Conversion map from quantitative parameter mapping to myelin water fraction: comparison with R1·R2* and myelin water fraction in white matter.

OBJECTIVE: To clarify the relationship between myelin water fraction (MWF) and R1⋅R2* and to develop a method to calculate MWF directly from parameters derived from QPM, i.e., MWF converted from QPM (MWFQPM). MATERIALS AND METHODS: Subjects were 12 healthy volunteers. On a 3 T MR scanner, dataset was acquired using spoiled gradient-echo sequence for QPM. MWF and R1⋅R2* maps were derived from the multi-gradient-echo (mGRE) dataset. Volume-of-interest (VOI) analysis using the JHU-white matter (WM) atlas was performed. All the data in the 48 WM regions measured VOI were plotted, and quadratic polynomial approximations of each region were derived from the relationship between R1·R2* and the two-pool model-MWF. The R1·R2* map was converted to MWFQPM map. MWF atlas template was generated using converted to MWF from R1·R2* per WM region. RESULTS: The mean MWF and R1·R2* values for the 48 WM regions were 11.96 ± 6.63%, and 19.94 ± 4.59 s-2, respectively. A non-linear relationship in 48 regions of the WM between MWF and R1·R2* values was observed by quadratic polynomial approximation (R2 ≥ 0.963, P < 0.0001). DISCUSSION: MWFQPM map improved image quality compared to the mGRE-MWF map. Myelin water atlas template derived from MWFQPM may be generated with combined multiple WM regions.

Determination of Alzheimer's disease based on morphology and atrophy using machine learning combined with automated segmentation.

BACKGROUND: To evaluate the degree of cerebral atrophy for Alzheimer's disease (AD), voxel-based morphometry has been performed with magnetic resonance imaging. Detailed morphological changes in a specific tissue area having the most evidence of atrophy were not considered by the machine-learning technique. PURPOSE: To develop a machine-learning system that can capture morphology features for determination of atrophy of brain tissue in early-stage AD and classification of healthy participants or patients. MATERIAL AND METHODS: Three-dimensional T1-weighted (3D-T1W) data were obtained from AD Neuroimaging Initiative (200 healthy controls and 200 patients with early-stage AD). Automated segmentation of 3D-T1W data was performed. Deep learning (DL) and support vector machine (SVM) were trained using 66-segmented volume values as input and AD diagnosis as output. DL was performed using 66 volume values or gray matter (GM) and white matter (WM) volume values. SVM learning was performed using 66 volume values and six regions with high variable importance. 3D convolutional neural network (3D-CNN) was trained using the segmented images. Accuracy and area under curve (AUC) were obtained. Variable importance was evaluated from logistic regression analysis. RESULTS: DL for GM and WM volume values, accuracy 0.6; SVM for all volume values, accuracy 0.82 and AUC 0.81; DL for all volume values, accuracy 0.82 and AUC 0.8; 3D-CNN using segmental images of the whole brain, accuracy 0.5 and AUC 0.51. SVM using volume values of six regions, accuracy 0.82; image-based 3D-CNN, highest accuracy 0.69. CONCLUSION: Our results show that atrophic features are more considerable than morphological features in the early detection of AD.

Utilization of Artificial Intelligence in Echocardiography.

Echocardiography has a central role in the diagnosis and management of cardiovascular disease. Precise and reliable echocardiographic assessment is required for clinical decision-making. Even if the development of new technologies (3-dimentional echocardiography, speckle-tracking, semi-automated analysis, etc.), the final decision on analysis is strongly dependent on operator experience. Diagnostic errors are a major unresolved problem. Moreover, not only can cardiologists differ from one another in image interpretation, but also the same observer may come to different findings when a reading is repeated. Daily high workloads in clinical practice may lead to this error, and all cardiologists require precise perception in this field. Artificial intelligence (AI) has the potential to improve analysis and interpretation of medical images to a new stage compared with previous algorithms. From our comprehensive review, we believe AI has the potential to improve accuracy of diagnosis, clinical management, and patient care. Although there are several concerns about the required large dataset and "black box" algorithm, AI can provide satisfactory results in this field. In the future, it will be necessary for cardiologists to adapt their daily practice to incorporate AI in this new stage of echocardiography.

[Evaluation of a Post-analysis Method for Cumulative Dose Distribution in Stereotactic Body Radiotherapy].

PURPOSE: The purpose of this study was to evaluate a post-analysis method for cumulative dose distribution in stereotactic body radiotherapy (SBRT) using volumetric modulated arc therapy (VMAT) . METHOD: VMAT is capable of acquiring respiratory signals derived from projection images and machine parameters based on machine logs during VMAT delivery. Dose distributions were reconstructed from the respiratory signals and machine parameters in the condition where respiratory signals were without division, divided into 4 and 10 phases. The dose distribution of each respiratory phase was calculated on the planned four-dimensional CT (4DCT). Summation of the dose distributions was carried out using deformable image registration (DIR), and cumulative dose distributions were compared with those of the corresponding plans. RESULTS AND DISCUSSION: Without division, dose differences between cumulative distribution and plan were not significant. In the condition where respiratory signals were divided, dose differences were observed over dose in cranial region and under dose in caudal region of planning target volume (PTV). Differences between 4 and 10 phases were not significant. CONCLUSION: The present method was feasible for evaluating cumulative dose distribution in VMAT-SBRT using 4DCT and DIR.

Evaluation of In Vivo Volumetric Dosimetry for Prostate Cancer Using Electronic Portal Imaging Device.

PURPOSE: Volumetric modulated arc therapy (VMAT) is capable of acquiring projection images using electronic portal imaging device (EPID). Commercial EPID-based dosimetry software, dosimetry check (DC), allows in vivo dosimetry using projection images. The purpose of this study was to evaluate in vivo dosimetry for prostate cancer using VMAT. METHOD: VMAT plans were generated for eight patients with prostate cancer using treatment planning system (TPS), and patient quality assurances (QAs) were carried out with phantom. We analyzed five plans as phantom study and five plans as patient study. Projection images were acquired during VMAT delivery. DC converted acquired images into fluence images and used a pencil beam algorithm to calculate dose distributions delivered on the CT images of the phantom and the patients. We evaluated isocenter point doses and gamma analysis in both studies and dose indexes of planning target volume (PTV), bladder and rectum in patient study. RESULTS AND DISCUSSION: Dose differences at the isocenter were less than a criterion in both studies. Pass rates of the gamma analysis were less than a criterion by two plans in the phantom study. Dose indexes of reconstructed distribution were lower than original plans and standard deviations of PTV in reconstructed distribution were larger than original plans. The errors were caused by some issues, such as the commissioning of DC, variations in patient anatomy, and patient positioning. CONCLUSION: The method was feasible to non-invasively perform in vivo dose evaluation for prostate cancer using VMAT.

[Dose reconstruction using respiratory signals and machine parameters during treatment in stereotactic body radiotherapy].

PURPOSE: Volumetric modulated arc therapy (VMAT) is a rotational intensity-modulated radiotherapy (IMRT) technique capable of acquiring projection images during treatment. The purpose of this study was to reconstruct the dose distribution from respiratory signals and machine parameters acquired during stereotactic body radiotherapy (SBRT). METHODS: The treatment plans created for VMAT-SBRT included the constraint of 1 mm/degree in multileaf collimator (MLC) for a moving phantom and three patients with lung tumors. The respiratory signals were derived from projection images acquired during VMAT delivery, while the machine parameters were derived from machine logs. The respiratory signals and machine parameters were then linked along with the gantry angle. With this data, the dose distribution of each respiratory phase was calculated on the planned four-dimensional CT (4D CT). The doses at the isocenter, the point of max dose and the centroid of the target were compared with those of the corresponding plans. RESULTS AND DISCUSSION: In the phantom study, the maximum dose difference between the plan and "in-treatment" results was -0.4% at the centroid of the target. In the patient study, the difference was -1.8 ± 0.4% at the centroid of the target. Dose differences of the evaluated points between 4 and 10 phases were not significant. CONCLUSION: The present method successfully reconstructed the dose distribution using the respiratory signals and machine parameters acquired during treatment. This is a feasible method for verifying the actual dose for a moving target.

Corticospinal tract-sparing intensity-modulated radiotherapy treatment planning.

AIM: To establish intensity-modulated radiotherapy (IMRT) planning procedures that spare the corticospinal tract by integrating diffusion tensor tractography into the treatment planning software. BACKGROUND: Organs at risk are generally contoured according to the outline of the organ as demonstrated by CT or MRI. But a part of the organ with specific function is difficult to protect, because such functional part of the organ cannot be delineated on CT or conventional sequence of MRI. METHODS: Diagnostic and treatment planning images of glioblastoma patients who had been treated by conventional 3-dimensional conformal radiotherapy were used for re-planning of IMRT. Three-dimensional fiber maps of the corticospinal tracts were created from the diffusion tensors obtained from the patients before the surgery, and were blended with the anatomical MR images (i.e. gadolinium-enhanced T1-weighted images or T2-weighted images). DICOM-formatted blended images were transferred and fused to the planning CT images. Then, IMRT plans were attempted. RESULTS: The corticospinal tracts could be contoured as organs at risk (OARs), because the blended images contained both anatomical information and fiber-tract maps. Other OARs were contoured in a way similar to that of ordinary IMRT planning. Gross tumor volumes, clinical target volumes, planning target volumes, and other OARs were contoured on the treatment planning software, and IMRT plans were made. CONCLUSIONS: IMRT plans with diminished doses to the corticospinal tract were attained. This technique enabled us to spare specific neuron fibers as OARs which were formerly "invisible" and to reduce the probability of late morbidities.

[Winston-lutz test and acquisition of flexmap using rotational irradiation].

PURPOSE: IGRT (image guided radiation therapy) is a useful technique for implementing precisely targeted radiation therapy. Quality assurance and quality control (QA/QC) medical linear accelerators with a portal imaging system (electronic portal imaging device: EPID) are the key to ensuring safe IGRT. The Winston-Lutz test (WLT) provides an evaluation of the MV isocenter, which is the intersection of radiation, collimator, and couch isocenters. A flexmap can indicate a displacement of EPID from the beam center axis as a function of gantry angles which can be removed from the images. The purpose of this study was to establish a novel method for simultaneously carrying out WLT and acquiring a flexmap using rotational irradiation. We also observed long-term changes in flexmaps over a period of five months. METHOD: We employed rotational irradiation with a rectangular field (30×30 mm). First, the displacement of EPID from the beam center axis, indicated by the ball bearing (BB) center, was evaluated using an in-house program. The location of the BB center was then modified according to WLT. Second, a second irradiation was used to acquire a flexmap. We performed this examination regularly and evaluated long-term changes in the flexmap. RESULTS AND DISCUSSION: It proved feasible to perform WLT and flexmap measurements using our proposed methods. The precision of WLT using rotational irradiation was 0.1 mm. In flexmap analysis, the maximum displacement from the mean value for each angle was 0.4 mm over five months. CONCLUSION: We have successfully established a novel method of simultaneously carrying out WLT and flexmap acquisition using rotational irradiation. Maximum displacement from the mean in each angle was 0.4 mm over five months.

Quantum annealing-based computed tomography using variational approach for a real-number image reconstruction.

Objective. Despite recent advancements in quantum computing, the limited number of available qubits has hindered progress in CT reconstruction. This study investigates the feasibility of utilizing quantum annealing-based computed tomography (QACT) with current quantum bit levels.Approach. The QACT algorithm aims to precisely solve quadratic unconstrained binary optimization problems. Furthermore, a novel approach is proposed to reconstruct images by approximating real numbers using the variational method. This approach allows for accurate CT image reconstruction using a small number of qubits. The study examines the impact of projection data quantity and noise on various image sizes ranging from 4 × 4 to 24 × 24 pixels. The reconstructed results are compared against conventional reconstruction algorithms, namely maximum likelihood expectation maximization (MLEM) and filtered back projection (FBP).Main result. By employing the variational approach and utilizing two qubits for each pixel of the image, accurate reconstruction was achieved with an adequate number of projections. Under conditions of abundant projections and lower noise levels, the image quality in QACT algorithm outperformed that of MLEM and FBP algorithms. However, in situations with limited projection data and in the presence of noise, the image quality in QACT was inferior to that in MLEM.Significance. This study developed the QACT reconstruction algorithm using the variational approach for real-number reconstruction. Remarkably, only 2 qubits were required for each pixel representation, demonstrating their sufficiency for accurate reconstruction.

Fine structural human phantom in dentistry and instance tooth segmentation.

In this study, we present the development of a fine structural human phantom designed specifically for applications in dentistry. This research focused on assessing the viability of applying medical computer vision techniques to the task of segmenting individual teeth within a phantom. Using a virtual cone-beam computed tomography (CBCT) system, we generated over 170,000 training datasets. These datasets were produced by varying the elemental densities and tooth sizes within the human phantom, as well as varying the X-ray spectrum, noise intensity, and projection cutoff intensity in the virtual CBCT system. The deep-learning (DL) based tooth segmentation model was trained using the generated datasets. The results demonstrate an agreement with manual contouring when applied to clinical CBCT data. Specifically, the Dice similarity coefficient exceeded 0.87, indicating the robust performance of the developed segmentation model even when virtual imaging was used. The present results show the practical utility of virtual imaging techniques in dentistry and highlight the potential of medical computer vision for enhancing precision and efficiency in dental imaging processes.

Simplified assessment for chemical exchanged saturation transfer (CEST) imaging: local offset frequency and CEST effect.

The aim of this study is to develop a novel phantom for the evaluation of clinical CEST imaging settings, e.g., B0 and B1 field inhomogeneities, CEST contrast, and post-processing. We made a phantom composed of two slice sections: a grid section for local offset frequency evaluation and a sample section for CEST effect evaluation using different concentrations of an egg white albumin solution. On a 3 Tesla MR scanner, a phantom study was performed using CEST imaging; the mean B1 amplitudes were set at 1.2 and 1.9 µT, and CEST images with and without B0 corrections were acquired. Next, region of interest (ROI) analysis was performed for each slice. Then, CEST images with and without B0 corrections were compared at each B1 amplitude. The B0 corrected Z-spectrums at each local region in the grid section showed a shifting of the curve bottom to 0 ppm. Z-spectrum at B1 = 1.9 µT showed a broader curve shape than that at 1.2 µT. Moreover, MTRasym values at 3.5 ppm for each albumin sample at B1 = 1.9 µT were about two times higher than those at 1.2 µT. Our phantom enabled us to evaluate and optimize B0 inhomogeneity and the CEST effect at the B1 amplitude.

Pixel-by-pixel correction of beam hardening artifacts by bowtie filter in fan-beam CT.

Objective. Beam hardening (BH) artifacts in computed tomography (CT) images originate from the polychromatic nature of x-ray photons. In a CT system with a bowtie filter, residual BH artifacts remain when polynomial fits are used. These artifacts lead to worse visuals, reduced contrast, and inaccurate CT numbers. This work proposes a pixel-by-pixel correction (PPC) method to reduce the residual BH artifacts caused by a bowtie filter.Approach. The energy spectrum for each pixel at the detector after the photons pass through the bowtie filter was calculated. Then, the spectrum was filtered through a series of water slabs with different thicknesses. The polychromatic projection corresponding to the thickness of the water slab for each detector pixel could be obtained. Next, we carried out a water slab experiment with a mono energyE= 69 keV to get the monochromatic projection. The polychromatic and monochromatic projections were then fitted with a 2nd-order polynomial. The proposed method was evaluated on digital phantoms in a virtual CT system and phantoms in a real CT machine.Main results. In the case of a virtual CT system, the standard deviation of the line profile was reduced by 23.8%, 37.3%, and 14.3%, respectively, in the water phantom with different shapes. The difference of the linear attenuation coefficients (LAC) in the central and peripheral areas of an image was reduced from 0.010 to 0.003cm-1and 0.007cm-1to 0 in the biological tissue phantom and human phantom, respectively. The method was also validated using CT projection data obtained from Activion16 (Canon Medical Systems, Japan). The difference in the LAC in the central and peripheral areas can be reduced by a factor of two.Significance. The proposed PPC method can successfully remove the cupping artifacts in both virtual and authentic CT images. The scanned object's shapes and materials do not affect the technique.

Differences of white matter structure for diffusion kurtosis imaging using voxel-based morphometry and connectivity analysis.

Objectives: In a clinical study, diffusion kurtosis imaging (DKI) has been used to visualize and distinguish white matter (WM) structures' details. The purpose of our study is to evaluate and compare the diffusion tensor imaging (DTI) and DKI parameter values to obtain WM structure differences of healthy subjects. Methods: Thirteen healthy volunteers (mean age, 25.2 years) were examined in this study. On a 3-T MRI system, diffusion dataset for DKI was acquired using an echo-planner imaging sequence, and T1-weghted (T1w) images were acquired. Imaging analysis was performed using Functional MRI of the brain Software Library (FSL). First, registration analysis was performed using the T1w of each subject to MNI152. Second, DTI (eg, fractional anisotropy [FA] and each diffusivity) and DKI (eg, mean kurtosis [MK], radial kurtosis [RK], and axial kurtosis [AK]) datasets were applied to above computed spline coefficients and affine matrices. Each DTI and DKI parameter value for WM areas was compared. Finally, tract-based spatial statistics (TBSS) analysis was performed using each parameter. Results: The relationship between FA and kurtosis parameters (MK, RK, and AK) for WM areas had a strong positive correlation (FA-MK, R2 = 0.93; FA-RK, R2 = 0.89) and a strong negative correlation (FA-AK, R2 = 0.92). When comparing a TBSS connection, we found that this could be observed more clearly in MK than in RK and FA. Conclusions: WM analysis with DKI enable us to obtain more detailed information for connectivity between nerve structures. Advances in knowledge: Quantitative indices of neurological diseases were determined using segmenting WM regions using voxel-based morphometry processing of DKI images.

A novel CT-based radiomics model for predicting response and prognosis of chemoradiotherapy in esophageal squamous cell carcinoma.

No clinically relevant biomarker has been identified for predicting the response of esophageal squamous cell carcinoma (ESCC) to chemoradiotherapy (CRT). Herein, we established a CT-based radiomics model with artificial intelligence (AI) to predict the response and prognosis of CRT in ESCC. A total of 44 ESCC patients (stage I-IV) were enrolled in this study; training (n = 27) and validation (n = 17) cohorts. First, we extracted a total of 476 radiomics features from three-dimensional CT images of cancer lesions in training cohort, selected 110 features associated with the CRT response by ROC analysis (AUC ≥ 0.7) and identified 12 independent features, excluding correlated features by Pearson's correlation analysis (r ≥ 0.7). Based on the 12 features, we constructed 5 prediction models of different machine learning algorithms (Random Forest (RF), Ridge Regression, Naive Bayes, Support Vector Machine, and Artificial Neural Network models). Among those, the RF model showed the highest AUC in the training cohort (0.99 [95%CI 0.86-1.00]) as well as in the validation cohort (0.92 [95%CI 0.71-0.99]) to predict the CRT response. Additionally, Kaplan-Meyer analysis of the validation cohort and all the patient data showed significantly longer progression-free and overall survival in the high-prediction score group compared with the low-prediction score group in the RF model. Univariate and multivariate analyses revealed that the radiomics prediction score and lymph node metastasis were independent prognostic biomarkers for CRT of ESCC. In conclusion, we have developed a CT-based radiomics model using AI, which may have the potential to predict the CRT response as well as the prognosis for ESCC patients with non-invasiveness and cost-effectiveness.

[Multidisciplinary medical care in radiation oncology].

Multidisciplinary medical care is now needed in the clinical setting of cancer treatment. In Japan, radiotherapy technologists and nurses have long played an important role in radiation oncology departments in many hospitals, resulting in spontaneously-arising multidisciplinary medical care teams. Multidisciplinary medical care, in which many medical staff work together on an equal footing, is based on multidisciplinary conferences, interdisciplinary communications, and most of all, doctors' decisions to transfer important daily work to paramedicals. The newly-introduced consultation fee for outpatient radiotherapy is a special charge for radiotherapy patients. To charge this fee, daily observation of patients by technologists or nurses is required, a practice which promotes multidisciplinary medical care in the field of radiation oncology. The number of technologists and nurses who specialize in radiation oncology has been increasing in recent years. When hospitals allow them to devote their specialized knowledge and skills entirely to radiation oncology practice, multidisciplinary medical care will develop within the clinical practice of radiation oncology.

X-ray energy spectrum estimation based on a virtual computed tomography system.

This paper presents a method for estimating the x-ray energy spectrum for computed tomography (CT) in the diagnostic energy range from the reconstructed CT image itself. To this end, a virtual CT system was developed, and datasets, including CT images for the Gammex phantom labeled by the corresponding energy spectra, were generated. Using these datasets, an artificial neural network (ANN) model was trained to reproduce the energy spectrum from the CT values in the Gammex inserts. In the actual application, an aluminum-based bow-tie filter was used in the virtual CT system, and an ANN model with a bow-tie filter was also developed. Both ANN models without/with a bow-tie filter can estimate the x-ray spectrum within the agreement, which is defined as one minus the absolute error, of more than 80% on average. The agreement increases as the tube voltage increases. The estimation was occasionally inaccurate when the amount of noise on the CT image was considerable. Image quality with a signal-to-noise ratio of more than 10 for the basis material of the Gammex phantom was required to predict the spectrum accurately. Based on the experimental data acquired from Activion16 (Canon Medical System, Japan), the ANN model with a bow-tie filter produced a reasonable energy spectrum by simultaneous optimization of the shape of the bow-tie filter. The present method requires a CT image for the Gammex phantom only, and no special setup, thus it is expected to be readily applied in clinical applications, such as beam hardening reduction, CT dose management, and material decomposition, all of which require exact information on the x-ray energy spectrum.

Radiomics analysis of [18F]-fluoro-2-deoxyglucose positron emission tomography for the prediction of cervical lymph node metastasis in tongue squamous cell carcinoma.

OBJECTIVES: This study aimed to create a predictive model for cervical lymph node metastasis (CLNM) in patients with tongue squamous cell carcinoma (SCC) based on radiomics features detected by [18F]-fluoro-2-deoxyglucose (18F-FDG) positron emission tomography (PET). METHODS: A total of 40 patients with tongue SCC who underwent 18F-FDG PET imaging during their first medical examination were enrolled. During the follow-up period (mean 28 months), 20 patients had CLNM, including six with late CLNM, whereas the remaining 20 patients did not have CLNM. Radiomics features were extracted from 18F-FDG PET images of all patients irrespective of metal artifact, and clinicopathological factors were obtained from the medical records. Late CLNM was defined as the CLNM that occurred after major treatment. The least absolute shrinkage and selection operator (LASSO) model was used for radiomics feature selection and sequential data fitting. The receiver operating characteristic curve analysis was used to assess the predictive performance of the 18F-FDG PET-based model and clinicopathological factors model (CFM) for CLNM. RESULTS: Six radiomics features were selected from LASSO analysis. The average values of the area under the curve (AUC), accuracy, sensitivity, and specificity of radiomics analysis for predicting CLNM from 18F-FDG PET images were 0.79, 0.68, 0.65, and 0.70, respectively. In contrast, those of the CFM were 0.54, 0.60, 0.60, and 0.60, respectively. The 18F-FDG PET-based model showed significantly higher AUC than that of the CFM. CONCLUSIONS: The 18F-FDG PET-based model has better potential for diagnosing CLNM and predicting late CLNM in patients with tongue SCC than the CFM.

Virtual cone-beam computed tomography simulator with human phantom library and its application to the elemental material decomposition.

PURPOSE: The purpose of this study is to develop a virtual CBCT simulator with a head and neck (HN) human phantom library and to demonstrate the feasibility of elemental material decomposition (EMD) for quantitative CBCT imaging using this virtual simulator. METHODS: The library of 36 HN human phantoms were developed by extending the ICRP 110 adult phantoms based on human age, height, and weight statistics. To create the CBCT database for the library, a virtual CBCT simulator that simulated the direct and scattered X-ray on a flat panel detector using ray-tracing and deep-learning (DL) models was used. Gaussian distributed noise was also included on the flat panel detector, which was evaluated using a real CBCT system. The usefulness of the virtual CBCT system was demonstrated through the application of the developed DL-based EMD model for case involving virtual phantom and real patient. RESULTS: The virtual simulator could generate various virtual CBCT images based on the human phantom library, and the prediction of the EMD could be successfully performed by preparing the CBCT database from the proposed virtual system, even for a real patient. The CBCT image degradation owing to the scattered X-ray and the statistical noise affected the prediction accuracy, although these effects were minimal. Furthermore, the elemental distribution using the real CBCT image was also predictable. CONCLUSIONS: This study demonstrated the potential of using computer vision for medical data preparation and analysis, which could have important implications for improving patient outcomes, especially in adaptive radiation therapy.

Correlation between bladder volume and irradiated dose of small bowel in CT-based planning of intracavitary brachytherapy for cervical cancer.

OBJECTIVE: To quantify the effect of bladder volume on the dose distribution of intracavitary brachytherapy in computed tomography-based treatment planning for cervical cancer. METHODS: Ten patients with cervical cancer were treated with high-dose rate radiation brachytherapy. For the three-dimensional analysis, pelvic computed tomographic scans were obtained from patients with indwelling catheters in place and from patients who received 50, 100, 150 and 200 cc injections of sterile water into their bladders ('200 cc' was defined as a full bladder). Additionally, scans were made in the prone position with the full bladder. RESULTS: Bladder fullness significantly affected the dose to the small bowel and bladder. The median of maximal doses to the small bowel was significantly greater with an empty bladder in all factors of hot spot (480 vs. 256 cGy on D-2cc). Although dosimetry revealed lower doses for larger volumes of bladder (D-50 and V-25%), the median maximal dose to the bladder was significantly greater with a full bladder (420 vs. 775 cGy on D-2cc). The rectosigmoid doses were not affected by bladder distension (476 vs. 467 cGy on D-2cc). After changing to the prone position, the hot spot dose of small bowel did not change but that of the bladder significantly decreased, although this procedure was very difficult. CONCLUSIONS: An increase in bladder volume resulted in a significant reduction in the hot spot dose of the small bowel at the expense of an increase in that of the bladder without changing the dose distribution of the rectosigmoid.