Image Slice Thickness Research Articles

Gross target volume contouring in canine extra-axial brain tumors: Effects of magnetic resonance image slice thickness and time between subsequent image sets.

Accurate determination of the gross target volume (GTV) is critical in radiation treatment planning, as errors could result in underdosing of the tumor or overdosing of nearby organs at risk. This multicenter retrospective observational serial measurement study evaluated the effects of variations in MRI slice thickness and a time delay between the diagnostic (MRI-1) and RT planning (MRI-2) MRIs GTV contouring in dogs with presumed meningiomas. The hypothesis was that the GTV would increase in size with time on T1-weighted sequences with contrast. Inclusion required paired MRI acquisition within 3 months. The GTV was contoured on each MRI. Forty-six dogs were included. Slice thickness was significantly different (P<.001) between MRIs: MRI-1 had a median of 3.9mm (range: 0.8-6mm; only two dogs <2mm), and MRI-2 had a median of 0.9mm (range: 0.6-4.5mm; only two dogs >2mm). The median time between MRIs was 22 days (range: 8-74 days). The MRI-1 GTV was significantly different from MRI-2 GTV (P<.0001); thirty (65%) were larger, five were equal in size, and 12 were smaller than the MRI-2 GTV. This difference in GTV is likely due to the slice thickness differences between MRI acquisitions rather than changes in tumor size due to the short time interval between MRI-1 and MRI-2. This finding highlights the differences between diagnostic and RT treatment-planning MRIs. For brain tumor target contouring, an MRI at the same time as the RT planning CT with <1mm slice thickness, 3D acquisitions, and anisotropic voxel is recommended.

Relationship between pericoronary adipose tissue attenuation value and image reconstruction parameters

Rationale and objectivesTo investigate the relationship between the pericoronary adipose tissue CT mean attenuation (PCATMA) measurement and image reconstruction parameters (adaptive statistical iterative reconstruction-veo (ASIR-V) percentage, kernel, and slice thickness). Materials and methodsOne hundred and ninety-eight consecutive patients underwent CT coronary angiography at 100 kilovoltage peak (kVp) (n = 102) and 120 kVp (n = 96) were included. All scans were reconstructed by three means: 1. with 11 different ASIR-V percentages, standard kernel and 0.625 mm; 2. with soft, standard, detail, and bone kernels, 60 % ASIR-V, and 0.625 mm; 3. at 0.625 mm and 1.25 mm slice thickness, standard kernel and 60 % ASIR-V. PCATMA of the three main coronary arteries was calculated using a dedicated software. Linear regression, analysis of variance (ANOVA), Friedman test, and paired t-test were used for statistical analysis. ResultsLinear regression of pooled average data showed that the PCATMA was positively and linearly correlated with the ASIR-V percentage (all R squared >0.99). Regression analysis of individual data showed that most R squared were greater than 0.8 or 0.9, but their slope consisted of a relatively wide range. The difference of PCATMA among different kernels for each coronary artery reached statistically significant levels (P < 0.001), particularly for the difference between standard and bone kernel. Most of the differences between 0.625 mm and 1.25 mm for LAD, LCX, and RCA at 100 kVp and 120 kVp reached statistical significance (P < 0.001). ConclusionsPCATMA correlates linearly with the strength of ASIR-V. Reconstruction kernel and slice thickness also affect PCATMA, especially for the sharp kernels.

Exploring CT pixel and voxel size effect on anatomic modeling in mandibular reconstruction

BackgroundComputer-aided modeling and design (CAM/CAD) of patient anatomy from computed tomography (CT) imaging and 3D printing technology enable the creation of tangible, patient-specific anatomic models that can be used for surgical guidance. These models have been associated with better patient outcomes; however, a lack of CT imaging guidelines risks the capture of unsuitable imaging for patient-specific modeling. This study aims to investigate how CT image pixel size (X-Y) and slice thickness (Z) impact the accuracy of mandibular models.MethodsSix cadaver heads were CT scanned at varying slice thicknesses and pixel sizes and turned into CAD models of the mandible for each scan. The cadaveric mandibles were then dissected and surface scanned, producing a CAD model of the true anatomy to be used as the gold standard for digital comparison. The root mean square (RMS) value of these comparisons, and the percentage of points that deviated from the true cadaveric anatomy by over 2.00 mm were used to evaluate accuracy. Two-way ANOVA and Tukey-Kramer post-hoc tests were used to determine significant differences in accuracy.ResultsTwo-way ANOVA demonstrated significant difference in RMS for slice thickness but not pixel size while post-hoc testing showed a significant difference in pixel size only between pixels of 0.32 mm and 1.32 mm. For slice thickness, post-hoc testing revealed significantly smaller RMS values for scans with slice thicknesses of 0.67 mm, 1.25 mm, and 3.00 mm compared to those with a slice thickness of 5.00 mm. No significant differences were found between 0.67 mm, 1.25 mm, and 3.00 mm slice thicknesses. Results for the percentage of points deviating from cadaveric anatomy greater than 2.00 mm agreed with those for RMS except when comparing pixel sizes of 0.75 mm and 0.818 mm against 1.32 mm in post-hoc testing, which showed a significant difference as well.ConclusionThis study suggests that slice thickness has a more significant impact on 3D model accuracy than pixel size, providing objective validation for guidelines favoring rigorous standards for slice thickness while recommending isotropic voxels. Additionally, our results indicate that CT scans up to 3.00 mm in slice thickness may provide an adequate 3D model for facial bony anatomy, such as the mandible, depending on the clinical indication.

IMG-21. HOW CAN WE BEST UTILISE HISTORICAL IMAGING DATASETS FOR RESEARCH IN PAEDIATRIC NEURO-ONCOLOGY? A REVIEW OF HISTORICAL PRE-OPERATIVE IMAGING PERFORMED FOR 92 PAEDIATRIC BRAIN TUMOUR PATIENTS WITH COMPARISON TO 2021 SIOPE GUIDELINES

Abstract BACKGROUND Utilisation of historical imaging is important for studying long-term outcomes in paediatric brain tumours often pooling data across multiple centres retrospectively over many years. Disparities in MRI sequence parameters, imaging planes, fields of view and resolution limit quantitative radiomic analysis and segmentation algorithms. We review historical imaging datasets, analysing the MRI scans performed assessing suitability for modern research techniques. METHODS Retrospective review of presenting MRI scans was performed in 92 paediatric brain tumour patients between 2006-2020. Whether imaging was performed internally at our paediatric neurosciences centre or externally at their referring hospitals was recorded. Image sequences, planes and slice thickness were compared with 2021 SIOPE guidelines for paediatric brain tumour imaging. T2 signal intensities for tumour, white and grey matter were compared for scans completed in different centres. Statistical testing was performed with T-Test and Paired T-Test. RESULTS 75% of patients had all SIOPE essential sequences and 60.9% were in the recommended planes. Only 1 patient had the desired slice thickness in all sequences recommended for 1.5T scanners. 38.5% had T1 and 27.8% had post-contrast T1 in the ≤1mm slice thickness recommended for 3T scanners. Comparison between 62 internal and 30 external scans revealed statistically significant differences in T2 signal intensities for regions of interest (ROI) within tumour, white and grey matter (p&amp;lt;0.01). Comparison of matched ROI for 12 patients imaged internally and externally demonstrated statistically significant differences in T2 signal intensities (p&amp;lt;0.05). Differences persisted despite normalisation of T2 signal intensity as ratios between tumour and the weighted mean of white and grey matter (p&amp;lt;0.01). CONCLUSIONS This study highlights the challenges of non-standardisation of imaging resolution and acquisition parameters in historical imaging. One approach to utilise these datasets is to use qualitative classifications where human experts label imaging features and encode data for future statistical or machine learning analysis.

Differentiation of radicular cysts and radicular granulomas via texture analysis of multi-slice computed tomography images.

This study aimed to establish a method for differentiating radicular cysts from granulomas via texture analysis (TA) of multi-slice computed tomography (CT) images. A total of 222 lesions with multi-slice computed tomography images acquired at our hospital between 2013 and 2022 that were pathologically diagnosed were included in this study. Cases of contrast-enhanced images, severe metallic artifacts, and lesions that were not sufficiently large to be analyzed were excluded. The images were chronologically divided into a training group and a validation group. The radiological characteristics were determined. Subsequently, a TA was performed. Pyradiomics software was used for the TA of three-dimensionally segmented volumes extracted from 2-mm slice thickness images with a soft-tissue algorithm. Features that differed significantly between the two lesions in the training group were extracted and used to create machine-learning models. The discriminative ability of these models was evaluated in the validation group using receiver operating characteristic curve analysis. A total of 131 lesions, comprising 28 radicular cysts and 103 granulomas, were analyzed. Forty-three texture features that exhibited significant variations were extracted. A support vector machine and decision tree model, with areas under the curves of 0.829 and 0.803, respectively, were created. These models showed high discriminative abilities, even for the validation group, with areas under the curve of 0.727 and 0.701, respectively. Both models showed superior performance compared with that of the models based on radiographic findings. Discriminatory models were established for the TA of radicular cysts and granulomas using CT images.

Qualitative and quantitative image quality of coronary CT angiography using photon-counting computed tomography: Standard and Ultra-high resolution protocols

PurposeWe aimed to identify the optimal reconstruction settings based on qualitative and quantitative image quality parameters on standard and ultra-high resolution (UHR) images using photon-counting CT (PCCT). MethodWe analysed 45 patients, 29 with standard and 16 with UHR acquisition, applying both smoother and sharper kernel settings. Coronary CT angiography images were performed on a dual-source PCCT system using standard (0.4/0.6 mm slice thickness, Bv40/Bv44 kernels, QIR levels 0–4) or UHR acquisition (0.2/0.4 mm slice thickness, Bv44/Bv56 kernels, QIR levels 0–4). Qualitative image quality was assessed using a 4-point Likert scale. Image noise (SD), signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated in both the proximal and distal segments. ResultsOn standard resolution, larger slice thickness resulted in an average increase of 12.5 % in CNR, whereas sharper kernel led to an average 8.7 % decrease in CNR. Highest CNR was measured on 0.6 mm, Bv40, QIR4 images and lowest on 0.4 mm, Bv44, QIR0 images: 25.8 ± 4.1vs.8.3 ± 1.6 (p < 0.001). On UHR images, highest CNR was observed on 0.4 mm, Bv40, QIR4 and lowest on 0.2 mm, Bv56 and QIR0 images: 21.5 ± 3.9vs.3.6 ± 0.8 (p < 0.001). Highest qualitative image quality was found on images with Bv44 kernel and QIR level 3/4 with both slice thicknesses on standard reconstruction. Additionally, Bv56 with QIR4 on 0.2 mm slice thickness images showed highest subjective image quality. Preserved distal vessel visualization was detected using QIR 2–4, Bv56 and 0.2 mm slice thickness. ConclusionsPhoton-counting CT demonstrated high qualitative and quantitative image quality for the assessment of coronaries and stents.

Three-dimensional volumetric analyses of temporal bone pneumatization from early childhood to early adulthood in a South African population.

A debate exists on whether the size of temporal bone pneumatization is a cause or consequence of otitis media (a global disease burden). However, a normal middle-ear mucosa is a prerequisite for normal temporal bone pneumatization. This study investigated the size of temporal bone pneumatization with age and the normal distribution of air cell volume in different stages of human growth postnatally. A three-dimensional computer-based volumetric-rendering technique was performed bilaterally on 248 head/brain and internal acoustic meatus computed tomography images of slice thickness ≤ 0.6 mm consisting of 133 males and 115 females with age range 0-35 years. The average volume of infant (0-2 years) pneumatization was 1920 mm3 with an expected rapid increase to about 4510 mm3 in childhood (6-9 years). The result also showed a significant increase (p < 0.001) in the volume of air cells up to the young adult stage I (19-25 years), followed by a significant decline in young adult stage II (26-35 years). However, the females were observed to experience an earlier increase than males. Also, population differences were observed as the Black South African population group showed a higher increase in volume with age than the White and Indian South African population groups, though the volumes of the latter increased up to young adult stage II. This study concludes that the pneumatization of a healthy temporal bone is expected to continue a linear increase up until at least adult stage I. Termination of temporal bone pneumatization in an individual before this stage could signify pathologic involvement of the middle ear during childhood.

Detection of brain lesions after catheter ablation depends on imaging criteria: insights from AXAFA-AFNET 5 trial.

Left atrial catheter ablation is well established in patients with symptomatic atrial fibrillation (AF) but associated with risk of embolism to the brain. The present analysis aims to assess the impact of diffusion-weighted imaging (DWI) slice thickness on the rate of magnetic resonance imaging (MRI)-detected ischaemic brain lesions after ablation. AXAFA-AFNET 5 trial (NCT02227550) participants underwent MRI using high-resolution (hr) DWI (slice thickness: 2.5-3 mm) and standard DWI (slice thickness: 5-6 mm) within 3-48 h after ablation. In 321 patients with analysable brain MRI (mean age 64 years, 33% female, median CHA2DS2-VASc 2), hrDWI detected at least one acute brain lesion in 84 (26.2%) patients and standard DWI in 60 (18.7%; P < 0.01) patients. High-resolution diffusion-weighted imaging detected more lesions compared to standard DWI (165 vs. 104; P < 0.01). The degree of agreement for lesion confirmation using hrDWI vs. standard DWI was substantial (κ = 0769). Comparing the proportion of DWI-detected lesions, lesion distribution, and total lesion volume per patient, there was no difference in the cohort of participants undergoing MRI at 1.5 T (n = 52) vs. 3 T (n = 269). The pre-specified AXAFA-AFNET 5 sub-analysis revealed significantly increased rates of MRI-detected acute brain lesions using hrDWI instead of standard DWI in AF patients undergoing ablation. In comparison to DWI slice thickness, MRI field strength had a no significant impact in the trial. Comparing the varying rates of ablation-related MRI-detected brain lesions across previous studies has to consider these technical parameters. Future studies should use hrDWI, as feasibility was demonstrated in the multicentre AXAFA-AFNET 5 trial.

Reproducibility of Facial Information in Three-Dimensional Reconstructed Head Images: An Exploratory Study.

Facial information acquired via three-dimensional reconstruction of head computed tomography (CT) data may be considered personal information, which can be problematic for neuroimaging studies. However, no study has verified the relationship between slice thickness and face reproducibility. This study determined the relationship and match rate between image slice thickness and face detection accuracy of face-recognition software in facial reconstructed models. Head CT data of 60 cases comprising entire faces obtained under conditions of non-contrast and 1-mm slice thickness were resampled to obtain 2-10-mm slice-thickness data. Facial models, reconstructed by image thresholding, were acquired from the data. We performed face detection tests per slice thickness on the models and calculated the face detection rate. The reconstructed facial models created from 1-mm slice-thickness data and other slice thicknesses were used as training and test data, respectively. Match confidence scores were obtained via three programs, match rates were calculated per slice thickness, and generalized estimating equations were used to evaluate the match rate trend. In general, the face detection rates for the 1-10-mm slice thicknesses were 100, 100, 98.3, 98.3, 95.0, 91.7, 86.7, 78.3, 68.3, and 61.7 %, respectively. The match rates for the 2-10-mm slice thicknesses were 100, 98.3, 98.3, 95.0, 85.0, 71.7, 53.3, 28.3, and 16.7 %, respectively. The reconstructed models tended to have higher match rates as the slice thickness decreased. Thus, thin-slice head CT imaging data may increase the possibility of the information becoming personally identifiable health information.

A Checklist for Radiation Therapy Planning: Update 2023

We developed a checklist to help navigate the radiation simulation and setup process. We distilled the tasks to be performed into an easily memorizable acronym P-P-I-I-I-T (Patient Prescription, Patient Position, Immobilization, Imaging, Image Guided Radiotherapy, and Treatment Technique). This checklist ensures coverage of all the key elements and improves patient safety and treatment planning quality. Since our initial publication, we applied the checklist on 600 patients and are now presenting an update. We updated the checklist to ensure inclusion of the American College of Radiology (ACR) recommendations for radiation planning and incorporated feedback we received from allied radiation oncology professionals. The inspiration behind the easily memorizable acronym, P-P-I-I-I-T, came from a similar published checklist for systematically evaluating of a radiotherapy treatment plan, CB-CHOP, which stands for Contours, Beam Arrangement, Coverage, Heterogeneity/Hot Spots, Organs at Risk and Prescription (Dean et al. 2017), The checklist provides a simplified and standardized approach to treatment plan evaluation. Similarly, P-P-I-I-I-T checklist was put into usage as aid for both learners and staff radiation oncologists, and applied to radiation planning for 600 patients treated in Iowa between 2019 to 2022. During each session another radiation oncologist, two dosimetrists, two radiation nurses, and one physicist were present. The other radiation oncologist acted as observer to ensure the checklist was being applied appropriately by the primary radiation oncologist. The rest of the treatment team provided feedback which was relevant to their professional domains. The feedback from all members was collected and incorporated to make revisions to an updated checklist which was put into use as of January 2023. The components of the revised list are as follows: The following are the components of the revised 2023 checklist: P - Patient Prescription: Confirms the proposed dose, fractionation, and scheduling (such as concurrent chemotherapy). P - Patient Position: Describes optimal position including head, shoulder, arms and legs, alternative positions, and changes due to unexpected issues. I - Immobilization: Considers type of device, 4D set-up, gating, and custom immobilization. I - Imaging: Chooses imaging modality, slice thickness, and contrast agent, as well as fusions for SBRT and SRS. Discusses image guidance incorporating ACR recommendations and reference points. T - Treatment Technique: Includes proposed technique such as 3D, VMAT, electrons, and bolus and energy to be used. Standardization of practical guidelines and safety checklists are increasingly common in medicine. P-P-I-I-I-T (P²I³T) continues to be a simple acronym that can be used by radiation oncology residents and attendings as a checklist to ensure a complete and accurate patient setup and simulation.

Practices and utility of imaging among urological communities for urolithiasis, observations, and inferences from a targeted survey.

To evaluate the general practice among urologists about the use of various radiological imaging and measurement tools, and to compare the different preferences in radiological evaluations between Turkish and European urologists. Our study was designed as a survey study. The survey comprised 22 questions which evaluated the epidemiological information, caseload of participants, general preferences of participants on CT image slice thickness, basic radiologic workup routines prior to surgery, use of special tools and measurements on CT. Data collection was conducted with both an online poll and printed copies. A total of 222 urologists from 23 different countries responded to the survey. The most performed endourologic surgery was semi-rigid URS which was performed more than 25 times/year by 90.1% (n = 200) of the participants. Although PCNL was performed more commonly by Turkish urologists (39.7%) compared to their European (17.3%) colleagues (p < 0.001), it was still the least often performed endourological surgery among all participants (31.5%). The stone size evaluation was the most performed measurement performed by the participants. Although the group of surgeons used size measurement tools, there were differences between the two groups. Turkish urologists used size measurement before PCNL (85.7%) as opposed to European urologists who used it mostly before URS (91.4%). Non-contrast CT images for urolithiasis are mainly evaluated by urologists themselves and a considerable number of urologists do not use additional measurement tools in evaluating CT images. Although there are similarities in the knowledge of various radiological tools, there are distinct regional differences.

Impact of preoperative computed tomography image slice thickness on the planning of deep brain stimulation surgery: A phantom study

BackgroundPreoperative Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) coregistration is often applied to perform deep brain stimulation (DBS) surgeries. Thin sliced (high dose) CT is used and recommended because of the high reported accuracy but there are no comparative studies. Thin sliced CT could lead to a higher radiation exposure for the patient in contrast to thicker sliced CT (low dose) due to a longer scanning time. In this study we investigated if the preoperative CT slice thickness had an effect on the registration accuracy in a preoperative simulated DBS setting. Material and methodsAn implanted phantom, a Citrullus lanatus (watermelon), was used to acquire an MRI data set (2 millimeter (mm) T2) with a fixed reference point. Two observers targeted the reference point independently and they recorded and compared the coordinates, ring, and arc angles from all coregistered series with different CT slice thicknesses of 0.5 mm, 1 mm, 1.5 mm, 2 mm, 3 mm, 4 mm, and 5 mm. Separately, both MRI and CT were used as reference series for coregistration and analysis. Lastly, inter-observer reliability was calculated with Kendall’s coefficient of concordance (W). With W> 0.9 defined as very good. ResultsOur results show no relevant effect on the preoperative registration accuracies for the different CT’s with all absolute differences in mm for the stereotactic coordinates< 0.5 and angles in degrees< 0.4. Additionally, the inter-observer reliability was high (W 0.991). ConclusionWe found no relevant effect of increased slice thickness of preoperative CT on the preoperative registration accuracy in a simulated DBS setting.

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.

Validation of two automated ASPECTS software on non-contrast computed tomography scans of patients with acute ischemic stroke.

The Alberta Stroke Program Early Computed Tomography Score (ASPECTS) was designed for semi-quantitative assessment of early ischemic changes on non-contrast computed tomography (NCCT) for acute ischemic stroke (AIS). We evaluated two automated ASPECTS software in comparison with reference standard. NCCT of 276 AIS patients were retrospectively reviewed (March 2018-June 2020). A three-radiologist consensus for ASPECTS was used as reference standard. Imaging data from both baseline and follow-up were evaluated for reference standard. Automated ASPECTS were calculated from baseline NCCT with 1-mm and 5-mm slice thickness, respectively. Agreement between automated ASPECTS and reference standard was assessed using intra-class correlation coefficient (ICC). Correlation of automated ASPECTS with baseline stroke severity (NIHSS) and follow-up ASPECTS were evaluated using Spearman correlation analysis. In score-based analysis, automated ASPECTS calculated from 5-mm slice thickness images agreed well with reference standard (software A: ICC = 0.77; software B: ICC = 0.65). Bland-Altman analysis revealed that the mean differences between automated ASPECTS and reference standard were ≤ 0.6. In region-based analysis, automated ASPECTS derived from 5-mm slice thickness images by software A showed higher sensitivity (0.60 vs. 0.54), lower specificity (0.91 vs. 0.94), and higher AUC (0.76 vs. 0.74) than those using 1-mm slice thickness images (p < 0.05). Automated ASPECTS derived from 5-mm slice thickness images by software B showed higher sensitivity (0.56 vs. 0.51), higher specificity (0.87 vs. 0.81), higher accuracy (0.80 vs. 0.73), and higher AUC (0.71 vs. 0.66) than those using 1-mm slice thickness images (p < 0.05). Automated ASPECTS were significantly associated with baseline NIHSS and follow-up ASPECTS. Automated ASPECTS showed good reliability and 5 mm was the optimal slice thickness.

PD22-07 A MULTICENTER STUDY PRESENTING AN EXPLAINABLE AI APPROACH TO AUTOMATIC SEGMENTATION OF RENAL TUMORS ON COMPUTED TOMOGRAPHY

You have accessJournal of UrologyCME1 Apr 2023PD22-07 A MULTICENTER STUDY PRESENTING AN EXPLAINABLE AI APPROACH TO AUTOMATIC SEGMENTATION OF RENAL TUMORS ON COMPUTED TOMOGRAPHY Annemarie Uhlig, Sophie Bachanek, Paul Romario Würzberg, Manuel Nietert, Lutz Trojan, Joachim Lotz, and Johannes Uhlig Annemarie UhligAnnemarie Uhlig More articles by this author , Sophie BachanekSophie Bachanek More articles by this author , Paul Romario WürzbergPaul Romario Würzberg More articles by this author , Manuel NietertManuel Nietert More articles by this author , Lutz TrojanLutz Trojan More articles by this author , Joachim LotzJoachim Lotz More articles by this author , and Johannes UhligJohannes Uhlig More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003295.07AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Automatic segmentation of renal tumors is a cornerstone for Artificial Intelligence (AI) based tumor diagnostics. This study presents an automatic segmentation algorithm and visualization method using CT-studies from renal tumor patients recruited in a multicenter setting. METHODS: Renal tumor patients diagnosed between 2018 and 2021 were retrospectively assessed for this study. Inclusion criteria were CT-imaging of renal tumors in corticomedullary (CM) or nephrogenic (NG) contrast media phase. Patients aged <18years, and those with cystic or infiltrative renal tumors (i.e. lymphomas) were excluded. Manual segmentation of the renal tumors was performed by a GU-radiologist on all axial CT slices. A convolutional neural network (UNET) was trained based on the radiologists´ manual segmentations. In an independent validation dataset, the accuracy of the UNETs predictions of renal tumor contours was compared to the references standard of manual segmentations and quantified using the DICE score. RESULTS: A total of n=394/ n=350 patients with renal tumors imaged in CM /NG phase, respectively, were included (median age 66y; 35% female; median tumor diameter 5.4cm). CT-studies from >20 radiological imaging centers were included with different imaging protocols and slice thickness. The UNET was trained on n=316 CM and n=294 NG contrast phase patients (n=7019 / n=6859 separate CT images). In the independent validation dataset (n=78 / n=56 patients with 1713 / 1298 CT images), the UNET achieved a DICE score of 0.88 and 0.90 for the corticomedullary and nephrogenic CM phase, respectively. The UNET predictions were visualized using a tile-based approach with color-coding and contour-lines that could be overlaid on CT-images to depict varying levels of prediction confidence. CONCLUSIONS: A UNET AI-approach yields a robust automatic delineation of renal tumors on CT-images acquired in clinical routine, irrespective of the contrast media phase. Using color-coding and contour-lines that could be overlaid on original CT-images provides an explainable approach to the UNETs predictions and might improve AI-acceptance in clinical practice. Source of Funding: This study received funding by the 2022 ESR Research Seed Grant. The 2022 ESR Research Seed Grants were kindly supported by an unrestricted, non-exclusive grant from GE Healthcare © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e667 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Annemarie Uhlig More articles by this author Sophie Bachanek More articles by this author Paul Romario Würzberg More articles by this author Manuel Nietert More articles by this author Lutz Trojan More articles by this author Joachim Lotz More articles by this author Johannes Uhlig More articles by this author Expand All Advertisement PDF downloadLoading ...

Segmented solenoid RF coils for MRI of ex vivo brain samples at ultra-high field preclinical and clinical scanners

Magnetic resonance imaging (MRI) is a well-known and widespread imaging modality for neuroscience studies and the clinical diagnoses of neurological disorders, mainly due to its capability to visualize brain microstructures and quantify various metabolites. Additionally, its noninvasive nature makes possible the correlation of high-resolution MRI from ex vivo brain samples with histology, supporting the study of neurodegenerative disorders such as Alzheimer's or Parkinson's disease. However, the quality and resolution of ex vivo MRI highly depend on the availability of specialized radiofrequency coils with maximized filling factors for the different sizes and shapes of the samples to be studied. For instance, small, dedicated radiofrequency (RF) coils are not always commercially available in ultrahigh field whole-body MRI scanners. Even for ultrahigh field preclinical scanners, specific RF coils for ex vivo MRI are expensive and not always available. Here, we describe the design and construction of two RF coils based on the solenoid geometry for ex vivo MRI of human brain tissues in a 7T whole-body scanner and for ex vivo MRI of marmoset brain samples in a 9.4T preclinical scanner. We designed the 7T solenoid RF coil to maximize the filling factor of human brain samples conditioned on cassettes for histology, while the 9.4T solenoid was constructed to accommodate marmoset brain samples conditioned in 50 ml centrifuge tubes. Both solenoid designs operate in transceiver mode. The measured B1+ maps show a high level of homogeneity in the imaging volume of interest, with a high signal-to-noise ratio over the imaging volume. High-resolution (80 µm in plane, 500 µm slice thickness) images of human brain samples were acquired with the 7T solenoid, while marmoset brain samples were acquired with an isotropic resolution of 60 µm using the 9.4T solenoid coil.

On the influence of computed tomography's slice thickness on computer tomography based finite element analyses results

BackgroundPatient-specific autonomous finite element analyses of femurs, based on clinical computed tomography scans may be used to monitor the progression of bone-related diseases. Some CT scan protocols provide lower resolution (slice thickness of 3 mm) that affects the accuracy. To investigate the impact of low-resolution scans on the CT-based finite element analyses results, identical CT raw data were reconstructed twice to generate a 1 mm (“gold standard”) and a 3 mm slice thickness scans. MethodsCT-based finite element analyses of twenty-four femurs (twelve patients) under stance and sideways fall loads were performed based on 1 and 3 mm slice thickness scans. Bone volume, load direction, and strains were extracted at different locations along the femurs and differences were evaluated. FindingsAverage differences in bone volume were 1.0 ± 1.5%. The largest average difference in strains in stance position was in the neck region (11.0 ± 13.4%), whereas in other regions these were much smaller. For sidewise fall loading, the average differences were at most 9.2 ± 16.0%. InterpretationWhole-body low dose CT scans (3 mm-slice thickness) are suboptimal for monitoring strain changes in patient's femurs but may allow longitudinal studies if larger than 5% in all areas and larger than 12% in the upper neck. CT-based finite element analyses with slice thickness of 3 mm may be used in clinical practice for patients with smoldering myeloma to associate changes in strains with progression to active myeloma if above ∼10%.

Diagnosing Drowning in Postmortem CT Images Using Artificial Intelligence.

Imaging features of the lung in postmortem computed tomography (CT) scans have been reported in drowning cases. However, it is difficult for forensic pathologists with limited experience to distinguish subtle differences in CT images. In this study, artificial intelligence (AI) with deep learning capability was used to diagnose drowning in postmortem CT images, and its performance was evaluated. The samples consisted of high-resolution CT images of the chest of 153 drowned and 160 non-drowned bodies captured by an 8- or 64-row multislice CT system. The images were captured with an image slice thickness of 1.0 mm and spacing of 30 mm, and 28 images were typically captured. A modified AlexNet was used as the AI architecture. The output result was the drowning probability for each component image. To evaluate the performance of the proposed model, the area under the receiver operating characteristic curve (AUC) was analyzed, and the AUC value of 0.95 was obtained. This indicates that the proposed AI architecture is a useful and powerful complementary testing approach for diagnosing drowning in postmortem CT images. Notably, the accuracy was 81% (62/77) for cases in which resuscitation was performed, and 92% (216/236) for cases in which resuscitation was not attempted. Therefore, the proposed AI method should not be used to diagnose the cause of death when aggressive cardiopulmonary resuscitation was performed. Additionally, because honeycomb lungs are likely to exhibit different morphologies, emphysema cases should also be treated with caution when the proposed AI method is used to diagnose drowning.

Computed tomography-based measurements of normative liver and spleen volumes in children.

Quantification of organ size has utility in clinical care and research for diagnostics, prognostics and surgical planning. Volumetry is regarded as the best measure of organ size and change in size over time. Scarce reference values exist for liver and spleen volumes in healthy children. To report liver and spleen volumes for a sample of children defined by manual segmentation of contrast-enhanced CT images with the goal of defining normal values and thresholds that might indicate disease. This retrospective study included clinically acquired contrast-enhanced CTs of the abdomen/pelvis for children and adolescents imaged between January 2018 and July 2021. Liver and spleen volumes were derived through manual segmentation of CTs reconstructed at 2.5-, 3- or 5-mm slice thickness. A subset of images (5%, n=16) was also segmented using 0.5-mm slice thickness reconstructions to define agreement based on image slice thickness. We used Pearson correlation and multivariable regression to assess associations between organ volumes and patient characteristics. We generated reference intervals for the 5th, 25th, 50th (median), 75th and 95th percentiles for organ volumes as a function of age and weight using quantile regression models. Finally, we calculated Bland-Altman plots and intraclass correlation coefficients (ICC) to quantify agreement. We included a total of 320 children (mean age ± standard deviation [SD] = 9±4.6years; mean weight 38.1±18.8kg; 160 female). Liver volume ranged from340-2,002mL, and spleen volume ranged from 28-480mL. Patient weight (kg) (β=12.5), age (months) (β=1.7) and sex (female) (β = -35.3) were independent predictors of liver volume, whereas patient weight (kg) (β=2.4) and age (months) (β=0.3) were independent predictors of spleen volume. There was excellent absolute agreement (ICC=0.99) and minimal absolute difference (4mL) in organ volumes based on reconstructed slice thickness. We report reference liver and spleen volumes for children without liver or spleen disease. These results provide reference ranges and potential thresholds to identify liver and spleen size abnormalities that might reflect disease in children.

Verification of dose distribution in high dose-rate brachytherapy for cervical cancer using a normoxic N-vinylpyrrolidone polymer gel dosimeter.

The polymer gel dosimeter has been proposed for use as a 3D dosimeter for complex dose distribution measurement of high dose-rate (HDR) brachytherapy. However, various shapes of catheter/applicator for sealed radioactive source transport used in clinical cases must be placed in the gel sample. The absorbed dose readout for the magnetic resonance (MR)-based polymer gel dosimeters requires calibration data for the dose-transverse relaxation rate (R2) response. In this study, we evaluated in detail the dose uncertainty and dose resolution of three calibration methods, the multi-sample and distance methods using the Ir-192 source and the linear accelerator (linac) method using 6MV X-rays. The use of Ir-192 sources increases dose uncertainty with steep dose gradients. We clarified that the uniformly irradiated gel sample improved the signal-to-noise ratio (SNR) due to the large slice thickness of MR images and could acquire an accurate calibration curve using the linac method. The curved tandem and ovoid applicator used for intracavitary irradiation of HDR brachytherapy for cervical cancer were reproduced with a glass tube to verify the dose distribution. The results of comparison with the treatment planning system (TPS) calculation by gamma analysis on the 3%/2mm criterion were in good agreement with a gamma pass rate of 90%. In addition, the prescription dose could be evaluated accurately. We conclude that it is easy to place catheter/applicator in the polymer gel dosimeters, making them a useful tool for verifying the 3D dose distribution of HDR brachytherapy with accurate calibration methods.