inferiorly (MCI) at 99, 94, 89, and 84 cm. Seven circular regions of interest were used, with one drawn at the center, four at the peripheral area of the phantom, and two at the background area of the image. ResultsFor the standard protocol, the mean CNR decreased uniformly as table height increased and significantly differed (p < 0.05) at +20 cm for MCS (435.70 ± 9.39) and −20 cm for MCI (438.91 ± 10.94) from the isocenter. Similarly, significant reductions (p < 0.05) were also noted for SNR for MCS (at +20 cm) and MCI (at −20 cm). For the low dose protocol, both CNR and SNR were significantly reduced (p < 0.05) at table heights of +20 and −20 cm from the isocenter. ConclusionMiscentering is proven to significantly affect the image quality in both low and standard dose protocols for head CT procedure. This study implies that accurate patient centering is one of the approaches that can improve CT optimization practice.

Estimation of pharmacokinetic parameters from DCE-MRI by extracting long and short time-dependent features using an LSTM network.

T1 -weighted dynamic contrast-enhanced Magnetic Resonance Imaging (DCE-MRI) is typically quantified by least squares (LS) fitting to a pharmacokinetic (PK) model to yield parameters of microvasculature and perfusion in normal and disease tissues. Such fitting is both time-consuming as well as subject to inaccuracy and instability in parameter estimates. Here, we propose a novel neural network approach to estimate the PK parameters by extracting long and short time-dependent features in DCE-MRI. A Long Short-Term Memory (LSTM) network, widely used for processing sequence data, was employed to map DCE-MRI time-series accompanied with an arterial input function to parameters of the extended Tofts model. Head and neck DCE-MRI from 103 patients were used for training and testing the LSTM model. Arterial input functions (AIFs) from 78 patients were used to generate synthetic DCE-MRI time-series for training, during which data augmentation was used to overcome the limited size of in vivo data. The model was tested on independent synthesized DCE data using AIFs from 25 patients. The LSTM performance was optimized for the numbers of layers and hidden state features. The performance of the LSTM was tested for different temporal resolution, total acquisition time, and contrast-to-noise ratio (CNR), and compared to the conventional LS fitting and a CNN-based method. Compared to LS fitting, the LSTM model had comparable accuracy in PK parameter estimations from fully temporal-sampled DCE-MRI data (~3s per frame), but much better accuracy for the data with temporally subsampling (4s or greater per frame), total acquisition time truncation by 48%-16%, or low CNR (5 and 10). The LSTM reduced normalized root mean squared error by 40.4%, 46.9%, and 53.0% for sampling intervals of 4s, 5s, and 6s, respectively, compared to LS fitting. Compared to the CNN model, the LSTM model reduced the error in the parameter estimates up to 55.2%. Also, the LSTM improved the inference time by~14 times on CPU compared to LS fitting. Our study suggests that the LSTM model could achieve improved robustness and computation speed for PK parameter estimation compared to LS fitting and the CNN based network, particularly for suboptimal data.

Atom column detection from simultaneously acquired ABF and ADF STEM images

In electron microscopy, the maximum a posteriori (MAP) probability rule has been introduced as a tool to determine the most probable atomic structure from high-resolution annular dark-field (ADF) scanning transmission electron microscopy (STEM) images exhibiting low contrast-to-noise ratio (CNR). Besides ADF imaging, STEM can also be applied in the annular bright-field (ABF) regime. The ABF STEM mode allows to directly visualize light-element atomic columns in the presence of heavy columns. Typically, light-element nanomaterials are sensitive to the electron beam, limiting the incoming electron dose in order to avoid beam damage and leading to images exhibiting low CNR. Therefore, it is of interest to apply the MAP probability rule not only to ADF STEM images, but to ABF STEM images as well. In this work, the methodology of the MAP rule, which combines statistical parameter estimation theory and model-order selection, is extended to be applied to simultaneously acquired ABF and ADF STEM images. For this, an extension of the commonly used parametric models in STEM is proposed. Hereby, the effect of specimen tilt has been taken into account, since small tilts from the crystal zone axis affect, especially, ABF STEM intensities. Using simulations as well as experimental data, it is shown that the proposed methodology can be successfully used to detect light elements in the presence of heavy elements.