Radiological Depth Research Articles

A deep learning-based dose calculation method for volumetric modulated arc therapy

BackgroundVolumetric modulated arc therapy (VMAT) planning optimization involves iterative adjustment of numerous parameters, and hence requires repeatedly dose recalculation. In this study, we used the deep learning method to develop a fast and accurate dose calculation method for VMAT.MethodsThe classical 3D UNet was adopted and trained to learn the physics principle of dose calculation. The inputs included the projected fluence map (FM), computed tomography (CT) images, the radiological depth and the source-to-voxel distance (SVD). The projected FM was generated by projecting the accumulated FM between two consecutive control points (CPs) onto the patient’s anatomy. The accumulated FM was calculated by simulating the movement of the multi-leaf collimator (MLC) from one CP to the next. The dose, calculated by the treatment planning system (TPS), was used as ground truth. 51 head and neck VMAT plans were used, with 43, 1 and 7 cases as training, validation, and testing datasets, respectively. Correspondingly, 7182, 180 and 1260 CP samples were included in the training, validation, and testing datasets.ResultsThis presented method was evaluated by comparing the derived dose distribution to the TPS calculated dose distribution. The dose profiles coincided for both the single CP and the entire plan (summation of all CPs). But the network derived dose was smoother than the TPS calculated dose. Gamma analysis was performed between the network derived dose and the TPS calculated dose. The average gamma pass rate was 96.56%, 98.75%, 98.03% and 99.30% under the criteria of 2% (tolerance) -2 mm (distance to agreement, DTA). 2%-3 mm, 3%-2 mm and 3%-3 mm. No significant difference was observed on the critical indices including the max, mean dose, and the relative volume covered by the 2000 cGy, 4000 cGy and the prescription dose. For one CP, the average computational time of the network and TPS was 0.09s and 0.53s. And for one patient, the average time was 16.51s and 95.60s.ConclusionThe dose distribution derived by the network showed good agreement with the TPS calculated dose distribution. The computational time was reduced to approximate one-sixth of its original duration. Therefore the presented deep learning-based dose calculation method has the potential to be used for planning optimization.

MRI and frozen section evaluation of mylohyoid muscle in determining surgical approach for T2-T3 floor of the mouth cancer.

The choice of surgical approach for floor of the mouth (FOM) cancer, particularly for intermediate-stage tumors (cT2-cT3), remains controversial. This study aims to evaluate a method considering mylohyoid muscle (MM) invasion as a determinant for surgical approach selection, utilizing magnetic resonance imaging (MRI) preoperatively and frozen section (FS) analysis intraoperatively. This observational retrospective cohort study analyzed patients undergoing surgical resection of cT2 and cT3 FOM squamous cell carcinoma (SCC) between January 2013 and June 2023. MM infiltration assessed by preoperative MRI determined the surgical approach: clear infiltration led to compartmental surgery (CS), while doubtful or absent infiltration led to transoral surgery (TOS). Conversion from TOS to CS occurred intraoperatively based on macroscopic evidence or positive FS. Data collected included demographic, clinical, surgical, and pathological variables. Survival analysis was conducted using Kaplan-Meier method. Among 44 patients included, majority had cT2 tumors (59.1%). MM resection was necessary in 22.7% of cases. Overall survival (OS) and progression-free survival (PFS) did not significantly differ between TOS and CS groups. Radiological depth of invasion (rDOI) < 10mm is correlated with MM preservation in 89% of cases, while rDOI > 10mm is correlated with MM resection only in 23.8% of cases. Pathological depth of invasion (pDOI) discrepancies were observed in the two groups: in CS group is shown a higher pDOI (> 10mm) confirmation (90%). Surgical complications and functional outcomes differed between TOS and CS groups. Considering MM invasion for surgical approach selection in cT2-cT3 FOM tumors appears oncologically safe, with better functional outcomes in muscle preservation. Preoperative MRI for MM assessment combined with intraoperative FS analysis provides reliable guidance for surgical decision-making.

Value of radiological depth of invasion in non-pT4 Oral tongue squamous cell carcinoma: implication for preoperative MR T-staging

ObjectiveThe prognostic stratification for oral tongue squamous cell carcinoma (OTSCC) is heavily based on postoperative pathological depth of invasion (pDOI). This study aims to propose a preoperative MR T-staging system based on tumor size for non-pT4 OTSCC.MethodsRetrospectively, 280 patients with biopsy-confirmed, non-metastatic, pT1-3 OTSCC, treated between January 2010 and December 2017, were evaluated. Multiple MR sequences, including axial T2-weighted imaging (WI), unenhanced T1WI, and axial, fat-suppressed coronal, and sagittal contrast-enhanced (CE) T1WI, were utilized to measure radiological depth of invasion (rDOI), tumor thickness, and largest diameter. Intra-class correlation (ICC) and univariate and multivariate analyses were used to evaluate measurement reproducibility, and factors’ significance, respectively. Cutoff values were established using an exhaustive method.ResultsIntra-observer (ICC = 0.81–0.94) and inter-observer (ICC = 0.79–0.90) reliability were excellent for rDOI measurements, and all measurements were significantly associated with overall survival (OS) (all p < .001). Measuring the rDOI on axial CE-T1WI with cutoffs of 8 mm and 12 mm yielded an optimal MR T-staging system for rT1-3 disease (5-year OS of rT1 vs rT2 vs rT3: 94.0% vs 72.8% vs 57.5%). Using multivariate analyses, the proposed T-staging exhibited increasingly worse OS (hazard ratio of rT2 and rT3 versus rT1, 3.56 [1.35–9.6], p = .011; 4.33 [1.59–11.74], p = .004; respectively), which outperformed pathological T-staging based on nonoverlapping Kaplan–Meier curves and improved C-index (0.682 vs. 0.639, p < .001).ConclusionsrDOI is a critical predictor of OTSCC mortality and facilitates preoperative prognostic stratification, which should be considered in future oral subsite MR T-staging.Clinical relevance statementUtilizing axial CE-T1WI, an MR T-staging system for non-pT4 OTSCC was developed by employing rDOI measurement with optimal thresholds of 8 mm and 12 mm, which is comparable with pathological staging and merits consideration in future preoperative oral subsite planning.Key Points• Tumor morphology, measuring sequences, and observers could impact MR-derived measurements and compromise the consistency with histology.• MR-derived measurements, including radiological depth of invasion (rDOI), tumor thickness, and largest diameter, have a prognostic impact on OS (all p < .001).• rDOI with cutoffs of 8 mm and 12 mm on axial CE-T1WI is an optimal predictor of OS and could facilitate risk stratification in non-pT4 OTSCC disease.

Focus stacking single-event particle radiography for high spatial resolution images and 3D feature localization

Objective. We demonstrate a novel focus stacking technique to improve spatial resolution of single-event particle radiography (pRad), and exploit its potential for 3D feature detection. Approach. Focus stacking, used typically in optical photography and microscopy, is a technique to combine multiple images with different focal depths into a single super-resolution image. Each pixel in the final image is chosen from the image with the largest gradient at that pixel's position. pRad data can be reconstructed at different depths in the patient based on an estimate of each particle's trajectory (called distance-driven binning; DDB). For a given feature, there is a depth of reconstruction for which the spatial resolution of DDB is maximal. Focus stacking can hence be applied to a series of DDB images reconstructed from a single pRad acquisition for different depths, yielding both a high-resolution projection and information on the features’ radiological depth at the same time. We demonstrate this technique with Geant4 simulated pRads of a water phantom (20 cm thick) with five bone cube inserts at different depths (1 × 1 × 1 cm3) and a lung cancer patient. Main results. For proton radiography of the cube phantom, focus stacking achieved a median resolution improvement of 136% compared to a state-of-the-art maximum likelihood pRad reconstruction algorithm and a median of 28% compared to DDB where the reconstruction depth was the center of each cube. For the lung patient, resolution was visually improved, without loss in accuracy. The focus stacking method also enabled to estimate the depth of the cubes within few millimeters accuracy, except for one shallow cube, where the depth was underestimated by 2.5 cm. Significance. Focus stacking utilizes the inherent 3D information encoded in pRad by the particle's scattering, overcoming current spatial resolution limits. It further opens possibilities for 3D feature localization. Therefore, focus stacking holds great potential for future pRad applications.

Deep Learning-Based Single-View Fluorescence Dose Reconstruction for 3D Dosimetry

3D dose distribution measurement is crucial for precise radiotherapy. Radiation-excited fluorescence imaging has potential for the 3D dosimetry with high spatial resolution, but multiple fluorescence images from different view-angles are required for analytical reconstruction techniques. Furthermore, the imaging data are contaminated by anisotropic Cherenkov light emission and statistical noise. This project aims to establish a novel deep learning-based model to predict 3D dose distributions from a single-view 2D fluorescence image while simultaneously removing the adverse effects of Cherenkov signals and other noises. A total of 124 single-aperture static photon beams were delivered to an acrylic tank containing 1 g/L quinine hemisulfate water solution with varying aperture shapes and collimator angle. The emitted optical signals were detected by a low-cost CMOS camera for 20 seconds, and image pre-processing was performed to obtain input 2D fluorescence images with 0.3 × 0.3 mm spatial resolution. 3D back-projected dose distribution images were also calculated from the input fluorescence images. Ground-truth of 3D dose distributions and 2D field map images were obtained from a clinical treatment planning system with 1.4 × 1.4 × 1.4 mm spatial resolution. The proposed deep learning-based dose reconstruction method involved 3 steps. First, 2D fluence map images at the bottom plane of the tank were predicted from the fluorescence images by using a customized convolutional neural network (CNN). Second, the predicted fluence map images were transformed into the 2D field map images on the isocenter plane by applying perspective transformation. Finally, 2D dose distributions at a given radiological depth were calculated by using the predicted field map images, the back-projected dose distribution images, and the radiological depth value as inputs of a shallow CNN. Both CNN models were trained separately, and the 3D dose distributions were predicted by concatenating the output 2D dose distributions at various radiological depths. The proposed CNN model yielded accurate 2D field map images. Averaged Dice similarity coefficient and mean absolute error of the field maps in the test data was 92.0% ± 4.6% and 0.0132 ± 0.0113, respectively. Moreover, our deep learning-based approach was able to predict accurate 3D dose distributions from the 2D fluorescence images. Mean squared error and averaged 3D gamma passing ratio (3%/3mm) were 9.55 mGy ± 6.8 mGy and 86.3% ± 9.86%, respectively. Theproposed deep learning-based method calculated accurate 3D dose distributions from a single-view 2D fluorescence image. Since this technique require only a single CMOS camera image and fluorescent material, it can be readily used for any external radiotherapy modalities, including SRS/SBRT with small fields. This method is useful for acquiring 3D dose distribution data for precise dose verification within a few seconds.

Towards MR contrast independent synthetic CT generation

The use of synthetic CT (sCT) in the radiotherapy workflow would reduce costs and scan time while removing the uncertainties around working with both MR and CT modalities. The performance of deep learning (DL) solutions for sCT generation is steadily increasing, however most proposed methods were trained and validated on private datasets of a single contrast from a single scanner. Such solutions might not perform equally well on other datasets, limiting their general usability and therefore value. Additionally, functional evaluations of sCTs such as dosimetric comparisons with CT-based dose calculations better show the impact of the methods, but the evaluations are more labor intensive than pixel-wise metrics.To improve the generalization of an sCT model, we propose to incorporate a pre-trained DL model to pre-process the input MR images by generating artificial proton density, T1 and T2 maps (i.e. contrast-independent quantitative maps), which are then used for sCT generation. Using a dataset of only T2w MR images, the robustness towards input MR contrasts of this approach is compared to a model that was trained using the MR images directly. We evaluate the generated sCTs using pixel-wise metrics and calculating mean radiological depths, as an approximation of the mean delivered dose.On T2w images acquired with the same settings as the training dataset, there was no significant difference between the performance of the models. However, when evaluated on T1w images, and a wide range of other contrasts and scanners from both public and private datasets, our approach outperforms the baseline model.Using a dataset of T2w MR images, our proposed model implements synthetic quantitative maps to generate sCT images, improving the generalization towards other contrasts. Our code and trained models are publicly available.

Efficacy of radiological depth of invasion measurements on magnetic resonance images acquired at different magnetic field strengths and imaging sequences in predicting cervical lymph node metastasis and other outcomes in tongue cancer

We investigated the correlation between magnetic resonance imaging (MRI) parameters and tumor pathological depth of invasion (pDOI), between pDOI and radiological DOI (rDOI), between rDOI and duration between biopsy and MRI, and between rDOI and duration between MRI and surgery to determine the efficacy of rDOI in identifying small lesions and other conditions. We examined 36 adult patients who had been diagnosed histopathologically with cancer of the tongue and had undergone a glossectomy. Using 1.5 Tesla (T) and 3.0T MRI, we measured rDOI at the deepest infiltration point on 4 MRI sequences. We calculated the correlations between rDOI and the variables examined by Spearman rho analysis and evaluated the diagnostic performance of rDOI by receiver operating characteristic curve analysis. Axial T2-weighted images using 1.5T MRI provided the closest approximation of pDOI. Although the correlation between rDOI and pDOI was significant, rDOI showed poor or acceptable discrimination in identifying small lesions and other conditions. There were no significant correlations between rDOI and the time between biopsy and MRI or between MRI and surgery. The correlation between rDOI and pDOI is significant, but rDOI is ineffective in predicting malignancy and other conditions. Axial T2-weighted images using 1.5T MRI provide the closest approximation of pDOI.

Correlation between radiologic depth of invasion and pathologic depth of invasion in oral cavity squamous cell carcinoma: A systematic review and meta-analysis

ObjectivesTo comprehensively assess the correlation between radiologic depth of invasion (rDOI) and pathologic depth of invasion (pDOI) in oral cavity squamous cell carcinoma (OSCC) by meta-analysis. Materials and MethodsPubMed and Embase databases were searched to find pertinent articles reporting rDOI of OSCC. Studies evaluating the correlations and mean differences (MDs) between rDOI and pDOI were included. The rDOI was measured based on ultrasound (US), computed tomography (CT), or magnetic resonance imaging (MRI). The correlation coefficients and MDs between rDOI and pDOI were meta-analytically pooled. Between-study heterogeneity was assessed using Higgins’ inconsistency index (I2). Subgroup analysis was performed based on imaging modality. ResultsTwenty-three studies with 1787 patients were included. The pooled correlation coefficient and MD were 0.86 (95 % confidence interval [CI], 0.82–0.90; I2 = 66.9 %) and 1.84 mm (95 % CI, 1.02–2.65 mm; I2 = 88.2 %), respectively. In subgroup analysis, MRI showed the largest MD (n = 12, 2.61 mm), followed by US (n = 2, –0.41 mm) and CT (n = 2, 0.12 mm). US showed the highest correlation coefficient (n = 3, 0.91), followed by MRI (n = 12, 0.85) and CT (n = 3, 0.82). ConclusionrDOI measured by US, CT, and MRI demonstrated excellent correlations with pDOI.

Diagnostic and prognostic value of magnetic resonance imaging in the detection of tumor depth of invasion and bone invasion in patients with oral cavity cancer.

To evaluate the accuracy of preoperative contrast-enhanced magnetic resonance imaging (MRI) in the assessment of radiological depth of invasion (rDOI) and bone invasion in patients with oral cavity cancer, and the prognostic value of preoperative rDOI. This retrospective study included patients with surgically resected oral cavity cancer and preoperative MRI acquired within four weeks before surgery. Two readers evaluated the MRI to assess the superficial and deep bone invasion, preoperative T stage, and measured the rDOI. The rDOI was compared to the histopathological DOI (pDOI), used as reference standard. Prognostic value of preoperative features for the disease-specific survival was evaluated using the Kaplan-Meier curve and multivariable Cox proportional hazards analysis. The final population included 80 patients (50 males, mean age 67.7 ± 13.6years). There was a strong statistically significant correlation between the rDOI (median 10mm) and the pDOI (median 9mm) (ρ: 0.978, p < 0.001). The agreement between MRI and histopathological T stage was excellent (k = 0.93, 95% CI 0.86, 0.99). The sensitivity and specificity of preoperative MRI were 93.3% and 98.8% for deep bone invasion, while they were 75.0% and 95.8% for superficial bone invasion, respectively. The rDOI > 10mm was associated with poorer disease-specific survival (log-rank p = 0.016). The rDOI remained the only independent preoperative predictor associated with poorer disease-specific survival at multivariable analysis (hazard ratio 5.5; 95% CI 1.14, 26.58; p = 0.033). Preoperative MRI is accurate for the assessment of DOI and bone invasion. The rDOI is an independent preoperative predictor of disease-specific survival in patients with oral cavity cancer.

Relationships between contrast-enhanced computed tomography features of hard palate cancer and pathological depth of invasion

The study aimed to determine the relationships between contrast-enhanced computed tomography (CECT) features of hard palate cancer (radiological depth of invasion [r-DOI], detectability of the lesion, and tumor invasion into the palatal bone) and the pathological DOI (p-DOI) of the tumors. In total, 36 lesions were retrospectively evaluated by 2 board-certified radiologists, who examined CECT scans for the radiological features, and 2 board-certified pathologists, who measured the p-DOI on histopathologic sections. Correlation between r-DOI and p-DOI was calculated. The Youden index was used to calculate the optimal p-DOI cutoff values to distinguish between detectable and undetectable lesions and between tumors with and without bony structure invasion. There was excellent agreement between r-DOI and p-DOI (intraclass correlation coefficient=0.80). The p-DOI of CECT-detectable lesions was significantly greater than that of CECT-undetectable lesions (P < .001), with a p-DOI cutoff value of 4 mm. The p-DOI of lesions that had invaded the palatal bone was significantly larger than that of lesions without invasion (P=.039), with a p-DOI cutoff value of 7 mm. Radiological DOI, tumor detectability, and invasion into the palatal bone can be useful in planning surgical treatment strategies for hard palate cancer.

A novel radiological method to evaluate the posterior tympanotomy depth for cochlear implantation: our experience in 257 patients

PurposeThis study aimed to validate our novel proposed radiological evaluation of the posterior tympanotomy (PT) depth. This dimension represents the bone of the facial recess needed to be drilled to get access into the middle ear during cochlear implantation.MethodsIt was a retrospective observational study that included 257 patients who underwent cochlear implantation from July 2018 to April 2021 in tertiary referral institutions. Two physicians evaluated the preoperative HRCT to measure the PT depth in the oblique para-sagittal cut. On the other hand, two other physicians evaluated the unedited surgical videos to judge the PT depth and classified it into an ordinary PT or deep PT. Then, the preoperative radiological measurements were correlated with the intraoperative findings.ResultsThe radiological PT depth ranged from 2.5 to 5.4 mm with a mean of 3.91 ± 0.886. Sixty-six patients had ordinary PT, and 191 patients had deep PT. Spearman’s correlation coefficient revealed a strong correlation between the preoperative radiological PT depth measurements and the intraoperative PT depth judgments (p value < 0.0001).ConclusionsWe created a novel radiological method to measure the posterior tympanotomy depth. This method was valid, reproducible, and reliable in the preoperative radiological evaluation of the PT depth with high sensitivity (91.71%), specificity (90.62%), and accuracy (91.44%). We also found a significant impact of the PT depth on the PT difficulty during cochlear implantation.

Sentinel lymph node biopsy for early oral cancer – accuracy and considerations in patient selection

Sentinel lymph node biopsy (SLNB) for staging oral squamous cell carcinoma (OSCC) patients presenting with early (T1 and T2 N0) disease in preference to elective neck dissection (END) remains controversial worldwide. A retrospective analysis of 145 patients who underwent sentinel lymph node biopsy for a previously untreated early oral cancer between 2010 and 2020 was performed. The primary outcome measures were predictors of occult metastases, accuracy of SLNB and disease specific plus overall survival. The negative predictive value, the false negative rate, and sensitivity for SLNB were 97%, 7.8%, and 92%, respectively. Depth of invasion (DOI) was a significant predictor of N status, overall survival, and disease specific survival. There was a significant difference in the incidence of the neck node metastasis in patients with DOI <5mm compared to those with DOI >5mm. For tumours >5mm there was a moderate to good correlation between radiological depth on contrast enhanced computed tomography (CECT) and histopathological DOI. Preoperative estimation of DOI may be a useful tool in the counselling of patients in the selection of either SLNB or END for N staging purposes in early OSCC.

The Usefulness of Intraoral Ultrasonography and Post-Contrast Three-Dimensional Fast Field Echo Imaging in the Evaluation of Radiological Depth of Invasion of Oral Tongue Squamous Cell Carcinoma: Comparison with Pathological Depth of Invasion Assessment

Aim Squamous cell carcinoma (SCC) is the most common malignancy in the oral tongue. Depth of invasion (DOI) of the tongue SCC is known as a predictor of recurrence and survival in patients. Both intraoral ultrasonography (ioUS) and post-contrast three-dimensional fast field echo (3D-FFE) imaging are useful for predicting pathological DOI and can contribute to effective treatment plans and a positive prognosis. The purpose of this study was to evaluate the usefulness of ioUS in comparison with post-contrast 3D-FFE imaging for assessing radiological DOI and compare radiological DOI and pathological DOI in patients with oral tongue SCC. Methods Fifteen patients were included in this study conducted between August 2018 and March 2020. Spearman's rank correlation coefficient was used to compare radiological DOI with pathological DOI. Results Both ioUS-derived DOI (9.98 mm) and 3D-FFE-derived DOI (9.86 mm) were larger than pathological DOI (5.7 mm). The Pearson’s correlation coefficient (PCC) between ioUS-derived DOI and pathological DOI was 0.63 and between 3D-FFE-derived DOI and pathological DOI was 0.58.

MRI before biopsy correlates with depth of invasion corrected for shrinkage rate of the histopathological specimen in tongue carcinoma

The purpose of this study was to evaluate which radiological depth of invasion (r-DOI) measurement is the most concordant to clinical DOI (c-DOI) derived from correction for the shrinkage rate of the histopathological specimens. We retrospectively reviewed 128 patients with tongue carcinoma who had undergone glossectomy between 2006 and 2019. At first, the width shrinkage rate during formalin fixation and preparation process of histopathological specimens was evaluated. From the shrinking rates, a formula to calculate c-DOI from pathological DOI (p-DOI) was developed. The correlation between c-DOI and r-DOI was evaluated. The specimen shrinkage rate during the histopathological specimen preparation process was 10.3%. Based on that, we yielded the correct formula for c-DOI based on p-DOI and preparation shrinkage rate: c-DOI = p-DOI × 100/89.7. The regression equations for the association of c-DOI with r-DOI measured by ultrasound (n = 128), MRI before biopsy (n = 18), and MRI after biopsy (n = 110) were y = 1.12 * x + 0.21, y = 0.89 * x − 0.26, and y = 0.52 * x + 2.63, respectively, while the coefficients of determination were 0.664, 0.891, and 0.422, respectively. In conclusion, r-DOI using MRI before biopsy most strongly correlated with c-DOI.

Correlation between the magnetic resonance imaging features of squamous cell carcinoma of the buccal mucosa and pathologic depth of invasion

The objective of this study was to determine correlations between magnetic resonance imaging (MRI) features including radiologic depth of invasion (r-DOI) and pathologic DOI (p-DOI) of squamous cell carcinoma of the buccal mucosa. In total, 31 lesions were retrospectively evaluated. MRI findings included detectability, buccinator muscle invasion (positive: BMI+, negative: BMI-), buccal fat pad invasion (positive: BFPI+, negative: BFPI-), and r-DOI measured on T2-weighted images (T2-DOI) and contrast-enhanced T1-weighted images (CET1-DOI). These findings were compared to the p-DOI of the tumors. The p-DOI values of undetectable lesions were smaller than those of detectable lesions (P < .001), and the cutoff value was 1 mm. BMI+ and BFPI+ lesions had significantly larger p-DOI values than the corresponding BMI- and BFPI- lesions (P < .001), with cutoff values of 5 and 6 mm, respectively. The correlation coefficient between CET1-DOI and p-DOI was 0.68 (P < .001). CET1-DOI values were larger than p-DOI (P < .001) and the average difference between them was 3.4 mm. T2-DOI was inconclusive in 50% of cases. Interobserver agreements of MRI evaluation were good to very good. MRI-derived parameters were useful in estimating p-DOI and may be helpful in predicting the depth of invasion of tumors and the risk of lymph node metastasis.

Usefulness of post contrast three-dimensional fast field echo imaging compared with Dixon T2-weighted imaging for the evaluation of radiological depth of invasion in oral tongue squamous cell carcinoma: comparison with pathological depth of invasion assessment

Usefulness of post contrast three-dimensional fast field echo imaging compared with Dixon T2-weighted imaging for the evaluation of radiological depth of invasion in oral tongue squamous cell carcinoma: comparison with pathological depth of invasion assessment

Endoscopic Anatomy and a Safe Surgical Corridor to the Anterior Skull Base

We describe the possibility to create precise preoperative planning for endonasal endoscopic approaches to the anterior skull base by overlapping endoscopic and radiologic anatomy. The important anatomic structures were marked. Morphometric measurements between these anatomic landmarks were performed endoscopically and compared with radiologic measurements of the same areas to ensure result compatibility. Seven cadaver heads injected intravascularly with colored silicone were used for this study. Thin-section brain and paranasal sinus computed tomography scans were obtained on all cadavers. Using 0-degree rigid endoscopes and endonasal endoscopic surgical instruments, the anterior skull base was examined binostrally in all cadavers. Bilateral middle turbinates were identified and preserved. Next, an inferior uncinectomy and middle meatal antrostomy were performed. After performing a frontal antrostomy, bilateral anterior and posterior ethmoidal cells were opened and the skull base was identified and followed to the posterior wall of the frontal sinus. A transnasal transethmoidal sphenoidotomy was done with full exposure to the entire anterior skull base. The anatomic landmarks for endonasal endoscopic skull base approaches were distinguished and measurements were made. The anterior skull base was divided into 3 compartments: anterior (area between the posterior inferior border of the frontal sinus and the course of anterior ethmoidal artery), middle (area between the course of the anterior ethmoidal artery and that of the posterior ethmoidal artery [PEA]), and posterior (area between the course of the PEA and the attachment point of the anterior border of the sphenoid sinus to the skull base) compartments. The distances between important anatomic markers and endoscopic depth measurements of this area were measured. During endonasal endoscopic anterior skull base surgery, the area between the anterior border of the sphenoid sinus and PEA artery was safe as the first dissection zone. Preoperative radiologic width and depth measurements facilitate orientation to the endoscopic anatomy during surgery and help predict the endonasal surgical corridor anatomy preoperatively.

Proximity-Based Sensor Fusion of Depth Cameras and Isotropic Rad-Detectors

Finding and tracking radioactive sources have numerous security applications in civilian energy installations, military facilities, and ports of entry. The price of radiological sensors varies proportionally to size and imaging characteristics such as angular resolution, and the cheapest devices are nearly isotropic–i.e., they integrate radiation from a sphere of directions centered at the sensor. While many radiation sensors have high aspect ratios or odd shapes, the sensors used here are right cylinders, with a near identical directional efficiency such that for analysis purposes, other aspects, such as counting statistics, would make nonisotropy of the sensor negligible. In this article, we propose a simple and robust way to integrate measurements from both isotropic radiological sensors and depth sensors, whose reliability and resolution benefit from recent advances in computer vision and imaging. Our key idea is to convert all sensor measurements into proximity signals based on radial distance variations over time. Based on this sensor fusion model, we show that for moving radiological sources even a simple Kalman filter can trade-off the complementary strength of high-resolution depth sensors and isotropic radiological sensors. We show novel results with a LIDAR sensor and a thermal stereo pair, and demonstrate applications such as tracking and rendering non-line-of-sight imagery behind obstacles and detecting multiple radiological sources in the same scene.

MR evaluation of tongue carcinoma in the assessment of depth of invasion with histopathological correlation: A single center experience.

Introduction:Magnetic resonance imaging (MRI) has become the cornerstone for pretreatment evaluation of carcinoma tongue and provides accurate information regarding the extent of the lesion and depth of invasion that helps the clinician to optimize treatment strategy. Aim of the study is to correlate MRI and histopathological findings, to evaluate the role of MRI in loco-regional tumor node metastasis (TNM) staging, and to assess the depth of invasion of tongue carcinoma. Materials and Methods:This study was undertaken on 30 patients with clinical diagnosis of tongue carcinoma referred for MR imaging at a tertiary care hospital over the 2-year period between July 2017 and June 2019. MRI was performed with GE 1.5 Tesla scanner, neurovascular (NV) array coil. Clinical and MRI staging of tongue carcinoma was done preoperatively and correlated. Post-surgery, histopathological TNM staging was done and correlated with clinical and MRI TNM staging. The cutoff value of histopathological (HP) depth that could determine the existence of nodal metastasis was 5 mm. Results:In 30 patients diagnosed with tongue carcinoma, the incidence was higher in males (92%). Moderate agreement (k = 0.512) was noted for T staging between clinical and MRI staging assessments. Fair agreement (k = 0.218) was noted for N stage between clinical and MRI staging assessments. There was good agreement (k = 0.871) for M stage between the clinical and MRI staging assessments. Good agreement (k = 0.822 and k = 0.767, respectively) was noted for both T staging and Nstaging between MRI and histopathology staging assessments. The agreement for the T stage was poor (k = 0.012) between the clinical and histopathology staging assessments. Agreement for the N stage was also poor (k = 0.091) between the clinical and histopathology staging assessments. Mean depth of invasion by histology and MRI was 14.22 mm and 16.12 mm, respectively. Moderate agreement (k = 0.541) was noted between clinical and pathological tumor depth and good agreement (k = 0.844) was noted between radiological and pathological tumor depth. As for the T1WGd MRI depth with a cutoff value of 5 mm, the nodal metastasis rate in the group with values >5 mm was 52%, and for those <5 mm was 24%, both of which were significantly different (P = 0.040). Pearson's correlation coefficient of HP depth and T1WGd MRI depth was 0.851 (P < 0.001) suggesting that HP depth shows a strong correlation with T1WGd MRI depth. Conclusion:MRI is the imaging modality of choice for evaluation of tongue carcinoma as MRI helps in accurate staging of the tumor using TNM classification which is crucial for optimizing treatment options. The current study shows a high correlation between MRI and histopathological findings regarding thickness of tumor and depth of invasion. MRI and histopathology assessments of tumor spread were equivalent to within 0.5 mm DOI. Estimation of invasion depth using MRI as a preoperative study in oral tongue carcinoma is essential in planning surgical treatment strategies such as the extent of elective neck dissection. Invasion depth, which greatly affects occult node metastases, must be included in the TNM staging of oral tongue carcinoma.

Impact of varying planning parameters on proton pencil beam scanning dose distributions in four commercial treatment planning systems.

In pencil beam scanning proton therapy, target coverage is achieved by scanning the pencil beam laterally in the x- and y-directions and delivering spots of dose to positions at a given radiological depth (layer). Dose is delivered to the spots on different layers by pencil beams of different energy until the entire volume has been irradiated. The aim of this study is to investigate the implementation of proton planning parameters (spot spacing, layer spacing and margins) in four commercial proton treatment planning systems (TPSs): Eclipse, Pinnacle3 , RayStation and XiO. Using identical beam data in each TPS, plans were created on uniform material synthetic phantoms with cubic targets. The following parameters were systematically varied in each TPS to observe their different implementations: spot spacing, layer spacing and margin. Additionally, plans were created in Eclipse to investigate the impact of these parameters on plan delivery and optimal values are suggested. It was found that all systems except Eclipse use a variable layer spacing per beam, based on the Bragg peak width of each energy layer. It is recommended that if this cannot be used, then a constant value of 5mm will ensure good dose homogeneity. Only RayStation varies the spot spacing according to the variable spot size with depth. If a constant spot spacing is to be used, a value of 5mm is recommended as a good compromise between dose homogeneity, plan robustness and planning time. It was found that both Pinnacle3 and RayStation position spots outside of the defined volume (target plus margin). All four systems are capable of delivering uniform dose distributions to simple targets, but their implementation of the various planning parameters is different. In this paper comparisons are made between the four systems and recommendations are made as to the values that will provide the best compromise in dose homogeneity and planning time.