[Accuracy Verification for Slice Sensitivity Profile Measurement Method by Averaging the Multiple Tilted Wires in Computed Tomography Image]

PURPOSE

Several studies present the unsuitability of the tilted-wire method for slice sensitivity profile (SSP) in helical scan. We compared the accuracy for SSP by the tilted-wire averaging method using multiple wire profiles and by the conventional micro-coin method.

METHODS

A micro-coin phantom positioned at the center or the off-center was scanned using a 64-detector row CT scanner in different positions where an X-ray tube starts scanning. In the same way, tilted-wire averaging phantoms, approximately 70 mm in diameter, in the shape of a donut, 8 wires tilted from the circumference toward the center, were scanned. Images were reconstructed with a slice thickness of 0.5 mm.

RESULTS

The relative errors of full width at half maximum (FWHM) by the tilted-wire averaging method were -0.015 mm to -0.004 mm (-1.98% to -0.56%) at the center compared to those by the micro-coin method, and it is almost the same value regardless of the number of wires. Relative errors were 0.001 mm to 0.029 mm (0.11% to 3.74%) at the upper 8 cm from the center, and 0.014 mm to 0.078 mm (1.86% to 10.25%) at the upper 16 cm, and the value of relative errors increased as it got farther from the center and as the number of wires went fewer.

CONCLUSION

This study indicated that accurate measurement of SSP may be achieved by using 4 (arranged every 90 degrees) or more averaging wires.

A challenge and solution for automatic thin slice thickness measurements on images of the Catphan phantom

Purpose. The use of the Hough transform for angle detection is quite accurate for relatively wide slice thickness. However, the Hough transform fails to accurately detect the angle for thin slice thickness. This study proposes a method for automatically measuring the thickness of thin slices on images of a Catphan phantom.Methods. In the proposed method, the angle of the phantom's orientation was determined based on the relative coordinates of the four hole objects in the phantom. After the angles of the wires were determined, the profiles of pixel values across the wire objects were constructed. Finally, their full widths at half maximum (FWHMs) were determined and multiplied bytan23° to obtain the slice thicknesses of the images. The results of the proposed method were compared to a previous method, which used the Hough transform to obtain the phantom's orientation. We used slice thicknesses ranging from 0.8 mm to 5.0 mm, and phantom angles from 0° to 10°.Results. Our proposed method detected the angle of the phantom accurately for thin slices, whereas a previous method did not accurately detect the angle. The results of the slice thickness using this current method were slightly higher (within 7.9%) compared to the previous method. However, the results of the two methods did not differ significantly (p-value > 0.05). Using different angles, the current method detected all the angles more accurately. Again, the slice thicknesses were not significantly different from the previous method (p-value > 0.05).Conclusion. The proposed method for measuring the thickness of thin slices in an image of a Catphan phantom, based on the relative coordinates of the four hole objects in the phantom, outperformed a previous method based on the Hough transform.

Investigating the slice thickness effect on noise and diagnostic content of single-source multi-slice computerized axial tomography

High-quality and detailed CT scan images are crucial for accurate diagnosis. Factors such as image noise and slice thickness affect image quality. This study aimed to determine the optimal slice thickness that minimized image noise while maintaining sufficient diagnostic information using the single-source computed tomography head protocol. Single-source CT images were examined using the Linux Operating system Ge Revolution 64-slice CT scanner, and a combination of statical analysis and DICOM CT image analysis was employed. The single-source energy head CT protocol was used to investigate the effect of slice thickness on noise and visibility in images. Different values of slice thickness 0.625, 1.25, 2.5, 3.75, 5, 7.5, and 10 were prepared, and then quantitative analysis was performed. Thinner slice thickness decreased image noise, increased visibility, and improved detection. Therefore, the balance between changing the thickness of the slice with the diagnostic content and image noise must be considered. Maximum slice thickness enhances CT image detail and structure despite more noise. Based on the results, a slice thickness of 1.25mm was identified as the optimal choice for reducing image noise and achieving better and more accurate detection using the single-source computed tomography head protocol. The study revealed that image noise tends to increase with greater slice thickness according to the Linux operating system. These findings can serve as a valuable guide for quality control methods in CT centers, emphasizing the need to determine the appropriate slice thickness to ensure an accurate diagnosis.

Automatic slice thickness measurement on three types of Catphan CT phantoms

OBJECTIVE

To develop an algorithm to measure slice thickness to run on three types of Catphan phantoms with the ability to adapt to any misalignment and rotation of the phantoms.&#xD;Method: Images of Catphan 500, 504, and 604 phantoms were examined. In addition, images with various slice thicknesses ranging from 1.5 to 10.0 mm, distance to the iso-center and phantom rotations were also examined. The automatic slice thickness algorithm was carried out by processing only objects within a circle having a diameter half that of the phantom diameter. A segmentation was performed within an inner circle with dynamic thresholds to produce binary images with wire and bead objects. Region properties were used to distinguish wire ramps and bead objects. At each identified wire ramp, the angle was detected using the Hough transform. Profile lines were then placed on each ramp based on the centroid coordinates and detected angles, and the full-width at half maximum (FWHM) determined for the average pixel profile. The slice thickness was obtained by multiplying the FWHM by the tangent of the ramp angle (23o). &#xD;Results: Automatic measurements work well and have only a small difference (<0.5 mm) from manual measurements. For variations of slice thickness, automatic measurement successfully performs segmentation and correctly locates the profile line on all wire ramps. The results show values that are close (< 3mm) to the nominal thickness on thin slices, but less close for thicker slices. There is a strong correlation (R2 = 0.873) between automatic and manual measurements. Testing the algorithm at various distances from the iso-center and phantom rotation angle also produced accurate results. &#xD;Conclusion: An automated algorithm for measuring slice thickness on three types of Catphan CT phantom images has been developed. The algorithm works well on various thicknesses, distances from the iso-center, and phantom rotations.

Automatic measurement of slice thickness in CT images of a siemens phantom

This study aims to develop a program in Python language for automatic measurement of slice thickness in computed tomography (CT) images of a Siemens phantom with different values of slice thickness, field of view (FOV), and pitch. ASiemens phantom was scanned using a Siemens 64-slice CT scanner with various slice thicknesses (i.e. 2, 4, 6, 8, and 10 mm), FOVs (i.e. 220, 260, and 300 mm), and pitch (i.e. 0.7, 0.9, and 1). Automatic measurement of slice thickness was performed by segmenting the ramp insert in the image and detecting angles of the ramp insert using the Hough transform. The resulting angles were subsequently used to rotate the image. Profiles of pixel along the ramp insert were made from the rotated images,and theslice thickness was calculated by determining the full-width at half maximum (FWHM) of the profiles. The product of the FWHM in pixels and the pixel size was corrected by the tangent of the ramp insert (i.e., 23o) to obtain the measured slice thickness. The results of the automatic measurements were compared with manual measurements carried out using a MicroDicom Viewer. The differences between the automatic and manual measurements at all slice thicknesses were less than 0.30 mm. The automatic and manual measurements had high linear correlations. For variations of the FOV and pitch, the differences between the automatic and manual measurement were less than 0.16 mm. The automatic and manual measurements were not significantly different (p-value > 0.05).&#xD.

Ultrahigh-Resolution Photon-Counting Detector CT of the Lungs: Association of Reconstruction Kernel and Slice Thickness With Image Quality

BACKGROUND. Prior work has shown improved image quality for photon-counting detector (PCD) CT of the lungs compared with energy-integrating detector CT. A paucity of the literature has compared PCD CT of the lungs using different reconstruction parameters. OBJECTIVE. The purpose of this study is to the compare the image quality of ultra-high-resolution (UHR) PCD CT image sets of the lungs that were reconstructed using different kernels and slice thicknesses. METHODS. This retrospective study included 29 patients (17 women and 12 men; median age, 56 years) who underwent noncontrast chest CT from February 15, 2022, to March 15, 2022, by use of a commercially available PCD CT scanner. All acquisitions used UHR mode (1024 × 1024 matrix). Nine image sets were reconstructed for all combinations of three sharp kernels (BI56, BI60, and BI64) and three slice thicknesses (0.2, 0.4, and 1.0 mm). Three radiologists independently reviewed reconstructions for measures of visualization of pulmonary anatomic structures and pathologies; reader assessments were pooled. Reconstructions were compared with the clinical reference reconstruction (obtained using the BI64 kernel and a 1.0-mm slice thickness [BI64_1.0-mm]). RESULTS. The median difference in the number of bronchial divisions identified versus the clinical reference reconstruction was higher for reconstructions with BI64_0.4-mm (0.5), BI60_0.4-mm (0.3), BI64_0.2-mm (0.5), and BI60_0.2-mm (0.2) (all p < .05). The median bronchial wall sharpness versus the clinical reference reconstruction was higher for reconstructions with BI64_0.4-mm (0.3) and BI64_0.2-mm (0.3) and was lower for BI56_1.0-mm (-0.7) and BI56_0.4-mm (-0.3) (all p < .05). Median pulmonary fissure sharpness versus the clinical reference reconstruction was higher for reconstructions with BI64_0.4-mm (0.3), BI60_0.4-mm (0.3), BI56_0.4-mm (0.5), BI64_0.2-mm (0.5), BI60_0.2-mm (0.5), and BI56_0.2-mm (0.3) (all p < .05). Median pulmonary vessel sharpness versus the clinical reference reconstruction was lower for reconstructions with BI56_1.0-mm (-0.3), BI60 0.4-mm (-0.3), BI56_0.4-mm (-0.7), BI64_0.2-mm (-0.7), BI60_0.2-mm (-0.7), and BI56_0.2-mm (-0.7). Median lung nodule conspicuity versus the clinical reference reconstruction was lower for reconstructions with BI56_1.0-mm (-0.3) and BI56_0.4-mm (-0.3) (both p < .05). Median conspicuity of all other pathologies versus the clinical reference reconstruction was lower for reconstructions with BI561.0 mm (-0.3), BI56_0.4-mm (-0.3), BI64_0.2-mm (-0.3), BI60_0.2-mm (-0.3), and BI56_0.2-mm (-0.3). Other comparisons among reconstructions were not significant (all p > .05). CONCLUSION. Only the reconstruction using BI64_0.4-mm yielded improved bronchial division identification and bronchial wall and pulmonary fissure sharpness without a loss in pulmonary vessel sharpness or conspicuity of nodules or other pathologies. CLINICAL IMPACT. The findings of this study may guide protocol optimization for UHR PCD CT of the lungs.

Effects of solar tunnel drying zones and slice thickness on the drying characteristics of taro (Colocasia esculenta (L.) Schott) slice

This study aims to investigate the effects of slice thicknesses (2, 4, and 6 mm) and solar tunnel drying zones (zone I, zone II, and zone III) on the drying characteristics and thermal properties of taro slices, which were dried using solar tunnel drying (STD). To assess the drying characteristics of taro slices, the data from the drying kinetics were fitted with five different models. The adequacy of fit for the proposed models was evaluated using the reduced chi-square (χ 2), determination of coefficient (R 2), mean relative percent error (P), and root means square error (RMSE). The results showed that, among the five drying models, the drying characteristics of taro are better expressed by the logarithmic model. The thinnest slices dried in zone III had the highest diffusivity (6.57 × 10-09 m2/s), lowest specific heat capacity (1.761 kJ/kg °C), and maximum thermal conductivity (0.268 W/m °C). It was also dried within a short period of time (5.5 h). The findings of this study provide evidence that STD zones and slice thickness have significant impact on the drying characteristics of dried taro slices.

Evaluating the effects of variation in CT scanning parameters on the image quality and Hounsfield units for optimization of dose in radiotherapy treatment planning: A semi-anthropomorphic thorax phantom study

Aim

The diagnosis accuracy of computed tomography (CT) systems and the reliability of calculated Hounsfield Units (HUs) are critical in tumor detection and cancer patients' treatment planning. This study evaluated the effects of scan parameters (Kilovoltage peak or kVp, milli-Ampere-second or mAS reconstruction kernels and algorithms, reconstruction field of view, and slice thickness) on image quality, HUs, and the calculated dose in the treatment planning system (TPS).

Materials and Methods

A quality dose verification phantom was scanned several times by a 16-slice Siemens CT scanner. The DOSIsoft ISO gray TPS was applied for dose calculations. The SPSS.24 software was used to analyze the results and the P-value <0.05 was considered significant.

Results

Reconstruction kernels and algorithms significantly affected noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). The noise increased and CNR decreased by raising the sharpness of reconstruction kernels. SNR and CNR had considerable increments at iterative reconstruction compared with the filtered back-projection algorithm. The noise decreased by raising mAS in soft tissues. Also, KVp had a significant effect on HUs. TPS--calculated dose variations were less than 2% for mediastinum and backbone and less than 8% for rib.

Conclusions

Although HU variation depends on image acquisition parameters across a clinically feasible range, its dosimetric impact on the calculated dose in TPS can be neglected. Hence, it can be concluded that the optimized values of scan parameters can be applied to obtain the maximum diagnostic accuracy and calculate HUs more precisely without affecting the calculated dose in the treatment planning of cancer patients.

Different CT slice thickness and contrast-enhancement phase in radiomics models on the differential performance of lung adenocarcinoma

Background

To investigate the effects of computed tomography (CT) reconstruction slice thickness and contrast‐enhancement phase on the differential diagnosis performance of radiomic signature in lung adenocarcinoma.

Methods

A total of 187 patients who had been pathologically confirmed with lung adenocarcinoma and nonadenocarcinoma were divided into a training cohort (n = 149) and validation cohort (n = 38). All the patients underwent contrast‐enhanced CT and the images were reconstructed with different slice thickness. The radiomic features were extracted from different slice thickness and scan phase. The logistic regression (LR) algorithm was used to build a machine learning model for each group. The area under the curve (AUC) obtained from the receiver operating characteristic (ROC) curve and DeLong test was used to evaluate its discriminating performance.

Results

Finally, 34 image features and five semantic features were selected to establish a radiomics model. Based on the three contrast‐enhanced CT phases and four reconstruction slice thickness, 12 groups of radiomics models showed good discrimination ability with the AUCs range from 0.9287 to 0.9631, sensitivity range from 0.8349 to 0.9083, specificity range from 0.825 to 0.925 in the training group. Similar results were observed in the validation group. However, there was no statistical significance between the different CT scan phase groups and different slice thickness (p > 0.05).

Conclusions

The radiomic analysis of contrast‐enhanced CT can be used for the differential diagnosis of lung adenocarcinoma. Moreover, different slice thickness and contrast‐enhanced scan phase did not affect the discriminating ability in the radiomics models.

CT reconstruction with thick slices not only underestimates lymph node size but also reduces data reproducibility in colorectal cancer

BACKGROUND

Reliable size measurement of lymph node (LN) metastases is important for the evaluation of cancer treatment. However, image analyses without proper settings may result in inappropriate diagnoses and staging.

PURPOSE

To investigate whether reconstruction slice thickness in computed tomography (CT) affects measurements of LN size and reproducibility.

MATERIAL AND METHODS

We analyzed 48 patients with histological diagnoses of sigmoid colon and rectal cancer who underwent contrast-enhanced CT colonography as part of a surgical treatment preparation. A board-certified radiologist selected 106 LNs whose short-axis diameter was ≥5 mm on 1-mm-thick images; the short-axis diameters were measured on 1- and 5-mm-thick images by the radiologist and residents and compared using Wilcoxon matched-pairs signed rank test. Data variation and reproducibility were evaluated using the F test and Bland-Altman analysis. P<0.05 was considered significant.

RESULTS

Short-axis diameters measured on 5-mm-thick images were significantly lower than those measured on 1-mm-thick images (P<0.01), even in the LNs whose short-axis diameters were over twice the slice thickness (P<0.05). Of the 106 LNs, 57 showed short-axis diameter <5 mm on 5-mm-thick images; the maximum short-axis diameter was 6.7 mm on a 1-mm thick image. Data variation was significantly larger on 5-mm thick images than 1-mm-thick images in small LNs (P<0.05) and reproducibility on 5-mm-thick images was inferior to that on 1-mm-thick images.

CONCLUSION

Thick reconstruction slices in CT can result in an underestimation of LN size and reduce data reproducibility. When measuring LN size, a thin reconstruction slice would be recommended based on targeted LN size.

Deep neural network-based approach to improving radiomics analysis reproducibility in liver cancer: effect on image resampling

Objectives.To test the effect of traditional up-sampling slice thickness (ST) methods on the reproducibility of CT radiomics features of liver tumors and investigate the improvement using a deep neural network (DNN) scheme.Methods.CT images with ≤ 1 mm ST in the public dataset were converted to low-resolution (3 mm, 5 mm) CT images. A DNN model was trained for the conversion from 3 mm ST and 5 mm ST to 1 mm ST and compared with conventional interpolation-based methods (cubic, linear, nearest) using structural similarity (SSIM) and peak-signal-to-noise-ratio (PSNR). Radiomics features were extracted from the tumor and tumor ring regions. The reproducibility of features from images converted using DNN and interpolation schemes were assessed using the concordance correlation coefficients (CCC) with the cutoff of 0.85. The paired t-test and Mann-Whitney U test were used to compare the evaluation metrics, where appropriate.Results.CT images of 108 patients were used for training (n = 63), validation (n = 11) and testing (n = 34). The DNN method showed significantly higher PSNR and SSIM values (p < 0.05) than interpolation-based methods. The DNN method also showed a significantly higher CCC value than interpolation-based methods. For features in the tumor region, compared with the cubic interpolation approach, the reproducible features increased from 393 (82%) to 422(88%) for the conversion of 3-1 mm, and from 305(64%) to 353(74%) for the conversion of 5-1 mm. For features in the tumor ring region, the improvement was from 395 (82%) to 431 (90%) and from 290 (60%) to 335 (70%), respectively.Conclusions.The DNN based ST up-sampling approach can improve the reproducibility of CT radiomics features in liver tumors, promoting the standardization of CT radiomics studies in liver cancer.

Determination of optimum pixel size and slice thickness for tractography and ulnar nerve diffusion tensor imaging at the cubital tunnel using 3T MRI

BACKGROUND

Small peripheral nerve tractography is challenging because of the trade-off among resolution, image acquisition time, and signal-to-noise ratio.

PURPOSE

To optimize pixel size and slice thickness parameters for fiber tractography and diffusion tensor imaging (DTI) of the ulnar nerve at the cubital tunnel using 3T magnetic resonance imaging (MRI).

MATERIAL AND METHODS

Fifteen healthy volunteers (mean age 30 ± 6.8 years) were recruited prospectively. Axial T2-weighted and DTI scans were acquired, covering the cubital tunnel, using different pixel sizes and slice thicknesses. Three-dimensional (3D) nerve tractography was evaluated for the median number and length of the reconstructed fiber tracts and visual score from 0 to 5. Two-dimensional (2D) cross-sectional DTI was evaluated for fractional anisotropy (FA) values throughout the length of the ulnar nerve.

RESULTS

A pixel size of 1.3 mm² revealed the highest number of reconstructed nerve fibers compared to that of 1.1 mm² (P = 0.048), with a good visual score. A slice thickness of 4 mm had the highest number of reconstructed nerve fibers and visual score compared with other thicknesses (all P < 0.05). In 2D cross-sectional images, the median FA values were in the range of 0.40-0.63 at the proximal, central, and distal portions of the cubital tunnel. Inter-observer agreement for all parameters was good to excellent.

CONCLUSION

For fiber tractography and DTI of the ulnar nerve at the cubital tunnel, optimal image quality was obtained using a 1.3-mm² pixel size and 4-mm slice thickness under MR parameters of this study at 3T.

High through-plane resolution CT imaging with self-supervised deep learning

CT images for radiotherapy planning are usually acquired in thick slices to reduce the imaging dose, especially for pediatric patients, and to lessen the need for contouring and treatment planning on more slices. However, low through-plane resolution may degrade the accuracy of dose calculations. In this paper, a self-supervised deep learning workflow is proposed to synthesize high through-plane resolution CT images by learning from their high in-plane resolution features. The proposed workflow was designed to facilitate neural networks to learn the mapping from low-resolution (LR) to high-resolution (HR) images in the axial plane. During the inference step, the HR sagittal and coronal images were generated by feeding two parallel-trained neural networks with the respective LR sagittal and coronal images to the trained neural networks. The CT simulation images of a cohort of 75 patients with head and neck cancer (1 mm slice thickness) and 200 CT images of a cohort of 20 lung cancer patients (3 mm slice thickness) were retrospectively investigated in a cross-validation manner. The HR images generated with the proposed method were qualitatively (visual quality, image intensity profiles and preliminary observer study) and quantitatively (mean absolute error, edge keeping index, structural similarity index measurement, information fidelity criterion and visual information fidelity in pixel domain) inspected, while taking the original CT images of the head and neck and lung cancer patients as the reference. The qualitative results showed the capability of the proposed method for generating high through-plane resolution CT images with data from both groups of cancer patients. All the improvements in the measure metrics were confirmed to be statistically significant with paired two-samplet-test analysis. The innovative point of the work is that the proposed deep learning workflow for CT image generation with high through-plane resolution in radiotherapy is self-supervised, meaning that it does not rely on ground truth CT images to train the network. In addition, the assumption that the in-plane HR information can supervise the through-plane HR generation is confirmed. We hope that this will inspire more research on this topic to further improve the through-plane resolution of medical images.

Automated procedure for slice thickness verification of computed tomography images: Variations of slice thickness, position from iso-center, and reconstruction filter

Purpose

The purpose of this study is to automate the slice thickness verification on the AAPM CT performance phantom and validate it for variations of slice thickness, position from iso‐center, and reconstruction filter.

Methods

An automatic procedure for slice thickness verification on AAPM CT performance phantom was developed using MATLAB R2015b. The stair object image within the phantom was segmented, and the middle stair object was located. Its angle was determined using the Hough transformation, and the image was rotated accordingly. The profile through this object was obtained, and its full‐width of half maximum (FWHM) was automatically measured. The FWHM indicated the slice thickness of the image. The automated procedure was applied with variations in three independent parameters, i.e., the slice thickness, the distance from the phantom to the iso‐center, and the reconstruction filter. The automated results were compared to manual measurements made using electronic calipers.

Results

The differences of the automated results from the nominal slice thicknesses were within 1.0 mm. The automated results are comparable to those from manual approach (i.e., the difference of both is within 12%). The automatic procedure accurately obtained slice thickness even when the phantom was moved from the iso‐center position by up to 4 cm above and 4 cm below the iso‐center. The automated results were similar (to within 0.1 mm) for various reconstruction filters.

Conclusions

We successfully developed an automated procedure of slice thickness verification and confirmed that the automated procedure provided accurate results. It provided an easy and effective method of determining slice thickness.

Evaluation of the linear interpolation method in correcting the influence of slice thicknesses on radiomic feature values in solid pulmonary nodules: a prospective patient study

Background

To investigate the influence of slice thickness on radiomic feature (RF) values in solid pulmonary nodules and evaluate the effect of a linear interpolation method in correcting the influence.

Methods

Thirty pulmonary nodules from 28 patients were selected prospectively with a thick-slice of 5 mm and a thin-slice of 1.25 mm on CT. A resampling method was used to normalize the voxel size of thick and thin slices CT images to 1×1×1 mm³ by linear interpolation. Lung nodules were segmented manually. A total of 396 radiomic features (RFs) were extracted from thick-slice and thin-slice images, together with the images resampled from thick (thick-r) and thin (thin-r) slices. The differences between the RF values were evaluated using a paired t-test. A comparison between groups was made using the Chi-squared test.

Results

Among the 396 RFs, 305 RFs showed an intraclass correlation coefficient ≥0.75 after test-retest analysis (including 22 histogram features, 20 geometry features, and 263 texture features). In the non-resampled data, 239 RF values (78.4%, 239/305) showed significant differences between thick and thin slice CT images. Resampling of thick images revealed that 202 RF values (66.2%, 202/305) showed significant differences between thick-r and thin slice CT images, showing a significant decrease in the number of different RF values when compared to non-resampled data (P<0.01). For the RF subgroups, only texture features showed a significant reduction in the number of different RF values after resampling (P<0.01). When both thick and thin slice images were resampled, the number of significantly different RF values between thick-r and thin-r images was increased to 247 (81.0%, 247/305), showing no significant difference when compared to non-resampled data (P=0.421).

Conclusions

Slice thickness demonstrated a considerable influence on RF values in solid pulmonary nodules, producing false results when CT images with different slice thicknesses were used. Linear interpolation of the resampling method was limited because of the relatively small correction effect.

Noise reduction in CT image using prior knowledge aware iterative denoising

The clinical demand for low image noise often limits the slice thickness used in many CT applications. However, a thick-slice image is more susceptible to longitudinal partial volume effects, which can blur key anatomic structures and pathologies of interest. In this work, we develop a prior-knowledge-aware iterative denoising (PKAID) framework that utilizes spatial data redundancy in the slice increment direction to generate low-noise, thin-slice images, and demonstrate its application in non-contrast head CT exams. The proposed technique takes advantage of the low-noise of thicker images and exploits the structural similarity between the thick- and thin-slice images to reduce noise in the thin-slice image. Phantom data and patient cases (n=3) of head CT were used to assess performance of this method. Images were reconstructed at clinically-utilized slice thickness (5 mm) and thinner slice thickness (2 mm). PKAID was used to reduce image noise in 2 mm images using the 5 mm images as low-noise prior. Noise amplitude, noise power spectra (NPS), modulation transfer function (MTF), and slice sensitivity profiles (SSP) of images before/after denoising were analyzed. The NPS and MTF analysis showed that PKAID preserved noise texture and resolution of the original thin-slice image, while reducing noise to the level of thick-slice image. The SSP analysis showed that the slice thickness of the original thin-slice image was retained. Patient examples demonstrated that PKAID-processed, thin-slice images better delineated brain structures and key pathologies such as subdural hematoma compared to the clinical 5 mm images, while additionally reducing image noise. To test an alternative PKAID utilization for dose reduction, a head exam with 40% dose reduction was simulated using projection-domain noise insertion. The image of 5 mm slice thickness was then denoised using PKAID. The results showed that the PKAID-processed reduced-dose images maintained similar noise and image quality compared to the full-dose images.

A precise, reproducible method for measuring ultrasound probe slice thickness using a Gammex 403 phantom

A precise, reproducible method for measuring ultrasound probe slice thickness has been developed for linear array ultrasound probes. The method used a custom built jig to draw the probe along the surface of a Gammex 403 phantom, with the image plane parallel to the filaments within the phantom. Still images at 0.5 mm intervals are saved for post-processing using in-house software. Slice thickness measurements with a precision of 0.1 mm are obtained. The method was shown to give reproducible estimates of probe slice thickness at several depths to within 0.4 mm during repeat tests. The method was able to provide information about the slice thickness of different sections of the probe face. It is expected that the method can quantify changes in probe performance due to lens wear or replacement over time that may elude both in-plane and in-air reverberation-based tests. A total of 18 linear probes were tested across eight centres, including six specialist vascular ultrasound centres.

Impact of CT slice thickness on volume and dose evaluation during thoracic cancer radiotherapy

Introduction

Accurate delineation of targets and organs at risk (OAR) is required to ensure treatment efficacy and minimize risk of normal tissue toxicity with radiotherapy. Therefore, we evaluated the impacts of computed tomography (CT) slice thickness and reconstruction methods on the volume and dose evaluations of targets and OAR.

Patients and methods

Eleven CT datasets from patients with thoracic cancer were included. 3D images with a slice thickness of 2 mm (2–CT) were created automatically. Images of other slice thickness (4–CT, 6–CT, 8–CT, 10–CT) were reconstructed manually by the selected 2D images using two methods; internal tumor information and external CT Reference markers. Structures and plans on 2–CT images, as a reference data, were copied to the reconstructed images.

Results

The maximum error of volume was 84.6% for the smallest target in 10–CT, and the maximum error (≥20 cm³) was 10.1%, 14.8% for the two reconstruction methods, internal tumor information and external CT Reference, respectively. Changes in conformity index for a target of <20 cm³ were 5.4% and 17.5% in 8–CT. Changes on V₃₀ and V₄₀ of the heart were considerable. In the internal tumor information method, volumes of hearts decreased by 3.2% in 6–CT, while V₃₀ and V₄₀ increased by 18.4% and 46.6%.

Conclusion

The image reconstruction method by internal tumor information was less affected by slice thickness than the image reconstruction method by external CT Reference markers. This study suggested that before positioning scanning, the largest section through the target should be determined and the optimal slice thickness should be estimated.

The Effect of Varying Slice Thickness and Interslice Gap on T1 and T2 Measured with the Multidynamic Multiecho Sequence

Purpose:

The purpose of our study was to investigate the effect of different slice thicknesses and/or interslice gaps on longitudinal and transverse relaxation times (T₁ and T₂) measured by a multi-dynamic, multi-echo (MDME) sequence.

Materials and Methods:

This retrospective study included nine healthy subjects who underwent MDME sequence (at 3T) with four different combinations of slice thicknesses and/or interslice gaps: slice thickness of 4 mm and interslice gap of 0 mm (TH4/G0), TH4/G1, TH5/G0, and TH5/G1. T₁ and T₂ were measured in various brain regions by a qualified neuroradiologist with 8 years of clinical experience: the frontal white matter (WM), occipital WM, genu, splenium, frontal cortex, thalamus, putamen, caudate head, and cerebrospinal fluid (CSF). The paired samples t-test was used to investigate the effect of different slice thicknesses and interslice gaps (TH4/G0 versus TH4/G1 and TH5/G0 versus TH5/G1). P < 0.013 was considered statistically significant.

Results:

T₂ in all brain regions and T₁ in the frontal WM, putamen, and CSF did not significantly change for different slice thicknesses and/or gaps (Ps > 0.013). In addition, T₁ in all brain regions of interest did not significantly change between TH4/G0, TH4/G1, TH5/G0 and TH5/G1. However, T₁ in some of the brain regions was higher with TH4/G0 than with TH5/G0 (occipital WM, frontal cortex, and caudate head) and with TH4/G1 than with TH5/G1 (occipital WM, genu, splenium and thalamus, all Ps < 0.013).

Conclusion:

T₂ estimated using the MDME sequence was stable regardless of slice thickness or gap. Although the sequence seems to provide stable relaxation values, identical slice thicknesses need to be used for follow-up to prevent potential T₁ changes.

[Basic Evaluation of the Acoustic Lens Narrow Aperture Method Devised for Improving Elevational Resolution of Ultrasonic Probe]

In general probe, the focal length in the elevational direction and resolution are fixed by the acoustic lens, so that sufficient resolution cannot be obtained in the region other than the focal point, except for special ultrasonic probes. Therefore, to improve the elevational resolution in the very shallow region, we devised the "narrow aperture method" for attaching a tape slit to an acoustic lens, and verified the method and its effect. The result of the study, for the slit, use Transpore™ tape overlaid on three layers and set the slit width to 1 mm. In addition, as a result of measurement of the slice thickness of the linear probe PLT-1204B manufactured by Canon Medical Systems, it was possible to reduce the slice thickness to a depth of 9 mm by 1 mm slit. The effectiveness of a 1 mm slit was also confirmed by probes from other companies. Moreover, it was found that the beam profile of 1 mm slit has almost the same shape even if the probe is different. The narrow aperture method of the acoustic lens can improve the elevational resolution in a very shallow region of a general ultrasonic probe.

Performance analysis of a computer-aided detection system for lung nodules in CT at different slice thicknesses

We study the performance of a computer-aided detection (CAD) system for lung nodules in computed tomography (CT) as a function of slice thickness. In addition, we propose and compare three different training methodologies for utilizing nonhomogeneous thickness training data (i.e., composed of cases with different slice thicknesses). These methods are (1) aggregate training using the entire suite of data at their native thickness, (2) homogeneous subset training that uses only the subset of training data that matches each testing case, and (3) resampling all training and testing cases to a common thickness. We believe this study has important implications for how CT is acquired, processed, and stored. We make use of 192 CT cases acquired at a thickness of 1.25 mm and 283 cases at 2.5 mm. These data are from the publicly available Lung Nodule Analysis 2016 dataset. In our study, CAD performance at 2.5 mm is comparable with that at 1.25 mm and is much better than at higher thicknesses. Also, resampling all training and testing cases to 2.5 mm provides the best performance among the three training methods compared in terms of accuracy, memory consumption, and computational time.

[Effect of Slice Thickness for Apparent Diffusion Coefficient Measurement of Mass]

Apparent diffusion coefficient (ADC) values calculated from diffusion-weighted magnetic resonance imaging (DW-MRI) can be used for differentiation of tumors. Clinically, ADC values are used for monitoring treatment response after chemotherapy or radiation. However, it is reported that the threshold of the ADC value differs among institutions. In addition, there are reports regarding the change factor of the ADC value. Slice thickness may induce error in the ADC value by the influence of the partial volume effect in thicker objects, and by the influence of signal-to-noise ratio (SNR) in thinner objects. Therefore, in this study, the effect of slice thickness was examined. The signal body of spherical high-diffusion coefficients of 6, 7.9, and 9.3 mm in diameter was fixed in the low-circumference material of the diffusion coefficient. These phantoms were imaged using DW imaging (DWI) of 1, 1.5, 2, 2.5, 3, 4, 5, 6, 7, 8, 9, 10, 15, and 20 mm slice thickness using the multi-b values. In addition, different SNR were imaged by changing field-of-view and the number of additions. ADC was calculated by DWI of the different b values. As a result, slice thickness showed a peak at 50-65% of the diameter of the signal body. Furthermore, ADC values fluctuated in the slice thickness in front of the peak with a change in SNR. In conclusion, the ADC value was most accurate at a setting of 50-65% of slice thickness for the object diameter.

Effects of MRI scanner parameters on breast cancer radiomics

PURPOSE

To assess the impact of varying magnetic resonance imaging (MRI) scanner parameters on the extraction of algorithmic features in breast MRI radiomics studies.

METHODS

In this retrospective study, breast imaging data for 272 patients were analyzed with magnetic resonance (MR) images. From the MR images, we assembled and implemented 529 algorithmic features of breast tumors and fibrograndular tissue (FGT). We divided the features into 10 groups based on the type of data used for the feature extraction and the nature of the extracted information. Three scanner parameters were considered: scanner manufacturer, scanner magnetic field strength, and slice thickness. We assessed the impact of each of the scanner parameters on each of the feature by testing whether the feature values are systematically diverse for different values of these scanner parameters. A two-sample t-test has been used to establish whether the impact of a scanner parameter on values of a feature is significant and receiver operating characteristics have been used for to establish the extent of that effect.

RESULTS

On average, higher proportion (69% FGT versus 20% tumor) of FGT related features were affected by the three scanner parameters. Of all feature groups and scanner parameters, the feature group related to the variation in FGT enhancement was found to be the most sensitive to the scanner manufacturer (AUC = 0.81 ± 0.14).

CONCLUSIONS

Features involving calculations from FGT are particularly sensitive to the scanner parameters.

The influence of change in slice thickness on the accuracy of reconstruction of cranium geometry

The article presents a comparative study of change in slice thickness on the accuracy of reconstruction of cranium geometry. Research was performed on 10 different patients. Digital Imaging and Communications in Medicine data were obtained on the Siemens Somatom Sensation Open 40 scanner. At the stage of reconstruction, the same parameters were utilized, while only slice thickness was changed. Modeling with voxel dimensions of 0.4 mm × 0.4 mm × 2.4 mm was chosen as the gold standard over the modeling approach comprising voxel dimensions of 0.4 mm × 0.4 mm × 4.8 mm. The influence of layer thickness on the accuracy of cranium geometry is very similar for the 10 presented patients. The average results show a distribution with a positive skew and kurtosis. The value of skewness is 0.284 (small asymmetry) and kurtosis is 3.746 (a distribution more peaked). Based on 95% confidence, the changes in layer thickness from 2.4 to 4.8 mm generated errors reconstructing the geometry of the cranium by 0.516 mm ± 1.345 mm. The presented research highlights new opportunities to control deviations at the stage of data processing and modeling geometry of the cranium.

Optimized approach to cine MRI of uterine peristalsis

PURPOSE

To determine the optimal slice thickness, playback rate, and scan time for uterine peristalsis with 3.0T magnetic resonance imaging (MRI).

MATERIALS AND METHODS

In all, 23 young female volunteers underwent a 3.0T MRI scan with different slice thicknesses of 3 mm (Cine_3mm ), 5 mm (Cine_5mm ), and 7 mm (Cine_7mm ) for 6 minutes. Subjective image quality score, signal-to-noise ratios (SNRs), and contrast-to-noise ratios (CNRs) of those MR images were evaluated by two radiologists independently. The number, intensity, and direction of uterine peristalsis with different thickness were compared at various playback rates. Also, the peristalsis frequency was counted and compared in different acquisition durations (1-6 minutes).

RESULTS

The subjective image quality score, peristalsis number, and intensity were significantly higher in Cine_7mm and Cine_5mm than Cine_3mm (P < 0.05), while the SNRs and CNRs of Cine_7mm were significantly higher than Cine_3mm (P < 0.05). Peristalsis numbers did not differ significantly at different playback rates with the same slice thickness (P = 0.548-0.962). However, peristalsis intensity at 12×, and 15× was significantly greater than that at 8× the actual speed for Cine_7mm and Cine_5mm (P < 0.05). The peristalsis frequency at 3, 4, 5, 6 minutes was significantly higher than that at 1 minute and 2 minutes (P < 0.05).

CONCLUSION

We recommend a slice thickness of 5 mm or 7 mm and a scan time of 3 minutes for uterine peristalsis with 3.0T MRI, and a playback rate of 12× or 15× the actual speed for peristalsis observation. J. Magn. Reson. Imaging 2016;44:1397-1404.

The effect of longitudinal CT resolution and pixel size (FOV) on target delineation and treatment planning in stereotactic radiosurgery

The acquisition of high-quality, anatomic images is essential for the accurate delineation of tumor volumes and critical structures used for stereotactic radiosurgery (SRS) treatment planning. This study investigates the effect of CT slice thickness and field of view (FOV), i.e., longitudinal and axial CT resolution, on volume delineation and treatment planning in SRS and suggests optimal CT acquisition parameters for brain SRS simulation. Optimization of such parameters will maximize clinical efficacy, alter data storage requirements, reduce dosimetric uncertainties, and may ultimately facilitate more favorable clinical outcomes. Changes in the extent, shape and the absolute volume of the GTV were recorded when the longitudinal and axial CT resolution were modified. These changes ultimately impacted the PTV dose coverage. Reducing CT slice thickness from 2mm to 1mm resulted in an average decrease of 8.6%±13.9% (max=52.2%) and 3.0 %±4.3% (max=13.1%) in PTV D_min and PTV D95, respectively. Increasing CT slice thickness from 2mm to 3mm resulted in an average decrease of 10%±9.9% (max=26.8%) and 5.8%±5.8% (max=17.4%) in PTV D_min and PTV D95, respectively. Similarly, on average, PTV coverage decreased when FOV decreased. The average decrease in PTV D_min and PTV D95 for a 350cm FOV was 5.2%±7.2% (max=21.4%) and 1.9%±3.2% (max=7.5%), respectively. Decreasing FOV to 250cm yielded similar results with the average decrease of 5.6%±5.0% (max=13.2%) and 1.6%±2.6% (max=6.3%) in PTV D_min and PTV D95, respectively. These results suggest that the slice thickness and FOV of CT images affect target delineation and may potentially compromise the quality of the target coverage.

Impact of slice thickness on semi-automated measurements for volume and whole-tumor attenuation of colorectal hepatic metastases in multislice computed tomography

BACKGROUND

Volumetric and whole-tumor attenuation assessment of tumor are of value in assessment of treatment.

PURPOSE

To assess the impact of slice thickness on semi-automatic analyses (volume, whole-tumor attenuation) for small colorectal hepatic metastases.

MATERIAL AND METHODS

Computed tomography (CT) data of patients with colorectal hepatic metastases at 1.5-, 3-, and 5-mm slice thickness were semi-automatically evaluated for volume and whole-tumor attenuation by two radiologists independently. Statistical analysis included paired samples t-test and concordance correlation coefficient (CCC) analysis according to the longest axial tumor diameter (10-20 mm, 20-30 mm, 30-40 mm).

RESULTS

A total of 62 patients (32 men and 30 women) with 62 target tumors were included. The mean volume was significantly higher at 3- and 5-mm slice thicknesses in comparison with the reference (1.5 mm) for the target tumors between 10 mm and 20 mm (P = 0.0295, CCC = 0.9394 for 3 mm; P = 0.0029, CCC = 0.5129 for 5 mm, respectively) and at 5 mm slice thickness for the target tumors between 20 mm and 30 mm (P = 0.0071, CCC = 0.9102). For whole-tumor attenuation measurements, the significant difference was only seen at 5-mm slice thicknesses in comparison with the reference (1.5 mm) for the target tumors between 10 and 20 mm (P = 0.0015, CCC = 0.9389).

CONCLUSION

Slice thickness of 1.5 mm might be suggested for semi-automated volumetric measurements, and slice thickness of no more than 3 mm for whole-tumor CT attenuation in hepatic metastasis between 10 mm and 20 mm.

Controlling FIB-SBEM slice thickness by monitoring the transmitted ion beam

Serial block-face electron microscopy with focused ion beam cutting suffers from cutting artefacts caused by changes in the relative position of beam and sample, which are, for example, inevitable when reconditioning the ion gun. The latter has to be done periodically, which limits the continuous stack-acquisition time to several days. Here, we describe a method for controlling the ion-beam position that is based on detecting that part of the ion beam that passes the sample (transmitted beam). We find that the transmitted-beam current decreases monotonically as the beam approaches the sample and can be used to determine the relative position of beam and sample to an accuracy of around one nanometre. By controlling the beam approach using this current as the feedback parameter, it is possible to ion-mill consecutive 5 nm slices without detectable variations in thickness even in the presence of substantial temperature fluctuations and to restart the acquisition of a stack seamlessly. In addition, the use of a silicon junction detector instead of the in-column detector is explored.

[Effects of different reconstruction parameters on CT volumetric measurement of pulmonary nodules]

BACKGROUND AND OBJECTIVE

It has been proven that volumetric measurements could detect subtle changes in small pulmonary nodules in serial CT scans, and thus may play an important role in the follow-up of indeterminate pulmonary nodules and in differentiating malignant nodules from benign nodules. The current study aims to evaluate the effects of different reconstruction parameters on the volumetric measurements of pulmonary nodules in chest CT scans.

METHODS

Thirty subjects who underwent chest CT scan because of indeterminate pulmonary nodules in General Hospital of Tianjin Medical University from December 2009 to August 2011 were retrospectively analyzed. A total of 52 pulmonary nodules were included, and all CT data were reconstructed using three reconstruction algorithms and three slice thicknesses. The volumetric measurements of the nodules were performed using the advanced lung analysis (ALA) software. The effects of the reconstruction algorithms, slice thicknesses, and nodule diameters on the volumetric measurements were assessed using the multivariate analysis of variance for repeated measures, the correlation analysis, and the Bland-Altman method.

RESULTS

The reconstruction algorithms (F=13.6, P<0.001) and slice thicknesses (F=4.4, P=0.02) had significant effects on the measured volume of pulmonary nodules. In addition, the coefficients of variation of nine measurements were inversely related with nodule diameter (r=-0.814, P<0.001). The volume measured at the 2.5 mm slice thickness had poor agreement with the volumes measured at 1.25 mm and 0.625 mm, respectively. Moreover, the best agreement was achieved between the slice thicknesses of 1.25 mm and 0.625 mm using the bone algorithm.

CONCLUSIONS

Reconstruction algorithms and slice thicknesses have significant impacts on the volumetric measurements of lung nodules, especially for the small nodules. Therefore, the reconstruction setting in serial CT scans should be consistent in the follow-up of indeterminate pulmonary nodules, more importantly for the small nodules.

Exploring CBCT-based DICOM files. A systematic review on the properties of images used to evaluate maxillofacial bone grafts

Previous studies suggests that cone beam computerized tomography (CBCT) images could provide reliable information regarding the fate of bone grafts in the maxillofacial region, but no systematic information regarding the standardization of CBCT settings and properties is available, i.e., there is a lack of information on how the images were generated, exported, and analyzed when bone grafts were evaluated. The aim of this study was to (1) do a systematic review on which type of CBCT-based DICOM images have been used for the evaluation of the fate of bone grafts in humans and (2) use a software suggested in the literature to test DICOM-based data sets, exemplifying the effect of variation in selected parameters (windowing/contrast control, plane definition, slice thickness, and number of measured slices) on the final image characteristics. The results from review identified three publications that used CBCT to evaluate maxillofacial bone grafts in humans, and in which the methodology/results comprised at least one of the expected outcomes (image acquisition protocol, image reconstruction, and image generation information). The experimental shows how the influence of information that was missing in the retrieved papers, can influence the reproducibility and the validity of image measurements. Although the use of CBCT-based images for the evaluation of bone grafts in humans has become more common, this does not reflect on a better standardization of the developed studies. Parameters regarding image acquisition and reconstruction, while important, are not addressed in the proper way in the literature, compromising the reproducibility and scientific impact of the studies.

Optimal slice thickness for cone-beam CT with on-board imager

PURPOSE

To find the optimal slice thickness (Δτ) setting for patient registration with kilovoltage cone-beam CT (kVCBCT) on the Varian On Board Imager (OBI) system by investigating the relationship of slice thickness to automatic registration accuracy and contrast-to-noise ratio.

MATERIALS AND METHOD

Automatic registration was performed on kVCBCT studies of the head and pelvis of a RANDO anthropomorphic phantom. Images were reconstructed with 1.0 ≤ Δτ (mm) ≤ 5.0 at 1.0 mm increments. The phantoms were offset by a known amount, and the suggested shifts were compared to the known shifts by calculating the residual error. A uniform cylindrical phantom with cylindrical inserts of various known CT numbers was scanned with kVCBCT at 1.0 ≤ Δτ (mm) ≤ 5.0 at increments of 0.5 mm. The contrast-to-noise ratios for the inserts were measured at each Δτ.

RESULTS

For the planning CT slice thickness used in this study, there was no significant difference in residual error below a threshold equal to the planning CT slice thickness. For Δτ > 3.0 mm, residual error increased for both the head and pelvis phantom studies. The contrast-to-noise ratio is proportional to slice thickness until Δτ = 2.5 mm. Beyond this point, the contrast-to-noise ratio was not affected by Δτ.

CONCLUSION

Automatic registration accuracy is greatest when 1.0 ≤ Δτ (mm) ≤ 3.0 is used. Contrast-to-noise ratio is optimal for the 2.5 ≤ Δτ (mm) ≤ 5.0 range. Therefore 2.5 ≤ Δτ (mm) ≤ 3.0 is recommended for kVCBCT patient registration where the planning CT is 3.0 mm.