Imaging And Detectors For Medical Physics Lecture 1 . - Cockcroft

Joint CI-JAI advanced accelerator lecture seriesImaging and detectors for medicalphysicsLecture 1: Medical imagingDr Barbara Camanzibarbara.camanzi@stfc.ac.uk

Course layoutAM 09.30 – 11.00PM 15.30 – 17.006th JuneLecture 1: Introduction tomedical imagingLecture 2: Detectors formedical imaging7th JuneLecture 3: X-ray imagingDayWeek 18th JuneTutorialWeek 213th JuneLecture 4: Radionuclides14th JuneLecture 5: Gammacameras16th JuneLecture 7: PETLecture 6: SPECTWeek 322nd JuneTutorialPage 2/29

Books1. N Barrie Smith & A WebbIntroduction to Medical ImagingCambridge University Press2. Edited by M A FlowerWebb’s Physics of Medical ImagingCRC Press3. A Del GuerraIonizing Radiation Detectors for Medical ImagingWorld Scientific4. W R LeoTechniques for Nuclear and Particle Physics ExperimentsSpringer-VerlagPage 3/29

Medical imaging: what is it? “Medical imaging is the technique and process ofcreating visual representations of the interior of abody for clinical analysis and medical intervention,as well as visual representation of the function ofsome organs or tissues.” – Wikipedia Used to:1. Diagnose the disease diagnostic imaging2. Plan and monitor the treatment of the disease Clinical speciality: radiology & radiography medical physicsPage 4/29

Origins of medical imagingRef. 2 – Chapter 1Taken from Ref. 2 pg. 8Page 5/29

Origins of medical imaging:some datesRef. 2 – Chapter 1 1895: X-ray discovery, Wilhelm C Röntgen1931: invention of cyclotron, Ernest O Lawrence1938: production of 99𝑇𝑐 𝑚 at cyclotron1946: radioisotopes available for public distribution nuclearmedicine1946: discovery of NMR, Felix Bloch and Edward M Purcell1958 – 1967: SPECT imagesEarly 1960s: first clinical PET images1972: first CT machine, Godfrey N Hounsfield1976: first MRI images1978: first commercial SPECT systemsPage 6/29

Medical imaging techniques Techniques not using ionising radiation:1.2.3.4.Ultrasound imaging (Ref. 1 – Chapter 4, Ref. 2 – Chapter 6)MRI (Ref. 1 – Chapter 5, Ref. 2 – Chapter 7)Infrared imaging (Ref. 2 – Chapter 8)Optical imaging (Ref. 2, Chapter 10) Techniques using ionising radiation:1.2.3.4.X-ray imaging: films, CTSPECTPETScintigraphy (Ref. 1 – Chapter 3)Page 7/29

Basic physics conceptsSee for ex. Ref. 4 – Chapters 1, 2, 3 and 4 X-ray energy spectrum Interaction of photons with matter1. Photoelectric effect2. Compton scattering3. Absorption and attenuation Probability distributions: Gaussian, Poisson, etc. Radioactive sources1. Radioactive decay Dosimetric unitsPage 8/29

The receiver operatingcharacteristic (ROC) curveDiagnosisActual itiveHealthyFalsenegativeTruenegativeTrue positive fraction10.5Area under the curvemeasures quality ofdiagnostic procedure𝑵𝒄𝒐𝒓𝒓𝒆𝒄𝒕 ��𝒓𝒂𝒄𝒚 𝑵𝒕𝒐𝒕𝒂𝒍 ��𝒊𝒕𝒊𝒗𝒊𝒕𝒚 𝑵𝒕𝒓𝒖𝒆 ��𝒆 𝒑𝒐𝒔𝒊𝒕𝒊𝒗𝒆𝒔 𝑵𝒇𝒂𝒍𝒔𝒆 ��𝒆 ��𝒊𝒇𝒊𝒄𝒊𝒕𝒚 𝑵𝒕𝒓𝒖𝒆 𝒏𝒆𝒈𝒂𝒗𝒊𝒕𝒆𝒔 𝑵𝒇𝒂𝒍𝒔𝒆 𝒑𝒐𝒔𝒊𝒕𝒊𝒗𝒆𝒔000.51False positive fractionPage 9/29

ExerciseTrue positive fractionExerciseDraw the ROC curve for whentrying to diagnose cardiacdisease by counting the numberof lesions in the brainSolution Cardiac disease completelyunrelated to brain lesions 50-50 chance of true positivesand false positives irrespectiveof number of brain lesionsfound10.5000.51False positive fractionPage 10/29

Important parameters for allimaging techniques Spatial resolution Signal-to-noise ratio, SNR Contrast-to-noise ratio, CNRPage 11/29

Spatial resolution Imaging systems not perfect introduce blurring no sharp edges finite spatial resolution Spatial resolution determines:1. The smallest feature that can be visualised2. The smallest distance between two features so thatthey can be resolved and not seen as one Measures of spatial resolution / blurring:1. Line spread function (LSF) – 1D2. Point spread function (PSF) – 3DPage 12/29

Line spread function (LSF) Measured by imaging a single thin line or set of linesTaken from Ref. 1 pg. 6 For many imaging systems 𝐿𝑆𝐹 is a Gaussian:1𝑦 𝑦0 2𝐿𝑆𝐹 𝑦 𝑒𝑥𝑝 222𝜎2𝜋𝜎𝜎 standard deviation𝐹𝑊𝐻𝑀 2 2 ln 2 𝜎 2.36𝜎Page 13/29

𝐿𝑆𝐹 and spatial 2 𝐻𝑀𝐿𝑆𝐹3 𝐹𝑊𝐻𝑀𝐿𝑆𝐹2 Two structures can beresolved if:𝑑 𝐹𝑊𝐻𝑀𝐿𝑆𝐹 𝑑 2.36𝜎𝐿𝑆𝐹𝑑 distance between twostructures The narrower 𝐿𝑆𝐹 smaller𝐹𝑊𝐻𝑀 the smaller thedistance between twostructures that can beresolved better spatialresolutionPage 14/29

Point spread function (PSF) Takes into account the spatial resolution maybecome poorer with depth in the body 3D PSF describes image of a point sourceTaken from Ref. 1 pg. 8Worsening PDF𝐼 𝑥, 𝑦, 𝑧 𝑂 𝑥, 𝑦, 𝑧 ℎ 𝑥, 𝑦, 𝑧3D image3D objectconvolution3D PSFPage 15/29

Signal-to-noise ratio SNR Noise random signal superimposed on top ofreal signal mean value zero standarddeviation 𝜎𝑁 Sources of noise different for different imagingmodalities Signal-to-noise ratio 𝑆𝑁𝑅:𝑆𝑖𝑔𝑛𝑎𝑙𝑆𝑁𝑅 𝜎𝑁 The higher 𝑆𝑁𝑅 the better the image:– Maximise signal when designing imaging systems– Average signal acquired over repeated scansPage 16/29

Example of effect of noise:MRI scanTaken from Ref. 1 pg. 11Page 17/29

Example of averaging the signal:MRI scanTaken from Ref. 1 pg. 11a.b.c.d.One scanAverage of two identical scansAverage of four identical scansAverage of 16 identical scansPage 18/29

Contrast-to-noise ration CNR Ability to distinguish between different tissues between healthy and pathological tissues Contrast-to-noise ratio 𝐶𝑁𝑅𝐴𝐵 between tissues 𝐴and 𝐵:𝐶𝐴𝐵𝑆𝐴 𝑆𝐵𝐶𝑁𝑅𝐴𝐵 𝑆𝑁𝑅𝐴 𝑆𝑁𝑅𝐵𝜎𝑁𝜎𝑁𝑆𝐴 , 𝑆𝐵 signals from tissues 𝐴 and 𝐵𝐶𝐴𝐵 contrast between tissues 𝐴 and 𝐵𝜎𝑁 standard deviation of noise The higher 𝐶𝑁𝑅𝐴𝐵 the better the imagePage 19/29

Dose 𝐴𝑏𝑠𝑜𝑟𝑏𝑒𝑑 𝑑𝑜𝑠𝑒 𝐷 𝐺𝑦 𝑟𝑎𝑑𝑖𝑎𝑡𝑖𝑜𝑛 𝑒𝑛𝑒𝑟𝑔𝑦 𝐸(𝐽)𝑘𝑔 𝑜𝑓 𝑡𝑖𝑠𝑠𝑢𝑒 𝑀𝑒𝑎𝑛 𝑎𝑏𝑠𝑜𝑟𝑏𝑒𝑑 𝑑𝑜𝑠𝑒 𝐷𝑇,𝑅 𝑖𝑛 𝑚𝑎𝑠𝑠 𝑚 𝑇 𝑜𝑓 𝑡𝑖𝑠𝑠𝑢𝑒 𝑇𝑓𝑟𝑜𝑚 𝑎𝑚𝑜𝑢𝑛𝑡 𝑜𝑓 𝑟𝑎𝑑𝑖𝑎𝑡𝑖𝑜𝑛 𝑅𝐷𝑇,𝑅1 𝑚𝑇𝐷𝑅 𝑑𝑚𝑚𝑇 𝐸𝑞𝑢𝑖𝑣𝑎𝑙𝑒𝑛𝑡 𝑑𝑜𝑠𝑒 𝐻𝑇 (𝑆𝑣) 𝑅 𝑤𝑅 𝐷𝑇,𝑅with 𝑤𝑅 𝑟𝑎𝑑𝑖𝑎𝑡𝑖𝑜𝑛 𝑤𝑒𝑖𝑔ℎ𝑡𝑖𝑛𝑔 𝑓𝑎𝑐𝑡𝑜𝑟Page 20/29

Dose damageRadiation effectsDescriptionProbabilityDeterministicCellular damage that leads toloss in tissue function- Dose threshold1- Zero or low at lowdoses below threshold- Climbs rapidly to unityabove thresholdStochasticCells are not killed but undergogenetic mutations cancer- No dose threshold- Increases with thedose- In the low dose regionnon negligible andhigher than fordeterministic effects1Thedoses in imaging procedures are usually below this thresholdPage 21/29

Effective dose Some tissues are more sensitive to radiation dosethan others Tissue weighting factor 𝑤𝑇 fraction of totalstochastic radiation risk𝑇 𝑤𝑇 1 𝐸𝑓𝑓𝑒𝑐𝑡𝑖𝑣𝑒 𝑑𝑜𝑠𝑒 𝐸 𝑇 𝑤𝑇 𝐻𝑇Page 22/29

Tissue weighting factorsTissue / organTissue weighting factor1Gonads0.2Bone marrow der0.05Liver0.05Thyroid0.05Oesophagus0.05Average (brain, small intestines, adrenals, kidney, pancreas, muscle, spleen,thymus, uterus)0.05Skin0.01Bone surface0.011For the gonads risk of hereditary conditions, for all others organs risk of cancerPage 23/29

Multimodality imagingRef. 2 – Chapter 15 Multimodality imaging (MMI) obtains informationfrom combination of image data Main use visualisation localise functional datawithin body anatomy for same patient Made possible by:1. Increasing computer power easily accessible2. Merging different imaging modalities: hybrid scanners3. Multidisciplinary teamsPage 24/29

MMI: image registration Image registration align image data sets to achievespatial correspondence for direct comparison– Images from different modalities– Images from same modality at different times– Images with standardised anatomy’s atlases1. Images produced already aligned nothing to be done– Set-up parameters identical for all scans2. Images not already aligned image transformation– Rigid-body registration– Non-rigid registrationPage 25/29

MMI: rigid-body registration Image transformation:𝑝𝑖′ 𝑹 𝑺𝒑′′𝒊 𝑻 𝑖 𝑖 1 𝑁𝑝𝑖′ , 𝑝𝑖′′ two 3D point sets𝑹 3x3 rotation matrix𝑺 3x3 scaling matrix (diagonal)𝑻 3x1 translation vector 3x1 ‘noise’ or ‘uncertainty’ vector 9 parameters to be determined:3 scaling factors 3 angles 3 translation factorsPage 26/29

MMI: Classification schemesMainly used with rigid-body registration:1. Point Matching Based on a landmark that can be considered as pointMinimum 3 non-coplanar points needed, accuracy increases withnumber of pointsAnatomical points accurate, functional data less accurate2. Line or Surface Matching Lines or Surfaces obtained from external frames or internal anatomy3. Volume Matching Derived from voxel information Various algorithms being developedPage 27/29

MMI: non-rigid-body registrationSee for ex. W R Crum, T Hartkens and D L G Hill “Non-rigid image registration:theory and practice”, Brit. Journ. Rad. 77 (2004), S140–S153 Anatomy not rigid (apart some bony structures) errors Elastic registration takes into account:1. Movement between different scans2. Motion during the scan Various methods developed Less commonly used due to high complexity andpitfalls of warping image data unrealisticallyPage 28/29

MMI registration accuracy Aim one-to-one spatial correspondencebetween each image’s element Potential primary sources of errors:1.2.3.4.Definition of features usedAccuracy and robustness of algorithm usedAccuracy of image transformationDifferences in anatomy or image parameters Absolute accuracy impossible to determine, onlyrelative accuracy between different registrationmethodsPage 29/29

Medical imaging: what is it? "Medical imaging is the technique and process of creating visual representations of the interior of a body for clinical analysis and medical intervention, as well as visual representation of the function of some organs or tissues." - Wikipedia Used to: 1. Diagnose the disease diagnostic imaging 2.