Radiotherapy planning using MRI

Abstract

The use of magnetic resonance imaging (MRI) in radiotherapy (RT) planning is rapidly expanding. We review the wide range of image contrast mechanisms available to MRI and the way they are exploited for RT planning. However a number of challenges are also considered: the requirements that MR images are acquired in the RT treatment position, that they are geometrically accurate, that effects of patient motion during the scan are minimized, that tissue markers are clearly demonstrated, that an estimate of electron density can be obtained. These issues are discussed in detail, prior to the consideration of a number of specific clinical applications. This is followed by a brief discussion on the development of real-time MRI-guided RT.

Figures

Figure 1

Comparison of axial CT and T2-weighted MR images to depict prostate tumours. In the CT image (left) only the boundaries of the prostate may be estimated. In the enlarged T2-weighted MR image (right) structure within the prostate is readily apparent. PZ = Peripheral Zone, CG = central gland, TU = tumour.

Figure 2

Example transaxial brain images of patient with glioblastoma. Note there is no signal from cortical bone. a) T1-weighted image. TR/TE = 8.3ms/3.8 ms. Fluid appears darker (e.g. cerebral spinal fluid in ventricles). b) T2-weighted image. TR/TE = 3000/80ms. Fluid appears bright. c) T1-weighted images as (a) following injection of contrast agent. Note brighter signal in blood vessels, and in tumour (TU) towards left of image (patient right). d) Fluid-attenuated inversion recovery image (FLAIR). This has a high degree of T2 weighting, but fluid is attenuated to enable other long-T2 tissues to be more conspicuous.

Figure 3

a.Top. T1-weighted transverse images through breast following administration of MRI contrast agent, acquired at 1 minute intervals. Below. The signal intensity from each of the five lesions indicated is shown for each time point. Qualitative analysis of contrast-agent uptake curves show different uptake patterns in multi-focal disease of the breast. Rapid wash in followed by a gradual wash out indicates highly vascularised lesions, most likely to be malignant. b. Pharmacokinetic analysis on DCE MRI of a head and neck squamous cell carcinoma. Parametric maps generated with in-house software (d'Arcy et al., 2006) are overlaid in colour showing heterogeneity within the lesion for the values of Ktrans, ve, Kep and IAUGC60 (Integrated area under the contrast-agent concentration curve in the first 60 seconds). Whole lesion uptake is summarised on the right.

Figure 4

Example transverse images of prostate a) T2-weighted image. b) Diffusion-weighted image. Tumour has restricted diffusion relative to normal prostate tissue and so appears bright c) Calculated image showing pixel-by-pixel values of calculated Apparent Diffusion Coefficient (ADC). In the ADC image the prostate lesion now appears dark.

Figure 5

Example MRSI spectra in prostate a) MRSI grid overlying T2-w image b) Enlarged selection from MRSI grid. Cho – choline, Cit = citrate, PA = polyamines, Cr = creatine. c) MRSI grid over entire prostate. Acquisition parameters: TR = 745 ms, TE = 99ms. Acquired using internal and external rf coils at 3.0T.

Figure 6

Receiver coil arrangement used at the Royal Marsden NHS Foundation Trust to perform Head and Neck MRI for RT planning, demonstrated on a volunteer. A standard MR-compatible baseboard is employed, enabling the use of a thermoplastic mask. The large flex-coil is positioned above the neck and used in conjunction with elements of the spine array.

Figure 7

Sagittal, Coronal and Transaxial Maximum Intensity Projections (MIPs) of images of the Linear Test Object described by Doran et al. (2005). The 3D datasets were acquired at 1.5T (Siemens Aera, Erlangen, Germany), using T1-weighted sequences normally used for RT planning. The maximum intensity projections (MPSs) on the bottom row show substantial distortions. The MIPS on top row were obtained after applying the built-in distortion correction software provided by the MRI manufacturer.

Figure 8

RT planning images of prostate bed (red outline) in subject with double hip replacement (Pinnacle Planning System, Philips, Best, Netherlands). CT image (top left) is degraded by streak artifacts which prevent demonstration of the prostate bed. MR image (top right) is degraded by signal loss in the vicinity of the metallic implants. In combination, the fused dataset (bottom row) allows successful RT planning, but successful registration is based on structures located away from the implant.

Figure 9

CT-MR fusion of rectal cancer patient. Both examinations were undertaken using a flat bed and the position of pelvic bones coincides almost perfectly. However, bladder filling is quite different and many soft tissues, including the rectum, are considerably displaced. The benefit of MRI for this particular RT treatment plan is thus limited.

Figure 10

RT planning study with prone breast examination with multi-modality skin marker (arrow), visible in CT (left) and MR (right). Surgical clips in the tumour bed appear dark in MR images and are surrounded by signal loss.

Figure 11

MR-CT fusion on patient with metallic fixation device for spinal stabilisation. The standard T2-weighted MR images (left) show a larger asymmetric area of signal loss surrounding the implant. The signal loss is reduced by employing specialist metal artifact reduction techniques (right).

Figure 12

T2-weighted MR images of three different types of MR compatible brachytherapy applicators, which appear dark in contrast with surrounding tissue. In the central figure, a fluid filled tube appears bright within applicator. In some cases an anterior saturation band is employed to eliminate signals from the abdominal wall, to prevent motion artifacts in the MR images.

Figure 13

T2-weighted (top left) and T2*-weighted (top right) MR liver images after insertion of markers for cyberknife stereotatic RT. T2W images allow delineation of the lesion (in red) but do not demonstrate the markers. T2*W images and the CT images (bottom left) allow visualisation of the markers, and therefore can be registered. The CTV is thus transferred to the CT images for RT planning. The combined final dataset (bottom right, created using Eclipse, Varian Medical Systems, Switzerland) shows the CTV outlined using the MRI dataset and the OARs outlined in the CT dataset.