Dose-volume relationships of planned versus estimated delivered radiation doses to pelvic organs at risk and side effects in patients treated with salvage radiotherapy for recurrent prostate cancer.

Purpose: To investigate estimated delivered dose distributions using weekly cone-beam computed tomography (CBCT) scans for pelvic organs at risk (OARs) in salvage radiotherapy (SRT) after radical prostatectomy. Furthermore, to compare them with the originally planned dose distributions and analyse associations with gastrointestinal (GI) and genitourinary (GU) side effects. Methods: This study is part of a phase II trial involving SRT for recurrent prostate cancer. Treatment was personalised based on PSA response during SRT, classifying patients as PSA responders or non-responders. Estimated radiation dose distributions were obtained using deformable image registration from weekly CBCT scans. GI and GU toxicities were assessed using the RTOG toxicity scale, while patient-reported symptoms were monitored through self-assessment questionnaires. Results: The study included 100 patients, with similar treatment-related side effects observed in both responders and non-responders. Differences in dose-volume metrics between the planned and estimated delivered doses for the examined OARs were mostly modest, although generally statistically significant. We identified statistically significant associations between QUANTEC-recommended dose-volume constraints and acute bowel toxicity, as well as late urinary patient-reported symptoms, for both the estimated delivered and planned dose distributions. Conclusion: We found small but statistically significant differences between estimated delivered and planned doses to OARs. These differences showed trends toward improved associations for estimated delivered dose distributions with side effects. Enhanced registration methods and imaging techniques could potentially further enhance the assessment of truly delivered doses and yield more reliable dose-volume constraints for future therapies.

Tensor-valued diffusion magnetic resonance imaging in a radiotherapy setting.

Background and purpose: Diagnostic information about cell density variations and microscopic tissue anisotropy can be gained from tensor-valued diffusion magnetic resonance imaging (MRI). These properties of tissue microstructure have the potential to become novel imaging biomarkers for radiotherapy response. However, tensor-valued diffusion encoding is more demanding than conventional encoding, and its compatibility with MR scanners that are dedicated to radiotherapy has not been established. Thus, our aim was to investigate the feasibility of tensor-valued diffusion MRI with radiotherapy dedicated MR equipment. Material and methods: A tensor-valued diffusion protocol was implemented, and five healthy volunteers were scanned with different resolutions using conventional head coil and radiotherapy coil setup with fixation masks. Signal-to-noise-ratio (SNR) was evaluated to assess the risk of signal bias due to rectified noise floor. We also evaluated the repeatability and reproducibility of the microstructure parameters. One patient with brain metastasis was scanned to investigate the image quality and the transferability of the setup to diseased tissue. Results: A resolution of 3 × 3 × 3 mm3 provided images with SNR > 3 for 93 % of the voxels using radiotherapy coil setup. The parameter maps and repeatability characteristics were comparable to those observed with a conventional head coil. The patient evaluation demonstrated successful parameter analysis also in tumor tissue, with SNR > 3 for 93 % of the voxels. Conclusion: We demonstrate that tensor-valued diffusion MRI is compatible with radiotherapy fixation masks and coil setup for investigations of microstructure parameters. The reported reproducibility may be used to plan future investigations of imaging biomarkers in brain cancer radiotherapy.

Separating Glioma Hyperintensities From White Matter by Diffusion-Weighted Imaging With Spherical Tensor Encoding.

Background: Tumor-related hyperintensities in high b-value diffusion-weighted imaging (DWI) are radiologically important in the workup of gliomas. However, the white matter may also appear as hyperintense, which may conflate interpretation. Purpose: To investigate whether DWI with spherical b-tensor encoding (STE) can be used to suppress white matter and enhance the conspicuity of glioma hyperintensities unrelated to white matter. Materials and Methods: Twenty-five patients with a glioma tumor and at least one pathology-related hyperintensity on DWI underwent conventional MRI at 3 T. The DWI was performed both with linear and spherical tensor encoding (LTE-DWI and STE-DWI). The LTE-DWI here refers to the DWI obtained with conventional diffusion encoding and averaged across diffusion-encoding directions. Retrospectively, the differences in contrast between LTE-DWI and STE-DWI, obtained at a b-value of 2,000 s/mm2, were evaluated by comparing hyperintensities and contralateral normal-appearing white matter (NAWM) both visually and quantitatively in terms of the signal intensity ratio (SIR) and contrast-to-noise ratio efficiency (CNReff). Results: The spherical tensor encoding DWI was more effective than LTE-DWI at suppressing signals from white matter and improved conspicuity of pathology-related hyperintensities. The median SIR improved in all cases and on average by 28%. The median (interquartile range) SIR was 1.9 (1.6 - 2.1) for STE and 1.4 (1.3 - 1.7) for LTE, with a significant difference of 0.4 (0.3 -0.5) (p < 10-4, paired U-test). In 40% of the patients, the SIR was above 2 for STE-DWI, but with LTE-DWI, the SIR was below 2 for all patients. The CNReff of STE-DWI was significantly higher than of LTE-DWI: 2.5 (2 - 3.5) vs. 2.3 (1.7 - 3.1), with a significant difference of 0.4 (-0.1 -0.6) (p < 10-3, paired U-test). The STE improved CNReff in 70% of the cases. We illustrate the benefits of STE-DWI in three patients, where STE-DWI may facilitate an improved radiological description of tumor-related hyperintensity, including one case that could have been missed out if only LTE-DWI was inspected. Conclusion: The contrast mechanism of high b-value STE-DWI results in a stronger suppression of white matter than conventional LTE-DWI, and may, therefore, be more sensitive and specific for assessment of glioma tumors and DWI-hyperintensities.

Dissecting Motor and Cognitive Component Processes of a Finger-Tapping Task With Hybrid Dopamine Positron Emission Tomography and Functional Magnetic Resonance Imaging.

Striatal dopamine is involved in facilitation of motor action as well as various cognitive and emotional functions. Positron emission tomography (PET) is the primary imaging method used to investigate dopamine function in humans. Previous PET studies have shown striatal dopamine release during simple finger tapping in both the putamen and the caudate. It is likely that dopamine release in the putamen is related to motor processes while dopamine release in the caudate could signal sustained cognitive component processes of the task, but the poor temporal resolution of PET has hindered firm conclusions. In this study we simultaneously collected [11C]Raclopride PET and functional Magnetic Resonance Imaging (fMRI) data while participants performed finger tapping, with fMRI being able to isolate activations related to individual tapping events. The results revealed fMRI-PET overlap in the bilateral putamen, which is consistent with a motor component process. Selective PET responses in the caudate, ventral striatum, and right posterior putamen, were also observed but did not overlap with fMRI responses to tapping events, suggesting that these reflect non-motor component processes of finger tapping. Our findings suggest an interplay between motor and non-motor-related dopamine release during simple finger tapping and illustrate the potential of hybrid PET-fMRI in revealing distinct component processes of cognitive functions.

Investigation of the clinical inter-observer bias in prostate fiducial marker image registration between CT and MR images.

BACKGROUND AND PURPOSE: Inter-modality image registration between computed tomography (CT) and magnetic resonance (MR) images is associated with systematic uncertainties and the magnitude of these uncertainties is not well documented. The purpose of this study was to investigate the potential uncertainty of gold fiducial marker (GFM) registration for localized prostate cancer and to estimate the inter-observer bias in a clinical setting. METHODS: Four experienced observers registered CT and MR images for 42 prostate cancer patients. Manual GFM identification was followed by a landmark-based registration. The absolute difference between observers in GFM identification and the displacement of the clinical target volume (CTV) was investigated. The CTV center of mass (CoM) vector displacements, DICE-index and Hausdorff distances for the observer registrations were compared against a clinical baseline registration. The time allocated for the manual registrations was compared. RESULTS: Absolute difference in GFM identification between observers ranged from 0.0 to 3.0 mm. The maximum CTV CoM displacement from the clinical baseline was 3.1 mm. Displacements larger than or equal to 1 mm, 2 mm and 3 mm were 46%, 18% and 4%, respectively. No statistically significant difference was detected between observers in terms of CTV displacement. Median DICE-index and Hausdorff distance for the CTV, with their respective ranges were 0.94 [0.70-1.00] and 2.5 mm [0.7-8.7]. CONCLUSIONS: Registration of CT and MR images using GFMs for localized prostate cancer patients was subject to inter-observer bias on an individual patient level. A CTV displacement as large as 3 mm occurred for individual patients. These results show that GFM registration in a clinical setting is associated with uncertainties, which motivates the removal of inter-modality registrations in the radiotherapy workflow and a transition to an MRI-only workflow for localized prostate cancer.

Multidimensional Diffusion Magnetic Resonance Imaging for Characterization of Tissue Microstructure in Breast Cancer Patients: A Prospective Pilot Study.

Diffusion-weighted imaging is a non-invasive functional imaging modality for breast tumor characterization through apparent diffusion coefficients. Yet, it has so far been unable to intuitively inform on tissue microstructure. In this IRB-approved prospective study, we applied novel multidimensional diffusion (MDD) encoding across 16 patients with suspected breast cancer to evaluate its potential for tissue characterization in the clinical setting. Data acquired via custom MDD sequences was processed using an algorithm estimating non-parametric diffusion tensor distributions. The statistical descriptors of these distributions allow us to quantify tissue composition in terms of metrics informing on cell densities, shapes, and orientations. Additionally, signal fractions from specific cell types, such as elongated cells (bin1), isotropic cells (bin2), and free water (bin3), were teased apart. Histogram analysis in cancers and healthy breast tissue showed that cancers exhibited lower mean values of "size" (1.43 ± 0.54 × 10-3 mm2/s) and higher mean values of "shape" (0.47 ± 0.15) corresponding to bin1, while FGT (fibroglandular breast tissue) presented higher mean values of "size" (2.33 ± 0.22 × 10-3 mm2/s) and lower mean values of "shape" (0.27 ± 0.11) corresponding to bin3 (p < 0.001). Invasive carcinomas showed significant differences in mean signal fractions from bin1 (0.64 ± 0.13 vs. 0.4 ± 0.25) and bin3 (0.18 ± 0.08 vs. 0.42 ± 0.21) compared to ductal carcinomas in situ (DCIS) and invasive carcinomas with associated DCIS (p = 0.03). MDD enabled qualitative and quantitative evaluation of the composition of breast cancers and healthy glands.

Rectal cancer: a methodological approach to matching PET/MRI to histopathology.

PURPOSE: To enable the evaluation of locoregional disease in the on-going RECTOPET (REctal Cancer Trial on PET/MRI/CT) study; a methodology to match mesorectal imaging findings to histopathology is presented, along with initial observations. METHODS: FDG-PET/MRI examinations were performed in twenty-four consecutively included patients with rectal adenocarcinoma. In nine patients, of whom five received neoadjuvant treatment, a postoperative MRI of the surgical specimen was performed. The pathological cut-out was performed according to clinical routine with the addition of photo documentation of each slice of the surgical specimen, meticulously marking the location, size, and type of pathology of each mesorectal finding. This allowed matching individual nodal structures from preoperative MRI, via the specimen MRI, to histopathology. RESULTS: Preoperative MRI identified 197 mesorectal nodal structures, of which 92 (47%) could be anatomically matched to histopathology. Of the matched nodal structures identified in both MRI and histopathology, 25% were found to be malignant. These malignant structures consisted of lymph nodes (43%), tumour deposits (48%), and extramural venous invasion (9%). One hundred eleven nodal structures (55%) could not be matched anatomically. Of these, 97 (87%) were benign lymph nodes, and 14 (13%) were malignant nodal structures. Five were malignant lymph nodes, and nine were tumour deposits, all of which had a short axis diameter < 5 mm. CONCLUSIONS: We designed a method able to anatomically match and study the characteristics of individual mesorectal nodal structures, enabling further research on the impact of each imaging modality. Initial observations suggest that small malignant nodal structures assessed as lymph nodes in MRI often comprise other forms of mesorectal tumour spread. TRIAL REGISTRATION: Clinical Trials Identifier: NCT03846882 .

Phantom-based quality assurance for multicenter quantitative MRI in locally advanced cervical cancer.

BACKGROUND AND PURPOSE: A wide variation of MRI systems is a challenge in multicenter imaging biomarker studies as it adds variation in quantitative MRI values. The aim of this study was to design and test a quality assurance (QA) framework based on phantom measurements, for the quantitative MRI protocols of a multicenter imaging biomarker trial of locally advanced cervical cancer. MATERIALS AND METHODS: Fifteen institutes participated (five 1.5 T and ten 3 T scanners). Each institute optimized protocols for T2, diffusion-weighted imaging, T1, and dynamic contrast-enhanced (DCE-)MRI according to system possibilities, institutional preferences and study-specific constraints. Calibration phantoms with known values were used for validation. Benchmark protocols, similar on all systems, were used to investigate whether differences resulted from variations in institutional protocols or from system variations. Bias, repeatability (%RC), and reproducibility (%RDC) were determined. Ratios were used for T2 and T1 values. RESULTS: The institutional protocols showed a range in bias of 0.88-0.98 for T2 (median %RC = 1%; %RDC = 12%), -0.007 to 0.029 × 10-3 mm2/s for the apparent diffusion coefficient (median %RC = 3%; %RDC = 18%), and 0.39-1.29 for T1 (median %RC = 1%; %RDC = 33%). For DCE a nonlinear vendor-specific relation was observed between measured and true concentrations with magnitude data, whereas the relation was linear when phase data was used. CONCLUSION: We designed a QA framework for quantitative MRI protocols and demonstrated for a multicenter trial for cervical cancer that measurement of consistent T2 and apparent diffusion coefficient values is feasible despite protocol differences. For DCE-MRI and T1 mapping with the variable flip angle method, this was more challenging.

PET/MRI and PET/CT hybrid imaging of rectal cancer - description and initial observations from the RECTOPET (REctal Cancer trial on PET/MRI/CT) study.

PURPOSE: The role of hybrid imaging using 18F-fluoro-2-deoxy-D-glucose positron-emission tomography (FDG-PET), computed tomography (CT) and magnetic resonance imaging (MRI) to improve preoperative evaluation of rectal cancer is largely unknown. To investigate this, the RECTOPET (REctal Cancer Trial on PET/MRI/CT) study has been launched with the aim to assess staging and restaging of primary rectal cancer. This report presents the study workflow and the initial experiences of the impact of PET/CT on staging and management of the first patients included in the RECTOPET study. METHODS: This prospective cohort study, initiated in September 2016, is actively recruiting patients from Region Västerbotten in Sweden. This pilot study includes patients recruited and followed up until December 2017. All patients had a biopsy-verified rectal adenocarcinoma and underwent a minimum of one preoperative FDG-PET/CT and FDG-PET/MRI examination. These patients were referred to the colorectal cancer multidisciplinary team meeting at Umeå University Hospital. All available data were evaluated when making management recommendations. The clinical course was noted and changes consequent to PET imaging were described; surgical specimens underwent dedicated MRI for anatomical matching between imaging and histopathology. RESULTS: Twenty-four patients have so far been included in the study. Four patients were deemed unresectable, while 19 patients underwent or were scheduled for surgery; one patient was enrolled in a watch-and-wait programme after restaging. Consequent to taking part in the study, two patients were upstaged to M1 disease: one patient was diagnosed with a solitary hepatic metastasis detected using PET/CT and underwent metastasectomy prior to rectal cancer surgery, while one patient with a small, but metabolically active, lung nodulus experienced no change of management. PET/MRI did not contribute to any recorded change in patient management. CONCLUSIONS: The RECTOPET study investigating the role of PET/CT and PET/MRI for preoperative staging of primary rectal cancer patients will provide novel data that clarify the value of adding hybrid to conventional imaging, and the role of PET/CT versus PET/MRI. TRIAL REGISTRATION: NCT03846882 .

Gray-level invariant Haralick texture features.

Haralick texture features are common texture descriptors in image analysis. To compute the Haralick features, the image gray-levels are reduced, a process called quantization. The resulting features depend heavily on the quantization step, so Haralick features are not reproducible unless the same quantization is performed. The aim of this work was to develop Haralick features that are invariant to the number of quantization gray-levels. By redefining the gray-level co-occurrence matrix (GLCM) as a discretized probability density function, it becomes asymptotically invariant to the quantization. The invariant and original features were compared using logistic regression classification to separate two classes based on the texture features. Classifiers trained on the invariant features showed higher accuracies, and had similar performance when training and test images had very different quantizations. In conclusion, using the invariant Haralick features, an image pattern will give the same texture feature values independent of image quantization.

Density estimation of grey-level co-occurrence matrices for image texture analysis.

The Haralick texture features are common in the image analysis literature, partly because of their simplicity and because their values can be interpreted. It was recently observed that the Haralick texture features are very sensitive to the size of the GLCM that was used to compute them, which led to a new formulation that is invariant to the GLCM size. However, these new features still depend on the sample size used to compute the GLCM, i.e. the size of the input image region-of-interest (ROI). The purpose of this work was to investigate the performance of density estimation methods for approximating the GLCM and subsequently the corresponding invariant features. Three density estimation methods were evaluated, namely a piece-wise constant distribution, the Parzen-windows method, and the Gaussian mixture model. The methods were evaluated on 29 different image textures and 20 invariant Haralick texture features as well as a wide range of different ROI sizes. The results indicate that there are two types of features: those that have a clear minimum error for a particular GLCM size for each ROI size, and those whose error decreases monotonically with increased GLCM size. For the first type of features, the Gaussian mixture model gave the smallest errors, and in particular for small ROI sizes (less than about [Formula: see text]). In conclusion, the Gaussian mixture model is the preferred method for the first type of features (in particular for small ROIs). For the second type of features, simply using a large GLCM size is preferred.

Technical Note: Adapting a GE SIGNA PET/MR scanner for radiotherapy.

PURPOSE: Simultaneous collection of PET and MR data for radiotherapy purposes are useful for, for example, target definition and dose escalations. However, a prerequisite for using PET/MR in the radiotherapy workflow is the ability to image the patient in treatment position. The aim of this work was to adapt a GE SIGNA PET/MR scanner to image patients for radiotherapy treatment planning and evaluate the impact on signal-to-noise (SNR) of the MR images, and the accuracy of the PET attenuation correction. METHOD: A flat tabletop and a coil holder were developed to image patients in the treatment position, avoid patient contour deformation, and facilitate attenuation correction of flex coils. Attenuation corrections for the developed hardware and an anterior array flex coil were also measured and implemented to the PET/MR system to minimize PET quantitation errors. The reduction of SNR in the MR images due to the added distance between the coils and the patient was evaluated using a large homogenous saline-doped water phantom, and the activity quantitation errors in PET imaging were evaluated with and without the developed attenuation corrections. RESULT: We showed that the activity quantitation errors in PET imaging were within ±5% when correcting for attenuation of the flat tabletop, coil holder, and flex coil. The SNR of the MRI images were reduced to 74% using the tabletop, and 66% using the tabletop and coil holders. CONCLUSION: We present a tabletop and coil holder for an anterior array coil to be used with a GE SIGNA PET/MR scanner, for scanning patients in the radiotherapy work flow. Implementing attenuation correction of the added hardware from the radiotherapy setup leads to acceptable PET image quantitation. The drop in SNR in MR images may require adjustment of the imaging protocols.

Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters.

In recent years, texture analysis of medical images has become increasingly popular in studies investigating diagnosis, classification and treatment response assessment of cancerous disease. Despite numerous applications in oncology and medical imaging in general, there is no consensus regarding texture analysis workflow, or reporting of parameter settings crucial for replication of results. The aim of this study was to assess how sensitive Haralick texture features of apparent diffusion coefficient (ADC) MR images are to changes in five parameters related to image acquisition and pre-processing: noise, resolution, how the ADC map is constructed, the choice of quantization method, and the number of gray levels in the quantized image. We found that noise, resolution, choice of quantization method and the number of gray levels in the quantized images had a significant influence on most texture features, and that the effect size varied between different features. Different methods for constructing the ADC maps did not have an impact on any texture feature. Based on our results, we recommend using images with similar resolutions and noise levels, using one quantization method, and the same number of gray levels in all quantized images, to make meaningful comparisons of texture feature results between different subjects.

Combining phase and magnitude information for contrast agent quantification in dynamic contrast-enhanced MRI using statistical modeling.

PURPOSE: The purpose of this study was to investigate, using simulations, a method for improved contrast agent (CA) quantification in DCE-MRI. METHODS: We developed a maximum likelihood estimator that combines the phase signal in the DCE-MRI image series with an additional CA estimate, e.g. the estimate obtained from magnitude data. A number of simulations were performed to investigate the ability of the estimator to reduce bias and noise in CA estimates. Noise levels ranging from that of a body coil to that of a dedicated head coil were investigated at both 1.5T and 3T. RESULTS: Using the proposed method, the root mean squared error in the bolus peak was reduced from 2.24 to 0.11 mM in the vessels and 0.16 to 0.08 mM in the tumor rim for a noise level equivalent of a 12-channel head coil at 3T. No improvements were seen for tissues with small CA uptake, such as white matter. CONCLUSION: Phase information reduces errors in the estimated CA concentrations. A larger phase response from higher field strengths or higher CA concentrations yielded better results. Issues such as background phase drift need to be addressed before this method can be applied in vivo.

ADC texture--an imaging biomarker for high-grade glioma?

PURPOSE: Survival for high-grade gliomas is poor, at least partly explained by intratumoral heterogeneity contributing to treatment resistance. Radiological evaluation of treatment response is in most cases limited to assessment of tumor size months after the initiation of therapy. Diffusion-weighted magnetic resonance imaging (MRI) and its estimate of the apparent diffusion coefficient (ADC) has been widely investigated, as it reflects tumor cellularity and proliferation. The aim of this study was to investigate texture analysis of ADC images in conjunction with multivariate image analysis as a means for identification of pretreatment imaging biomarkers. METHODS: Twenty-three consecutive high-grade glioma patients were treated with radiotherapy (2 Gy/60 Gy) with concomitant and adjuvant temozolomide. ADC maps and T1-weighted anatomical images with and without contrast enhancement were collected prior to treatment, and (residual) tumor contrast enhancement was delineated. A gray-level co-occurrence matrix analysis was performed on the ADC maps in a cuboid encapsulating the tumor in coronal, sagittal, and transversal planes, giving a total of 60 textural descriptors for each tumor. In addition, similar examinations and analyses were performed at day 1, week 2, and week 6 into treatment. Principal component analysis (PCA) was applied to reduce dimensionality of the data, and the five largest components (scores) were used in subsequent analyses. MRI assessment three months after completion of radiochemotherapy was used for classifying tumor progression or regression. RESULTS: The score scatter plots revealed that the first, third, and fifth components of the pretreatment examinations exhibited a pattern that strongly correlated to survival. Two groups could be identified: one with a median survival after diagnosis of 1099 days and one with 345 days, p = 0.0001. CONCLUSIONS: By combining PCA and texture analysis, ADC texture characteristics were identified, which seems to hold pretreatment prognostic information, independent of known prognostic factors such as age, stage, and surgical procedure. These findings encourage further studies with a larger patient cohort.

Uncertainty estimation in dynamic contrast-enhanced MRI.

Using dynamic contrast-enhanced MRI (DCE-MRI), it is possible to estimate pharmacokinetic (PK) parameters that convey information about physiological properties, e.g., in tumors. In DCE-MRI, errors propagate in a nontrivial way to the PK parameters. We propose a method based on multivariate linear error propagation to calculate uncertainty maps for the PK parameters. Uncertainties in the PK parameters were investigated for the modified Kety model. The method was evaluated with Monte Carlo simulations and exemplified with in vivo brain tumor data. PK parameter uncertainties due to noise in dynamic data were accurately estimated. Noise with standard deviation up to 15% in the baseline signal and the baseline T1 map gave estimated uncertainties in good agreement with the Monte Carlo simulations. Good agreement was also found for up to 15% errors in the arterial input function amplitude. The method was less accurate for errors in the bolus arrival time with disagreements of 23%, 32%, and 29% for K(trans) , ve , and vp , respectively, when the standard deviation of the bolus arrival time error was 5.3 s. In conclusion, the proposed method provides efficient means for calculation of uncertainty maps, and it was applicable to a wide range of sources of uncertainty.

Registration accuracy for MR images of the prostate using a subvolume based registration protocol.

BACKGROUND: In recent years, there has been a considerable research effort concerning the integration of magnetic resonance imaging (MRI) into the external radiotherapy workflow motivated by the superior soft tissue contrast as compared to computed tomography. Image registration is a necessary step in many applications, e.g. in patient positioning and therapy response assessment with repeated imaging. In this study, we investigate the dependence between the registration accuracy and the size of the registration volume for a subvolume based rigid registration protocol for MR images of the prostate. METHODS: Ten patients were imaged four times each over the course of radiotherapy treatment using a T2 weighted sequence. The images were registered to each other using a mean square distance metric and a step gradient optimizer for registration volumes of different sizes. The precision of the registrations was evaluated using the center of mass distance between the manually defined prostates in the registered images. The optimal size of the registration volume was determined by minimizing the standard deviation of these distances. RESULTS: We found that prostate position was most uncertain in the anterior-posterior (AP) direction using traditional full volume registration. The improvement in standard deviation of the mean center of mass distance between the prostate volumes using a registration volume optimized to the prostate was 3.9 mm (p < 0.001) in the AP direction. The optimum registration volume size was 0 mm margin added to the prostate gland as outlined in the first image series. CONCLUSIONS: Repeated MR imaging of the prostate for therapy set-up or therapy assessment will both require high precision tissue registration. With a subvolume based registration the prostate registration uncertainty can be reduced down to the order of 1 mm (1 SD) compared to several millimeters for registration based on the whole pelvis.