The NCS code of practice for the quality assurance of treatment planning systems (NCS-35).

A subcommittee of the Netherlands Commission on Radiation Dosimetry (NCS) was initiated in 2018 with the task to update and extend a previous publication (NCS-15) on the quality assurance of treatment planning systems (TPS) (Bruinviset al2005). The field of treatment planning has changed considerably since 2005. Whereas the focus of the previous report was more on the technical aspects of the TPS, the scope of this report is broader with a focus on a department wide implementation of the TPS. New sections about education, automated planning, information technology (IT) and updates are therefore added. Although the scope is photon therapy, large parts of this report will also apply to all other treatment modalities. This paper is a condensed version of these guidelines; the full version of the report in English is freely available from the NCS website (http://radiationdosimetry.org/ncs/publications). The paper starts with the scope of this report in relation to earlier reports on this subject. Next, general aspects of the commissioning process are addressed, like e.g. project management, education, and safety. It then focusses more on technical aspects such as beam commissioning and patient modeling, dose representation, dose calculation and (automated) plan optimisation. The final chapters deal with IT-related subjects and scripting, and the process of updating or upgrading the TPS.

Robust deep learning-based forward dose calculations for VMAT on the 1.5T MR-linac.

In this work we present a framework for robust deep learning-based VMAT forward dose calculations for the 1.5T MR-linac. A convolutional neural network was trained on the dose of individual multi-leaf-collimator VMAT segments and was used to predict the dose per segment for a set of MR-linac-deliverable VMAT test plans. The training set consisted of prostate, rectal, lung and esophageal tumour data. All patients were previously treated in our clinic with VMAT on a conventional linac. The clinical data were converted to an MR-linac environment prior to training. During training time, gantry and collimator angles were randomized for each training sample, while the multi-leaf-collimator shapes were rigidly shifted to ensure robust learning. A Monte Carlo dose engine was used for the generation of the ground truth data at 1% statistical uncertainty per control point. For a set of 17 MR-linac-deliverable VMAT test plans, generated on a research treatment planning system, our method predicted highly accurate dose distributions, reporting 99.7% ± 0.5% for the full plan prediction at the 3%/3 mm gamma criterion. Additional evaluation on previously unseen IMRT patients passed all clinical requirements resulting in 99.0% ± 0.6% for the 3%/3 mm analysis. The overall performance of our method makes it a promising plan validation solution for IMRT and VMAT workflows, robust to tumour anatomies and tissue density variations.

Validation of skeletal muscle mass assessment at the level of the third cervical vertebra in patients with head and neck cancer.

BACKGROUND: Low skeletal muscle mass (SMM) is associated with adverse outcomes. SMM is often assessed at the third lumbar vertebra (L3) on abdominal imaging. Abdominal imaging is not routinely performed in patients with head and neck cancer (HNC). We aim to validate SMM measurement at the level of the third cervical vertebra (C3) on head and neck imaging. MATERIAL AND METHODS: Patients with pre-treatment whole-body computed tomography (CT) between 2010 and 2018 were included. Cross-sectional muscle area (CSMA) was manually delineated at the level of C3 and L3. Correlation coefficients and intraclass correlation coefficients (ICCs) were calculated. Cohen's kappa was used to assess the reliability of identifying a patient with low SMM. RESULTS: Two hundred patients were included. Correlation between CSMA at the level of C3 and L3 was good (r = 0.75, p < 0.01). Using a multivariate formula to estimate CSMA at L3, including gender, age, and weight, correlation improved (r = 0.82, p < 0.01). The agreement between estimated and actual CSMA at L3 was good (ICC 0.78, p < 0.01). There was moderate agreement in the identification of patients with low SMM based on the estimated lumbar skeletal muscle mass index (LSMI) and actual LSMI (Cohen's κ: 0.57, 95%CI 0.45-0.69). CONCLUSIONS: CSMA at C3 correlates well with CSMA at L3. There is moderate agreement in the identification of patients with low SMM based on the estimated lumbar SMI (based on measurement at C3) and actual LSMI.

DeepDose: a robust deep learning-based dose engine for abdominal tumours in a 1.5 T MRI radiotherapy system.

We present a robust deep learning-based framework for dose calculations of abdominal tumours in a 1.5 T MRI radiotherapy system. For a set of patient plans, a convolutional neural network is trained on the dose of individual multi-leaf-collimator segments following the DeepDose framework. It can then be used to predict the dose distribution per segment for a set of patient anatomies. The network was trained using data from three anatomical sites of the abdomen: prostate, rectal and oligometastatic tumours. A total of 216 patient fractions were used, previously treated in our clinic with fixed-beam IMRT using the Elekta MR-linac. For the purpose of training, 176 fractions were used with random gantry angles assigned to each segment, while 20 fractions were used for the validation of the network. The ground truth data were calculated with a Monte Carlo dose engine at 1% statistical uncertainty per segment. For a total of 20 independent abdominal test fractions with the clinical angles, the network was able to accurately predict the dose distributions, achieving 99.4% ± 0.6% for the whole plan prediction at the 3%/3 mm gamma test. The average dose difference and standard deviation per segment was 0.3% ± 0.7%. Additional dose prediction on one cervical and one pancreatic case yielded high dose agreement of 99.9% and 99.8% respectively for the 3%/3 mm criterion. Overall, we show that our deep learning-based dose engine calculates highly accurate dose distributions for a variety of abdominal tumour sites treated on the MR-linac, in terms of performance and generality.

Proof-of-concept delivery of intensity modulated arc therapy on the Elekta Unity 1.5 T MR-linac.

In this work we present the first delivery of intensity modulated arc therapy on the Elekta Unity 1.5 T MR-linac. The machine's current intensity modulated radiation therapy based control system was modified suitably to enable dynamic delivery of radiation, for the purpose of exploring MRI-guided radiation therapy adaptation modes in a research setting. The proof-of-concept feasibility was demonstrated by planning and delivering two types of plans, each investigating the performance of different parts of a dynamic treatment. A series of fixed-speed arc plans was used to show the high-speed capabilities of the gantry during radiation, while several fully modulated prostate plans-optimised following the volumetric modulated arc therapy approach-were delivered in order to establish the performance of its multi-leaf collimator and diaphragms. These plans were delivered to Delta4 Phantom+ MR and film phantoms passing the clinical quality assurance criteria used in our clinic. In addition, we also performed some initial MR imaging experiments during dynamic therapy, demonstrating that the impact of radiation and moving gantry/collimator components on the image quality is negligible. These results show that arc therapy is feasible on the Elekta Unity system. The machine's high performance components enable dynamic delivery during fast gantry rotation and can be controlled in a stable fashion to deliver fully modulated plans.

DeepDose: Towards a fast dose calculation engine for radiation therapy using deep learning.

We present DeepDose, a deep learning framework for fast dose calculations in radiation therapy. Given a patient anatomy and linear-accelerator IMRT multi-leaf-collimator shape or segment, a novel set of physics-based inputs is calculated that encode the linac machine parameters into the underlying anatomy. These inputs are then used to train a deep convolutional network to derive the dose distribution of individual MLC shapes on a given patient anatomy. In this work we demonstrate the proof-of-concept application of DeepDose on 101 prostate patients treated in our clinic with fixed-beam IMRT. The ground-truth data used for training, validation and testing of the prediction were calculated with a state-of-the-art Monte Carlo dose engine at 1% statistical uncertainty per segment. A deep convolution network was trained using the data of 80 patients at the clinically used 3 mm3 grid spacing while 10 patients were used for validation. For another 11 independent test patients, the network was able to accurately estimate the segment doses from the clinical plans of each patient passing the clinical QA when compared with the Monte Carlo calculations, yielding on average 99.9%±0.3% for the forward calculated patient plans at 3%/3 mm gamma tests. Dose prediction using the trained network was very fast at approximately 0.9 seconds for the input generation and 0.6 seconds for single GPU inference per segment and 1 minute per patient in total. The overall performance of this dose calculation framework in terms of both accuracy and inference speed, makes it compelling for online adaptive workflows where fast segment dose calculations are needed.

Impact of body fat distribution and sarcopenia on the overall survival in patients with spinal metastases receiving radiotherapy treatment: a prospective cohort study.

Introduction: An increasing number of patients is diagnosed with spinal metastases due to elevated cancer incidence and improved overall survival. Patients with symptomatic spinal bone metastases often receive radiotherapy with or without surgical stabilisation. Patients with a life expectancy of less than 3 months are generally deemed unfit for surgery, therefore adequate pre-treatment assessment of life expectancy is necessary. The aim of this study was to assess new factors associated with overall survival for this category of patients.Patients and methods: Patients who received radiotherapy for thoracic or lumbar spinal metastases from June 2013 to December 2016 were included in this study. The pre-treatment planning CT for radiotherapy treatment was used to assess the patient's visceral fat area, subcutaneous fat area, total muscle area and skeletal muscle density on a single transverse slice at the L3 level. The total muscle area was used to assess sarcopenia. Furthermore, data were collected on age, sex, primary tumour, Karnofsky performance score, medical history, number of bone metastases, non-bone metastases and neurological symptoms. Univariable and multivariable cox regressions were performed to determine the association between our variables of interest and the survival at 90 and 365 days.Results: A total of 310 patients was included. The median age was 67 years. Overall survival rates for 90 and 365 days were 71% and 36% respectively. For 90- and 365-day survival, the Karnofsky performance score, muscle density and primary tumour were independently significantly associated. The visceral or subcutaneous fat area and their ratio and sarcopenia were not independently associated with overall survival.Conclusions: Of the body morphology, only muscle density was statistically significant associated with overall survival after 90 and 365 days in patients with spinal bone metastases. Body fat distribution was not significantly associated with overall survival.

Interobserver agreement of skeletal muscle mass measurement on head and neck CT imaging at the level of the third cervical vertebra.

OBJECTIVES: Skeletal muscle mass (SMM) is most often assessed in cancer patients on abdominal computed tomography (CT) imaging at the level of the third lumbar vertebra (L3). Abdominal CT imaging is not routinely performed in head and neck cancer (HNC) patients. Recently, a novel method to assess SMM on a single transversal CT slice at the level of the third cervical vertebra (C3) was published. The objective of this study was to assess the robustness of this novel C3 measurement method in terms of interobserver agreement. PATIENTS AND METHODS: Patients diagnosed with locally advanced head and neck squamous cell carcinoma (LA-HNSCC) at our center between 2007 and 2011 were evaluated. Fifty-four patients with were randomly selected for analysis. Six observers independently measured the cross-sectional muscle area (CSMA) at the level of C3 using a predefined, written protocol as instruction. Interobserver agreement was assessed using intraclass correlation coefficients (ICCs), a Bland-Altman plot and Fleiss' kappa (κ). RESULTS: The agreement in vertebra selection between all observers was excellent (Fleiss' κ: 0.96). There was a substantial agreement between all observers in single slice selection (Fleiss' κ: 0.61). For all CSMA measurements, ICCs were excellent (0.763-0.969; all p < 0.001). The Bland-Altman plot showed good agreement between measurements, with narrow limits of agreement. CONCLUSION: Interobserver agreement for SMM measurement at the level of C3 was excellent. Assessment of SMM at the level of C3 is easy and robust and can performed on routinely available imaging in HNC patients.

First patients treated with a 1.5 T MRI-Linac: clinical proof of concept of a high-precision, high-field MRI guided radiotherapy treatment.

The integration of 1.5 T MRI functionality with a radiotherapy linear accelerator (linac) has been pursued since 1999 by the UMC Utrecht in close collaboration with Elekta and Philips. The idea behind this integrated device is to offer unrivalled, online and real-time, soft-tissue visualization of the tumour and the surroundings for more precise radiation delivery. The proof of concept of this device was given in 2009 by demonstrating simultaneous irradiation and MR imaging on phantoms, since then the device has been further developed and commercialized by Elekta. The aim of this work is to demonstrate the clinical feasibility of online, high-precision, high-field MRI guidance of radiotherapy using the first clinical prototype MRI-Linac. Four patients with lumbar spine bone metastases were treated with a 3 or 5 beam step-and-shoot IMRT plan. The IMRT plan was created while the patient was on the treatment table and based on the online 1.5 T MR images; pre-treatment CT was deformably registered to the online MRI to obtain Hounsfield values. Bone metastases were chosen as the first site as these tumors can be clearly visualized on MRI and the surrounding spine bone can be detected on the integrated portal imager. This way the portal images served as an independent verification of the MRI based guidance to quantify the geometric precision of radiation delivery. Dosimetric accuracy was assessed post-treatment from phantom measurements with an ionization chamber and film. Absolute doses were found to be highly accurate, with deviations ranging from 0.0% to 1.7% in the isocenter. The geometrical, MRI based targeting as confirmed using portal images was better than 0.5 mm, ranging from 0.2 mm to 0.4 mm. In conclusion, high precision, high-field, 1.5 T MRI guided radiotherapy is clinically feasible.

Towards fast online intrafraction replanning for free-breathing stereotactic body radiation therapy with the MR-linac.

The hybrid MRI-radiotherapy machines, like the MR-linac (Elekta AB, Stockholm, Sweden) installed at the UMC Utrecht (Utrecht, The Netherlands), will be able to provide real-time patient imaging during treatment. In order to take advantage of the system's capabilities and enable online adaptive treatments, a new generation of software should be developed, ranging from motion estimation to treatment plan adaptation. In this work we present a proof of principle adaptive pipeline designed for high precision stereotactic body radiation therapy (SBRT) suitable for sites affected by respiratory motion, like renal cell carcinoma (RCC). We utilized our research MRL treatment planning system (MRLTP) to simulate a single fraction 25 Gy free-breathing SBRT treatment for RCC by performing inter-beam replanning for two patients and one volunteer. The simulated pipeline included a combination of (pre-beam) 4D-MRI and (online) 2D cine-MR acquisitions. The 4DMRI was used to generate the mid-position reference volume, while the cine-MRI, via an in-house motion model, provided three-dimensional (3D) deformable vector fields (DVFs) describing the anatomical changes during treatment. During the treatment fraction, at an inter-beam interval, the mid-position volume of the patient was updated and the delivered dose was accurately reconstructed on the underlying motion calculated by the model. Fast online replanning, targeting the latest anatomy and incorporating the previously delivered dose was then simulated with MRLTP. The adaptive treatment was compared to a conventional mid-position SBRT plan with a 3 mm planning target volume margin reconstructed on the same motion trace. We demonstrate that our system produced tighter dose distributions and thus spared the healthy tissue, while delivering more dose to the target. The pipeline was able to account for baseline variations/drifts that occurred during treatment ensuring target coverage at the end of the treatment fraction.

Development and clinical introduction of automated radiotherapy treatment planning for prostate cancer.

To develop an automated radiotherapy treatment planning and optimization workflow to efficiently create patient specifically optimized clinical grade treatment plans for prostate cancer and to implement it in clinical practice. A two-phased planning and optimization workflow was developed to automatically generate 77Gy 5-field simultaneously integrated boost intensity modulated radiation therapy (SIB-IMRT) plans for prostate cancer treatment. A retrospective planning study (n = 100) was performed in which automatically and manually generated treatment plans were compared. A clinical pilot (n = 21) was performed to investigate the usability of our method. Operator time for the planning process was reduced to <5 min. The retrospective planning study showed that 98 plans met all clinical constraints. Significant improvements were made in the volume receiving 72Gy (V72Gy) for the bladder and rectum and the mean dose of the bladder and the body. A reduced plan variance was observed. During the clinical pilot 20 automatically generated plans met all constraints and 17 plans were selected for treatment. The automated radiotherapy treatment planning and optimization workflow is capable of efficiently generating patient specifically optimized and improved clinical grade plans. It has now been adopted as the current standard workflow in our clinic to generate treatment plans for prostate cancer.

A new methodology for inter- and intrafraction plan adaptation for the MR-linac.

The new era of hybrid MRI and linear accelerator machines, including the MR-linac currently being installed in the University Medical Center Utrecht (Utrecht, The Netherlands), will be able to provide the actual anatomy and real-time anatomy changes of the patient's target(s) and organ(s) at risk (OARs) during radiation delivery. In order to be able to take advantage of this input, a new generation of treatment planning systems is needed, that will allow plan adaptation to the latest anatomy state in an online regime. In this paper, we present a treatment planning algorithm for intensity-modulated radiotherapy (IMRT), which is able to compensate for patient anatomy changes. The system consists of an iterative sequencing loop open to anatomy updates and an inter- and intrafraction adaptation scheme that enables convergence to the ideal dose distribution without the need of a final segment weight optimization (SWO). The ability of the system to take into account organ motion and adapt the plan to the latest anatomy state is illustrated using artificial baseline shifts created for three different kidney cases. Firstly, for two kidney cases of different target volumes, we show that the system can account for intrafraction motion, delivering the intended dose to the target with minimal dose deposition to the surroundings compared to conventional plans. Secondly, for a third kidney case we show that our algorithm combined with the interfraction scheme can be used to deliver the prescribed dose while adapting to the changing anatomy during multi-fraction treatments without performing a final SWO.

Dosimetric feasibility of intensity modulated proton therapy in a transverse magnetic field of 1.5 T.

Proton therapy promises higher dose conformality in comparison with regular radiotherapy techniques. Also, image guidance has an increasing role in radiotherapy and MRI is a prime candidate for this imaging. Therefore, in this paper the dosimetric feasibility of Intensity Modulated Proton Therapy (IMPT) in a magnetic field of 1.5 T and the effect on the generated dose distributions compared to those at 0 T is evaluated, using the Monte Carlo software TOol for PArticle Simulation (TOPAS). For three different anatomic sites IMPT plans are generated. It is shown that the generation of an IMPT plan in a magnetic field is feasible, the impact of the magnetic field is small, and the resulting dose distributions are equivalent for 0 T and 1.5 T. Also, the framework of Monte Carlo simulation combined with an inverse optimization method can be used to generate IMPT plans. These plans can be used in future dosimetric comparisons with e.g. IMRT and conventional IMPT. Finally, this study shows that IMPT in a 1.5 T magnetic field is dosimetrically feasible.

Towards adaptive IMRT sequencing for the MR-linac.

The MRI linear accelerator (MR-linac) that is currently being installed in the University Medical Center Utrecht (Utrecht, The Netherlands), will be able to track the patient's target(s) and Organ(s) At Risk during radiation delivery. In this paper, we present a treatment planning system for intensity-modulated radiotherapy (IMRT). It is capable of Adaptive Radiotherapy and consists of a GPU Monte Carlo dose engine, an inverse dose optimization algorithm and a novel adaptive sequencing algorithm. The system is able to compensate for patient anatomy changes and enables radiation delivery immediately from the first calculated segment. IMRT plans meeting all clinical constraints were generated for two breast cases, one spinal bone metastasis case, two prostate cases with integrated boost regions and one head and neck case. These plans were generated by the segment weighted version of our algorithm, in a 0 T environment in order to test the feasibility of the new sequencing strategy in current clinical conditions, yielding very small differences between the fluence and sequenced distributions. All plans went through stringent experimental quality assurance on Delta4 and passed all clinical tests currently performed in our institute. A new inter-fraction adaptation scheme built on top of this algorithm is also proposed that enables convergence to the ideal dose distribution without the need of a final segment weight optimization. The first results of this method confirm that convergence is achieved within the first fractions of the treatment. These features combined will lead to a fully adaptive intra-fraction planning system able to take into account patient anatomy updates during treatment.

Compensating for the impact of non-stationary spherical air cavities on IMRT dose delivery in transverse magnetic fields.

With the development of the 1.5 T MRI linear accelerator and the clinical introduction of the 0.35 T ViewRay™ system, delivering intensity-modulated radiotherapy (IMRT) in a transverse magnetic field becomes increasingly important. When delivering dose in the presence of a transverse magnetic field, one of the most prominent phenomena occurs around air cavities: the electron return effect (ERE). For stationary, spherical air cavities which are centrally located in the phantom, the ERE can be compensated by using opposing beams configurations in combination with IMRT. In this paper we investigate the effects of non-stationary spherical air cavities, centrally located within the target in a phantom containing no organs at risk, on IMRT dose delivery in 0.35 T and 1.5 T transverse magnetic fields by using Monte Carlo simulations. We show that IMRT can be used for compensating ERE around those air cavities, except for intrafraction appearing or disappearing air cavities. For these cases, gating or plan re-optimization should be used. We also analyzed the option of using IMRT plans optimized at 0 T to be delivered in the presence of 0.35 T and 1.5 T magnetic field. When delivering dose at 0.35 T, IMRT plans optimized at 0 T and 0.35 T perform equally well regarding ERE compensation. Within a 1.5 T environment, the 1.5 T optimized plans perform slightly better for the static and random intra- and interfraction air cavity movement cases than the 0 T optimized plans. For non-stationary spherical air cavities with a baseline shift (intra- and interfraction) the 0 T optimized plans perform better. These observations show the intrinsic ERE compensation by equidistant and opposing beam configurations for spherical air cavities within the target area. IMRT gives some additional compensation, but only in case of correct positioning of the air cavity according to the IMRT compensation. For intrafraction appearing or disappearing air cavities this correct positioning is absent and gating or plan re-optimization should be used.

Virtual couch shift (VCS): accounting for patient translation and rotation by online IMRT re-optimization.

When delivering conventional intensity modulated radiotherapy (IMRT), discrepancies between the pre-treatment CT/MRI/PET based patient geometry and the daily patient geometry are minimized by performing couch translations and/or small rotations. However, full compensation of, in particular, rotations is usually not possible. In this paper, we introduce an online 'virtual couch shift (VCS)': we translate and/or rotate the pre-treatment dose distribution to compensate for the changes in patient anatomy and generate a new plan which delivers the transformed dose distribution automatically. We show for a phantom and a cervical cancer patient case that VCS accounts for both translations and large rotations equally well in terms of DVH results and 2%/2 mm γ analyses and when the various aspects of the clinical workflow can be implemented successfully, VCS can potentially outperform physical couch translations and/or rotations. This work is performed in the context of our hybrid 1.5 T MRI linear accelerator, which can provide translations and rotations but also deformations of the anatomy. The VCS is the first step toward compensating all of these anatomical changes by online re-optimization of the IMRT dose distribution.

Fast online Monte Carlo-based IMRT planning for the MRI linear accelerator.

The MRI accelerator, a combination of a 6 MV linear accelerator with a 1.5 T MRI, facilitates continuous patient anatomy updates regarding translations, rotations and deformations of targets and organs at risk. Accounting for these demands high speed, online intensity-modulated radiotherapy (IMRT) re-optimization. In this paper, a fast IMRT optimization system is described which combines a GPU-based Monte Carlo dose calculation engine for online beamlet generation and a fast inverse dose optimization algorithm. Tightly conformal IMRT plans are generated for four phantom cases and two clinical cases (cervix and kidney) in the presence of the magnetic fields of 0 and 1.5 T. We show that for the presented cases the beamlet generation and optimization routines are fast enough for online IMRT planning. Furthermore, there is no influence of the magnetic field on plan quality and complexity, and equal optimization constraints at 0 and 1.5 T lead to almost identical dose distributions.

Patient position verification using small IMRT fields.

A commonly used approach to quantify and minimize patient setup errors is by using electronic portal imaging devices (EPIDs). The position of the tumor can be verified indirectly by matching the bony anatomy to a reference image containing the same structures. In this paper we present two off-line methods for detecting the position of the bony anatomy automatically, even if every single portal image of each segment of an IMRT treatment beam contains insufficient matching information. Extra position verification fields will no longer be necessary, which reduces the total dose to the patient. The first method, the stack matching method (SMM), stacks the portal image of each segment of a beam to a three dimensional (3D) volume, and this volume is subsequently used during the matching phase. The second method [the averaged projection matching method (APMM)], is a simplification of the first one, since the initially created volume is reduced again to a 2D artificial image, which speeds up the matching procedure considerably, without a significant loss of accuracy. Matching is based on normalized mutual information. We demonstrate our methods by comparing them to existing matching routines, such as matching based on the largest segment. Both phantom and patient experiments show that our methods are comparable with the results obtained from standard position verification methods. The matches are verified by means of visual inspection. Furthermore, we show that when a distinct area of 40-60 cm2 of the EPID is exposed during one treatment beam, both SMM and APMM are able to deliver a good matching result.

Assessment of rigid multi-modality image registration consistency using the multiple sub-volume registration (MSR) method.

Registration of different imaging modalities such as CT, MRI, functional MRI (fMRI), positron (PET) and single photon (SPECT) emission tomography is used in many clinical applications. Determining the quality of any automatic registration procedure has been a challenging part because no gold standard is available to evaluate the registration. In this note we present a method, called the 'multiple sub-volume registration' (MSR) method, for assessing the consistency of a rigid registration. This is done by registering sub-images of one data set on the other data set, performing a crude non-rigid registration. By analysing the deviations (local deformations) of the sub-volume registrations from the full registration we get a measure of the consistency of the rigid registration. Registration of 15 data sets which include CT, MR and PET images for brain, head and neck, cervix, prostate and lung was performed utilizing a rigid body registration with normalized mutual information as the similarity measure. The resulting registrations were classified as good or bad by visual inspection. The resulting registrations were also classified using our MSR method. The results of our MSR method agree with the classification obtained from visual inspection for all cases (p < 0.02 based on ANOVA of the good and bad groups). The proposed method is independent of the registration algorithm and similarity measure. It can be used for multi-modality image data sets and different anatomic sites of the patient.